diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..8b7acad --- /dev/null +++ b/.dockerignore @@ -0,0 +1,11 @@ +runs/ +pretrain/ +models/ +models_old/ +tests/ +predictions/ +predictions_docker/ +test_images/ +tests/ +old/ +*.cmd \ No newline at end of file diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..5e20edf --- /dev/null +++ b/.gitignore @@ -0,0 +1,110 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.idea/ +runs/ +/xview2.zip +models/ +models_old/ +pretrain/ diff --git a/Dockerfile-pytorch14-37 b/Dockerfile-pytorch14-37 new file mode 100644 index 0000000..bdca3dd --- /dev/null +++ b/Dockerfile-pytorch14-37 @@ -0,0 +1,26 @@ +FROM python:3.7 + +RUN apt-get update && apt-get install -y libgeos-dev + +RUN mkdir /xView2 +WORKDIR /xView2 + +ADD requirements_docker_pytorch14.txt /xView2 +RUN pip3 install -r requirements_docker_pytorch14.txt + +ADD . /xView2 + +RUN mkdir -p /input +RUN mkdir -p /output + +# https://github.com/pytorch/pytorch/issues/27971 +ENV LRU_CACHE_CAPACITY 1 + +# set environment variables +# Prevents Python from writing pyc files to disc +ENV PYTHONDONTWRITEBYTECODE 1 + +# Prevents Python from buffering stdout and stderr +ENV PYTHONUNBUFFERED 1 + +ENTRYPOINT ["python3", "docker_submission_37.py"] \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..766613e --- /dev/null +++ b/README.md @@ -0,0 +1,20 @@ +# 3rd place solution for xView2 Damage Assessment Challenge + +Eugene Khvedchenya, February 2020 + +This repository contains source code for my solution to [xView2 challenge](https://xview2.com). My solution was scored second (0.803) on public LB and third (0.807) on private hold-out dataset. + +# Approach in a nutshell + +- Ensemble of semantic segmentation models. +- Trained with weighted CE to address class imbalance. +- Heavy augmentations to prevent over-fitting and increase robustness to misalignment of pre- and post- images. +- Shared encoder for pre- and post- images. Extracted feature are concatenated and sent to decoder. +- Bunch of encoders (ResNets, Densenets, EfficientNets) and two decoders: Unet and FPN. +- 1 round of Pseudolabeling +- Ensemble using weighted averaging. Weights optimized for every model on corresponding validation data. + +# Training + +- Install dependencies from `requirements.txt` +- Follow `train.sh` \ No newline at end of file diff --git a/black.toml b/black.toml new file mode 100644 index 0000000..d6600cc --- /dev/null +++ b/black.toml @@ -0,0 +1,25 @@ +# Example configuration for Black. + +# NOTE: you have to use single-quoted strings in TOML for regular expressions. +# It's the equivalent of r-strings in Python. Multiline strings are treated as +# verbose regular expressions by Black. Use [ ] to denote a significant space +# character. + +[tool.black] +line-length = 119 +target-version = ['py35', 'py36', 'py37', 'py38'] +include = '\.pyi?$' +exclude = ''' +/( + \.eggs + | \.git + | \.hg + | \.mypy_cache + | \.tox + | \.venv + | _build + | buck-out + | build + | dist +)/ +''' diff --git a/build_push_docker_37.cmd b/build_push_docker_37.cmd new file mode 100644 index 0000000..eb1c37a --- /dev/null +++ b/build_push_docker_37.cmd @@ -0,0 +1,5 @@ +set mydate=%date:~10,4%%date:~4,2%%date:~7,2% + +docker build -t xview2:37_pytorch14 -f Dockerfile-pytorch14-37 . +docker tag xview2:37_pytorch14 ekhvedchenya/xview2:37_pytorch14_%mydate% +START docker push ekhvedchenya/xview2:37_pytorch14_%mydate% \ No newline at end of file diff --git a/convert_crops.py b/convert_crops.py new file mode 100644 index 0000000..0eb726e --- /dev/null +++ b/convert_crops.py @@ -0,0 +1,116 @@ +import argparse +import os + +import cv2 +from skimage.measure import label +from tqdm import tqdm +import pandas as pd + +from pytorch_toolbelt.utils import fs +import numpy as np + +from xview.dataset import make_dual_dataframe, read_image +from xview.utils.inference_image_output import create_inference_image, open_json, create_instance_image +from PIL import Image + + +def bbox1(img): + a = np.where(img != 0) + bbox = np.min(a[0]), np.max(a[0]) + 1, np.min(a[1]), np.max(a[1]) + 1 + return bbox + + +def convert_dir(df: pd.DataFrame, dir) -> pd.DataFrame: + crops_dir = os.path.join(dir, "crops") + os.makedirs(crops_dir, exist_ok=True) + + building_crops = [] + + global_crop_index = 0 + + for i, row in tqdm(df.iterrows(), total=len(df)): + image_fname_pre = read_image(os.path.join(dir, row["image_fname_pre"])) + image_fname_post = read_image(os.path.join(dir, row["image_fname_post"])) + + mask_fname_post = row["mask_fname_post"] + json_fname_post = fs.change_extension(mask_fname_post.replace("masks", "labels"), ".json") + inference_data = open_json(os.path.join(dir, json_fname_post)) + instance_image, labels = create_instance_image(inference_data) + + for label_index, damage_label in zip(range(1, instance_image.max() + 1), labels): + try: + instance_mask = instance_image == label_index + rmin, rmax, cmin, cmax = bbox1(instance_mask) + + max_size = max(rmax - rmin, cmax - cmin) + if max_size < 16: + print( + "Skipping crop since it's too small", + fs.id_from_fname(mask_fname_post), + "label_index", + label_index, + "min_size", + max_size + ) + continue + + rpadding = (rmax - rmin) // 4 + cpadding = (cmax - cmin) // 4 + + pre_crop = image_fname_pre[ + max(0, rmin - rpadding) : rmax + rpadding, max(0, cmin - cpadding) : cmax + cpadding + ] + post_crop = image_fname_post[ + max(0, rmin - rpadding) : rmax + rpadding, max(0, cmin - cpadding) : cmax + cpadding + ] + + image_id_pre = row["image_id_pre"] + image_id_post = row["image_id_post"] + + pre_crop_fname = f"{global_crop_index:06}_{image_id_pre}.png" + post_crop_fname = f"{global_crop_index:06}_{image_id_post}.png" + global_crop_index += 1 + + cv2.imwrite(os.path.join(crops_dir, pre_crop_fname), pre_crop) + cv2.imwrite(os.path.join(crops_dir, post_crop_fname), post_crop) + + building_crops.append( + { + "pre_crop_fname": pre_crop_fname, + "post_crop": post_crop_fname, + "label": damage_label, + "event_name": row["event_name_post"], + "fold": row["fold_post"], + "rmin": rmin, + "rmax": rmax, + "cmin": cmin, + "cmax": cmax, + "max_size": max_size, + "rpadding": rpadding, + "cpadding": cpadding + } + ) + except Exception as e: + print(e) + print(mask_fname_post) + + df = pd.DataFrame.from_records(building_crops) + return df + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("-dd", "--data-dir", type=str, default="c:\\datasets\\xview2") + args = parser.parse_args() + + data_dir = args.data_dir + + df = pd.read_csv(os.path.join(data_dir, "train_folds.csv")) + df = make_dual_dataframe(df) + + df_crops = convert_dir(df, data_dir) + df_crops.to_csv(os.path.join(data_dir, "train_crops.csv"), index=None) + + +if __name__ == "__main__": + main() diff --git a/convert_masks.py b/convert_masks.py new file mode 100644 index 0000000..0dc7b26 --- /dev/null +++ b/convert_masks.py @@ -0,0 +1,80 @@ +import argparse +import os + +import pandas as pd +from pytorch_toolbelt.utils import fs +from skimage.measure import label +from tqdm import tqdm + +from xview.utils.inference_image_output import create_inference_image + + +def convert_dir(dir, folder): + jsons_dir = os.path.join(dir, "labels") + masks_dir = os.path.join(dir, "masks") + os.makedirs(masks_dir, exist_ok=True) + jsons = [fname for fname in fs.find_in_dir(jsons_dir) if fname.endswith(".json")] + + items = [] + for json_fname in tqdm(jsons): + mask_fname = os.path.join(masks_dir, fs.id_from_fname(json_fname) + ".png") + mask = create_inference_image(json_fname, mask_fname) + + non_damaged_mask = mask == 1 + light = mask == 2 + medium = mask == 3 + destroyed = mask == 4 + + non_damaged_pixels = non_damaged_mask.sum() + light_pixels = light.sum() + medium_pixels = medium.sum() + destroyed_pixels = destroyed.sum() + + # guatemala-volcano_00000000_post_disaster + event_name, sample_id, event_type, disaster = fs.id_from_fname(json_fname).split("_") + assert disaster == "disaster" + + image_id = fs.id_from_fname(json_fname) + items.append( + { + "image_fname": os.path.join(folder, "images", image_id + ".png"), + "mask_fname": os.path.join(folder, "masks", image_id + ".png"), + "folder": folder, + "image_id": image_id, + "event_name": event_name, + "sample_id": sample_id, + "event_type": event_type, + "non_damaged_pixels": non_damaged_pixels, + "light_damaged_pixels": light_pixels, + "medium_damaged_pixels": medium_pixels, + "destroyed_pixels": destroyed_pixels, + "non_damaged_buildings": label(non_damaged_mask, return_num=True)[1], + "light_damaged_buildings": label(light, return_num=True)[1], + "medium_damaged_buildings": label(medium, return_num=True)[1], + "destroyed_buildings": label(destroyed, return_num=True)[1], + } + ) + return items + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("-dd", "--data-dir", type=str, default="c:\\datasets\\xview2") + args = parser.parse_args() + + data_dir = args.data_dir + + train_dir = os.path.join(data_dir, "train") + tier3_dir = os.path.join(data_dir, "tier3") + + items = [] + + items += convert_dir(train_dir, folder="train") + items += convert_dir(tier3_dir, folder="tier3") + + df = pd.DataFrame.from_records(items) + df.to_csv(os.path.join(data_dir, "train.csv"), index=None) + + +if __name__ == "__main__": + main() diff --git a/docker_submission_37.py b/docker_submission_37.py new file mode 100644 index 0000000..dab1e16 --- /dev/null +++ b/docker_submission_37.py @@ -0,0 +1,266 @@ +import argparse +import os +import time + +import albumentations as A + +import cv2 +import numpy as np +import pandas as pd +import torch +from pytorch_toolbelt.utils import fs +from pytorch_toolbelt.utils.torch_utils import tensor_from_rgb_image, to_numpy + +from xview.dataset import OUTPUT_MASK_KEY, read_image +from xview.inference import model_from_checkpoint, ApplyWeights, MultiscaleTTA, HFlipTTA, Ensembler +from xview.postprocessing import make_predictions_naive +from xview.utils.inference_image_output import colorize_mask + + +def weighted_model(checkpoint_fname: str, weights, activation: str): + model, info = model_from_checkpoint( + fs.auto_file(checkpoint_fname, where="ensemble"), activation_after=activation, report=False, classifiers=False + ) + model = ApplyWeights(model, weights) + return model, info + + +@torch.no_grad() +def main(): + start = time.time() + + torch.set_num_threads(1) + torch.set_num_interop_threads(1) + + parser = argparse.ArgumentParser() + parser.add_argument("pre_image", type=str) + parser.add_argument("post_image", type=str) + parser.add_argument("loc_image", type=str) + parser.add_argument("dmg_image", type=str) + parser.add_argument("--raw", action="store_true") + parser.add_argument("--color-mask", action="store_true") + parser.add_argument("--gpu", action="store_true") + args = parser.parse_args() + + pre_image = args.pre_image + post_image = args.post_image + localization_fname = args.loc_image + damage_fname = args.dmg_image + save_raw = args.raw + color_mask = args.color_mask + use_gpu = args.gpu + + size = 1024 + postprocess = "naive" + image_size = size, size + + print("pre_image ", pre_image) + print("post_image ", post_image) + print("loc_image ", localization_fname) + print("dmg_image ", damage_fname) + print("Size ", image_size) + print("Postprocess ", postprocess) + print("Colorize ", color_mask) + raw_predictions_file = fs.change_extension(damage_fname, ".npy") + print("raw_predictions_file", raw_predictions_file) + print(*torch.__config__.show().split("\n"), sep="\n") + + if not os.path.isdir(os.path.dirname(localization_fname)): + print("Output directory does not exists", localization_fname) + return -1 + + if not os.access(os.path.dirname(localization_fname), os.W_OK): + print("Output directory does not have write access", localization_fname) + return -2 + + if not os.path.isdir(os.path.dirname(damage_fname)): + print("Output directory does not exists", damage_fname) + return -1 + + if not os.access(os.path.dirname(damage_fname), os.W_OK): + print("Output directory does not have write access", damage_fname) + return -2 + + fold_0_models_dict = [ + # ( + # "Dec15_21_41_resnet101_fpncatv2_256_512_fold0_fp16_crops.pth", + # [0.45136154, 1.4482629, 1.42098208, 0.6839698, 0.96800456], + # ), + # ( + # "Dec16_08_26_resnet34_unet_v2_512_fold0_fp16_crops.pth", + # [0.92919105, 1.03831743, 1.03017048, 0.98257118, 1.0241164], + # ), + # ( + # "Dec21_21_54_densenet161_deeplab256_512_fold0_fp16_crops.pth", + # [0.48157651, 1.02084685, 1.36264406, 1.03175205, 1.11758873], + # ), + # 0.762814651939279 0.854002889559006 0.7237339786736817 [0.9186602573598759, 0.5420118318644089, 0.7123870673168781, 0.8405837378060299] coeffs [0.51244243 1.42747062 1.23648384 0.90290896 0.88912514] + ( + "Dec30_15_34_resnet34_unet_v2_512_fold0_fp16_pseudo_crops.pth", + [0.51244243, 1.42747062, 1.23648384, 0.90290896, 0.88912514], + ), + # 0.7673669954814148 0.8582940771677703 0.7283982461872626 [0.919932857782992, 0.5413880912001547, 0.731840942842999, 0.8396640419159087] coeffs [0.50847073 1.15392272 1.2059733 1.1340391 1.03196719] + ( + "Dec30_15_34_resnet101_fpncatv2_256_512_fold0_fp16_pseudo_crops.pth", + [0.50847073, 1.15392272, 1.2059733, 1.1340391, 1.03196719], + ), + ] + + fold_1_models_dict = [ + # ( + # "Dec16_18_59_densenet201_fpncatv2_256_512_fold1_fp16_crops.pth", + # [0.64202075, 1.04641224, 1.23015655, 1.03203408, 1.12505602], + # ), + # ( + # "Dec17_01_52_resnet34_unet_v2_512_fold1_fp16_crops.pth", + # [0.69605759, 0.89963168, 0.9232137, 0.92938775, 0.94460875], + # ), + ( + "Dec22_22_24_seresnext50_unet_v2_512_fold1_fp16_crops.pth", + [0.54324459, 1.76890163, 1.20782899, 0.85128004, 0.83100698], + ), + ( + "Dec31_02_09_resnet34_unet_v2_512_fold1_fp16_pseudo_crops.pth", + # Maybe suboptimal + [0.48269921, 1.22874469, 1.38328066, 0.96695393, 0.91348539], + ), + ( + "Dec31_03_55_densenet201_fpncatv2_256_512_fold1_fp16_pseudo_crops.pth", + [0.48804137, 1.14809462, 1.24851827, 1.11798428, 1.00790482] + ) + ] + + fold_2_models_dict = [ + # ( + # "Dec17_19_19_resnet34_unet_v2_512_fold2_fp16_crops.pth", + # [0.65977938, 1.50252452, 0.97098732, 0.74048182, 1.08712367], + # ), + # 0.7674290884579319 0.8107652756500724 0.7488564368041575 [0.9228529822124596, 0.5900700454049471, 0.736806959757804, 0.8292099253270483] coeffs [0.34641084 1.63486251 1.14186036 0.86668715 1.12193125] + ( + "Dec17_19_12_inceptionv4_fpncatv2_256_512_fold2_fp16_crops.pth", + [0.34641084, 1.63486251, 1.14186036, 0.86668715, 1.12193125], + ), + # 0.7683650436367244 0.8543981047493 0.7314937317313349 [0.9248137307721042, 0.5642011151253543, 0.7081016179096937, 0.831720163492164] coeffs [0.51277498 1.4475809 0.8296623 0.97868596 1.34180805] + ( + "Dec27_14_08_densenet169_unet_v2_512_fold2_fp16_crops.pth", + [0.55429115, 1.34944309, 1.1087044, 0.89542089, 1.17257541], + ), + ( + "Dec31_12_45_resnet34_unet_v2_512_fold2_fp16_pseudo_crops.pth", + # Copied from Dec17_19_19_resnet34_unet_v2_512_fold2_fp16_crops + [0.65977938, 1.50252452, 0.97098732, 0.74048182, 1.08712367], + ) + ] + + fold_3_models_dict = [ + ( + "Dec15_23_24_resnet34_unet_v2_512_fold3_crops.pth", + [0.84090623, 1.02953555, 1.2526516, 0.9298182, 0.94053529], + ), + # ( + # "Dec18_12_49_resnet34_unet_v2_512_fold3_fp16_crops.pth", + # [0.55555375, 1.18287119, 1.10997173, 0.85927596, 1.18145368], + # ), + # ( + # "Dec19_14_59_efficientb4_fpncatv2_256_512_fold3_fp16_crops.pth", + # [0.59338243, 1.17347438, 1.186104, 1.06860638, 1.03041829], + # ), + ( + "Dec21_11_50_seresnext50_unet_v2_512_fold3_fp16_crops.pth", + [0.43108046, 1.30222898, 1.09660616, 0.94958969, 1.07063753], + ), + ( + "Dec31_18_17_efficientb4_fpncatv2_256_512_fold3_fp16_pseudo_crops.pth", + # Copied from Dec19_14_59_efficientb4_fpncatv2_256_512_fold3_fp16_crops + [0.59338243, 1.17347438, 1.186104, 1.06860638, 1.03041829] + ) + ] + + fold_4_models_dict = [ + ( + "Dec19_06_18_resnet34_unet_v2_512_fold4_fp16_crops.pth", + [0.83915734, 1.02560309, 0.77639015, 1.17487775, 1.05632771], + ), + ( + "Dec27_14_37_resnet101_unet_v2_512_fold4_fp16_crops.pth", + [0.57414314, 1.19599486, 1.05561912, 0.98815567, 1.2274592], + ), + ] + + infos = [] + + resize = A.Resize(1024, 1024) + normalize = A.Normalize(mean=(0.485, 0.456, 0.406, 0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225, 0.229, 0.224, 0.225)) + transform = A.Compose([resize, normalize]) + + # Very dumb way but it matches 1:1 with inferencing + pre, post = read_image(pre_image), read_image(post_image) + image = np.dstack([pre, post]) + image = transform(image=image)["image"] + pre_image = image[..., 0:3] + post_image = image[..., 3:6] + + models = [] + for models_dict in [ + fold_0_models_dict, + fold_1_models_dict, + fold_2_models_dict, + fold_3_models_dict, + fold_4_models_dict, + ]: + for checkpoint, weights in models_dict: + model, info = weighted_model(checkpoint, weights, activation="model") + models.append(model) + infos.append(info) + + model = Ensembler(models, outputs=[OUTPUT_MASK_KEY]) + model = HFlipTTA(model, outputs=[OUTPUT_MASK_KEY], average=True) + model = MultiscaleTTA(model, outputs=[OUTPUT_MASK_KEY], size_offsets=[-128, +128], average=True) + model = model.eval() + + df = pd.DataFrame.from_records(infos) + pd.set_option("display.max_rows", None) + pd.set_option("display.max_columns", None) + pd.set_option("display.width", None) + pd.set_option("display.max_colwidth", -1) + + print(df) + print("score ", df["score"].mean(), df["score"].std()) + print("localization ", df["localization"].mean(), df["localization"].std()) + print("damage ", df["damage"].mean(), df["damage"].std()) + + input_image = tensor_from_rgb_image(np.dstack([pre_image, post_image])).unsqueeze(0) + + if use_gpu: + print("Using GPU for inference") + input_image = input_image.cuda() + model = model.cuda() + + output = model(input_image) + masks = output[OUTPUT_MASK_KEY] + predictions = to_numpy(masks.squeeze(0)).astype(np.float32) + + if save_raw: + np.save(raw_predictions_file, predictions) + + localization_image, damage_image = make_predictions_naive(predictions) + + if color_mask: + localization_image = colorize_mask(localization_image) + localization_image.save(localization_fname) + + damage_image = colorize_mask(damage_image) + damage_image.save(damage_fname) + else: + cv2.imwrite(localization_fname, localization_image) + cv2.imwrite(damage_fname, damage_image) + + print("Saved output to ", localization_fname, damage_fname) + + done = time.time() + elapsed = done - start + print("Inference time", elapsed, "(s)") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/evaluate_postprocessing.py b/evaluate_postprocessing.py new file mode 100644 index 0000000..c779db0 --- /dev/null +++ b/evaluate_postprocessing.py @@ -0,0 +1,102 @@ +import argparse +import os +from collections import defaultdict +from functools import partial +from multiprocessing.pool import Pool + +import cv2 +from tqdm import tqdm + +from xview.dataset import read_mask +from xview.metric import CompetitionMetricCallback +from xview.postprocessing import make_predictions_dominant, make_predictions_naive, make_predictions_floodfill + +from pytorch_toolbelt.utils import fs +import pandas as pd +from datetime import datetime +import numpy as np + + +def _compute_fn(args, postprocessing_fn): + xi, yi = args + dmg_pred = np.load(xi) + dmg_true = read_mask(yi) + + loc_pred, dmg_pred = postprocessing_fn(dmg_pred) + + if loc_pred.shape[0] != 1024: + loc_pred = cv2.resize(loc_pred, dsize=(1024, 1024), interpolation=cv2.INTER_NEAREST) + dmg_pred = cv2.resize(dmg_pred, dsize=(1024, 1024), interpolation=cv2.INTER_NEAREST) + + row = CompetitionMetricCallback.get_row_pair(loc_pred, dmg_pred, dmg_true, dmg_true) + return row + + +def optimize_postprocessing(y_pred_filenames, y_true_filenames, workers: int, postprocessing_fn): + input = list(zip(y_pred_filenames, y_true_filenames)) + + all_rows = [] + process = partial(_compute_fn, postprocessing_fn=postprocessing_fn) + with Pool(workers) as wp: + for row in tqdm(wp.imap_unordered(process, input, chunksize=8), total=len(input)): + all_rows.append(row) + + return CompetitionMetricCallback.compute_metrics(all_rows) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("predictions", nargs="+") + parser.add_argument("-w", "--workers", type=int, default=0, help="") + parser.add_argument("-dd", "--data-dir", type=str, default="data", help="Data directory") + args = parser.parse_args() + + targets = fs.find_in_dir(os.path.join(args.data_dir, "tier3", "masks")) + fs.find_in_dir( + os.path.join(args.data_dir, "train", "masks") + ) + targets_post = dict((fs.id_from_fname(fname), fname) for fname in targets if "_post_" in fname) + + df = defaultdict(list) + + postprocessings = { + "naive": make_predictions_naive, + "dominant": make_predictions_dominant, + "floodfill": make_predictions_floodfill, + } + + for predictions_dir in args.predictions: + try: + prediction_files = fs.find_in_dir(predictions_dir) + prediction_files_post = dict( + (fs.id_from_fname(fname), fname) for fname in prediction_files if "_post_" in fname + ) + + y_true_filenames = [targets_post[image_id_post] for image_id_post in prediction_files_post.keys()] + y_pred_filenames = [prediction_files_post[image_id_post] for image_id_post in prediction_files_post.keys()] + + for name, fn in postprocessings.items(): + score, localization_f1, damage_f1, damage_f1s = optimize_postprocessing( + y_pred_filenames, y_true_filenames, postprocessing_fn=fn, workers=args.workers + ) + + print(name, score) + + df["samples"].append(len(y_pred_filenames)) + df["predictions_dir"].append(predictions_dir) + df["postprocessing"].append(name) + df["score"].append(score) + df["localization_f1"].append(localization_f1) + df["damage_f1"].append(damage_f1) + except Exception as e: + print("Failed to process", predictions_dir, e) + + df = pd.DataFrame.from_dict(df) + print(df) + + current_time = datetime.now().strftime("%b%d_%H_%M") + + df.to_csv(f"postprocessing_eval_{current_time}.csv", index=None) + + +if __name__ == "__main__": + main() diff --git a/finetune.py b/finetune.py new file mode 100644 index 0000000..e8e9713 --- /dev/null +++ b/finetune.py @@ -0,0 +1,517 @@ +from __future__ import absolute_import + +import argparse +import collections +import json +import os +from datetime import datetime + +from catalyst.dl import SupervisedRunner, OptimizerCallback, SchedulerCallback +from catalyst.dl.callbacks import CriterionAggregatorCallback, AccuracyCallback +from catalyst.utils import load_checkpoint, unpack_checkpoint +from pytorch_toolbelt.utils import fs, torch_utils +from pytorch_toolbelt.utils.catalyst import ShowPolarBatchesCallback, ConfusionMatrixCallback +from pytorch_toolbelt.utils.random import set_manual_seed +from pytorch_toolbelt.utils.torch_utils import count_parameters, transfer_weights, get_optimizable_parameters +from torch import nn +from torch.optim.lr_scheduler import CyclicLR +from torch.utils.data import DataLoader + +from xview.dataset import ( + INPUT_IMAGE_KEY, + OUTPUT_MASK_KEY, + INPUT_MASK_KEY, + get_datasets, + OUTPUT_MASK_4_KEY, + UNLABELED_SAMPLE, + get_pseudolabeling_dataset, + DISASTER_TYPE_KEY, + UNKNOWN_DISASTER_TYPE_CLASS, + DISASTER_TYPES, + OUTPUT_EMBEDDING_KEY, + DAMAGE_TYPE_KEY, + OUTPUT_MASK_8_KEY, + OUTPUT_MASK_16_KEY, + OUTPUT_MASK_32_KEY, +) +from xview.metric import CompetitionMetricCallback +from xview.models import get_model +from xview.optim import get_optimizer +from xview.scheduler import get_scheduler +from xview.train_utils import clean_checkpoint, report_checkpoint, get_criterion_callback +from xview.visualization import draw_predictions + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("-acc", "--accumulation-steps", type=int, default=1, help="Number of batches to process") + parser.add_argument("--seed", type=int, default=42, help="Random seed") + parser.add_argument("-v", "--verbose", action="store_true") + parser.add_argument("--fast", action="store_true") + parser.add_argument( + "-dd", "--data-dir", type=str, required=True, help="Data directory for INRIA sattelite dataset" + ) + parser.add_argument("-m", "--model", type=str, default="resnet34_fpncat128", help="") + parser.add_argument("-b", "--batch-size", type=int, default=8, help="Batch Size during training, e.g. -b 64") + parser.add_argument("-e", "--epochs", type=int, default=100, help="Epoch to run") + # parser.add_argument('-es', '--early-stopping', type=int, default=None, help='Maximum number of epochs without improvement') + # parser.add_argument('-fe', '--freeze-encoder', type=int, default=0, help='Freeze encoder parameters for N epochs') + # parser.add_argument('-ft', '--fine-tune', action='store_true') + parser.add_argument("-lr", "--learning-rate", type=float, default=1e-3, help="Initial learning rate") + parser.add_argument( + "--disaster-type-loss", + type=str, + default=None, # [["ce", 1.0]], + action="append", + nargs="+", + help="Criterion for classifying disaster type", + ) + parser.add_argument( + "--damage-type-loss", + type=str, + default=None, # [["bce", 1.0]], + action="append", + nargs="+", + help="Criterion for classifying presence of building with particular damage type", + ) + + parser.add_argument("-l", "--criterion", type=str, default=None, action="append", nargs="+", help="Criterion") + parser.add_argument( + "--mask4", type=str, default=None, action="append", nargs="+", help="Criterion for mask with stride 4" + ) + parser.add_argument( + "--mask8", type=str, default=None, action="append", nargs="+", help="Criterion for mask with stride 8" + ) + parser.add_argument( + "--mask16", type=str, default=None, action="append", nargs="+", help="Criterion for mask with stride 16" + ) + parser.add_argument( + "--mask32", type=str, default=None, action="append", nargs="+", help="Criterion for mask with stride 32" + ) + parser.add_argument("--embedding", type=str, default=None) + + parser.add_argument("-o", "--optimizer", default="RAdam", help="Name of the optimizer") + parser.add_argument( + "-c", "--checkpoint", type=str, default=None, help="Checkpoint filename to use as initial model weights" + ) + parser.add_argument("-w", "--workers", default=8, type=int, help="Num workers") + parser.add_argument("-a", "--augmentations", default="safe", type=str, help="Level of image augmentations") + parser.add_argument("--transfer", default=None, type=str, help="") + parser.add_argument("--fp16", action="store_true") + parser.add_argument("--size", default=512, type=int) + parser.add_argument("--fold", default=0, type=int) + parser.add_argument("-s", "--scheduler", default="multistep", type=str, help="") + parser.add_argument("-x", "--experiment", default=None, type=str, help="") + parser.add_argument("-d", "--dropout", default=0.0, type=float, help="Dropout before head layer") + parser.add_argument("-pl", "--pseudolabeling", type=str, required=True) + parser.add_argument("-wd", "--weight-decay", default=0, type=float, help="L2 weight decay") + parser.add_argument("--show", action="store_true") + parser.add_argument("--dsv", action="store_true") + parser.add_argument("--balance", action="store_true") + parser.add_argument("--only-buildings", action="store_true") + parser.add_argument("--freeze-bn", action="store_true") + parser.add_argument("--crops", action="store_true", help="Train on random crops") + parser.add_argument("--post-transform", action="store_true") + + args = parser.parse_args() + set_manual_seed(args.seed) + + data_dir = args.data_dir + num_workers = args.workers + num_epochs = args.epochs + learning_rate = args.learning_rate + model_name = args.model + optimizer_name = args.optimizer + image_size = args.size, args.size + fast = args.fast + augmentations = args.augmentations + fp16 = args.fp16 + scheduler_name = args.scheduler + experiment = args.experiment + dropout = args.dropout + segmentation_losses = args.criterion + verbose = args.verbose + show = args.show + accumulation_steps = args.accumulation_steps + weight_decay = args.weight_decay + fold = args.fold + balance = args.balance + only_buildings = args.only_buildings + freeze_bn = args.freeze_bn + train_on_crops = args.crops + enable_post_image_transform = args.post_transform + disaster_type_loss = args.disaster_type_loss + train_batch_size = args.batch_size + embedding_criterion = args.embedding + damage_type_loss = args.damage_type_loss + pseudolabels_dir = args.pseudolabeling + + # Compute batch size for validaion + if train_on_crops: + valid_batch_size = max(1, (train_batch_size * (image_size[0] * image_size[1])) // (1024 ** 2)) + else: + valid_batch_size = train_batch_size + + run_train = num_epochs > 0 + + model: nn.Module = get_model(model_name, dropout=dropout).cuda() + + if args.transfer: + transfer_checkpoint = fs.auto_file(args.transfer) + print("Transfering weights from model checkpoint", transfer_checkpoint) + checkpoint = load_checkpoint(transfer_checkpoint) + pretrained_dict = checkpoint["model_state_dict"] + + transfer_weights(model, pretrained_dict) + + if args.checkpoint: + checkpoint = load_checkpoint(fs.auto_file(args.checkpoint)) + unpack_checkpoint(checkpoint, model=model) + + print("Loaded model weights from:", args.checkpoint) + report_checkpoint(checkpoint) + + if freeze_bn: + torch_utils.freeze_bn(model) + print("Freezing bn params") + + runner = SupervisedRunner(input_key=INPUT_IMAGE_KEY, output_key=None) + main_metric = "weighted_f1" + cmd_args = vars(args) + + current_time = datetime.now().strftime("%b%d_%H_%M") + checkpoint_prefix = f"{current_time}_{args.model}_{args.size}_fold{fold}" + + if fp16: + checkpoint_prefix += "_fp16" + + if fast: + checkpoint_prefix += "_fast" + + if pseudolabels_dir: + checkpoint_prefix += "_pseudo" + + if train_on_crops: + checkpoint_prefix += "_crops" + + if experiment is not None: + checkpoint_prefix = experiment + + log_dir = os.path.join("runs", checkpoint_prefix) + os.makedirs(log_dir, exist_ok=False) + + config_fname = os.path.join(log_dir, f"{checkpoint_prefix}.json") + with open(config_fname, "w") as f: + train_session_args = vars(args) + f.write(json.dumps(train_session_args, indent=2)) + + default_callbacks = [ + CompetitionMetricCallback(input_key=INPUT_MASK_KEY, output_key=OUTPUT_MASK_KEY, prefix="weighted_f1"), + ConfusionMatrixCallback( + input_key=INPUT_MASK_KEY, + output_key=OUTPUT_MASK_KEY, + class_names=["land", "no_damage", "minor_damage", "major_damage", "destroyed"], + ignore_index=UNLABELED_SAMPLE, + ), + ] + + if show: + default_callbacks += [ + ShowPolarBatchesCallback(draw_predictions, metric=main_metric + "_batch", minimize=False) + ] + + train_ds, valid_ds, train_sampler = get_datasets( + data_dir=data_dir, + image_size=image_size, + augmentation=augmentations, + fast=fast, + fold=fold, + balance=balance, + only_buildings=only_buildings, + train_on_crops=train_on_crops, + crops_multiplication_factor=1, + enable_post_image_transform=enable_post_image_transform, + ) + + if run_train: + loaders = collections.OrderedDict() + callbacks = default_callbacks.copy() + criterions_dict = {} + losses = [] + + unlabeled_train = get_pseudolabeling_dataset( + data_dir, + include_masks=True, + image_size=image_size, + augmentation="medium_nmd", + train_on_crops=train_on_crops, + enable_post_image_transform=enable_post_image_transform, + pseudolabels_dir=pseudolabels_dir, + ) + + train_ds = train_ds + unlabeled_train + + print("Using online pseudolabeling with ", len(unlabeled_train), "samples") + + loaders["train"] = DataLoader( + train_ds, + batch_size=train_batch_size, + num_workers=num_workers, + pin_memory=True, + drop_last=True, + shuffle=True, + ) + + loaders["valid"] = DataLoader(valid_ds, batch_size=valid_batch_size, num_workers=num_workers, pin_memory=True) + + # Create losses + for criterion in segmentation_losses: + if isinstance(criterion, (list, tuple)) and len(criterion) == 2: + loss_name, loss_weight = criterion + else: + loss_name, loss_weight = criterion[0], 1.0 + + cd, criterion, criterion_name = get_criterion_callback( + loss_name, + prefix="segmentation", + input_key=INPUT_MASK_KEY, + output_key=OUTPUT_MASK_KEY, + loss_weight=float(loss_weight), + ) + criterions_dict.update(cd) + callbacks.append(criterion) + losses.append(criterion_name) + print(INPUT_MASK_KEY, "Using loss", loss_name, loss_weight) + + if args.mask4 is not None: + for criterion in args.mask4: + if isinstance(criterion, (list, tuple)): + loss_name, loss_weight = criterion + else: + loss_name, loss_weight = criterion, 1.0 + + cd, criterion, criterion_name = get_criterion_callback( + loss_name, + prefix="mask4", + input_key=INPUT_MASK_KEY, + output_key=OUTPUT_MASK_4_KEY, + loss_weight=float(loss_weight), + ) + criterions_dict.update(cd) + callbacks.append(criterion) + losses.append(criterion_name) + print(OUTPUT_MASK_4_KEY, "Using loss", loss_name, loss_weight) + + if args.mask8 is not None: + for criterion in args.mask8: + if isinstance(criterion, (list, tuple)): + loss_name, loss_weight = criterion + else: + loss_name, loss_weight = criterion, 1.0 + + cd, criterion, criterion_name = get_criterion_callback( + loss_name, + prefix="mask8", + input_key=INPUT_MASK_KEY, + output_key=OUTPUT_MASK_8_KEY, + loss_weight=float(loss_weight), + ) + criterions_dict.update(cd) + callbacks.append(criterion) + losses.append(criterion_name) + print(OUTPUT_MASK_8_KEY, "Using loss", loss_name, loss_weight) + + if args.mask16 is not None: + for criterion in args.mask16: + if isinstance(criterion, (list, tuple)): + loss_name, loss_weight = criterion + else: + loss_name, loss_weight = criterion, 1.0 + + cd, criterion, criterion_name = get_criterion_callback( + loss_name, + prefix="mask16", + input_key=INPUT_MASK_KEY, + output_key=OUTPUT_MASK_16_KEY, + loss_weight=float(loss_weight), + ) + criterions_dict.update(cd) + callbacks.append(criterion) + losses.append(criterion_name) + print(OUTPUT_MASK_16_KEY, "Using loss", loss_name, loss_weight) + + if args.mask32 is not None: + for criterion in args.mask32: + if isinstance(criterion, (list, tuple)): + loss_name, loss_weight = criterion + else: + loss_name, loss_weight = criterion, 1.0 + + cd, criterion, criterion_name = get_criterion_callback( + loss_name, + prefix="mask32", + input_key=INPUT_MASK_KEY, + output_key=OUTPUT_MASK_32_KEY, + loss_weight=float(loss_weight), + ) + criterions_dict.update(cd) + callbacks.append(criterion) + losses.append(criterion_name) + print(OUTPUT_MASK_32_KEY, "Using loss", loss_name, loss_weight) + + if disaster_type_loss is not None: + callbacks += [ + ConfusionMatrixCallback( + input_key=DISASTER_TYPE_KEY, + output_key=DISASTER_TYPE_KEY, + class_names=DISASTER_TYPES, + ignore_index=UNKNOWN_DISASTER_TYPE_CLASS, + prefix=f"{DISASTER_TYPE_KEY}/confusion_matrix", + ), + AccuracyCallback( + input_key=DISASTER_TYPE_KEY, + output_key=DISASTER_TYPE_KEY, + prefix=f"{DISASTER_TYPE_KEY}/accuracy", + activation="Softmax", + ), + ] + + for criterion in disaster_type_loss: + if isinstance(criterion, (list, tuple)): + loss_name, loss_weight = criterion + else: + loss_name, loss_weight = criterion, 1.0 + + cd, criterion, criterion_name = get_criterion_callback( + loss_name, + prefix=DISASTER_TYPE_KEY, + input_key=DISASTER_TYPE_KEY, + output_key=DISASTER_TYPE_KEY, + loss_weight=float(loss_weight), + ignore_index=UNKNOWN_DISASTER_TYPE_CLASS, + ) + criterions_dict.update(cd) + callbacks.append(criterion) + losses.append(criterion_name) + print(DISASTER_TYPE_KEY, "Using loss", loss_name, loss_weight) + + if damage_type_loss is not None: + callbacks += [ + # MultilabelConfusionMatrixCallback( + # input_key=DAMAGE_TYPE_KEY, + # output_key=DAMAGE_TYPE_KEY, + # class_names=DAMAGE_TYPES, + # prefix=f"{DAMAGE_TYPE_KEY}/confusion_matrix", + # ), + AccuracyCallback( + input_key=DAMAGE_TYPE_KEY, + output_key=DAMAGE_TYPE_KEY, + prefix=f"{DAMAGE_TYPE_KEY}/accuracy", + activation="Sigmoid", + threshold=0.5, + ) + ] + + for criterion in damage_type_loss: + if isinstance(criterion, (list, tuple)): + loss_name, loss_weight = criterion + else: + loss_name, loss_weight = criterion, 1.0 + + cd, criterion, criterion_name = get_criterion_callback( + loss_name, + prefix=DAMAGE_TYPE_KEY, + input_key=DAMAGE_TYPE_KEY, + output_key=DAMAGE_TYPE_KEY, + loss_weight=float(loss_weight), + ) + criterions_dict.update(cd) + callbacks.append(criterion) + losses.append(criterion_name) + print(DAMAGE_TYPE_KEY, "Using loss", loss_name, loss_weight) + + if embedding_criterion is not None: + cd, criterion, criterion_name = get_criterion_callback( + embedding_criterion, + prefix="embedding", + input_key=INPUT_MASK_KEY, + output_key=OUTPUT_EMBEDDING_KEY, + loss_weight=1.0, + ) + criterions_dict.update(cd) + callbacks.append(criterion) + losses.append(criterion_name) + print(OUTPUT_EMBEDDING_KEY, "Using loss", embedding_criterion) + + callbacks += [ + CriterionAggregatorCallback(prefix="loss", loss_keys=losses), + OptimizerCallback(accumulation_steps=accumulation_steps, decouple_weight_decay=False), + ] + + optimizer = get_optimizer( + optimizer_name, get_optimizable_parameters(model), learning_rate, weight_decay=weight_decay + ) + scheduler = get_scheduler( + scheduler_name, optimizer, lr=learning_rate, num_epochs=num_epochs, batches_in_epoch=len(loaders["train"]) + ) + if isinstance(scheduler, CyclicLR): + callbacks += [SchedulerCallback(mode="batch")] + + print("Train session :", checkpoint_prefix) + print(" FP16 mode :", fp16) + print(" Fast mode :", args.fast) + print(" Epochs :", num_epochs) + print(" Workers :", num_workers) + print(" Data dir :", data_dir) + print(" Log dir :", log_dir) + print("Data ") + print(" Augmentations :", augmentations) + print(" Train size :", len(loaders["train"]), len(train_ds)) + print(" Valid size :", len(loaders["valid"]), len(valid_ds)) + print(" Image size :", image_size) + print(" Train on crops :", train_on_crops) + print(" Balance :", balance) + print(" Buildings only :", only_buildings) + print(" Post transform :", enable_post_image_transform) + print(" Pseudolabels :", pseudolabels_dir) + print("Model :", model_name) + print(" Parameters :", count_parameters(model)) + print(" Dropout :", dropout) + print("Optimizer :", optimizer_name) + print(" Learning rate :", learning_rate) + print(" Weight decay :", weight_decay) + print(" Scheduler :", scheduler_name) + print(" Batch sizes :", train_batch_size, valid_batch_size) + print(" Criterion :", segmentation_losses) + print(" Damage type :", damage_type_loss) + print(" Disaster type :", disaster_type_loss) + print(" Embedding :", embedding_criterion) + + # model training + runner.train( + fp16=fp16, + model=model, + criterion=criterions_dict, + optimizer=optimizer, + scheduler=scheduler, + callbacks=callbacks, + loaders=loaders, + logdir=os.path.join(log_dir, "opl"), + num_epochs=num_epochs, + verbose=verbose, + main_metric=main_metric, + minimize_metric=False, + checkpoint_data={"cmd_args": cmd_args}, + ) + + # Training is finished. Let's run predictions using best checkpoint weights + best_checkpoint = os.path.join(log_dir, "main", "checkpoints", "best.pth") + + model_checkpoint = os.path.join(log_dir, "main", "checkpoints", f"{checkpoint_prefix}.pth") + clean_checkpoint(best_checkpoint, model_checkpoint) + + del optimizer, loaders + + +if __name__ == "__main__": + main() diff --git a/fit_predict.py b/fit_predict.py new file mode 100644 index 0000000..0307e52 --- /dev/null +++ b/fit_predict.py @@ -0,0 +1,614 @@ +from __future__ import absolute_import + +import argparse +import collections +import gc +import json +import os +from datetime import datetime + +import torch +from catalyst.dl import SupervisedRunner, OptimizerCallback, SchedulerCallback +from catalyst.dl.callbacks import CriterionAggregatorCallback, AccuracyCallback +from catalyst.utils import load_checkpoint, unpack_checkpoint +from pytorch_toolbelt.optimization.functional import get_lr_decay_parameters +from pytorch_toolbelt.utils import fs, torch_utils +from pytorch_toolbelt.utils.catalyst import ShowPolarBatchesCallback, ConfusionMatrixCallback +from pytorch_toolbelt.utils.random import set_manual_seed +from pytorch_toolbelt.utils.torch_utils import count_parameters, transfer_weights, get_optimizable_parameters +from torch import nn +from torch.optim.lr_scheduler import CyclicLR +from torch.utils.data import DataLoader + +from xview.dataset import ( + INPUT_IMAGE_KEY, + OUTPUT_MASK_KEY, + INPUT_MASK_KEY, + get_datasets, + OUTPUT_MASK_4_KEY, + UNLABELED_SAMPLE, + get_pseudolabeling_dataset, + DISASTER_TYPE_KEY, + UNKNOWN_DISASTER_TYPE_CLASS, + DISASTER_TYPES, + OUTPUT_EMBEDDING_KEY, + DAMAGE_TYPE_KEY, + OUTPUT_MASK_8_KEY, OUTPUT_MASK_16_KEY, OUTPUT_MASK_32_KEY) +from xview.metric import CompetitionMetricCallback +from xview.models import get_model +from xview.optim import get_optimizer +from xview.pseudo import CEOnlinePseudolabelingCallback2d +from xview.scheduler import get_scheduler +from xview.train_utils import clean_checkpoint, report_checkpoint, get_criterion_callback +from xview.visualization import draw_predictions + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("-acc", "--accumulation-steps", type=int, default=1, help="Number of batches to process") + parser.add_argument("--seed", type=int, default=42, help="Random seed") + parser.add_argument("-v", "--verbose", action="store_true") + parser.add_argument("--fast", action="store_true") + parser.add_argument( + "-dd", "--data-dir", type=str, required=True, help="Data directory for INRIA sattelite dataset" + ) + parser.add_argument("-m", "--model", type=str, default="resnet34_fpncat128", help="") + parser.add_argument("-b", "--batch-size", type=int, default=8, help="Batch Size during training, e.g. -b 64") + parser.add_argument("-e", "--epochs", type=int, default=100, help="Epoch to run") + # parser.add_argument('-es', '--early-stopping', type=int, default=None, help='Maximum number of epochs without improvement') + # parser.add_argument('-fe', '--freeze-encoder', type=int, default=0, help='Freeze encoder parameters for N epochs') + # parser.add_argument('-ft', '--fine-tune', action='store_true') + parser.add_argument("-lr", "--learning-rate", type=float, default=1e-3, help="Initial learning rate") + parser.add_argument( + "--disaster-type-loss", + type=str, + default=None, # [["ce", 1.0]], + action="append", + nargs="+", + help="Criterion for classifying disaster type", + ) + parser.add_argument( + "--damage-type-loss", + type=str, + default=None, # [["bce", 1.0]], + action="append", + nargs="+", + help="Criterion for classifying presence of building with particular damage type", + ) + + parser.add_argument("-l", "--criterion", type=str, default=None, action="append", nargs="+", help="Criterion") + parser.add_argument("--mask4", type=str, default=None, action="append", nargs="+", help="Criterion for mask with stride 4") + parser.add_argument("--mask8", type=str, default=None, action="append", nargs="+", help="Criterion for mask with stride 8") + parser.add_argument("--mask16", type=str, default=None, action="append", nargs="+", help="Criterion for mask with stride 16") + parser.add_argument("--mask32", type=str, default=None, action="append", nargs="+", help="Criterion for mask with stride 32") + parser.add_argument("--embedding", type=str, default=None) + + parser.add_argument("-o", "--optimizer", default="RAdam", help="Name of the optimizer") + parser.add_argument( + "-c", "--checkpoint", type=str, default=None, help="Checkpoint filename to use as initial model weights" + ) + parser.add_argument("-w", "--workers", default=8, type=int, help="Num workers") + parser.add_argument("-a", "--augmentations", default="safe", type=str, help="Level of image augmentations") + parser.add_argument("--transfer", default=None, type=str, help="") + parser.add_argument("--fp16", action="store_true") + parser.add_argument("--size", default=512, type=int) + parser.add_argument("--fold", default=0, type=int) + parser.add_argument("-s", "--scheduler", default="multistep", type=str, help="") + parser.add_argument("-x", "--experiment", default=None, type=str, help="") + parser.add_argument("-d", "--dropout", default=0.0, type=float, help="Dropout before head layer") + parser.add_argument("--opl", action="store_true") + parser.add_argument( + "--warmup", default=0, type=int, help="Number of warmup epochs with reduced LR on encoder parameters" + ) + parser.add_argument("-wd", "--weight-decay", default=0, type=float, help="L2 weight decay") + parser.add_argument("--show", action="store_true") + parser.add_argument("--dsv", action="store_true") + parser.add_argument("--balance", action="store_true") + parser.add_argument("--only-buildings", action="store_true") + parser.add_argument("--freeze-bn", action="store_true") + parser.add_argument("--crops", action="store_true", help="Train on random crops") + parser.add_argument("--post-transform", action="store_true") + + args = parser.parse_args() + set_manual_seed(args.seed) + + data_dir = args.data_dir + num_workers = args.workers + num_epochs = args.epochs + learning_rate = args.learning_rate + model_name = args.model + optimizer_name = args.optimizer + image_size = args.size, args.size + fast = args.fast + augmentations = args.augmentations + fp16 = args.fp16 + scheduler_name = args.scheduler + experiment = args.experiment + dropout = args.dropout + online_pseudolabeling = args.opl + segmentation_losses = args.criterion + verbose = args.verbose + warmup = args.warmup + show = args.show + accumulation_steps = args.accumulation_steps + weight_decay = args.weight_decay + fold = args.fold + balance = args.balance + only_buildings = args.only_buildings + freeze_bn = args.freeze_bn + train_on_crops = args.crops + enable_post_image_transform = args.post_transform + disaster_type_loss = args.disaster_type_loss + train_batch_size = args.batch_size + embedding_criterion = args.embedding + damage_type_loss = args.damage_type_loss + + # Compute batch size for validaion + if train_on_crops: + valid_batch_size = max(1, (train_batch_size * (image_size[0] * image_size[1])) // (1024 ** 2)) + else: + valid_batch_size = train_batch_size + + run_train = num_epochs > 0 + + model: nn.Module = get_model(model_name, dropout=dropout).cuda() + + if args.transfer: + transfer_checkpoint = fs.auto_file(args.transfer) + print("Transfering weights from model checkpoint", transfer_checkpoint) + checkpoint = load_checkpoint(transfer_checkpoint) + pretrained_dict = checkpoint["model_state_dict"] + + transfer_weights(model, pretrained_dict) + + if args.checkpoint: + checkpoint = load_checkpoint(fs.auto_file(args.checkpoint)) + unpack_checkpoint(checkpoint, model=model) + + print("Loaded model weights from:", args.checkpoint) + report_checkpoint(checkpoint) + + if freeze_bn: + torch_utils.freeze_bn(model) + print("Freezing bn params") + + runner = SupervisedRunner(input_key=INPUT_IMAGE_KEY, output_key=None) + main_metric = "weighted_f1" + cmd_args = vars(args) + + current_time = datetime.now().strftime("%b%d_%H_%M") + checkpoint_prefix = f"{current_time}_{args.model}_{args.size}_fold{fold}" + + if fp16: + checkpoint_prefix += "_fp16" + + if fast: + checkpoint_prefix += "_fast" + + if online_pseudolabeling: + checkpoint_prefix += "_opl" + + if train_on_crops: + checkpoint_prefix += "_crops" + + if experiment is not None: + checkpoint_prefix = experiment + + log_dir = os.path.join("runs", checkpoint_prefix) + os.makedirs(log_dir, exist_ok=False) + + config_fname = os.path.join(log_dir, f"{checkpoint_prefix}.json") + with open(config_fname, "w") as f: + train_session_args = vars(args) + f.write(json.dumps(train_session_args, indent=2)) + + default_callbacks = [ + CompetitionMetricCallback(input_key=INPUT_MASK_KEY, output_key=OUTPUT_MASK_KEY, prefix="weighted_f1"), + ConfusionMatrixCallback( + input_key=INPUT_MASK_KEY, + output_key=OUTPUT_MASK_KEY, + class_names=["land", "no_damage", "minor_damage", "major_damage", "destroyed"], + ignore_index=UNLABELED_SAMPLE, + ), + ] + + if show: + default_callbacks += [ + ShowPolarBatchesCallback(draw_predictions, metric=main_metric + "_batch", minimize=False) + ] + + train_ds, valid_ds, train_sampler = get_datasets( + data_dir=data_dir, + image_size=image_size, + augmentation=augmentations, + fast=fast, + fold=fold, + balance=balance, + only_buildings=only_buildings, + train_on_crops=train_on_crops, + enable_post_image_transform=enable_post_image_transform, + ) + + # Pretrain/warmup + if warmup: + callbacks = default_callbacks.copy() + criterions_dict = {} + losses = [] + + for criterion in segmentation_losses: + if isinstance(criterion, (list, tuple)): + loss_name, loss_weight = criterion + else: + loss_name, loss_weight = criterion, 1.0 + + cd, criterion, criterion_name = get_criterion_callback( + loss_name, input_key=INPUT_MASK_KEY, output_key=OUTPUT_MASK_KEY, loss_weight=float(loss_weight) + ) + criterions_dict.update(cd) + callbacks.append(criterion) + losses.append(criterion_name) + print("Using loss", loss_name, loss_weight) + + if args.mask4 is not None: + for criterion in args.mask4: + if isinstance(criterion, (list, tuple)): + loss_name, loss_weight = criterion + else: + loss_name, loss_weight = criterion, 1.0 + + cd, criterion, criterion_name = get_criterion_callback( + loss_name, input_key=INPUT_MASK_KEY, output_key=OUTPUT_MASK_4_KEY, loss_weight=float(loss_weight) + ) + criterions_dict.update(cd) + callbacks.append(criterion) + losses.append(criterion_name) + print("Using loss", loss_name, loss_weight) + + callbacks += [ + CriterionAggregatorCallback(prefix="loss", loss_keys=losses), + OptimizerCallback(accumulation_steps=accumulation_steps, decouple_weight_decay=False), + ] + + parameters = get_lr_decay_parameters(model.named_parameters(), learning_rate, {"encoder": 0.1}) + optimizer = get_optimizer("RAdam", parameters, learning_rate=learning_rate * 0.1) + + loaders = collections.OrderedDict() + loaders["train"] = DataLoader( + train_ds, + batch_size=train_batch_size, + num_workers=num_workers, + pin_memory=True, + drop_last=True, + shuffle=train_sampler is None, + sampler=train_sampler, + ) + + loaders["valid"] = DataLoader(valid_ds, batch_size=valid_batch_size, num_workers=num_workers, pin_memory=True) + + runner.train( + fp16=fp16, + model=model, + criterion=criterions_dict, + optimizer=optimizer, + scheduler=None, + callbacks=callbacks, + loaders=loaders, + logdir=os.path.join(log_dir, "warmup"), + num_epochs=warmup, + verbose=verbose, + main_metric=main_metric, + minimize_metric=False, + checkpoint_data={"cmd_args": cmd_args}, + ) + + del optimizer, loaders + + best_checkpoint = os.path.join(log_dir, "warmup", "checkpoints", "best.pth") + model_checkpoint = os.path.join(log_dir, "warmup", "checkpoints", f"{checkpoint_prefix}_warmup.pth") + clean_checkpoint(best_checkpoint, model_checkpoint) + + torch.cuda.empty_cache() + gc.collect() + + if run_train: + loaders = collections.OrderedDict() + callbacks = default_callbacks.copy() + criterions_dict = {} + losses = [] + + if online_pseudolabeling: + unlabeled_label = get_pseudolabeling_dataset( + data_dir, include_masks=False, image_size=image_size, augmentation=None + ) + + unlabeled_train = get_pseudolabeling_dataset( + data_dir, + include_masks=True, + image_size=image_size, + augmentation=augmentations, + train_on_crops=train_on_crops, + enable_post_image_transform=enable_post_image_transform, + ) + + loaders["label"] = DataLoader( + unlabeled_label, batch_size=valid_batch_size, num_workers=num_workers, pin_memory=True + ) + + train_ds = train_ds + unlabeled_train + train_sampler = None + + callbacks += [ + CEOnlinePseudolabelingCallback2d( + unlabeled_train, + pseudolabel_loader="label", + prob_threshold=0.75, + output_key=OUTPUT_MASK_KEY, + unlabeled_class=UNLABELED_SAMPLE, + label_frequency=5, + ) + ] + + print("Using online pseudolabeling with ", len(unlabeled_label), "samples") + + loaders["train"] = DataLoader( + train_ds, + batch_size=train_batch_size, + num_workers=num_workers, + pin_memory=True, + drop_last=True, + shuffle=train_sampler is None, + sampler=train_sampler, + ) + + loaders["valid"] = DataLoader(valid_ds, batch_size=valid_batch_size, num_workers=num_workers, pin_memory=True) + + # Create losses + for criterion in segmentation_losses: + if isinstance(criterion, (list, tuple)) and len(criterion) == 2: + loss_name, loss_weight = criterion + else: + loss_name, loss_weight = criterion[0], 1.0 + + cd, criterion, criterion_name = get_criterion_callback( + loss_name, + prefix="segmentation", + input_key=INPUT_MASK_KEY, + output_key=OUTPUT_MASK_KEY, + loss_weight=float(loss_weight), + ) + criterions_dict.update(cd) + callbacks.append(criterion) + losses.append(criterion_name) + print(INPUT_MASK_KEY, "Using loss", loss_name, loss_weight) + + if args.mask4 is not None: + for criterion in args.mask4: + if isinstance(criterion, (list, tuple)): + loss_name, loss_weight = criterion + else: + loss_name, loss_weight = criterion, 1.0 + + cd, criterion, criterion_name = get_criterion_callback( + loss_name, + prefix="mask4", + input_key=INPUT_MASK_KEY, + output_key=OUTPUT_MASK_4_KEY, + loss_weight=float(loss_weight), + ) + criterions_dict.update(cd) + callbacks.append(criterion) + losses.append(criterion_name) + print(OUTPUT_MASK_4_KEY, "Using loss", loss_name, loss_weight) + + if args.mask8 is not None: + for criterion in args.mask8: + if isinstance(criterion, (list, tuple)): + loss_name, loss_weight = criterion + else: + loss_name, loss_weight = criterion, 1.0 + + cd, criterion, criterion_name = get_criterion_callback( + loss_name, + prefix="mask8", + input_key=INPUT_MASK_KEY, + output_key=OUTPUT_MASK_8_KEY, + loss_weight=float(loss_weight), + ) + criterions_dict.update(cd) + callbacks.append(criterion) + losses.append(criterion_name) + print(OUTPUT_MASK_8_KEY, "Using loss", loss_name, loss_weight) + + if args.mask16 is not None: + for criterion in args.mask16: + if isinstance(criterion, (list, tuple)): + loss_name, loss_weight = criterion + else: + loss_name, loss_weight = criterion, 1.0 + + cd, criterion, criterion_name = get_criterion_callback( + loss_name, + prefix="mask16", + input_key=INPUT_MASK_KEY, + output_key=OUTPUT_MASK_16_KEY, + loss_weight=float(loss_weight), + ) + criterions_dict.update(cd) + callbacks.append(criterion) + losses.append(criterion_name) + print(OUTPUT_MASK_16_KEY, "Using loss", loss_name, loss_weight) + + if args.mask32 is not None: + for criterion in args.mask32: + if isinstance(criterion, (list, tuple)): + loss_name, loss_weight = criterion + else: + loss_name, loss_weight = criterion, 1.0 + + cd, criterion, criterion_name = get_criterion_callback( + loss_name, + prefix="mask32", + input_key=INPUT_MASK_KEY, + output_key=OUTPUT_MASK_32_KEY, + loss_weight=float(loss_weight), + ) + criterions_dict.update(cd) + callbacks.append(criterion) + losses.append(criterion_name) + print(OUTPUT_MASK_32_KEY, "Using loss", loss_name, loss_weight) + + if disaster_type_loss is not None: + callbacks += [ + ConfusionMatrixCallback( + input_key=DISASTER_TYPE_KEY, + output_key=DISASTER_TYPE_KEY, + class_names=DISASTER_TYPES, + ignore_index=UNKNOWN_DISASTER_TYPE_CLASS, + prefix=f"{DISASTER_TYPE_KEY}/confusion_matrix", + ), + AccuracyCallback( + input_key=DISASTER_TYPE_KEY, + output_key=DISASTER_TYPE_KEY, + prefix=f"{DISASTER_TYPE_KEY}/accuracy", + activation="Softmax", + ), + ] + + for criterion in disaster_type_loss: + if isinstance(criterion, (list, tuple)): + loss_name, loss_weight = criterion + else: + loss_name, loss_weight = criterion, 1.0 + + cd, criterion, criterion_name = get_criterion_callback( + loss_name, + prefix=DISASTER_TYPE_KEY, + input_key=DISASTER_TYPE_KEY, + output_key=DISASTER_TYPE_KEY, + loss_weight=float(loss_weight), + ignore_index=UNKNOWN_DISASTER_TYPE_CLASS, + ) + criterions_dict.update(cd) + callbacks.append(criterion) + losses.append(criterion_name) + print(DISASTER_TYPE_KEY, "Using loss", loss_name, loss_weight) + + if damage_type_loss is not None: + callbacks += [ + # MultilabelConfusionMatrixCallback( + # input_key=DAMAGE_TYPE_KEY, + # output_key=DAMAGE_TYPE_KEY, + # class_names=DAMAGE_TYPES, + # prefix=f"{DAMAGE_TYPE_KEY}/confusion_matrix", + # ), + AccuracyCallback( + input_key=DAMAGE_TYPE_KEY, + output_key=DAMAGE_TYPE_KEY, + prefix=f"{DAMAGE_TYPE_KEY}/accuracy", + activation="Sigmoid", + threshold=0.5, + ) + ] + + for criterion in damage_type_loss: + if isinstance(criterion, (list, tuple)): + loss_name, loss_weight = criterion + else: + loss_name, loss_weight = criterion, 1.0 + + cd, criterion, criterion_name = get_criterion_callback( + loss_name, + prefix=DAMAGE_TYPE_KEY, + input_key=DAMAGE_TYPE_KEY, + output_key=DAMAGE_TYPE_KEY, + loss_weight=float(loss_weight), + ) + criterions_dict.update(cd) + callbacks.append(criterion) + losses.append(criterion_name) + print(DAMAGE_TYPE_KEY, "Using loss", loss_name, loss_weight) + + if embedding_criterion is not None: + cd, criterion, criterion_name = get_criterion_callback( + embedding_criterion, + prefix="embedding", + input_key=INPUT_MASK_KEY, + output_key=OUTPUT_EMBEDDING_KEY, + loss_weight=1.0, + ) + criterions_dict.update(cd) + callbacks.append(criterion) + losses.append(criterion_name) + print(OUTPUT_EMBEDDING_KEY, "Using loss", embedding_criterion) + + callbacks += [ + CriterionAggregatorCallback(prefix="loss", loss_keys=losses), + OptimizerCallback(accumulation_steps=accumulation_steps, decouple_weight_decay=False), + ] + + optimizer = get_optimizer( + optimizer_name, get_optimizable_parameters(model), learning_rate, weight_decay=weight_decay + ) + scheduler = get_scheduler( + scheduler_name, optimizer, lr=learning_rate, num_epochs=num_epochs, batches_in_epoch=len(loaders["train"]) + ) + if isinstance(scheduler, CyclicLR): + callbacks += [SchedulerCallback(mode="batch")] + + print("Train session :", checkpoint_prefix) + print(" FP16 mode :", fp16) + print(" Fast mode :", args.fast) + print(" Epochs :", num_epochs) + print(" Workers :", num_workers) + print(" Data dir :", data_dir) + print(" Log dir :", log_dir) + print("Data ") + print(" Augmentations :", augmentations) + print(" Train size :", len(loaders["train"]), len(train_ds)) + print(" Valid size :", len(loaders["valid"]), len(valid_ds)) + print(" Image size :", image_size) + print(" Train on crops :", train_on_crops) + print(" Balance :", balance) + print(" Buildings only :", only_buildings) + print(" Post transform :", enable_post_image_transform) + print("Model :", model_name) + print(" Parameters :", count_parameters(model)) + print(" Dropout :", dropout) + print("Optimizer :", optimizer_name) + print(" Learning rate :", learning_rate) + print(" Weight decay :", weight_decay) + print(" Scheduler :", scheduler_name) + print(" Batch sizes :", train_batch_size, valid_batch_size) + print(" Criterion :", segmentation_losses) + print(" Damage type :", damage_type_loss) + print(" Disaster type :", disaster_type_loss) + print(" Embedding :", embedding_criterion) + + # model training + runner.train( + fp16=fp16, + model=model, + criterion=criterions_dict, + optimizer=optimizer, + scheduler=scheduler, + callbacks=callbacks, + loaders=loaders, + logdir=os.path.join(log_dir, "main"), + num_epochs=num_epochs, + verbose=verbose, + main_metric=main_metric, + minimize_metric=False, + checkpoint_data={"cmd_args": vars(args)}, + ) + + # Training is finished. Let's run predictions using best checkpoint weights + best_checkpoint = os.path.join(log_dir, "main", "checkpoints", "best.pth") + + model_checkpoint = os.path.join(log_dir, "main", "checkpoints", f"{checkpoint_prefix}.pth") + clean_checkpoint(best_checkpoint, model_checkpoint) + + del optimizer, loaders + + +if __name__ == "__main__": + main() diff --git a/make_folds.py b/make_folds.py new file mode 100644 index 0000000..9a19b14 --- /dev/null +++ b/make_folds.py @@ -0,0 +1,53 @@ +import argparse +import os + +import pandas as pd +from sklearn.model_selection import StratifiedKFold +from iterstrat.ml_stratifiers import MultilabelStratifiedKFold +from sklearn.preprocessing import LabelEncoder +import numpy as np + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("-dd", "--data-dir", type=str, default="c:\\datasets\\xview2") + args = parser.parse_args() + + data_dir = args.data_dir + + df = pd.read_csv(os.path.join(data_dir, "train.csv")) + df = df.sort_values(by="sample_id") + df["fold"] = -1 + + df_pre = df[df["event_type"] == "pre"].copy() + df_post = df[df["event_type"] == "post"].copy() + + # Use only post samples to split data + + # destroyed_buildings,destroyed_pixels,event_name,event_type,light_damaged_buildings,light_damaged_pixels,medium_damaged_buildings,medium_damaged_pixels,non_damaged_buildings,non_damaged_pixels,sample_id + y = np.column_stack( + [ + df_post["non_damaged_buildings"].values > 0, + df_post["light_damaged_buildings"].values > 0, + df_post["medium_damaged_buildings"].values > 0, + df_post["destroyed_buildings"].values > 0, + LabelEncoder().fit_transform(df_post["event_name"].tolist()), + ] + ) + + mskf = MultilabelStratifiedKFold(n_splits=5, shuffle=True, random_state=42) + + folds = np.ones(len(y), dtype=int) * -1 + + for fold, (train_index, test_index) in enumerate(mskf.split(df_post, y)): + folds[test_index] = fold + + df_pre["fold"] = folds + df_post["fold"] = folds + df = pd.concat((df_pre, df_post)) + + df.to_csv(os.path.join(data_dir, "train_folds.csv"), index=None) + + +if __name__ == "__main__": + main() diff --git a/optimize_softmax.py b/optimize_softmax.py new file mode 100644 index 0000000..3ce9f74 --- /dev/null +++ b/optimize_softmax.py @@ -0,0 +1,79 @@ +import argparse +import os +from collections import defaultdict +import numpy as np + +from xview.rounder import OptimizedRounder + +from pytorch_toolbelt.utils import fs +import pandas as pd +from datetime import datetime + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("checkpoints", nargs="+") + parser.add_argument("-w", "--workers", type=int, default=1, help="") + parser.add_argument("-dd", "--data-dir", type=str, default="data", help="Data directory") + parser.add_argument("-a", "--activation", type=str, default="pre", help="") + args = parser.parse_args() + + targets = fs.find_in_dir(os.path.join(args.data_dir, "tier3", "masks")) + fs.find_in_dir( + os.path.join(args.data_dir, "train", "masks") + ) + targets_post = dict((fs.id_from_fname(fname), fname) for fname in targets if "_post_" in fname) + + df = defaultdict(list) + + current_time = datetime.now().strftime("%b%d_%H_%M") + + print("Checkpoints ", args.checkpoints) + print("Activation ", args.activation) + + for model_checkpoint in args.checkpoints: + model_checkpoint = fs.auto_file(model_checkpoint) + predictions_dir = os.path.join( + os.path.dirname(model_checkpoint), fs.id_from_fname(model_checkpoint) + "_oof_predictions" + ) + + prediction_files = fs.find_in_dir(predictions_dir) + prediction_files_post = dict( + (fs.id_from_fname(fname), fname) for fname in prediction_files if "_post_" in fname + ) + + y_true_filenames = [targets_post[image_id_post] for image_id_post in prediction_files_post.keys()] + y_pred_filenames = [prediction_files_post[image_id_post] for image_id_post in prediction_files_post.keys()] + + rounder = OptimizedRounder(workers=args.workers, apply_softmax=args.activation) + + raw_score, raw_localization_f1, raw_damage_f1, raw_damage_f1s = rounder.predict( + y_pred_filenames, y_true_filenames, np.array([1, 1, 1, 1, 1], dtype=np.float32) + ) + + rounder.fit(y_pred_filenames, y_true_filenames) + + score, localization_f1, damage_f1, damage_f1s = rounder.predict( + y_pred_filenames, y_true_filenames, rounder.coefficients() + ) + + print(rounder.coefficients()) + + df["checkpoint"].append(fs.id_from_fname(model_checkpoint)) + df["coefficients"].append(rounder.coefficients()) + df["samples"].append(len(y_true_filenames)) + + df["raw_score"].append(raw_score) + df["raw_localization"].append(raw_localization_f1) + df["raw_damage"].append(raw_damage_f1) + + df["opt_score"].append(score) + df["opt_localization"].append(localization_f1) + df["opt_damage"].append(damage_f1) + + dataframe = pd.DataFrame.from_dict(df) + dataframe.to_csv(f"optimized_weights_{current_time}.csv", index=None) + print(df) + + +if __name__ == "__main__": + main() diff --git a/predict.py b/predict.py new file mode 100644 index 0000000..e8331ed --- /dev/null +++ b/predict.py @@ -0,0 +1,130 @@ +import argparse +import os +from collections import defaultdict +from datetime import datetime + +import torch +import pandas as pd +from pytorch_toolbelt.utils import fs + +from xview.dataset import get_test_dataset, OUTPUT_MASK_KEY +from xview.inference import Ensembler, model_from_checkpoint, run_inference_on_dataset, ApplySoftmaxTo, MultiscaleTTA, \ + HFlipTTA, D4TTA + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("models", nargs="+") + parser.add_argument("-o", "--output-dir", type=str) + parser.add_argument("--fast", action="store_true") + parser.add_argument("--tta", type=str, default=None) + parser.add_argument("-b", "--batch-size", type=int, default=1, help="Batch Size during training, e.g. -b 64") + parser.add_argument("-w", "--workers", type=int, default=0, help="") + parser.add_argument("-dd", "--data-dir", type=str, default="data", help="Data directory") + parser.add_argument("-p", "--postprocessing", type=str, default="dominant") + parser.add_argument("--size", default=1024, type=int) + parser.add_argument("--activation", default="model", type=str) + parser.add_argument("--weights", default=None, type=float, nargs="+") + parser.add_argument("--fp16", action="store_true") + parser.add_argument("--align", action="store_true") + + args = parser.parse_args() + + workers = args.workers + data_dir = args.data_dir + fast = args.fast + tta = args.tta + image_size = args.size, args.size + model_checkpoints = args.models + batch_size = args.batch_size + activation_after = args.activation + fp16 = args.fp16 + align = args.align + postprocessing=args.postprocessing + weights = args.weights + assert weights is None or len(weights) == 5 + + current_time = datetime.now().strftime("%b%d_%H_%M") + if args.output_dir is None and len(model_checkpoints) == 1: + output_dir = os.path.join( + os.path.dirname(model_checkpoints[0]), fs.id_from_fname(model_checkpoints[0]) + "_test_predictions" + ) + if weights is not None: + output_dir += "_weighted" + if tta is not None: + output_dir += f"_{tta}" + else: + output_dir = args.output_dir or f"output_dir_{current_time}" + + print("Size", image_size) + print("Output dir", output_dir) + print("Postproc ", postprocessing) + # Load models + + models = [] + infos = [] + for model_checkpoint in model_checkpoints: + try: + model, info = model_from_checkpoint( + fs.auto_file(model_checkpoint), tta=None, activation_after=activation_after, report=False + ) + models.append(model) + infos.append(info) + except Exception as e: + print(e) + print(model_checkpoint) + return + + df = pd.DataFrame.from_records(infos) + print(df) + + print("score ", df["score"].mean(), df["score"].std()) + print("localization ", df["localization"].mean(), df["localization"].std()) + print("damage ", df["damage"].mean(), df["damage"].std()) + + if len(models) > 1: + model = Ensembler(models, [OUTPUT_MASK_KEY]) + if activation_after == "ensemble": + model = ApplySoftmaxTo(model, OUTPUT_MASK_KEY) + print("Applying activation after ensemble") + + if tta == "multiscale": + print(f"Using {tta}") + model = MultiscaleTTA(model, outputs=[OUTPUT_MASK_KEY], size_offsets=[-128, +128], average=True) + + if tta == "flip": + print(f"Using {tta}") + model = HFlipTTA(model, outputs=[OUTPUT_MASK_KEY], average=True) + + if tta == "flipscale": + print(f"Using {tta}") + model = HFlipTTA(model, outputs=[OUTPUT_MASK_KEY], average=True) + model = MultiscaleTTA(model, outputs=[OUTPUT_MASK_KEY], size_offsets=[-128, +128], average=True) + + if tta == "multiscale_d4": + print(f"Using {tta}") + model = D4TTA(model, outputs=[OUTPUT_MASK_KEY], average=True) + model = MultiscaleTTA(model, outputs=[OUTPUT_MASK_KEY], size_offsets=[-128, +128], average=True) + + if activation_after == "tta": + model = ApplySoftmaxTo(model, OUTPUT_MASK_KEY) + + else: + model = models[0] + + test_ds = get_test_dataset(data_dir=data_dir, image_size=image_size, fast=fast, align_post=align) + + run_inference_on_dataset( + model=model, + dataset=test_ds, + output_dir=output_dir, + batch_size=batch_size, + workers=workers, + weights=weights, + fp16=fp16, + postprocessing=postprocessing, + ) + + +if __name__ == "__main__": + main() diff --git a/predict_37_weighted.py b/predict_37_weighted.py new file mode 100644 index 0000000..40874c7 --- /dev/null +++ b/predict_37_weighted.py @@ -0,0 +1,239 @@ +import argparse + +import pandas as pd +import torch +from pytorch_toolbelt.utils import fs + +from xview.dataset import OUTPUT_MASK_KEY, get_test_dataset +from xview.inference import ( + model_from_checkpoint, + ApplyWeights, + Ensembler, + ApplySoftmaxTo, + MultiscaleTTA, + HFlipTTA, + D4TTA, + run_inference_on_dataset, +) + + +def weighted_model(checkpoint_fname: str, weights, activation: str): + model, info = model_from_checkpoint(fs.auto_file(checkpoint_fname, where="models"), activation_after=activation, report=False) + model = ApplyWeights(model, weights) + return model, info + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("-o", "--output-dir", type=str, default="models/predict_37_weighted") + parser.add_argument("--tta", type=str, default=None) + parser.add_argument("-b", "--batch-size", type=int, default=1, help="Batch Size during training, e.g. -b 64") + parser.add_argument("-w", "--workers", type=int, default=0, help="") + parser.add_argument("-dd", "--data-dir", type=str, default="c:\\datasets\\xview2", help="Data directory") + parser.add_argument("-p", "--postprocessing", type=str, default=None) + parser.add_argument("--size", default=1024, type=int) + parser.add_argument("--activation", default="model", type=str) + parser.add_argument("--fp16", action="store_true") + + args = parser.parse_args() + + workers = args.workers + data_dir = args.data_dir + tta = args.tta + image_size = args.size, args.size + batch_size = args.batch_size + activation_after = args.activation + fp16 = args.fp16 + postprocessing = args.postprocessing + output_dir = args.output_dir + + print("Size ", image_size) + print("Output dir", output_dir) + print("Postproc ", postprocessing) + + fold_0_models_dict = [ + # ( + # "Dec15_21_41_resnet101_fpncatv2_256_512_fold0_fp16_crops.pth", + # [0.45136154, 1.4482629, 1.42098208, 0.6839698, 0.96800456], + # ), + # ( + # "Dec16_08_26_resnet34_unet_v2_512_fold0_fp16_crops.pth", + # [0.92919105, 1.03831743, 1.03017048, 0.98257118, 1.0241164], + # ), + # ( + # "Dec21_21_54_densenet161_deeplab256_512_fold0_fp16_crops.pth", + # [0.48157651, 1.02084685, 1.36264406, 1.03175205, 1.11758873], + # ), + # 0.762814651939279 0.854002889559006 0.7237339786736817 [0.9186602573598759, 0.5420118318644089, 0.7123870673168781, 0.8405837378060299] coeffs [0.51244243 1.42747062 1.23648384 0.90290896 0.88912514] + ( + "Dec30_15_34_resnet34_unet_v2_512_fold0_fp16_pseudo_crops.pth", + [0.51244243, 1.42747062, 1.23648384, 0.90290896, 0.88912514], + ), + # 0.7673669954814148 0.8582940771677703 0.7283982461872626 [0.919932857782992, 0.5413880912001547, 0.731840942842999, 0.8396640419159087] coeffs [0.50847073 1.15392272 1.2059733 1.1340391 1.03196719] + ( + "Dec30_15_34_resnet101_fpncatv2_256_512_fold0_fp16_pseudo_crops.pth", + [0.50847073, 1.15392272, 1.2059733, 1.1340391, 1.03196719], + ), + ] + + fold_1_models_dict = [ + # ( + # "Dec16_18_59_densenet201_fpncatv2_256_512_fold1_fp16_crops.pth", + # [0.64202075, 1.04641224, 1.23015655, 1.03203408, 1.12505602], + # ), + # ( + # "Dec17_01_52_resnet34_unet_v2_512_fold1_fp16_crops.pth", + # [0.69605759, 0.89963168, 0.9232137, 0.92938775, 0.94460875], + # ), + ( + "Dec22_22_24_seresnext50_unet_v2_512_fold1_fp16_crops.pth", + [0.54324459, 1.76890163, 1.20782899, 0.85128004, 0.83100698], + ), + ( + "Dec31_02_09_resnet34_unet_v2_512_fold1_fp16_pseudo_crops.pth", + # Maybe suboptimal + [0.48269921, 1.22874469, 1.38328066, 0.96695393, 0.91348539], + ), + ( + "Dec31_03_55_densenet201_fpncatv2_256_512_fold1_fp16_pseudo_crops.pth", + [0.48804137, 1.14809462, 1.24851827, 1.11798428, 1.00790482] + ) + ] + + fold_2_models_dict = [ + # ( + # "Dec17_19_19_resnet34_unet_v2_512_fold2_fp16_crops.pth", + # [0.65977938, 1.50252452, 0.97098732, 0.74048182, 1.08712367], + # ), + # 0.7674290884579319 0.8107652756500724 0.7488564368041575 [0.9228529822124596, 0.5900700454049471, 0.736806959757804, 0.8292099253270483] coeffs [0.34641084 1.63486251 1.14186036 0.86668715 1.12193125] + ( + "Dec17_19_12_inceptionv4_fpncatv2_256_512_fold2_fp16_crops.pth", + [0.34641084, 1.63486251, 1.14186036, 0.86668715, 1.12193125], + ), + # 0.7683650436367244 0.8543981047493 0.7314937317313349 [0.9248137307721042, 0.5642011151253543, 0.7081016179096937, 0.831720163492164] coeffs [0.51277498 1.4475809 0.8296623 0.97868596 1.34180805] + ( + "Dec27_14_08_densenet169_unet_v2_512_fold2_fp16_crops.pth", + [0.55429115, 1.34944309, 1.1087044, 0.89542089, 1.17257541], + ), + ( + "Dec31_12_45_resnet34_unet_v2_512_fold2_fp16_pseudo_crops.pth", + # Copied from Dec17_19_19_resnet34_unet_v2_512_fold2_fp16_crops + [0.65977938, 1.50252452, 0.97098732, 0.74048182, 1.08712367], + ) + ] + + fold_3_models_dict = [ + ( + "Dec15_23_24_resnet34_unet_v2_512_fold3_crops.pth", + [0.84090623, 1.02953555, 1.2526516, 0.9298182, 0.94053529], + ), + # ( + # "Dec18_12_49_resnet34_unet_v2_512_fold3_fp16_crops.pth", + # [0.55555375, 1.18287119, 1.10997173, 0.85927596, 1.18145368], + # ), + # ( + # "Dec19_14_59_efficientb4_fpncatv2_256_512_fold3_fp16_crops.pth", + # [0.59338243, 1.17347438, 1.186104, 1.06860638, 1.03041829], + # ), + ( + "Dec21_11_50_seresnext50_unet_v2_512_fold3_fp16_crops.pth", + [0.43108046, 1.30222898, 1.09660616, 0.94958969, 1.07063753], + ), + ( + "Dec31_18_17_efficientb4_fpncatv2_256_512_fold3_fp16_pseudo_crops.pth", + # Copied from Dec19_14_59_efficientb4_fpncatv2_256_512_fold3_fp16_crops + [0.59338243, 1.17347438, 1.186104, 1.06860638, 1.03041829] + ) + ] + + fold_4_models_dict = [ + ( + "Dec19_06_18_resnet34_unet_v2_512_fold4_fp16_crops.pth", + [0.83915734, 1.02560309, 0.77639015, 1.17487775, 1.05632771], + ), + ( + "Dec27_14_37_resnet101_unet_v2_512_fold4_fp16_crops.pth", + [0.57414314, 1.19599486, 1.05561912, 0.98815567, 1.2274592], + ), + ] + + infos = [] + models = [] + + for models_dict in [ + fold_0_models_dict, + fold_1_models_dict, + fold_2_models_dict, + fold_3_models_dict, + fold_4_models_dict, + ]: + for checkpoint, weights in models_dict: + model, info = weighted_model(checkpoint, weights, activation_after) + models.append(model) + infos.append(info) + + model = Ensembler(models, outputs=[OUTPUT_MASK_KEY]) + + df = pd.DataFrame.from_records(infos) + + pd.set_option("display.max_rows", None) + pd.set_option("display.max_columns", None) + pd.set_option("display.width", None) + pd.set_option("display.max_colwidth", -1) + + print(df) + print("score ", df["score"].mean(), df["score"].std()) + print("localization ", df["localization"].mean(), df["localization"].std()) + print("damage ", df["damage"].mean(), df["damage"].std()) + + if activation_after == "ensemble": + model = ApplySoftmaxTo(model, OUTPUT_MASK_KEY) + print("Applying activation after ensemble") + + if tta == "multiscale": + print(f"Using {tta}") + model = MultiscaleTTA(model, outputs=[OUTPUT_MASK_KEY], size_offsets=[-128, +128], average=True) + + if tta == "flip": + print(f"Using {tta}") + model = HFlipTTA(model, outputs=[OUTPUT_MASK_KEY], average=True) + + if tta == "flipscale": + print(f"Using {tta}") + model = HFlipTTA(model, outputs=[OUTPUT_MASK_KEY], average=True) + model = MultiscaleTTA(model, outputs=[OUTPUT_MASK_KEY], size_offsets=[-128, +128], average=True) + + if tta == "flipscale2": + print(f"Using {tta}") + model = HFlipTTA(model, outputs=[OUTPUT_MASK_KEY], average=True) + model = MultiscaleTTA(model, outputs=[OUTPUT_MASK_KEY], size_offsets=[-256, -128, +128, +256], average=True) + + if tta == "multiscale_d4": + print(f"Using {tta}") + model = D4TTA(model, outputs=[OUTPUT_MASK_KEY], average=True) + model = MultiscaleTTA(model, outputs=[OUTPUT_MASK_KEY], size_offsets=[-128, +128], average=True) + + if tta is not None: + output_dir += "_" + tta + + if activation_after == "tta": + model = ApplySoftmaxTo(model, OUTPUT_MASK_KEY) + print("Applying activation after TTA") + + test_ds = get_test_dataset(data_dir=data_dir, image_size=image_size) + + run_inference_on_dataset( + model=model, + dataset=test_ds, + output_dir=output_dir, + batch_size=batch_size, + workers=workers, + fp16=fp16, + postprocessing=postprocessing, + save_pseudolabels=False, + cpu=False + ) + + +if __name__ == "__main__": + main() diff --git a/predict_oof.py b/predict_oof.py new file mode 100644 index 0000000..c2677b7 --- /dev/null +++ b/predict_oof.py @@ -0,0 +1,106 @@ +import argparse +import os + +import torch +from pytorch_toolbelt.utils import fs + +from xview.dataset import get_datasets +from xview.inference import model_from_checkpoint, run_inference_on_dataset_oof +import numpy as np + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("model", type=str, nargs="+") + parser.add_argument("-o", "--output-dir", type=str, default=None) + parser.add_argument("--fast", action="store_true") + parser.add_argument("--tta", type=str, default=None) + parser.add_argument("-b", "--batch-size", type=int, default=1, help="Batch Size during training, e.g. -b 64") + parser.add_argument("-w", "--workers", type=int, default=0, help="") + parser.add_argument("-dd", "--data-dir", type=str, default="data", help="Data directory") + parser.add_argument("--size", default=1024, type=int) + parser.add_argument("--fold", default=None, type=int) + parser.add_argument("--no-save", action="store_true") + parser.add_argument("--fp16", action="store_true") + parser.add_argument("--activation", default="model", type=str) + parser.add_argument("--align", action="store_true") + + args = parser.parse_args() + + fp16 = args.fp16 + activation = args.activation + + average_score = [] + average_dmg = [] + average_loc = [] + + for model_checkpoint in args.model: + model_checkpoint = fs.auto_file(model_checkpoint) + checkpoint = torch.load(model_checkpoint) + + print("Model :", model_checkpoint) + print( + "Metrics :", + checkpoint["epoch_metrics"]["valid"]["weighted_f1"], + checkpoint["epoch_metrics"]["valid"]["weighted_f1/localization_f1"], + checkpoint["epoch_metrics"]["valid"]["weighted_f1/damage_f1"], + ) + + workers = args.workers + data_dir = args.data_dir + fast = args.fast + tta = args.tta + no_save = args.no_save + image_size = args.size or checkpoint["checkpoint_data"]["cmd_args"]["size"] + batch_size = args.batch_size or checkpoint["checkpoint_data"]["cmd_args"]["batch_size"] + fold = args.fold or checkpoint["checkpoint_data"]["cmd_args"]["fold"] + align = args.align + + print("Image size :", image_size) + print("Fold :", fold) + print("Align :", align) + print("Workers :", workers) + print("Save :", not no_save) + + output_dir = None + if not no_save: + output_dir = args.output_dir or os.path.join( + os.path.dirname(model_checkpoint), fs.id_from_fname(model_checkpoint) + "_oof_predictions" + ) + print("Output dir :", output_dir) + + # Load models + model, info = model_from_checkpoint(model_checkpoint, tta=tta, activation_after=None, report=False) + print(info) + _, valid_ds, _ = get_datasets(data_dir=data_dir, image_size=(image_size, image_size), fast=fast, fold=fold, align_post=align) + + score, localization_f1, damage_f1, damage_f1s = run_inference_on_dataset_oof( + model=model, + dataset=valid_ds, + output_dir=output_dir, + batch_size=batch_size, + workers=workers, + save=not no_save, + fp16=fp16 + ) + + average_score.append(score) + average_dmg.append(damage_f1) + average_loc.append(localization_f1) + + print("Score :", score) + print("Localization :", localization_f1) + print("Damage :", damage_f1) + print("Per class :", damage_f1s) + print() + + print("Average") + if len(average_score) > 1: + print("Score :", np.mean(average_score), np.std(average_score)) + print("Localization :", np.mean(average_loc), np.std(average_loc)) + print("Damage :", np.mean(average_dmg), np.std(average_dmg)) + + +if __name__ == "__main__": + torch.backends.cudnn.benchmark = True + main() diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..727e0b1 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,7 @@ +torch>=1.3 +catalyst==19.11.5 +albumentations==0.4.3 +git+https://github.com/BloodAxe/pytorch-toolbelt@038b93d00b10a89b6a89e07ba24cf7fb8f76b27a +opencv-python>=4.0 +shapely==1.6.4 +apex \ No newline at end of file diff --git a/requirements_docker_pytorch14.txt b/requirements_docker_pytorch14.txt new file mode 100644 index 0000000..1c81c6d --- /dev/null +++ b/requirements_docker_pytorch14.txt @@ -0,0 +1,17 @@ +torch==1.4.0+cpu +torchvision==0.5.0 +albumentations==0.4.3 +opencv-python==4.1.1.26 +pillow==6.1.0 +git+https://github.com/BloodAxe/pytorch-toolbelt@038b93d00b10a89b6a89e07ba24cf7fb8f76b27a +shapely==1.6.4 +scikit-image==0.15.0 +scikit-learn==0.21.3 +scipy==1.3.1 +pandas==0.24.2 +numpy==1.16.4 +torchnet +tqdm +jpeg4py==0.1.4 +catalyst==19.12 +-f https://download.pytorch.org/whl/cpu/torch_stable.html \ No newline at end of file diff --git a/run_tensorboard.cmd b/run_tensorboard.cmd new file mode 100644 index 0000000..184373d --- /dev/null +++ b/run_tensorboard.cmd @@ -0,0 +1,3 @@ +@call c:\Anaconda3\Scripts\activate.bat tb +set CUDA_VISIBLE_DEVICES= +tensorboard --logdir runs --host 0.0.0.0 --port 5555 \ No newline at end of file diff --git a/run_tensorboard.sh b/run_tensorboard.sh new file mode 100644 index 0000000..c717e76 --- /dev/null +++ b/run_tensorboard.sh @@ -0,0 +1,3 @@ +#!/usr/bin/env bash +export CUDA_VISIBLE_DEVICES= +tensorboard --logdir runs --host 0.0.0.0 --port 5555 \ No newline at end of file diff --git a/run_tensorboard_3389.sh b/run_tensorboard_3389.sh new file mode 100644 index 0000000..290aee4 --- /dev/null +++ b/run_tensorboard_3389.sh @@ -0,0 +1,3 @@ +#!/usr/bin/env bash +export CUDA_VISIBLE_DEVICES= +tensorboard --logdir runs --host 0.0.0.0 --port 3389 \ No newline at end of file diff --git a/test_docker_pytorch14_37.cmd b/test_docker_pytorch14_37.cmd new file mode 100644 index 0000000..4f84579 --- /dev/null +++ b/test_docker_pytorch14_37.cmd @@ -0,0 +1,26 @@ +docker build -t xview2:37_pytorch14 -f Dockerfile-pytorch14-37 . +docker tag xview2:37_pytorch14 ekhvedchenya/xview2:37_pytorch14 + +docker run --rm --memory=7g --memory-swap=7g --memory-swappiness=0 --kernel-memory=7g --cpus=1^ + -v j:\xview2\test\images:/input^ + -v j:\xview2\test_predictions:/output^ + ekhvedchenya/xview2:37_pytorch14^ + /input/test_pre_00000.png /input/test_post_00000.png /output/test_localization_00000_pytorch14_v37.png /output/test_damage_00000_pytorch14_v37.png --color-mask --raw + +docker run --rm --memory=7g --memory-swap=7g --memory-swappiness=0 --kernel-memory=7g --cpus=1^ + -v j:\xview2\test\images:/input^ + -v j:\xview2\test_predictions:/output^ + ekhvedchenya/xview2:37_pytorch14^ + /input/test_pre_00284.png /input/test_post_00284.png /output/test_localization_00284_pytorch14_v37.png /output/test_damage_00284_pytorch14_v37.png --color-mask --raw + +docker run --rm --memory=7g --memory-swap=7g --memory-swappiness=0 --kernel-memory=7g --cpus=1^ + -v j:\xview2\test\images:/input^ + -v j:\xview2\test_predictions:/output^ + ekhvedchenya/xview2:37_pytorch14^ + /input/test_pre_00033.png /input/test_post_00033.png /output/test_localization_00033_pytorch14_v37.png /output/test_damage_00033_pytorch14_v37.png --color-mask --raw + +docker run --rm --memory=7g --memory-swap=7g --memory-swappiness=0 --kernel-memory=7g --cpus=1^ + -v j:\xview2\test\images:/input^ + -v j:\xview2\test_predictions:/output^ + ekhvedchenya/xview2:37_pytorch14^ + /input/test_pre_00096.png /input/test_post_00096.png /output/test_localization_00096_pytorch14_v37.png /output/test_damage_00096_pytorch14_v37.png --color-mask --raw diff --git a/tests/guatemala-volcano_00000000_post_disaster.png b/tests/guatemala-volcano_00000000_post_disaster.png new file mode 100644 index 0000000..e067211 Binary files /dev/null and b/tests/guatemala-volcano_00000000_post_disaster.png differ diff --git a/tests/hurricane-florence_00000115_post_disaster.png b/tests/hurricane-florence_00000115_post_disaster.png new file mode 100644 index 0000000..80e8b45 Binary files /dev/null and b/tests/hurricane-florence_00000115_post_disaster.png differ diff --git a/tests/hurricane-florence_00000475_post_disaster.png b/tests/hurricane-florence_00000475_post_disaster.png new file mode 100644 index 0000000..db9a9e8 Binary files /dev/null and b/tests/hurricane-florence_00000475_post_disaster.png differ diff --git a/tests/post.png b/tests/post.png new file mode 100644 index 0000000..c933a53 Binary files /dev/null and b/tests/post.png differ diff --git a/tests/pre.png b/tests/pre.png new file mode 100644 index 0000000..2b7aef8 Binary files /dev/null and b/tests/pre.png differ diff --git a/tests/test_damage_00121_prediction.png b/tests/test_damage_00121_prediction.png new file mode 100644 index 0000000..0cd8252 Binary files /dev/null and b/tests/test_damage_00121_prediction.png differ diff --git a/tests/test_dataset.py b/tests/test_dataset.py new file mode 100644 index 0000000..e9165cd --- /dev/null +++ b/tests/test_dataset.py @@ -0,0 +1,126 @@ +import cv2 + +from xview.augmentations import old_post_transform_augs, light_post_image_transform, medium_post_transform_augs +from xview.dataset import make_dual_dataframe +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt + + +def test_dataset(): + df = pd.read_csv("../train_folds.csv") + train_df = make_dual_dataframe(df) + + non_damaged_buildings = train_df["non_damaged_buildings_post"].values + light_damaged_buildings = train_df["light_damaged_buildings_post"].values + medium_damaged_buildings = train_df["medium_damaged_buildings_post"].values + destroyed_buildings = train_df["destroyed_buildings_post"].values + + non_damaged_pixels = train_df["non_damaged_pixels_post"].values + light_damaged_pixels = train_df["light_damaged_pixels_post"].values + medium_damaged_pixels = train_df["medium_damaged_pixels_post"].values + destroyed_pixels = train_df["destroyed_pixels_post"].values + + print( + non_damaged_buildings.sum(), + light_damaged_buildings.sum(), + medium_damaged_buildings.sum(), + destroyed_buildings.sum(), + ) + print( + (1024 * 1024) * len(train_df) + - non_damaged_pixels.sum() + - light_damaged_pixels.sum() + - medium_damaged_pixels.sum() + - destroyed_pixels.sum(), + non_damaged_pixels.sum(), + light_damaged_pixels.sum(), + medium_damaged_pixels.sum(), + destroyed_pixels.sum(), + ) + + +def test_post_transform(): + image = cv2.imread("guatemala-volcano_00000000_post_disaster.png") + image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) + + post_transform = old_post_transform_augs() + + image_acc = image.astype(np.long) + + n = 1000 + + for i in range(n): + image_t = post_transform(image=image)["image"] + image_acc += image_t + + image_acc = (image_acc * (1. / n)).astype(np.uint8) + + plt.figure() + plt.imshow(image) + plt.show() + + plt.figure() + plt.imshow(image_acc) + plt.show() + + + +def test_light_post_image_transform(): + image = cv2.imread("guatemala-volcano_00000000_post_disaster.png") + image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) + + post_transform = light_post_image_transform() + + image_acc = image.astype(np.long) + + n = 1000 + + for i in range(n): + image_t = post_transform(image=image)["image"] + image_acc += image_t + + image_acc = (image_acc * (1. / n)).astype(np.uint8) + + plt.figure() + plt.imshow(image) + plt.show() + + plt.figure() + plt.imshow(image_acc) + plt.show() + + + +def test_medium_post_transform_augs(): + image = cv2.imread("guatemala-volcano_00000000_post_disaster.png") + image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) + + plt.figure() + plt.imshow(image) + plt.show() + + post_transform = medium_post_transform_augs() + + k = 10 + for i in range(k): + image_t = post_transform(image=image)["image"] + + plt.figure() + plt.imshow(image_t) + plt.show() + + image_acc = image.astype(np.long) + + n = 100 + + for i in range(n): + image_t = post_transform(image=image)["image"] + image_acc += image_t + + image_acc = (image_acc * (1. / n)).astype(np.uint8) + + + plt.figure() + plt.imshow(image_acc) + plt.show() diff --git a/tests/test_load_mask.py b/tests/test_load_mask.py new file mode 100644 index 0000000..f6f8914 --- /dev/null +++ b/tests/test_load_mask.py @@ -0,0 +1,43 @@ +import cv2 +import numpy as np +from PIL import Image +import matplotlib.pyplot as plt + +from xview.dataset import read_mask +from xview.utils.inference_image_output import resize_mask_one_hot + + +def test_load_paletted(): + fname = "d:\\datasets\\xview2\\train\\masks\\hurricane-harvey_00000402_post_disaster.png" + + a = cv2.imread(fname) + b = cv2.imread(fname, cv2.IMREAD_GRAYSCALE) + c = cv2.imread(fname, cv2.IMREAD_ANYCOLOR) + d = cv2.imread(fname, cv2.IMREAD_ANYCOLOR) + e = cv2.imread(fname, cv2.IMREAD_ANYDEPTH) + f = cv2.imread(fname, cv2.IMREAD_UNCHANGED) + g = np.array(Image.open(fname)) + + print(a.shape, np.unique(a)) + print(b.shape, np.unique(b)) + print(c.shape, np.unique(c)) + print(d.shape, np.unique(d)) + print(e.shape, np.unique(e)) + print(f.shape, np.unique(f)) + print(g.shape, np.unique(g)) + + + +def test_mask_resize(): + fname = "d:\\datasets\\xview2\\train\\masks\\hurricane-harvey_00000402_post_disaster.png" + mask = read_mask(fname) + + mask2 = cv2.resize(mask, (512,512), interpolation=cv2.INTER_NEAREST) + mask3 = resize_mask_one_hot(mask, (512,512)) + + cv2.imshow("Original", mask * 255) + cv2.imshow("Nearest", mask2 * 255) + cv2.imshow("Smart", mask3 * 255) + + cv2.waitKey(-1) + diff --git a/tests/test_localization_00121_prediction.png b/tests/test_localization_00121_prediction.png new file mode 100644 index 0000000..04a2fcb Binary files /dev/null and b/tests/test_localization_00121_prediction.png differ diff --git a/tests/test_models.py b/tests/test_models.py new file mode 100644 index 0000000..dd5760d --- /dev/null +++ b/tests/test_models.py @@ -0,0 +1,157 @@ +import torch +from pytorch_toolbelt.utils.torch_utils import count_parameters +from torch import nn + +from xview.dataset import OUTPUT_MASK_KEY +from xview.losses import ArcFaceLoss2d, OHEMCrossEntropyLoss +from xview.models.deeplab import resnet34_deeplab128 +from xview.models.fpn_v2 import ( + resnet101_fpncatv2_256, + densenet201_fpncatv2_256, + efficientb4_fpncatv2_256, + inceptionv4_fpncatv2_256, +) +from xview.models.hrnet_arc import hrnet18_arc +from xview.models.segcaps import SegCaps +from xview.models.unet import resnet18_unet32 +from xview.models.unetv2 import inceptionv4_unet_v2, resnet101_unet_v2 + + +def test_ohem_ce(): + x = torch.randn((8, 5, 128, 128)).cuda() + y = torch.randint(0, 5, (8, 128, 128)).long().cuda() + + loss = OHEMCrossEntropyLoss() + l = loss(x, y) + print(l) + + +def test_conv_transpose(): + x = torch.randn((1, 32, 128, 128)).cuda() + + module = nn.ConvTranspose2d(32, 5, kernel_size=8, stride=4, padding=2).cuda() + + y = module(x) + print(y.size()) + + +@torch.no_grad() +def test_hrnet18_arc(): + x = torch.randn((1, 6, 256, 256)) + net = hrnet18_arc().eval() + + out = net(x) + tgt = torch.randint(0, 5, (1, 256, 256)).long() + criterion = ArcFaceLoss2d() + loss = criterion(out[OUTPUT_MASK_KEY], tgt) + + print(out) + + +@torch.no_grad() +def test_resnet18_unet(): + x = torch.randn((1, 6, 256, 256)) + net = resnet18_unet32().eval() + print(count_parameters(net)) + out = net(x) + print(out) + + +@torch.no_grad() +def test_resnet34_deeplab128(): + x = torch.randn((1, 6, 512, 512)) + net = resnet34_deeplab128().eval() + print(count_parameters(net)) + out = net(x) + print(out) + + +def test_seg_caps(): + net = SegCaps(num_classes=5) + print(count_parameters(net)) + x = torch.randn((4, 3, 256, 256)) + y = net(x) + print(y.size()) + + +def test_selim_unet(): + from xview.models.selim.unet import DensenetUnet + + d = DensenetUnet(5, backbone_arch="densenet121") + d.eval() + import numpy as np + + with torch.no_grad(): + images = torch.from_numpy(np.zeros((16, 3, 256, 256), dtype="float32")) + i = d(images) + print(i.shape) + + print(d) + + +@torch.no_grad() +def test_inception_unet_like_selim(): + d = inceptionv4_unet_v2().cuda().eval() + print(count_parameters(d)) + + print(d.decoder.decoder_features) + print(d.decoder.bottlenecks) + print(d.decoder.decoder_stages) + + images = torch.rand(4, 6, 512, 512).cuda() + i = d(images) + print(i[OUTPUT_MASK_KEY].size()) + + +@torch.no_grad() +def test_inception_unet_like_selim(): + d = resnet101_unet_v2().cuda().eval() + print(count_parameters(d)) + + print(d.decoder.decoder_features) + print(d.decoder.bottlenecks) + print(d.decoder.decoder_stages) + + images = torch.rand(4, 6, 512, 512).cuda() + i = d(images) + print(i[OUTPUT_MASK_KEY].size()) + + +@torch.no_grad() +def test_resnet101_fpncatv2_256(): + d = resnet101_fpncatv2_256().cuda().eval() + print(count_parameters(d)) + + images = torch.rand(2, 6, 512, 512).cuda() + i = d(images) + print(i[OUTPUT_MASK_KEY].size()) + + +@torch.no_grad() +def test_densenet201_fpncatv2_256(): + d = densenet201_fpncatv2_256().cuda().eval() + print(count_parameters(d)) + + images = torch.rand(4, 6, 512, 512).cuda() + i = d(images) + print(i[OUTPUT_MASK_KEY].size()) + + +@torch.no_grad() +def test_inceptionv4_fpncatv2_256(): + d = inceptionv4_fpncatv2_256().cuda().eval() + print(count_parameters(d)) + + images = torch.rand(2, 6, 512, 512).cuda() + i = d(images) + print(i[OUTPUT_MASK_KEY].size()) + + +@torch.no_grad() +def test_efficientb4_fpncatv2_256(): + d = efficientb4_fpncatv2_256().cuda().eval() + print(count_parameters(d)) + + images = torch.rand(4, 6, 512, 512).cuda() + i = d(images) + print(i[OUTPUT_MASK_KEY].size()) diff --git a/tests/test_post_00121.png b/tests/test_post_00121.png new file mode 100644 index 0000000..4c8550f Binary files /dev/null and b/tests/test_post_00121.png differ diff --git a/tests/test_postprocessing.py b/tests/test_postprocessing.py new file mode 100644 index 0000000..c304e8f --- /dev/null +++ b/tests/test_postprocessing.py @@ -0,0 +1,63 @@ +import cv2 +import numpy as np + +from xview.dataset import read_mask + +import matplotlib.pyplot as plt + +from xview.postprocessing import make_predictions_floodfill, make_predictions_dominant_v2 +from xview.utils.inference_image_output import make_rgb_image +import pytest + + +@pytest.mark.parametrize(["actual", "expected"], [ + ("hurricane-florence_00000115_post_disaster.npy", "hurricane-florence_00000115_post_disaster.png"), + ("hurricane-florence_00000475_post_disaster.npy", "hurricane-florence_00000475_post_disaster.png"), +]) +def test_watershed(actual, expected): + dmg = np.load(actual) + dmg_true = read_mask(expected) + + loc_cls, dmg_cls = make_predictions_dominant_v2(dmg) + + plt.figure() + plt.imshow(make_rgb_image(dmg_true)) + plt.show() + + plt.figure() + plt.imshow(make_rgb_image(np.argmax(dmg, axis=0))) + plt.show() + + plt.figure() + plt.imshow(make_rgb_image(loc_cls)) + plt.show() + + plt.figure() + plt.imshow(make_rgb_image(dmg_cls)) + plt.show() + + +def test_watershed_with_image(): + dmg = read_mask("test_damage_00121_prediction.png") + loc = read_mask("test_localization_00121_prediction.png") + img = cv2.imread("test_post_00121.png") + + # Fix mask + dmg[loc == 0] = 0 + + seed = dmg.copy() + seed[loc == 0] = 0 + markers = cv2.watershed(img, seed.astype(int)) + markers[markers == 0] = 1 + + plt.figure() + plt.imshow(dmg) + plt.show() + + plt.figure() + plt.imshow(loc) + plt.show() + + plt.figure() + plt.imshow(markers) + plt.show() diff --git a/tests/test_registration.py b/tests/test_registration.py new file mode 100644 index 0000000..2b3f4e1 --- /dev/null +++ b/tests/test_registration.py @@ -0,0 +1,105 @@ +import pytest +import cv2 +import numpy as np + +import matplotlib.pyplot as plt + +from xview.alignment import align_post_image_pyramid + + +def test_ecc(): + pre = cv2.imread("d:\\datasets\\xview2\\train\\images\\guatemala-volcano_00000002_pre_disaster.png") + post = cv2.imread("d:\\datasets\\xview2\\train\\images\\guatemala-volcano_00000002_post_disaster.png") + + warpMatrix = np.zeros((3, 3), dtype=np.float32) + warpMatrix[0, 0] = warpMatrix[1, 1] = warpMatrix[2, 2] = 1.0 + + stop_criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 200, 0.0001) + + retval = False + + try: + retval, warpMatrix = cv2.findTransformECC( + cv2.cvtColor(pre, cv2.COLOR_RGB2GRAY), + cv2.cvtColor(post, cv2.COLOR_RGB2GRAY), + warpMatrix, + cv2.MOTION_HOMOGRAPHY, + stop_criteria, + None, + 5, + ) + post_warped = cv2.warpPerspective(post, warpMatrix, dsize=(1024, 1024), flags=cv2.WARP_INVERSE_MAP) + except: + retval = False + post_warped = post.copy() + + plt.figure() + plt.imshow(pre) + plt.show() + + plt.figure() + plt.imshow(post) + plt.show() + + plt.figure() + plt.imshow(post_warped) + plt.show() + + + +def test_ecc_pyramid(): + pre = cv2.imread("c:\\datasets\\xview2\\train\\images\\guatemala-volcano_00000001_pre_disaster.png") + post = cv2.imread("c:\\datasets\\xview2\\train\\images\\guatemala-volcano_00000001_post_disaster.png") + post_warped = align_post_image_pyramid(pre, post) + + plt.figure() + plt.imshow(pre) + plt.show() + + plt.figure() + plt.imshow(post) + plt.show() + + plt.figure() + plt.imshow(post_warped) + plt.show() + + + +def test_ecc_simple(): + pre = cv2.imread("pre.png") + post = cv2.imread("post.png") + + warpMatrix = np.zeros((3, 3), dtype=np.float32) + warpMatrix[0, 0] = warpMatrix[1, 1] = warpMatrix[2, 2] = 1.0 + + stop_criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 20, 0.0001) + + retval = False + + try: + retval, warpMatrix = cv2.findTransformECC( + cv2.cvtColor(pre, cv2.COLOR_RGB2GRAY), + cv2.cvtColor(post, cv2.COLOR_RGB2GRAY), + warpMatrix, + cv2.MOTION_HOMOGRAPHY, + stop_criteria, + None, + 5, + ) + post_warped = cv2.warpPerspective(post, warpMatrix, dsize=(256, 256), flags=cv2.WARP_INVERSE_MAP) + except: + retval = False + post_warped = post.copy() + + plt.figure() + plt.imshow(pre) + plt.show() + + plt.figure() + plt.imshow(post) + plt.show() + + plt.figure() + plt.imshow(post_warped) + plt.show() diff --git a/train.csv b/train.csv new file mode 100644 index 0000000..6c971e8 --- /dev/null +++ b/train.csv @@ -0,0 +1,18337 @@ +destroyed_buildings,destroyed_pixels,event_name,event_type,folder,image_fname,image_id,light_damaged_buildings,light_damaged_pixels,mask_fname,medium_damaged_buildings,medium_damaged_pixels,non_damaged_buildings,non_damaged_pixels,sample_id 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index 0000000..c1f25fc --- /dev/null +++ b/train.sh @@ -0,0 +1,99 @@ +# Step 0 +python convert_masks.py -dd $XVIEW2_DATA_DIR + +# Train base models (no pseudolabeling, no optimized weights) +# Batch size tuned to fit into p3.8xlarge instance or 4x1080Ti +# Estimated training time ~1 week + +# Step 1 +python fit_predict.py --seed 330 -dd $XVIEW2_DATA_DIR -x fold0_resnet34_unet_v2 --model resnet34_unet_v2 --batch-size 64 --epochs 150 --learning-rate 0.0001 --criterion [['weighted_ce', '1']] -w 16 -a medium --fp16 --fold 0 --scheduler cos -wd 0.0001 --only-buildings True --crops True --post-transform True +python fit_predict.py --seed 332 -dd $XVIEW2_DATA_DIR -x fold0_resnet101_fpncatv2_256 --model resnet101_fpncatv2_256 --batch-size 48 --epochs 150 --learning-rate 0.0001 --criterion [['weighted_ce', '1']] -w 16 -a medium --fp16 --fold 0 --scheduler cos -wd 0.0001 --only-buildings True --crops True --post-transform True + +python fit_predict.py --seed 13 -dd $XVIEW2_DATA_DIR -x fold1_seresnext50_unet_v2 --model seresnext50_unet_v2 --batch-size 32 --epochs 150 --learning-rate 0.001 --criterion [['weighted_ce', '1'], ['focal', '1']] -w 16 -a medium --fp16 --fold 1 --scheduler simple -wd 1e-05 --only-buildings True --crops True --post-transform True +python fit_predict.py --seed 331 -dd $XVIEW2_DATA_DIR -x fold1_resnet34_unet_v2 --model resnet34_unet_v2 --batch-size 64 --epochs 150 --learning-rate 0.0001 --criterion [['weighted_ce', '1']] -w 16 -a medium --fp16 --fold 1 --scheduler cos -wd 0.0001 --only-buildings True --crops True --post-transform True +python fit_predict.py --seed 1331 -dd $XVIEW2_DATA_DIR -x fold1_densenet201_fpncatv2_256 --model densenet201_fpncatv2_256 --batch-size 32 --epochs 150 --learning-rate 0.0001 --criterion [['weighted_ce', '1']] -w 16 -a medium --fp16 --fold 1 --scheduler cos -wd 0.0001 --only-buildings True --crops True --post-transform True + +python fit_predict.py --seed 333 -dd $XVIEW2_DATA_DIR -x fold2_inceptionv4_fpncatv2_256 --model inceptionv4_fpncatv2_256 --batch-size 48 --epochs 150 --learning-rate 0.001 --criterion [['weighted_ce', '1']] -w 16 -a medium --fp16 --fold 2 --scheduler poly -wd 1e-05 --only-buildings True --crops True --post-transform True +python fit_predict.py --seed 303 -dd $XVIEW2_DATA_DIR -x fold2_densenet169_unet_v2 --model densenet169_unet_v2 --batch-size 32 --epochs 150 --learning-rate 0.001 --criterion [['ohem_ce', '1']] -w 16 -a medium --fp16 --fold 2 --scheduler simple -wd 1e-05 --only-buildings True --crops True --post-transform True +python fit_predict.py --seed 332 -dd $XVIEW2_DATA_DIR -x fold2_resnet34_unet_v2 --model resnet34_unet_v2 --batch-size 64 --epochs 150 --learning-rate 0.0001 --criterion [['weighted_ce', '1']] -w 16 -a medium --fp16 --fold 2 --scheduler cos -wd 0.0001 --only-buildings True --crops True --post-transform True + +python fit_predict.py --seed 50 -dd $XVIEW2_DATA_DIR -x fold3_resnet34_unet_v2 --model resnet34_unet_v2 --batch-size 16 --epochs 150 --learning-rate 0.0003 --criterion [['weighted_ce', '1']] -w 16 -a medium --fp16 --fold 3 --scheduler cos -wd 1e-05 --only-buildings True --crops True --post-transform True +python fit_predict.py --seed 3 -dd $XVIEW2_DATA_DIR -x fold3_seresnext50_unet_v2 --model seresnext50_unet_v2 --batch-size 32 --epochs 150 --learning-rate 0.001 --criterion [['weighted_ce', '1'], ['focal', '1']] -w 16 -a medium --fp16 --fold 3 --scheduler simple -wd 1e-05 --only-buildings True --crops True --post-transform True +python fit_predict.py --seed 3334 -dd $XVIEW2_DATA_DIR -x fold3_efficientb4_fpncatv2_256 --model efficientb4_fpncatv2_256 --batch-size 32 --epochs 150 --learning-rate 0.0001 --criterion [['weighted_ce', '1']] -w 16 -a medium --fp16 --fold 3 --scheduler cos -wd 0.0001 --only-buildings True --crops True --post-transform True + +python fit_predict.py --seed 334 -dd $XVIEW2_DATA_DIR -x fold4_resnet34_unet_v2 --model resnet34_unet_v2 --batch-size 64 --epochs 150 --learning-rate 0.001 --criterion [['weighted_ce', '1']] -w 16 -a medium --fp16 --fold 4 --scheduler cos -wd 1e-05 --only-buildings True --crops True --post-transform True +python fit_predict.py --seed 133 -dd $XVIEW2_DATA_DIR -x fold4_resnet101_unet_v2 --model resnet101_unet_v2 --batch-size 40 --epochs 150 --learning-rate 0.001 --criterion [['weighted_ce', '1'], ['focal', '1']] -w 16 -a medium --fp16 --fold 4 --scheduler simple -wd 0.0001 --only-buildings True --crops True --post-transform True + +# Run inference on test dataset +python predict.py -dd $XVIEW2_DATA_DIR -tta flipscale -p naive -o stage1_predictions -b 16 --fp16 \ + fold0_resnet34_unet_v2.pth\ + fold0_resnet101_fpncatv2_256.pth\ + fold1_seresnext50_unet_v2.pth\ + fold1_resnet34_unet_v2.pth\ + fold1_densenet201_fpncatv2_256.pth\ + fold2_inceptionv4_fpncatv2_256.pth\ + fold2_densenet169_unet_v2.pth\ + fold2_resnet34_unet_v2.pth\ + fold3_resnet34_unet_v2.pth\ + fold3_seresnext50_unet_v2.pth\ + fold3_efficientb4_fpncatv2_256.pth\ + fold4_resnet34_unet_v2.pth\ + fold4_resnet101_unet_v2.pth + +# Step 2 +# Fine-tune using pseudo-label predictions +# Estimated training time ~3-4 days +python finetune.py --seed 330 -dd $XVIEW2_DATA_DIR -pl stage1_predictions_pseudolabeling -x fold0_pl_resnet34_unet_v2 --model resnet34_unet_v2 --batch-size 64 --epochs 50 --learning-rate 0.0001 --criterion [['weighted_ce', '1']] -w 16 -a medium --fp16 --fold 0 --scheduler cos -wd 0.0001 --only-buildings True --crops True --post-transform True +python finetune.py --seed 332 -dd $XVIEW2_DATA_DIR -pl stage1_predictions_pseudolabeling -x fold0_pl_resnet101_fpncatv2_256 --model resnet101_fpncatv2_256 --batch-size 48 --epochs 50 --learning-rate 0.0001 --criterion [['weighted_ce', '1']] -w 16 -a medium --fp16 --fold 0 --scheduler cos -wd 0.0001 --only-buildings True --crops True --post-transform True + +python finetune.py --seed 13 -dd $XVIEW2_DATA_DIR -pl stage1_predictions_pseudolabeling -x fold1_pl_seresnext50_unet_v2 --model seresnext50_unet_v2 --batch-size 32 --epochs 50 --learning-rate 0.0001 --criterion [['weighted_ce', '1'], ['focal', '1']] -w 16 -a medium --fp16 --fold 1 --scheduler simple -wd 1e-05 --only-buildings True --crops True --post-transform True +python finetune.py --seed 331 -dd $XVIEW2_DATA_DIR -pl stage1_predictions_pseudolabeling -x fold1_pl_resnet34_unet_v2 --model resnet34_unet_v2 --batch-size 64 --epochs 50 --learning-rate 0.0001 --criterion [['weighted_ce', '1']] -w 16 -a medium --fp16 --fold 1 --scheduler cos -wd 0.0001 --only-buildings True --crops True --post-transform True +python finetune.py --seed 1331 -dd $XVIEW2_DATA_DIR -pl stage1_predictions_pseudolabeling -x fold1_pl_densenet201_fpncatv2_256 --model densenet201_fpncatv2_256 --batch-size 32 --epochs 50 --learning-rate 0.0001 --criterion [['weighted_ce', '1']] -w 16 -a medium --fp16 --fold 1 --scheduler cos -wd 0.0001 --only-buildings True --crops True --post-transform True + +python finetune.py --seed 333 -dd $XVIEW2_DATA_DIR -pl stage1_predictions_pseudolabeling -x fold2_pl_inceptionv4_fpncatv2_256 --model inceptionv4_fpncatv2_256 --batch-size 48 --epochs 50 --learning-rate 0.001 --criterion [['weighted_ce', '1']] -w 16 -a medium --fp16 --fold 2 --scheduler poly -wd 1e-05 --only-buildings True --crops True --post-transform True +python finetune.py --seed 303 -dd $XVIEW2_DATA_DIR -pl stage1_predictions_pseudolabeling -x fold2_pl_densenet169_unet_v2 --model densenet169_unet_v2 --batch-size 32 --epochs 50 --learning-rate 0.0001 --criterion [['ohem_ce', '1']] -w 16 -a medium --fp16 --fold 2 --scheduler simple -wd 1e-05 --only-buildings True --crops True --post-transform True +python finetune.py --seed 332 -dd $XVIEW2_DATA_DIR -pl stage1_predictions_pseudolabeling -x fold2_pl_resnet34_unet_v2 --model resnet34_unet_v2 --batch-size 64 --epochs 50 --learning-rate 0.0001 --criterion [['weighted_ce', '1']] -w 16 -a medium --fp16 --fold 2 --scheduler cos -wd 0.0001 --only-buildings True --crops True --post-transform True + +python finetune.py --seed 50 -dd $XVIEW2_DATA_DIR -pl stage1_predictions_pseudolabeling -x fold3_pl_resnet34_unet_v2 --model resnet34_unet_v2 --batch-size 16 --epochs 50 --learning-rate 0.0003 --criterion [['weighted_ce', '1']] -w 16 -a medium --fp16 --fold 3 --scheduler cos -wd 1e-05 --only-buildings True --crops True --post-transform True +python finetune.py --seed 3 -dd $XVIEW2_DATA_DIR -pl stage1_predictions_pseudolabeling -x fold3_pl_seresnext50_unet_v2 --model seresnext50_unet_v2 --batch-size 32 --epochs 50 --learning-rate 0.0001 --criterion [['weighted_ce', '1'], ['focal', '1']] -w 16 -a medium --fp16 --fold 3 --scheduler simple -wd 1e-05 --only-buildings True --crops True --post-transform True +python finetune.py --seed 3334 -dd $XVIEW2_DATA_DIR -pl stage1_predictions_pseudolabeling -x fold3_pl_efficientb4_fpncatv2_256 --model efficientb4_fpncatv2_256 --batch-size 32 --epochs 50 --learning-rate 0.0001 --criterion [['weighted_ce', '1']] -w 16 -a medium --fp16 --fold 3 --scheduler cos -wd 0.0001 --only-buildings True --crops True --post-transform True + +python finetune.py --seed 334 -dd $XVIEW2_DATA_DIR -pl stage1_predictions_pseudolabeling -x fold4_pl_resnet34_unet_v2 --model resnet34_unet_v2 --batch-size 64 --epochs 50 --learning-rate 0.0001 --criterion [['weighted_ce', '1']] -w 16 -a medium --fp16 --fold 4 --scheduler cos -wd 1e-05 --only-buildings True --crops True --post-transform True +python finetune.py --seed 133 -dd $XVIEW2_DATA_DIR -pl stage1_predictions_pseudolabeling -x fold4_pl_resnet101_unet_v2 --model resnet101_unet_v2 --batch-size 40 --epochs 50 --learning-rate 0.0001 --criterion [['weighted_ce', '1'], ['focal', '1']] -w 16 -a medium --fp16 --fold 4 --scheduler simple -wd 0.0001 --only-buildings True --crops True --post-transform True + + +# Make OOF predictions on fine-tuned models +# This would require up to 1Tb to save raw masks in NPY format +python predict_off.py -dd $XVIEW2_DATA_DIR\ + fold0_pl_resnet34_unet_v2.pth\ + fold0_pl_resnet101_fpncatv2_256.pth\ + fold1_pl_seresnext50_unet_v2.pth\ + fold1_pl_resnet34_unet_v2.pth\ + fold1_pl_densenet201_fpncatv2_256.pth\ + fold2_pl_inceptionv4_fpncatv2_256.pth\ + fold2_pl_densenet169_unet_v2.pth\ + fold2_pl_resnet34_unet_v2.pth\ + fold3_pl_resnet34_unet_v2.pth\ + fold3_pl_seresnext50_unet_v2.pth\ + fold3_pl_efficientb4_fpncatv2_256.pth\ + fold4_pl_resnet34_unet_v2.pth\ + fold4_pl_resnet101_unet_v2.pth + +# Optimize per-class weights. As a result, you will get optimized_weights_%timestamp%.csv file +# This is very CPU and IO consuming operation +# Exhaustive search of optimal weights checkpoint may take up to several hours PER ONE checkpoint. +python optimize_softmax.py -dd $XVIEW2_DATA_DIR\ + fold0_pl_resnet34_unet_v2.pth\ + fold0_pl_resnet101_fpncatv2_256.pth\ + fold1_pl_seresnext50_unet_v2.pth\ + fold1_pl_resnet34_unet_v2.pth\ + fold1_pl_densenet201_fpncatv2_256.pth\ + fold2_pl_inceptionv4_fpncatv2_256.pth\ + fold2_pl_densenet169_unet_v2.pth\ + fold2_pl_resnet34_unet_v2.pth\ + fold3_pl_resnet34_unet_v2.pth\ + fold3_pl_seresnext50_unet_v2.pth\ + fold3_pl_efficientb4_fpncatv2_256.pth\ + fold4_pl_resnet34_unet_v2.pth\ + fold4_pl_resnet101_unet_v2.pth + diff --git a/train_folds.csv b/train_folds.csv new file mode 100644 index 0000000..6da020a --- /dev/null +++ b/train_folds.csv @@ -0,0 +1,18337 @@ 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(cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 200, 0.0001) + + retval = False + post_warped: np.ndarray = None + + try: + retval, warpMatrix = cv2.findTransformECC( + cv2.cvtColor(pre, cv2.COLOR_RGB2GRAY), + cv2.cvtColor(post, cv2.COLOR_RGB2GRAY), + warpMatrix, + cv2.MOTION_HOMOGRAPHY, + stop_criteria, + None, + 5, + ) + post_warped = cv2.warpPerspective(post, warpMatrix, dsize=(1024, 1024), flags=cv2.WARP_INVERSE_MAP) + except: + retval = False + post_warped = post.copy() + + return post_warped + + + + +def align_post_image_pyramid(pre, post): + pre_pyrs = [cv2.cvtColor(pre, cv2.COLOR_RGB2GRAY)] + pre_pyrs.append(cv2.pyrDown(pre_pyrs[-1])) + pre_pyrs.append(cv2.pyrDown(pre_pyrs[-1])) + pre_pyrs.append(cv2.pyrDown(pre_pyrs[-1])) + + post_pyrs = [cv2.cvtColor(post, cv2.COLOR_RGB2GRAY)] + post_pyrs.append(cv2.pyrDown(post_pyrs[-1])) + post_pyrs.append(cv2.pyrDown(post_pyrs[-1])) + post_pyrs.append(cv2.pyrDown(post_pyrs[-1])) + + stop_criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.0001) + + warpMatrix = np.zeros((3, 3), dtype=np.float32) + warpMatrix[0, 0] = warpMatrix[1, 1] = warpMatrix[2, 2] = 1.0 + + scale_up = np.zeros((3, 3), dtype=np.float32) + scale_up[0, 0] = scale_up[1, 1] = 0.5 + scale_up[2, 2] = 1.0 + + M = np.zeros((3, 3), dtype=np.float32) + M[0, 0] = M[1, 1] = M[2, 2] = 1.0 + + for pre_i, post_i in zip(reversed(pre_pyrs), reversed(post_pyrs)): + warpMatrix = np.zeros((3, 3), dtype=np.float32) + warpMatrix[0, 0] = warpMatrix[1, 1] = warpMatrix[2, 2] = 1.0 + + retval = False + + post_i_refined = cv2.warpPerspective(post_i, M, + dsize=(post_i.shape[1], post_i.shape[0]), + flags=cv2.WARP_INVERSE_MAP) + + try: + retval, warpMatrix = cv2.findTransformECC( + pre_i, + post_i_refined, + warpMatrix, + cv2.MOTION_HOMOGRAPHY, + stop_criteria, + None, + 5, + ) + + if retval: + M = np.dot(warpMatrix, M) + # M = np.dot(np.dot(scale_up, warpMatrix), M) + # M = np.dot(np.dot(warpMatrix, scale_up), M) + # M = np.dot(M, np.dot(warpMatrix, scale_up)) + # M = np.dot(M, np.dot(scale_up, warpMatrix)) + except: + pass + + post_warped = cv2.warpPerspective(post, M, + dsize=(post.shape[1], post.shape[0]), + flags=cv2.WARP_INVERSE_MAP) + + return post_warped diff --git a/xview/augmentations.py b/xview/augmentations.py new file mode 100644 index 0000000..928a79c --- /dev/null +++ b/xview/augmentations.py @@ -0,0 +1,221 @@ +from typing import Tuple + +import albumentations as A +import cv2 + +__all__ = [ + "safe_color_augmentations", + "safe_spatial_augmentations", + "light_color_augmentations", + "light_spatial_augmentations", + "light_post_image_transform", + "medium_color_augmentations", + "medium_spatial_augmentations", + "medium_post_transform_augs", + "hard_spatial_augmentations", + "hard_color_augmentations", + "old_light_augmentations", + "old_post_transform_augs" +] + + +def safe_color_augmentations(): + return A.Compose([A.RandomBrightnessContrast(brightness_limit=0.1, contrast_limit=0.1, brightness_by_max=True)]) + + +def safe_spatial_augmentations(image_size: Tuple[int, int]): + return A.Compose( + [ + A.ShiftScaleRotate( + shift_limit=0.05, + scale_limit=0.1, + rotate_limit=5, + border_mode=cv2.BORDER_CONSTANT, + value=0, + mask_value=0, + ), + A.MaskDropout(10), + A.Compose([A.Transpose(), A.RandomRotate90()]), + ] + ) + + +def light_color_augmentations(): + return A.Compose( + [ + A.RandomBrightnessContrast(brightness_limit=0.1, contrast_limit=0.1, brightness_by_max=True), + A.RandomGamma(gamma_limit=(90, 110)), + ] + ) + + +def light_spatial_augmentations(image_size: Tuple[int, int]): + return A.Compose( + [ + A.ShiftScaleRotate(scale_limit=0.1, rotate_limit=10, border_mode=cv2.BORDER_CONSTANT), + # D4 Augmentations + A.Compose([A.Transpose(), A.RandomRotate90()]), + # Spatial-preserving augmentations: + A.RandomBrightnessContrast(), + + A.MaskDropout(max_objects=10), + ] + ) + + +def old_light_augmentations(image_size: Tuple[int, int]): + return A.Compose( + [ + A.ShiftScaleRotate(scale_limit=0.1, rotate_limit=10, border_mode=cv2.BORDER_CONSTANT), + # D4 Augmentations + A.Compose([A.Transpose(), A.RandomRotate90()]), + # Spatial-preserving augmentations: + A.RandomBrightnessContrast(), + A.ElasticTransform(border_mode=cv2.BORDER_CONSTANT, value=0, mask_value=0), + ] + ) + + +def light_post_image_transform(): + return A.OneOf( + [ + A.NoOp(), + A.Compose( + [ + A.PadIfNeeded(1024 + 10, 1024 + 10, border_mode=cv2.BORDER_CONSTANT, value=0, mask_value=0), + A.RandomSizedCrop((1024 - 5, 1024 + 5), 1024, 1024), + ], + p=0.2, + ), + A.ShiftScaleRotate( + shift_limit=0.02, + rotate_limit=3, + scale_limit=0.02, + border_mode=cv2.BORDER_CONSTANT, + mask_value=0, + value=0, + p=0.2, + ), + ] + ) + + +def old_post_transform_augs(): + return A.OneOf( + [ + A.NoOp(), + A.Compose( + [ + A.PadIfNeeded(1024 + 20, 1024 + 20, border_mode=cv2.BORDER_CONSTANT, value=0), + A.RandomSizedCrop((1024 - 10, 1024 + 10), 1024, 1024), + ], + p=0.2, + ), + A.ShiftScaleRotate( + shift_limit=0.0625, rotate_limit=3, scale_limit=0.05, border_mode=cv2.BORDER_CONSTANT, value=0, p=0.2 + ), + ] + ) + + +def medium_post_transform_augs(): + return A.OneOf( + [ + A.NoOp(), + A.Compose( + [ + A.PadIfNeeded(1024 + 40, 1024 + 40, border_mode=cv2.BORDER_CONSTANT, value=0), + A.RandomSizedCrop((1024 - 20, 1024 + 20), 1024, 1024), + ] + ), + A.ShiftScaleRotate( + shift_limit=0.1, rotate_limit=5, scale_limit=0.075, border_mode=cv2.BORDER_CONSTANT, value=0 + ), + ] + ) + + +def medium_color_augmentations(): + return A.Compose( + [ + A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, brightness_by_max=True), + A.RandomGamma(gamma_limit=(90, 110)), + A.OneOf( + [ + A.NoOp(p=0.8), + A.HueSaturationValue(hue_shift_limit=10, sat_shift_limit=10, val_shift_limit=10), + A.RGBShift(r_shift_limit=10, g_shift_limit=10, b_shift_limit=10), + ], + p=0.2, + ), + ] + ) + + +def medium_spatial_augmentations(image_size: Tuple[int, int], no_mask_dropout=False): + return A.Compose( + [ + A.OneOf( + [ + A.NoOp(p=0.8), + A.RandomGridShuffle(grid=(4, 4), p=0.2), + A.RandomGridShuffle(grid=(3, 3), p=0.2), + A.RandomGridShuffle(grid=(2, 2), p=0.2), + ], p=1 + ), + + A.ShiftScaleRotate(scale_limit=0.1, rotate_limit=10, border_mode=cv2.BORDER_CONSTANT), + # D4 Augmentations + A.Compose([A.Transpose(), A.RandomRotate90()]), + # Spatial-preserving augmentations: + A.RandomBrightnessContrast(), + + A.NoOp() if no_mask_dropout else A.MaskDropout(max_objects=10), + ] + ) + + + +def hard_color_augmentations(): + return A.Compose( + [ + A.RandomBrightnessContrast(brightness_limit=0.3, contrast_limit=0.3, brightness_by_max=True), + A.RandomGamma(gamma_limit=(90, 110)), + A.OneOf([A.NoOp(), A.MultiplicativeNoise(), A.GaussNoise(), A.ISONoise()]), + A.OneOf([A.RGBShift(), A.HueSaturationValue(), A.NoOp()]), + A.RandomFog(fog_coef_lower=0.05, fog_coef_upper=0.3), + ] + ) + + +def hard_spatial_augmentations(image_size: Tuple[int, int], rot_angle=45): + return A.Compose( + [ + A.OneOf( + [ + A.NoOp(), + A.RandomGridShuffle(grid=(4, 4)), + A.RandomGridShuffle(grid=(3, 3)), + A.RandomGridShuffle(grid=(2, 2)), + ] + ), + A.MaskDropout(max_objects=10), + A.OneOf( + [ + A.ShiftScaleRotate( + scale_limit=0.1, rotate_limit=rot_angle, border_mode=cv2.BORDER_CONSTANT, value=0, mask_value=0 + ), + A.NoOp(), + ] + ), + A.OneOf( + [ + A.ElasticTransform(border_mode=cv2.BORDER_CONSTANT, value=0, mask_value=0), + A.GridDistortion(border_mode=cv2.BORDER_CONSTANT, value=0, mask_value=0), + A.NoOp(), + ] + ), + # D4 + A.Compose([A.Transpose(), A.RandomRotate90()]), + ] + ) diff --git a/xview/averaging_rounder.py b/xview/averaging_rounder.py new file mode 100644 index 0000000..a637e37 --- /dev/null +++ b/xview/averaging_rounder.py @@ -0,0 +1,99 @@ +from functools import partial +from multiprocessing.pool import Pool + +import cv2 +import numpy as np +import scipy as sp +import torch +from pytorch_toolbelt.utils.torch_utils import to_numpy +from tqdm import tqdm + +from xview.dataset import read_mask +from xview.metric import CompetitionMetricCallback +from xview.postprocessing import make_predictions_naive + + +def _compute_fn(args, coef_exp): + xi, dmg_true = args + + loc_pred, dmg_pred = make_predictions_naive(xi.astype(np.float32) * coef_exp) + row = CompetitionMetricCallback.get_row_pair(loc_pred, dmg_pred, dmg_true, dmg_true) + return row + + +class AveragingOptimizedRounder(object): + def __init__(self, apply_softmax, workers=0): + self.coef_ = 0 + self.workers = workers + self.apply_softmax = apply_softmax + + @torch.no_grad() + def _prepare_data(self, X, y): + X_data = [] + n = len(X[0]) + m = len(X) + + for i in tqdm(range(n), desc="Loading predictions"): + x_preds = [] + for j in range(m): + x = np.load(X[j][i]) + if self.apply_softmax == "pre": + x = torch.from_numpy(x).float().softmax(dim=0).numpy().astype(np.float16) + x_preds.append(x) + + x = np.mean(np.stack(x_preds), axis=0) + + if self.apply_softmax == "post": + x = torch.from_numpy(x).float().softmax(dim=0).numpy().astype(np.float16) + + X_data.append(x) + + Y_data = [read_mask(yi) for yi in tqdm(y, desc="Loading ground-truths")] + assert len(X_data) == len(Y_data) + + print("Loaded data into memory") + return X_data, Y_data + + def _target_metric_loss(self, coef, X, y): + coef_exp = np.expand_dims(np.expand_dims(coef, -1), -1) + + all_rows = [] + proc_fn = partial(_compute_fn, coef_exp=coef_exp) + + with Pool(self.workers) as wp: + for row in wp.imap_unordered(proc_fn, zip(X, y)): + all_rows.append(row) + + score, localization_f1, damage_f1, damage_f1s = CompetitionMetricCallback.compute_metrics(all_rows) + print(score, localization_f1, damage_f1, damage_f1s, "coeffs", coef) + return 1.0 - score + + def fit(self, X, y): + X_data, Y_data = self._prepare_data(X, y) + loss_partial = partial(self._target_metric_loss, X=X_data, y=Y_data) + initial_coef = [1.0, 1.0, 1.0, 1.0, 1.0] + self.coef_ = sp.optimize.minimize( + loss_partial, initial_coef, method="nelder-mead", options={"maxiter": 100, "xatol": 0.001} + ) + del X_data, Y_data + return self.coefficients() + + def predict(self, X, y, coef: np.ndarray): + coef_exp = np.expand_dims(np.expand_dims(coef, -1), -1) + + all_rows = [] + + X_data, Y_data = self._prepare_data(X, y) + + proc_fn = partial(_compute_fn, coef_exp=coef_exp) + + with Pool(self.workers) as wp: + for row in wp.imap_unordered(proc_fn, zip(X_data, Y_data)): + all_rows.append(row) + + score, localization_f1, damage_f1, damage_f1s = CompetitionMetricCallback.compute_metrics(all_rows) + del X_data, Y_data + return score, localization_f1, damage_f1, damage_f1s + + def coefficients(self): + return self.coef_["x"] diff --git a/xview/dataset.py b/xview/dataset.py new file mode 100644 index 0000000..945e669 --- /dev/null +++ b/xview/dataset.py @@ -0,0 +1,608 @@ +import os +from typing import List, Optional + +import albumentations as A +import cv2 +import numpy as np +import pandas as pd +import torch +from PIL import Image +from pytorch_toolbelt.utils import fs +from pytorch_toolbelt.utils.catalyst import PseudolabelDatasetMixin +from pytorch_toolbelt.utils.torch_utils import tensor_from_rgb_image +from scipy.ndimage import binary_dilation, binary_fill_holes +from sklearn.utils import compute_sample_weight, compute_class_weight +from torch.utils.data import Dataset, WeightedRandomSampler, ConcatDataset + +from .alignment import align_post_image +from .augmentations import * + +from .utils.inference_image_output import colorize_mask + +INPUT_IMAGE_KEY = "image" + +INPUT_IMAGE_PRE_KEY = "image_pre" +INPUT_IMAGE_POST_KEY = "image_post" + +INPUT_IMAGE_ID_KEY = "image_id" +INPUT_MASK_KEY = "mask" +INPUT_MASK_PRE_KEY = "mask_pre" +INPUT_MASK_POST_KEY = "mask_post" + +OUTPUT_EMBEDDING_KEY = "embedding" +OUTPUT_MASK_KEY = "mask" +OUTPUT_MASK_ARC_KEY = "mask_arc" +OUTPUT_MASK_PRE_KEY = "mask_pre" +OUTPUT_MASK_POST_KEY = "mask_post" + +INPUT_INDEX_KEY = "index" +DISASTER_TYPE_KEY = "disaster_type" +DAMAGE_TYPE_KEY = "damage_type" + +# Smaller masks for deep supervision +OUTPUT_MASK_4_KEY = "mask_4" +OUTPUT_MASK_8_KEY = "mask_8" +OUTPUT_MASK_16_KEY = "mask_16" +OUTPUT_MASK_32_KEY = "mask_32" + +OUTPUT_CLASS_KEY = "classes" + +UNLABELED_SAMPLE = 5 + +DAMAGE_TYPES = ["no_damage", "minor_damage", "major_damage", "destroyed"] +DISASTER_TYPES = ["volcano", "fire", "tornado", "tsunami", "flooding", "earthquake", "hurricane"] +UNKNOWN_DISASTER_TYPE_CLASS = -100 + + +def get_disaster_class_from_fname(fname: str) -> int: + image_id = fs.id_from_fname(fname) + + for i, disaster_name in enumerate(DISASTER_TYPES): + if disaster_name in image_id: + return i + + return UNKNOWN_DISASTER_TYPE_CLASS + + +def read_image(fname): + image = cv2.imread(fname) + if image is None: + raise FileNotFoundError(fname) + return image + + +def read_mask(fname): + mask = np.array(Image.open(fname)) # Read using PIL since it supports palletted image + if len(mask.shape) == 3: + mask = np.squeeze(mask, axis=-1) + return mask + + +def compute_boundary_mask(mask: np.ndarray) -> np.ndarray: + dilated = binary_dilation(mask, structure=np.ones((5, 5), dtype=np.bool)) + dilated = binary_fill_holes(dilated) + + diff = dilated & ~mask + diff = cv2.dilate(diff, kernel=(5, 5)) + diff = diff & ~mask + return diff.astype(np.uint8) + + +class ImageLabelDataset(Dataset): + def __init__( + self, + pre_image_filenames: List[str], + post_image_filenames: List[str], + targets: Optional[np.ndarray], + spatial_transform: A.Compose, + color_transform: A.Compose = None, + post_image_transform=None, + image_loader=read_image, + ): + assert len(pre_image_filenames) == len(post_image_filenames) + + self.pre_image_filenames = pre_image_filenames + self.post_image_filenames = post_image_filenames + + self.targets = targets + + self.get_image = image_loader + + self.spatial_transform = spatial_transform + self.color_transform = color_transform + self.post_image_transform = post_image_transform + + def __len__(self): + return len(self.pre_image_filenames) + + def __getitem__(self, index): + pre_image = self.get_image(self.pre_image_filenames[index]) + post_image = self.get_image(self.post_image_filenames[index]) + + if self.color_transform is not None: + pre_image = self.color_transform(image=pre_image)["image"] + post_image = self.color_transform(image=post_image)["image"] + + if self.post_image_transform is not None: + post_image = self.post_image_transform(image=post_image)["image"] + + image = np.dstack([pre_image, post_image]) + data = {"image": image} + data = self.spatial_transform(**data) + + sample = { + INPUT_INDEX_KEY: index, + INPUT_IMAGE_ID_KEY: fs.id_from_fname(self.pre_image_filenames[index]), + INPUT_IMAGE_KEY: tensor_from_rgb_image(data["image"]), + } + + if self.targets is not None: + target = int(self.targets[index]) + sample[DAMAGE_TYPE_KEY] = target + + return sample + + +class ImageMaskDataset(Dataset, PseudolabelDatasetMixin): + def __init__( + self, + pre_image_filenames: List[str], + post_image_filenames: List[str], + post_mask_filenames: Optional[List[str]], + spatial_transform: A.Compose, + color_transform: A.Compose = None, + post_image_transform=None, + image_loader=read_image, + mask_loader=read_mask, + use_edges=False, + align_post=False, + ): + assert len(pre_image_filenames) == len(post_image_filenames) + + self.use_edges = use_edges + + self.pre_image_filenames = pre_image_filenames + self.post_image_filenames = post_image_filenames + self.post_mask_filenames = post_mask_filenames + + self.get_image = image_loader + self.get_mask = mask_loader + + self.spatial_transform = spatial_transform + self.color_transform = color_transform + self.post_image_transform = post_image_transform + self.align_post = align_post + + def __len__(self): + return len(self.pre_image_filenames) + + def __getitem__(self, index): + pre_image = self.get_image(self.pre_image_filenames[index]) + post_image = self.get_image(self.post_image_filenames[index]) + + if self.align_post: + post_image = align_post_image(pre_image, post_image) + + if self.color_transform is not None: + pre_image = self.color_transform(image=pre_image)["image"] + post_image = self.color_transform(image=post_image)["image"] + + if self.post_image_transform is not None: + post_image = self.post_image_transform(image=post_image)["image"] + + image = np.dstack([pre_image, post_image]) + data = {"image": image} + + if self.post_mask_filenames is not None: + post_mask = self.get_mask(self.post_mask_filenames[index]) + # assert np.all((post_mask >= 0) & (post_mask < 5)), f"Mask for sample {index} {self.post_mask_filenames[index]} contains values {np.unique(post_mask)}" + + data["mask"] = post_mask + else: + data["mask"] = np.ones(image.shape[:2], dtype=int) * UNLABELED_SAMPLE + + data = self.spatial_transform(**data) + + sample = { + INPUT_INDEX_KEY: index, + INPUT_IMAGE_ID_KEY: fs.id_from_fname(self.pre_image_filenames[index]), + INPUT_IMAGE_KEY: tensor_from_rgb_image(data["image"]), + DISASTER_TYPE_KEY: get_disaster_class_from_fname(self.pre_image_filenames[index]), + } + + if "mask" in data: + post_mask = data["mask"] + sample[INPUT_MASK_KEY] = torch.from_numpy(post_mask).long() + sample[DAMAGE_TYPE_KEY] = torch.tensor( + [(post_mask == 1).any(), (post_mask == 2).any(), (post_mask == 3).any(), (post_mask == 4).any()] + ).float() + + return sample + + def set_target(self, index: int, value: np.ndarray): + mask_fname = self.post_mask_filenames[index] + + value = value.astype(np.uint8) + value = colorize_mask(value) + value.save(mask_fname) + + +def get_transforms(image_size, augmentation, train_on_crops, enable_post_image_transform): + if train_on_crops: + train_crop_or_resize = A.RandomSizedCrop( + (int(image_size[0] * 0.8), int(image_size[0] * 1.2)), image_size[0], image_size[1] + ) + valid_crop_or_resize = A.NoOp() + print("Training on crops", train_crop_or_resize.min_max_height) + else: + if image_size[0] != 1024 or image_size[1] != 1024: + train_crop_or_resize = A.Resize(image_size[0], image_size[1]) + else: + train_crop_or_resize = A.NoOp() + valid_crop_or_resize = train_crop_or_resize + + normalize = A.Normalize( + mean=(0.485, 0.456, 0.406, 0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225, 0.229, 0.224, 0.225) + ) + + train_spatial_augs = A.NoOp() + train_color_augs = None + + # This transform slightly moves post- image to simulate imperfect alignment of satellite + post_image_transform = None + + if augmentation == "hard": + train_color_augs = hard_color_augmentations() + train_spatial_augs = hard_spatial_augmentations(image_size) + post_image_transform = light_post_image_transform() + elif augmentation == "medium": + train_color_augs = medium_color_augmentations() + train_spatial_augs = medium_spatial_augmentations(image_size) + post_image_transform = medium_post_transform_augs() + elif augmentation == "medium_nmd": + train_color_augs = medium_color_augmentations() + train_spatial_augs = medium_spatial_augmentations(image_size, no_mask_dropout=True) + post_image_transform = medium_post_transform_augs() + elif augmentation == "light": + train_color_augs = light_color_augmentations() + train_spatial_augs = light_spatial_augmentations(image_size) + post_image_transform = light_post_image_transform() + elif augmentation == "old": + train_color_augs = None + train_spatial_augs = old_light_augmentations(image_size) + post_image_transform = old_post_transform_augs() + elif augmentation == "safe": + train_color_augs = safe_color_augmentations() + train_spatial_augs = safe_spatial_augmentations(image_size) + post_image_transform = old_post_transform_augs() + + train_transform = A.Compose([train_crop_or_resize, train_spatial_augs, normalize]) + valid_transform = A.Compose([valid_crop_or_resize, normalize]) + + if enable_post_image_transform: + print("Enabling post-image spatial transformation") + else: + post_image_transform = None + + return train_transform, train_color_augs, valid_transform, post_image_transform + + +def get_datasets( + data_dir: str, + image_size=(512, 512), + augmentation="safe", + use_edges=False, + sanity_check=False, + fast=False, + fold=0, + only_buildings=False, + balance=False, + train_on_crops=False, + enable_post_image_transform=False, + align_post=False, + crops_multiplication_factor=3 +): + """ + Create train and validation data loaders + :param data_dir: Inria dataset directory + :param fast: Fast training model. Use only one image per location for training and one image per location for validation + :param image_size: Size of image crops during training & validation + :param use_edges: If True, adds 'edge' target mask + :param augmentation: Type of image augmentations to use + :param train_mode: + 'random' - crops tiles from source images randomly. + 'tiles' - crop image in overlapping tiles (guaranteed to process entire dataset) + :return: (train_loader, valid_loader) + """ + + df = pd.read_csv(os.path.join(data_dir, "train_folds.csv")) + df = make_dual_dataframe(df) + + train_transform, train_color_augs, valid_transform, post_image_transform = get_transforms( + image_size=image_size, + augmentation=augmentation, + train_on_crops=train_on_crops, + enable_post_image_transform=enable_post_image_transform, + ) + + train_df = df[df["fold_post"] != fold] + valid_df = df[df["fold_post"] == fold] + + if only_buildings: + only_buildings_mask = train_df["non_damaged_buildings_pre"] > 0 + total = len(train_df) + percentage = only_buildings_mask.sum() / float(total) + train_df = train_df[only_buildings_mask] + print("Using only images with buildings for training", percentage) + + if fast: + train_df = train_df[:128] + valid_df = valid_df[:128] + train_sampler = None + + train_img_pre = [os.path.join(data_dir, fname) for fname in train_df["image_fname_pre"]] + train_img_post = [os.path.join(data_dir, fname) for fname in train_df["image_fname_post"]] + train_mask_post = [os.path.join(data_dir, fname) for fname in train_df["mask_fname_post"]] + + valid_img_pre = [os.path.join(data_dir, fname) for fname in valid_df["image_fname_pre"]] + valid_img_post = [os.path.join(data_dir, fname) for fname in valid_df["image_fname_post"]] + valid_mask_post = [os.path.join(data_dir, fname) for fname in valid_df["mask_fname_post"]] + + trainset = ImageMaskDataset( + train_img_pre, + train_img_post, + train_mask_post, + use_edges=use_edges, + spatial_transform=train_transform, + color_transform=train_color_augs, + post_image_transform=post_image_transform, + ) + validset = ImageMaskDataset( + valid_img_pre, + valid_img_post, + valid_mask_post, + use_edges=use_edges, + spatial_transform=valid_transform, + align_post=align_post + ) + + train_sampler = None + if balance: + # destroyed_buildings, destroyed_pixels, event_name, event_type, folder, image_fname, image_id, , light_damaged_pixels, mask_fname, , medium_damaged_pixels, , non_damaged_pixels, sample_id, fold + + non_damaged_buildings = train_df["non_damaged_buildings_post"].values > 0 + light_damaged_buildings = train_df["light_damaged_buildings_post"].values > 0 + medium_damaged_buildings = train_df["medium_damaged_buildings_post"].values > 0 + destroyed_buildings = train_df["destroyed_buildings_post"].values > 0 + labels = ( + non_damaged_buildings * 1 + + light_damaged_buildings * 2 + + medium_damaged_buildings * 4 + + destroyed_buildings * 8 + ) + + num_samples = 4 * min( + sum(non_damaged_buildings), + sum(light_damaged_buildings), + sum(medium_damaged_buildings), + sum(destroyed_buildings), + ) + weights = compute_sample_weight("balanced", labels) + train_sampler = WeightedRandomSampler(weights, int(num_samples), replacement=bool(num_samples > len(train_df))) + print("Using balancing for training", num_samples, weights.min(), weights.mean(), weights.max()) + elif train_on_crops: + # If we're training on crops, make 3 crops for each sample + trainset = ConcatDataset([trainset] * int(crops_multiplication_factor)) + + if sanity_check: + first_batch = [trainset[i] for i in range(32)] + return first_batch * 50, first_batch, None + + return trainset, validset, train_sampler + + +def make_dual_dataframe(df: pd.DataFrame) -> pd.DataFrame: + columns_to_rename = [ + "destroyed_buildings", + "destroyed_pixels", + "event_name", + "event_type", + "folder", + "image_fname", + "image_id", + "light_damaged_buildings", + "light_damaged_pixels", + "mask_fname", + "medium_damaged_buildings", + "medium_damaged_pixels", + "non_damaged_buildings", + "non_damaged_pixels", + "sample_id", + # "fold" + ] + + df = df.sort_values(by=["image_id"]) + df_pre = df[df["event_type"] == "pre"].copy().reset_index(drop=True) + df_post = df[df["event_type"] == "post"].copy().reset_index(drop=True) + + df = df_pre.merge(df_post, left_index=True, right_index=True, suffixes=["_pre", "_post"]) + return df + + +def get_test_dataset(data_dir: str, image_size=(224, 224), use_edges=False, fast=False, align_post=False): + """ + Create train and validation data loaders + :param data_dir: Inria dataset directory + :param fast: Fast training model. Use only one image per location for training and one image per location for validation + :param image_size: Size of image crops during training & validation + :param use_edges: If True, adds 'edge' target mask + :param augmentation: Type of image augmentations to use + :param train_mode: + 'random' - crops tiles from source images randomly. + 'tiles' - crop image in overlapping tiles (guaranteed to process entire dataset) + :return: (train_loader, valid_loader) + """ + resize = A.Resize(image_size[0], image_size[1]) + normalize = A.Normalize( + mean=(0.485, 0.456, 0.406, 0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225, 0.229, 0.224, 0.225) + ) + + valid_transform = A.Compose([resize, normalize]) + + test_images_post = [ + fname + for fname in fs.find_images_in_dir(os.path.join(data_dir, "test", "images")) + if "post_" in fs.id_from_fname(fname) + ] + + test_images_pre = [fname.replace("_post_", "_pre_") for fname in test_images_post] + + if fast: + test_images_pre = test_images_pre[:128] + test_images_post = test_images_post[:128] + + validset = ImageMaskDataset( + test_images_pre, + test_images_post, + None, + use_edges=use_edges, + spatial_transform=valid_transform, + align_post=align_post, + ) + + return validset + + +def get_pseudolabeling_dataset( + data_dir, + image_size, + include_masks, + augmentation, + use_edges=False, + train_on_crops=False, + enable_post_image_transform=False, + pseudolabels_dir=None +): + + train_transform, train_color_augs, valid_transform, post_image_transform = get_transforms( + image_size=image_size, + augmentation=augmentation, + train_on_crops=train_on_crops, + enable_post_image_transform=enable_post_image_transform, + ) + + images_dir = os.path.join(data_dir, "test", "images") + masks_dir = pseudolabels_dir or os.path.join(data_dir, "test", "masks") + os.makedirs(masks_dir, exist_ok=True) + + test_images_post = [fname for fname in fs.find_images_in_dir(images_dir) if "_post_" in fs.id_from_fname(fname)] + test_images_pre = [fname.replace("_post_", "_pre_") for fname in test_images_post] + + if include_masks: + test_masks_post = [os.path.join(masks_dir, os.path.basename(fname)) for fname in test_images_post] + else: + test_masks_post = None + + validset = ImageMaskDataset( + test_images_pre, + test_images_post, + test_masks_post, + use_edges=use_edges, + color_transform=train_color_augs, + spatial_transform=train_transform, + post_image_transform=post_image_transform, + ) + + return validset + + +def get_classification_datasets( + data_dir: str, + min_size=64, + image_size=(224, 224), + augmentation="safe", + sanity_check=False, + fast=False, + fold=0, + enable_post_image_transform=False, +): + """ + Create train and validation data loaders + :param data_dir: Inria dataset directory + :param fast: Fast training model. Use only one image per location for training and one image per location for validation + :param image_size: Size of image crops during training & validation + :param use_edges: If True, adds 'edge' target mask + :param augmentation: Type of image augmentations to use + :param train_mode: + 'random' - crops tiles from source images randomly. + 'tiles' - crop image in overlapping tiles (guaranteed to process entire dataset) + :return: (train_loader, valid_loader) + """ + + resize_op = A.Compose( + [ + A.LongestMaxSize(max(image_size[0], image_size[1])), + A.PadIfNeeded(image_size[0], image_size[1], border_mode=cv2.BORDER_CONSTANT, value=0), + ] + ) + + normalize = A.Normalize( + mean=(0.485, 0.456, 0.406, 0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225, 0.229, 0.224, 0.225) + ) + + df = pd.read_csv(os.path.join(data_dir, "train_crops.csv")) + + post_transform = None + if augmentation == "safe": + augment = A.Compose([A.RandomRotate90(), A.Transpose()]) + elif augmentation == "light": + augment = A.Compose([A.RandomRotate90(), A.Transpose()]) + else: + print("Unsupported augmentation", augmentation) + augment = A.NoOp() + + train_transform = A.Compose([resize_op, augment, normalize]) + + valid_transform = A.Compose([resize_op, normalize]) + + train_sampler = None + + df = df[df["max_size"] >= min_size] + + train_df = df[df["fold"] != fold] + valid_df = df[df["fold"] == fold] + + if fast: + train_df = train_df[:128] + valid_df = valid_df[:128] + train_sampler = None + + train_img_pre = [os.path.join(data_dir, "crops", fname) for fname in train_df["pre_crop_fname"]] + train_img_post = [os.path.join(data_dir, "crops", fname) for fname in train_df["post_crop"]] + train_targets = np.array(train_df["label"]) - 1 # Targets in CSV starting from 1 + + valid_img_pre = [os.path.join(data_dir, "crops", fname) for fname in valid_df["pre_crop_fname"]] + valid_img_post = [os.path.join(data_dir, "crops", fname) for fname in valid_df["post_crop"]] + valid_targets = np.array(valid_df["label"]) - 1 # Targets in CSV starting from 1 + + print( + "Sample weights (train,val)", + compute_class_weight("balanced", np.arange(len(DAMAGE_TYPES)), train_targets), + compute_class_weight("balanced", np.arange(len(DAMAGE_TYPES)), valid_targets), + ) + + trainset = ImageLabelDataset( + train_img_pre, + train_img_post, + train_targets, + spatial_transform=train_transform, + color_transform=None, + post_image_transform=None, + ) + validset = ImageLabelDataset(valid_img_pre, valid_img_post, valid_targets, spatial_transform=valid_transform) + + if sanity_check: + first_batch = [trainset[i] for i in range(32)] + return first_batch * 50, first_batch, None + + return trainset, validset, train_sampler diff --git a/xview/factory.py b/xview/factory.py new file mode 100644 index 0000000..fdd87fe --- /dev/null +++ b/xview/factory.py @@ -0,0 +1,146 @@ +from multiprocessing.pool import Pool +from typing import List, Dict + +import albumentations as A +import cv2 +import numpy as np +import torch +from pytorch_toolbelt.inference.tiles import CudaTileMerger, ImageSlicer +from pytorch_toolbelt.inference.tta import TTAWrapper, fliplr_image2mask, d4_image2mask +from pytorch_toolbelt.utils.torch_utils import tensor_from_rgb_image, to_numpy, rgb_image_from_tensor +from torch import nn +from torch.nn import functional as F +from torch.utils.data import Dataset, DataLoader +from tqdm import tqdm + +from .dataset import INPUT_IMAGE_KEY, INPUT_MASK_KEY, INPUT_IMAGE_ID_KEY, OUTPUT_MASK_KEY + + +class InMemoryDataset(Dataset): + def __init__(self, data: List[Dict], transform: A.Compose): + self.data = data + self.transform = transform + + def __len__(self): + return len(self.data) + + def __getitem__(self, item): + return self.transform(**self.data[item]) + + +def _tensor_from_rgb_image(image: np.ndarray, **kwargs): + return tensor_from_rgb_image(image) + + +class PickModelOutput(nn.Module): + def __init__(self, model, key): + super().__init__() + self.model = model + self.target_key = key + + def forward(self, input): + output = self.model(input) + return output[self.target_key] + + +@torch.no_grad() +def predict(model: nn.Module, image: np.ndarray, image_size, normalize=A.Normalize(), batch_size=1) -> np.ndarray: + + tile_step = (image_size[0] // 2, image_size[1] // 2) + + tile_slicer = ImageSlicer(image.shape, image_size, tile_step) + tile_merger = CudaTileMerger(tile_slicer.target_shape, 1, tile_slicer.weight) + patches = tile_slicer.split(image) + + transform = A.Compose([normalize, A.Lambda(image=_tensor_from_rgb_image)]) + + data = list( + {"image": patch, "coords": np.array(coords, dtype=np.int)} + for (patch, coords) in zip(patches, tile_slicer.crops) + ) + for batch in DataLoader(InMemoryDataset(data, transform), pin_memory=True, batch_size=batch_size): + image = batch["image"].cuda(non_blocking=True) + coords = batch["coords"] + mask_batch = model(image) + tile_merger.integrate_batch(mask_batch, coords) + + mask = tile_merger.merge() + + mask = np.moveaxis(to_numpy(mask), 0, -1) + mask = tile_slicer.crop_to_orignal_size(mask) + + return mask + + +def __compute_ious(args): + thresholds = np.arange(0, 256) + gt, pred = args + gt = cv2.imread(gt) > 0 # Make binary {0,1} + pred = cv2.imread(pred) + + pred_i = np.zeros_like(gt) + + intersection = np.zeros(len(thresholds)) + union = np.zeros(len(thresholds)) + + gt_sum = gt.sum() + for index, threshold in enumerate(thresholds): + np.greater(pred, threshold, out=pred_i) + union[index] += gt_sum + pred_i.sum() + + np.logical_and(gt, pred_i, out=pred_i) + intersection[index] += pred_i.sum() + + return intersection, union + + +def optimize_threshold(gt_images, pred_images): + thresholds = np.arange(0, 256) + + intersection = np.zeros(len(thresholds)) + union = np.zeros(len(thresholds)) + + with Pool(32) as wp: + for i, u in tqdm(wp.imap_unordered(__compute_ious, zip(gt_images, pred_images)), total=len(gt_images)): + intersection += i + union += u + + return thresholds, intersection / (union - intersection) + + +def visualize_inria_predictions( + input: dict, + output: dict, + mean=(0.485, 0.456, 0.406), + std=(0.229, 0.224, 0.225), + input_image_key=INPUT_IMAGE_KEY, + input_mask_key=INPUT_MASK_KEY, + input_image_id_key=INPUT_IMAGE_ID_KEY, + output_mask_key=OUTPUT_MASK_KEY, +): + images = [] + for image, target, image_id, logits in zip( + input[input_image_key], input[input_mask_key], input[input_image_id_key], output[output_mask_key] + ): + image = rgb_image_from_tensor(image, mean, std) + target = to_numpy(target).squeeze(0) + logits = to_numpy(logits).squeeze(0) + + overlay = np.zeros_like(image) + true_mask = target > 0 + pred_mask = logits > 0 + + overlay[true_mask & pred_mask] = np.array( + [0, 250, 0], dtype=overlay.dtype + ) # Correct predictions (Hits) painted with green + overlay[true_mask & ~pred_mask] = np.array([250, 0, 0], dtype=overlay.dtype) # Misses painted with red + overlay[~true_mask & pred_mask] = np.array( + [250, 250, 0], dtype=overlay.dtype + ) # False alarm painted with yellow + + image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) + overlay = cv2.addWeighted(image, 0.5, overlay, 0.5, 0, dtype=cv2.CV_8U) + cv2.putText(overlay, str(image_id), (10, 15), cv2.FONT_HERSHEY_PLAIN, 1, (250, 250, 250)) + + images.append(overlay) + return images diff --git a/xview/inference.py b/xview/inference.py new file mode 100644 index 0000000..c9bcadc --- /dev/null +++ b/xview/inference.py @@ -0,0 +1,500 @@ +import os + +import torch +from typing import Optional, Dict, List, Tuple + +from pytorch_toolbelt.inference.tiles import CudaTileMerger, ImageSlicer +from pytorch_toolbelt.utils import fs +from torch.nn import functional as F +from pytorch_toolbelt.utils.torch_utils import to_numpy +from torch import nn +from torch.utils.data import DataLoader +from tqdm import tqdm +import pytorch_toolbelt.inference.functional as AF + +from xview.dataset import ( + OUTPUT_MASK_POST_KEY, + OUTPUT_MASK_PRE_KEY, + INPUT_IMAGE_ID_KEY, + INPUT_IMAGE_PRE_KEY, + INPUT_IMAGE_POST_KEY, + OUTPUT_MASK_KEY, + INPUT_IMAGE_KEY, + DAMAGE_TYPE_KEY, + INPUT_MASK_KEY, +) +from xview.metric import CompetitionMetricCallback +from xview.models import get_model +from xview.postprocessing import ( + make_predictions_dominant, + make_predictions_naive, + make_predictions_floodfill, + make_predictions_dominant_v2, + make_pseudolabeling_target, +) +from xview.train_utils import report_checkpoint +from xview.utils.inference_image_output import colorize_mask + +import numpy as np + + +class ApplySigmoidTo(nn.Module): + def __init__(self, model, input_key="logits"): + super().__init__() + self.model = model + self.input_key = input_key + + def forward(self, *input, **kwargs) -> Dict: + output = self.model(*input, **kwargs) + if self.input_key in output: + output[self.input_key] = output[self.input_key].sigmoid() + return output + + +class ApplySoftmaxTo(nn.Module): + def __init__(self, model, input_key="logits"): + super().__init__() + self.model = model + self.input_key = input_key + + def forward(self, *input, **kwargs) -> Dict: + output = self.model(*input, **kwargs) + if self.input_key in output: + output[self.input_key] = output[self.input_key].softmax(dim=1) + return output + + +class HFlipTTA(nn.Module): + def __init__(self, model, outputs, average=True): + super().__init__() + self.model = model + self.outputs = outputs + self.average = average + + def forward(self, image): + outputs = self.model(image) + outputs_flip = self.model(AF.torch_fliplr(image)) + + for output_key in self.outputs: + outputs[output_key] += AF.torch_fliplr(outputs_flip[output_key]) + + if self.average: + averaging_scale = 0.5 + for output_key in self.outputs: + outputs[output_key] *= averaging_scale + + return outputs + + +class D4TTA(nn.Module): + def __init__(self, model, outputs, average=True): + super().__init__() + self.model = model + self.outputs = outputs + self.average = average + + def forward(self, image): + outputs = self.model(image) + + augment = [AF.torch_rot90, AF.torch_rot180, AF.torch_rot270] + deaugment = [AF.torch_rot270, AF.torch_rot180, AF.torch_rot90] + + for aug, deaug in zip(augment, deaugment): + input = aug(image) + aug_output = self.model(input) + + for output_key in self.outputs: + outputs[output_key] += deaug(aug_output[output_key]) + + image_t = AF.torch_transpose(image) + + augment = [AF.torch_none, AF.torch_rot90, AF.torch_rot180, AF.torch_rot270] + deaugment = [AF.torch_none, AF.torch_rot270, AF.torch_rot180, AF.torch_rot90] + + for aug, deaug in zip(augment, deaugment): + input = aug(image_t) + aug_output = self.model(input) + + for output_key in self.outputs: + x = deaug(aug_output[output_key]) + outputs[output_key] += AF.torch_transpose(x) + + if self.average: + averaging_scale = 1.0 / 8.0 + for output_key in self.outputs: + outputs[output_key] *= averaging_scale + + return outputs + + +class MultiscaleTTA(nn.Module): + def __init__(self, model, outputs, size_offsets: List[int], average=True): + super().__init__() + self.model = model + self.outputs = outputs + self.size_offsets = size_offsets + self.average = average + + def integrate(self, outputs, input, augment, deaugment): + aug_input = augment(input) + aug_output = self.model(aug_input) + + for output_key in self.outputs: + outputs[output_key] += deaugment(aug_output[output_key]) + + def forward(self, image): + outputs = self.model(image) + x_size_orig = image.size()[2:] + + for image_size_offset in self.size_offsets: + x_size_modified = x_size_orig[0] + image_size_offset, x_size_orig[1] + image_size_offset + self.integrate( + outputs, + image, + lambda x: F.interpolate(x, size=x_size_modified, mode="bilinear", align_corners=False), + lambda x: F.interpolate(x, size=x_size_orig, mode="bilinear", align_corners=False), + ) + + if self.average: + averaging_scale = 1.0 / (len(self.size_offsets) + 1) + for output_key in self.outputs: + outputs[output_key] *= averaging_scale + return outputs + + +class Ensembler(nn.Module): + def __init__(self, models: List[nn.Module], outputs: List[str]): + super().__init__() + self.models = nn.ModuleList(models) + self.outputs = outputs + + def forward(self, *input, **kwargs): + num_models = len(self.models) + + with tqdm(total=num_models, desc="Inference") as tq: + output_0 = self.models[0](*input, **kwargs) + tq.update() + + for i in range(1, num_models): + output_i = self.models[i](*input, **kwargs) + tq.update() + + # Aggregate predictions + for key in self.outputs: + output_0[key] += output_i[key] + + scale = 1.0 / num_models + return {key: output_0[key] * scale for key in self.outputs} + + +class ApplyWeights(nn.Module): + def __init__(self, model, weights, output_key=OUTPUT_MASK_KEY): + if not isinstance(weights, torch.Tensor): + weights = torch.tensor(weights).float().view(1, -1, 1, 1) + + super().__init__() + self.model = model + self.register_buffer("weights", weights) + self.output_key = output_key + + def forward(self, x): + output = self.model(x) + output[self.output_key] *= self.weights + return output + + +def model_from_checkpoint( + model_checkpoint: str, tta: Optional[str] = None, activation_after="model", model=None, report=True, classifiers=True +) -> Tuple[nn.Module, Dict]: + checkpoint = torch.load(model_checkpoint, map_location="cpu") + model_name = model or checkpoint["checkpoint_data"]["cmd_args"]["model"] + + score = float(checkpoint["epoch_metrics"]["valid"]["weighted_f1"]) + loc = float(checkpoint["epoch_metrics"]["valid"]["weighted_f1/localization_f1"]) + dmg = float(checkpoint["epoch_metrics"]["valid"]["weighted_f1/damage_f1"]) + fold = int(checkpoint["checkpoint_data"]["cmd_args"]["fold"]) + + if report: + print(model_checkpoint, model_name) + report_checkpoint(checkpoint) + + model = get_model(model_name, pretrained=False, classifiers=classifiers) + + model.load_state_dict(checkpoint["model_state_dict"], strict=False) + del checkpoint + + if activation_after == "model": + model = ApplySoftmaxTo(model, OUTPUT_MASK_KEY) + + if tta == "multiscale": + print(f"Using {tta}") + model = MultiscaleTTA(model, outputs=[OUTPUT_MASK_KEY], size_offsets=[-256, -128, +128, +256], average=True) + + if tta == "flip": + print(f"Using {tta}") + model = HFlipTTA(model, outputs=[OUTPUT_MASK_KEY], average=True) + + if tta == "flipscale": + print(f"Using {tta}") + model = HFlipTTA(model, outputs=[OUTPUT_MASK_KEY], average=True) + model = MultiscaleTTA(model, outputs=[OUTPUT_MASK_KEY], size_offsets=[-256, -128, +128, +256], average=True) + + if tta == "multiscale_d4": + print(f"Using {tta}") + model = D4TTA(model, outputs=[OUTPUT_MASK_KEY], average=True) + model = MultiscaleTTA(model, outputs=[OUTPUT_MASK_KEY], size_offsets=[-256, -128, +128, +256], average=True) + + if activation_after == "tta": + model = ApplySoftmaxTo(model, OUTPUT_MASK_KEY) + + info = { + "model": fs.id_from_fname(model_checkpoint), + "model_name": model_name, + "fold": fold, + "score": score, + "localization": loc, + "damage": dmg, + } + return model, info + + +@torch.no_grad() +def run_inference_on_dataset( + model, dataset, output_dir, batch_size=1, workers=0, weights=None, fp16=False, cpu=False, postprocessing="naive", save_pseudolabels=True +): + if not cpu: + if fp16: + model = model.half() + model = model.cuda() + if torch.cuda.device_count() > 1: + model = nn.DataParallel(model) + print("Using multi-GPU inference") + + model = model.eval() + + if weights is not None: + print("Using weights", weights) + weights = torch.tensor(weights).float().view(1, -1, 1, 1) + + if not cpu: + if fp16: + weights = weights.half() + weights = weights.cuda() + + data_loader = DataLoader(dataset, batch_size=batch_size, pin_memory=not cpu, num_workers=workers) + + pseudolabeling_dir = os.path.join(output_dir + "_pseudolabeling") + + os.makedirs(output_dir, exist_ok=True) + os.makedirs(pseudolabeling_dir, exist_ok=True) + + postprocessings = {} + if postprocessing == "naive": + postprocessings[postprocessing] = make_predictions_naive + elif postprocessing == "dominant": + postprocessings[postprocessing] = make_predictions_dominant + elif postprocessing in {"dominant2", "dominantv2", "dominant_v2"}: + postprocessings[postprocessing] = make_predictions_dominant_v2 + elif postprocessing == "floodfill": + postprocessings[postprocessing] = make_predictions_floodfill + elif postprocessing is None: + postprocessings = { + "naive": make_predictions_naive, + "dominant": make_predictions_dominant, + "dominantv2": make_predictions_dominant_v2, + "floodfill": make_predictions_floodfill, + } + + for batch in tqdm(data_loader): + image = batch[INPUT_IMAGE_KEY] + + if not cpu: + if fp16: + image = image.half() + image = image.cuda(non_blocking=True) + + image_ids = batch[INPUT_IMAGE_ID_KEY] + + output = model(image) + + masks = output[OUTPUT_MASK_KEY] + + if weights is not None: + masks *= weights + + if masks.size(2) != 1024 or masks.size(3) != 1024: + masks = F.interpolate(masks, size=(1024, 1024), mode="bilinear", align_corners=False) + masks = to_numpy(masks).astype(np.float32) + + for i, image_id in enumerate(image_ids): + _, _, image_uuid = image_id.split("_") + + # Save pseudolabeling target + if save_pseudolabels: + pseudo_mask = make_pseudolabeling_target(masks[i]) + pseudo_mask = pseudo_mask.astype(np.uint8) + pseudo_mask = colorize_mask(pseudo_mask) + pseudo_mask.save(os.path.join(pseudolabeling_dir, f"test_post_{image_uuid}.png")) + + for postprocessing_name, postprocessing_fn in postprocessings.items(): + + output_dir_for_postprocessing = os.path.join(output_dir + "_" + postprocessing_name) + os.makedirs(output_dir_for_postprocessing, exist_ok=True) + + localization_image, damage_image = postprocessing_fn(masks[i]) + + localization_fname = os.path.join( + output_dir_for_postprocessing, f"test_localization_{image_uuid}_prediction.png" + ) + localization_image = colorize_mask(localization_image) + localization_image.save(localization_fname) + + damage_fname = os.path.join(output_dir_for_postprocessing, f"test_damage_{image_uuid}_prediction.png") + damage_image = colorize_mask(damage_image) + damage_image.save(damage_fname) + + del data_loader + + +@torch.no_grad() +def run_inference_on_dataset_oof(model, dataset, output_dir, batch_size=1, workers=0, save=True, fp16=False): + model = model.cuda() + if torch.cuda.device_count() > 1: + model = nn.DataParallel(model) + model = model.eval() + if fp16: + model = model.half() + + data_loader = DataLoader(dataset, batch_size=batch_size, pin_memory=True, num_workers=workers) + + if save: + os.makedirs(output_dir, exist_ok=True) + + allrows = [] + + for batch in tqdm(data_loader): + image = batch[INPUT_IMAGE_KEY] + if fp16: + image = image.half() + + image = image.cuda(non_blocking=True) + image_ids = batch[INPUT_IMAGE_ID_KEY] + + dmg_true = to_numpy(batch[INPUT_MASK_KEY]).astype(np.float32) + + output = model(image) + + masks = output[OUTPUT_MASK_KEY] + masks = to_numpy(masks) + + for i, image_id in enumerate(image_ids): + damage_mask = masks[i] + + if save: + damage_fname = os.path.join(output_dir, fs.change_extension(image_id.replace("_pre", "_post"), ".npy")) + np.save(damage_fname, damage_mask.astype(np.float16)) + + loc_pred, dmg_pred = make_predictions_naive(damage_mask) + row = CompetitionMetricCallback.get_row_pair(loc_pred, dmg_pred, dmg_true[i], dmg_true[i]) + allrows.append(row) + + if save: + if DAMAGE_TYPE_KEY in output: + damage_type = to_numpy(output[DAMAGE_TYPE_KEY].sigmoid()).astype(np.float32) + + for i, image_id in enumerate(image_ids): + damage_fname = os.path.join( + output_dir, fs.change_extension(image_id.replace("_pre", "_damage_type"), ".npy") + ) + np.save(damage_fname, damage_type[i]) + + del data_loader + + return CompetitionMetricCallback.compute_metrics(allrows) + + +@torch.no_grad() +def run_dual_inference_on_dataset(model, dataset, output_dir, batch_size=1, workers=0): + model = model.cuda() + if torch.cuda.device_count() > 1: + model = nn.DataParallel(model) + model = model.eval() + + data_loader = DataLoader(dataset, batch_size=batch_size, pin_memory=True, num_workers=workers) + + os.makedirs(output_dir, exist_ok=True) + + for batch in tqdm(data_loader): + image_pre = batch[INPUT_IMAGE_PRE_KEY].cuda(non_blocking=True) + image_post = batch[INPUT_IMAGE_POST_KEY].cuda(non_blocking=True) + image_ids = batch[INPUT_IMAGE_ID_KEY] + + output = model(image_pre=image_pre, image_post=image_post) + + masks_pre = output[OUTPUT_MASK_PRE_KEY] + if masks_pre.size(2) != 1024 or masks_pre.size(3) != 1024: + masks_pre = F.interpolate(masks_pre, size=(1024, 1024), mode="bilinear", align_corners=False) + masks_pre = to_numpy(masks_pre.squeeze(1)).astype(np.float32) + + masks_post = output[OUTPUT_MASK_POST_KEY] + if masks_post.size(2) != 1024 or masks_post.size(3) != 1024: + masks_post = F.interpolate(masks_post, size=(1024, 1024), mode="bilinear", align_corners=False) + masks_post = to_numpy(masks_post).astype(np.float32) + + for i, image_id in enumerate(image_ids): + _, _, image_uuid = image_id.split("_") + localization_image = masks_pre[i] + damage_image = masks_post[i] + + localization_fname = os.path.join(output_dir, f"test_localization_{image_uuid}_prediction.png") + localization_image = (localization_image > 0.5).astype(np.uint8) + localization_image = colorize_mask(localization_image) + localization_image.save(localization_fname) + + damage_fname = os.path.join(output_dir, f"test_damage_{image_uuid}_prediction.png") + damage_image = np.argmax(damage_image, axis=0).astype(np.uint8) + damage_image = colorize_mask(damage_image) + damage_image.save(damage_fname) + + del data_loader + + +@torch.no_grad() +def run_dual_inference_on_dataset_oof(model, dataset, output_dir, batch_size=1, workers=0): + model = model.cuda() + if torch.cuda.device_count() > 1: + model = nn.DataParallel(model) + model = model.eval() + + data_loader = DataLoader(dataset, batch_size=batch_size, pin_memory=True, num_workers=workers) + + os.makedirs(output_dir, exist_ok=True) + + for batch in tqdm(data_loader): + image_pre = batch[INPUT_IMAGE_PRE_KEY].cuda(non_blocking=True) + image_post = batch[INPUT_IMAGE_POST_KEY].cuda(non_blocking=True) + image_ids = batch[INPUT_IMAGE_ID_KEY] + + output = model(image_pre=image_pre, image_post=image_post) + + masks_pre = output[OUTPUT_MASK_PRE_KEY] + if masks_pre.size(2) != 1024 or masks_pre.size(3) != 1024: + masks_pre = F.interpolate(masks_pre, size=(1024, 1024), mode="bilinear", align_corners=False) + masks_pre = to_numpy(masks_pre.squeeze(1)).astype(np.float32) + + masks_post = output[OUTPUT_MASK_POST_KEY] + if masks_post.size(2) != 1024 or masks_post.size(3) != 1024: + masks_post = F.interpolate(masks_post, size=(1024, 1024), mode="bilinear", align_corners=False) + masks_post = to_numpy(masks_post).astype(np.float32) + + for i, image_id in enumerate(image_ids): + localization_image = masks_pre[i] + damage_image = masks_post[i] + + localization_fname = os.path.join(output_dir, fs.change_extension(image_id, ".npy")) + np.save(localization_fname, localization_image) + + damage_fname = os.path.join(output_dir, fs.change_extension(image_id.replace("_pre", "_post"), ".npy")) + np.save(damage_fname, damage_image) + + del data_loader diff --git a/xview/losses.py b/xview/losses.py new file mode 100644 index 0000000..4c4b303 --- /dev/null +++ b/xview/losses.py @@ -0,0 +1,231 @@ +import math + +import torch +from pytorch_toolbelt.losses import * +import torch.nn.functional as F + +__all__ = ["get_loss", "AdaptiveMaskLoss2d"] + +from torch import nn +from torch.nn import Module, Parameter + +from .dataset import UNLABELED_SAMPLE +from .ssim_loss import SSIM +from .utils.inference_image_output import resize_mask_one_hot + + +class LabelSmoothingCrossEntropy2d(Module): + """ + + Original implementation: fast.ai + """ + + def __init__(self, eps: float = 0.1, reduction="mean", weight=None, ignore_index=-100): + super().__init__() + self.eps = eps + self.reduction = reduction + self.ignore_index = ignore_index + self.register_buffer("weight", weight) + + def forward(self, output, target): + num_classes = output.size(1) + log_preds = F.log_softmax(output, dim=1) + if self.reduction == "sum": + loss = -log_preds.sum() + else: + loss = -log_preds.sum(dim=1) + if self.reduction == "mean": + loss = loss.mean() + + return loss * self.eps / num_classes + (1 - self.eps) * F.nll_loss( + log_preds, target, weight=self.weight, ignore_index=self.ignore_index, reduction=self.reduction + ) + + +class OHEMCrossEntropyLoss(nn.CrossEntropyLoss): + """ + Online hard example mining CE loss + + https://arxiv.org/pdf/1812.05802.pdf + """ + + def __init__(self, weight=None, fraction=0.3, ignore_index=-100, reduction="mean"): + super().__init__(weight, ignore_index=ignore_index, reduction=reduction) + self.fraction = fraction + + def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + batch_size = input.size(0) + + with torch.no_grad(): + positive_mask = (target > 0).view(batch_size, -1) + Cp = torch.sum(positive_mask, dim=1) # Number of positive pixels + Cn = torch.sum(~positive_mask, dim=1) # Number of negative pixels + Chn = torch.max((Cn / 4).clamp_min(5), 2 * Cp) + + losses = F.cross_entropy( + input, target, weight=self.weight, ignore_index=self.ignore_index, reduction="none" + ).view(target.size(0), -1) + + loss = 0 + num_samples = 0 + + for i in range(batch_size): + positive_losses = losses[i, positive_mask[i]] + negative_losses = losses[i, ~positive_mask[i]] + + num_negatives = Chn[i] + hard_negative_losses, _ = negative_losses.sort(descending=True)[:num_negatives] + + loss = positive_losses.sum() + hard_negative_losses.sum() + loss + + num_samples += positive_losses.size(0) + num_samples += hard_negative_losses.size(0) + + loss /= float(num_samples) + return loss + + +def get_loss(loss_name: str, ignore_index=UNLABELED_SAMPLE): + if loss_name.lower() == "bce": + return BCELoss(ignore_index=ignore_index) + + if loss_name.lower() == "ce": + return nn.CrossEntropyLoss(ignore_index=ignore_index) + + if loss_name.lower() == "ohem_ce": + return OHEMCrossEntropyLoss(ignore_index=ignore_index, weight=torch.tensor([1.0, 1.0, 3.0, 3.0, 3.0])).cuda() + + if loss_name.lower() == "weighted_ce": + return nn.CrossEntropyLoss(ignore_index=ignore_index, weight=torch.tensor([1.0, 1.0, 3.0, 3.0, 3.0])).cuda() + + if loss_name.lower() == "weighted2_ce": + return nn.CrossEntropyLoss(ignore_index=ignore_index, weight=torch.tensor([1.0, 1.0, 3.0, 2.0, 1.0])).cuda() + + if loss_name.lower() == "dsv_ce": + return AdaptiveMaskLoss2d( + nn.CrossEntropyLoss(ignore_index=ignore_index, weight=torch.tensor([1.0, 1.0, 3.0, 3.0, 3.0])) + ).cuda() + + if loss_name.lower() in {"ce_building_only", "ce_buildings_only"}: + # This ignores predictions on "non-building" pixels + return nn.CrossEntropyLoss(ignore_index=0) + + if loss_name.lower() == "soft_bce": + return SoftBCELoss(smooth_factor=0.1, ignore_index=ignore_index) + + if loss_name.lower() == "soft_ce": + return LabelSmoothingCrossEntropy2d(eps=0.1, ignore_index=ignore_index) + + if loss_name.lower() == "binary_focal": + return BinaryFocalLoss(alpha=None, gamma=2, ignore_index=ignore_index) + + if loss_name.lower() == "focal": + return FocalLoss(alpha=None, gamma=2, ignore_index=ignore_index, reduction="mean") + + if loss_name.lower() == "nfl": + return FocalLoss(alpha=None, gamma=2, ignore_index=ignore_index, normalized=True, reduction="sum") + + if loss_name.lower() == "dice": + return DiceLoss(mode="multiclass") + + if loss_name.lower() == "log_dice": + return DiceLoss(mode="multiclass", log_loss=True) + + if loss_name.lower() == "am-softmax": + return AmSoftmax2d(weight=torch.tensor([1.0, 1.0, 3.0, 3.0, 3.0])).cuda() + + if loss_name.lower() == "arcface": + return ArcFaceLoss2d(ignore_index=ignore_index) + + if loss_name.lower() == "ssim": + return SSIM(5).cuda() + + raise KeyError(loss_name) + + +class AdaptiveMaskLoss2d(nn.Module): + """ + Works only with sigmoid masks and bce loss + Rescales target mask to predicted mask + """ + + def __init__(self, loss): + super().__init__() + self.loss = loss + + def forward(self, input: torch.Tensor, target: torch.Tensor): + with torch.no_grad(): + target_one_hot = F.one_hot(target, int(input.size(1))).permute(0, 3, 1, 2).type(input.dtype) + + scale = int(target.size(2)) // int(input.size(2)) + while scale > 2: + target_one_hot = F.interpolate(target_one_hot, scale_factor=0.5, mode="bilinear", align_corners=False) + scale = int(target_one_hot.size(2)) // int(input.size(2)) + + target_one_hot = F.interpolate(target_one_hot, size=input.size()[2:], mode="bilinear", align_corners=False) + + target = target_one_hot.argmax(dim=1).type(target.dtype) + return self.loss(input, target) + + +class ArcFaceLoss2d(nn.modules.Module): + """ + https://www.kaggle.com/c/human-protein-atlas-image-classification/discussion/78109#latest-560973 + """ + + def __init__(self, s=30.0, m=0.35, gamma=1, ignore_index=-100): + super(ArcFaceLoss2d, self).__init__() + self.gamma = gamma + self.classify_loss = nn.CrossEntropyLoss(ignore_index=ignore_index) + self.s = s + self.easy_margin = False + self.cos_m = float(math.cos(m)) + self.sin_m = float(math.sin(m)) + self.th = float(math.cos(math.pi - m)) + self.mm = float(math.sin(math.pi - m) * m) + + def forward(self, cos_theta: torch.Tensor, labels): + num_classes = cos_theta.size(1) + sine = torch.sqrt(1.0 - torch.pow(cos_theta, 2)) + phi = (cos_theta * self.cos_m - sine * self.sin_m).type(cos_theta.dtype) + if self.easy_margin: + phi = torch.where(cos_theta > 0, phi, cos_theta) + else: + phi = torch.where(cos_theta > self.th, phi, cos_theta - self.mm) + + one_hot = F.one_hot(labels, num_classes).type(cos_theta.dtype) + one_hot = one_hot.permute(0, 3, 1, 2) + # -------------torch.where(out_i = {x_i if condition_i else y_i) ------------- + output = (one_hot * phi) + ((1.0 - one_hot) * cos_theta) + output *= self.s + loss1 = self.classify_loss(output, labels) + loss2 = self.classify_loss(cos_theta, labels) + + loss = (loss1 + self.gamma * loss2) / (1 + self.gamma) + return loss + + +class AmSoftmax2d(Module): + # implementation of additive margin softmax loss in https://arxiv.org/abs/1801.05599 + def __init__(self, ignore_index=UNLABELED_SAMPLE, weight=None): + super(AmSoftmax2d, self).__init__() + # initial kernel + + self.m = 0.35 # additive margin recommended by the paper + self.s = 30.0 # see normface https://arxiv.org/abs/1704.06369 + self.classify_loss = nn.CrossEntropyLoss(ignore_index=ignore_index, weight=weight) + + def forward(self, cos_theta, labels): + + cos_theta = cos_theta.clamp(-1, 1) # for numerical stability + phi = cos_theta - self.m + + num_classes = cos_theta.size(1) + + one_hot = F.one_hot(labels, num_classes) # .type(embbedings.dtype) + one_hot = one_hot.permute(0, 3, 1, 2) + + output = (one_hot * phi) + ((1.0 - one_hot) * cos_theta) + output *= self.s # scale up in order to make softmax work, first introduced in normface + + return self.classify_loss(output, labels) diff --git a/xview/metric.py b/xview/metric.py new file mode 100644 index 0000000..423acf7 --- /dev/null +++ b/xview/metric.py @@ -0,0 +1,232 @@ +from typing import List + +import numpy as np +import pandas as pd +import torch +from catalyst.dl import Callback, RunnerState, CallbackOrder +from pytorch_toolbelt.utils.catalyst import get_tensorboard_logger +from pytorch_toolbelt.utils.torch_utils import to_numpy +from pytorch_toolbelt.utils.visualization import render_figure_to_tensor, plot_confusion_matrix +from torchnet.meter import ConfusionMeter + +from .dataset import OUTPUT_MASK_PRE_KEY, OUTPUT_MASK_POST_KEY, INPUT_MASK_PRE_KEY, INPUT_MASK_POST_KEY +from .xview2_metrics import F1Recorder + + +class CompetitionMetricCallback(Callback): + """ + """ + + def __init__( + self, + input_key: str = "targets", + output_key: str = "logits", + image_id_key: str = "image_id", + prefix: str = "weighted_f1", + ): + super().__init__(CallbackOrder.Metric) + """ + :param input_key: input key to use for precision calculation; specifies our `y_true`. + :param output_key: output key to use for precision calculation; specifies our `y_pred`. + """ + self.prefix = prefix + self.output_key = output_key + self.input_key = input_key + self.image_id_key = image_id_key + self.all_rows = [] + + def on_loader_start(self, state): + self.all_rows = [] + + @staticmethod + def extract_buildings(x: np.ndarray): + """ Returns a mask of the buildings in x """ + buildings = x.copy() + buildings[x > 0] = 1 + return buildings + + @staticmethod + def compute_tp_fn_fp(pred: np.ndarray, targ: np.ndarray, c: int) -> List[int]: + """ + Computes the number of TPs, FNs, FPs, between a prediction (x) and a target (y) for the desired class (c) + + Args: + pred (np.ndarray): prediction + targ (np.ndarray): target + c (int): positive class + """ + TP = np.logical_and(pred == c, targ == c).sum() + FN = np.logical_and(pred != c, targ == c).sum() + FP = np.logical_and(pred == c, targ != c).sum() + return [TP, FN, FP] + + @classmethod + def get_row_pair(cls, lp, dp, lt, dt): + """ + Builds a row of TPs, FNs, and FPs for both the localization dataframe and the damage dataframe. + This pair of rows are built in the same function as damages are only assessed where buildings are predicted. + + Args: + lp: localization predictions + dp: damage predictions + lt: localization targets + dt: damage targets + """ + lp_b, lt_b, dt_b = map(cls.extract_buildings, (lp, lt, dt)) # convert all damage scores 1-4 to 1 + + dp = dp * lp_b # only give credit to damages where buildings are predicted + dp, dt = dp[dt_b == 1], dt[dt_b == 1] # only score damage where there exist buildings in target damage + + lrow = cls.compute_tp_fn_fp(lp_b, lt_b, 1) + drow = [] + for i in range(1, 5): + drow += cls.compute_tp_fn_fp(dp, dt, i) + return lrow, drow + + def on_batch_end(self, state: RunnerState): + image_ids = state.input[self.image_id_key] + outputs = to_numpy(torch.argmax(state.output[self.output_key].detach(), dim=1)) + targets = to_numpy(state.input[self.input_key].detach()) + + rows = [] + for image_id, y_true, y_pred in zip(image_ids, targets, outputs): + row = self.get_row_pair(y_pred, y_pred, y_true, y_true) + rows.append(row) + + self.all_rows.extend(rows) + + score, localization_f1, damage_f1, damage_f1s = self.compute_metrics(rows) + state.metrics.add_batch_value(self.prefix + "_batch" + "/localization_f1", localization_f1) + state.metrics.add_batch_value(self.prefix + "_batch" + "/damage_f1", damage_f1) + state.metrics.add_batch_value(self.prefix + "_batch", score) + + @staticmethod + def compute_metrics(rows): + lcolumns = ["lTP", "lFN", "lFP"] + ldf = pd.DataFrame([lrow for lrow, drow in rows], columns=lcolumns) + + dcolumns = ["dTP1", "dFN1", "dFP1", "dTP2", "dFN2", "dFP2", "dTP3", "dFN3", "dFP3", "dTP4", "dFN4", "dFP4"] + ddf = pd.DataFrame([drow for lrow, drow in rows], columns=dcolumns) + + TP = ldf["lTP"].sum() + FP = ldf["lFP"].sum() + FN = ldf["lFN"].sum() + lf1r = F1Recorder(TP, FP, FN, "Buildings") + + dmg2str = { + 1: f"No damage (1) ", + 2: f"Minor damage (2) ", + 3: f"Major damage (3) ", + 4: f"Destroyed (4) ", + } + + df1rs = [] + for i in range(1, 5): + TP = ddf[f"dTP{i}"].sum() + FP = ddf[f"dFP{i}"].sum() + FN = ddf[f"dFN{i}"].sum() + df1rs.append(F1Recorder(TP, FP, FN, dmg2str[i])) + + localization_f1 = lf1r.f1 + damage_f1s = [F1.f1 for F1 in df1rs] + harmonic_mean = lambda xs: len(xs) / sum((x + 1e-6) ** -1 for x in xs) + damage_f1 = harmonic_mean(damage_f1s) + + score = 0.3 * localization_f1 + 0.7 * damage_f1 + + return score, localization_f1, damage_f1, damage_f1s + + def on_loader_end(self, state): + score, localization_f1, damage_f1, damage_f1s = self.compute_metrics(self.all_rows) + + state.metrics.epoch_values[state.loader_name][self.prefix + "/localization_f1"] = localization_f1 + state.metrics.epoch_values[state.loader_name][self.prefix + "/damage_f1"] = damage_f1 + state.metrics.epoch_values[state.loader_name][self.prefix] = score + + class_names = ["no_damage", "minor_damage", "major_damage", "destroyed"] + for i in range(4): + state.metrics.epoch_values[state.loader_name][self.prefix + f"/{class_names[i]}"] = damage_f1s[i] + + +def default_multilabel_activation(x): + return (x.sigmoid() > 0.5).long() + + +class MultilabelConfusionMatrixCallback(Callback): + """ + Compute and log confusion matrix to Tensorboard. + For use with Multiclass classification/segmentation. + """ + + def __init__( + self, + input_key: str = "targets", + output_key: str = "logits", + prefix: str = "confusion_matrix", + class_names: List[str] = None, + num_classes: int = None, + ignore_index=None, + activation_fn=default_multilabel_activation, + ): + """ + :param input_key: input key to use for precision calculation; + specifies our `y_true`. + :param output_key: output key to use for precision calculation; + specifies our `y_pred`. + :param ignore_index: same meaning as in nn.CrossEntropyLoss + """ + super().__init__(CallbackOrder.Metric) + self.prefix = prefix + self.class_names = class_names + self.num_classes = num_classes if class_names is None else len(class_names) + self.output_key = output_key + self.input_key = input_key + self.ignore_index = ignore_index + self.confusion_matrix = None + self.activation_fn = activation_fn + + def on_loader_start(self, state): + self.confusion_matrix = ConfusionMeter(self.num_classes) + + def on_batch_end(self, state: RunnerState): + outputs: torch.Tensor = state.output[self.output_key].detach().cpu() + outputs: torch.Tensor = self.activation_fn(outputs) + + targets: torch.Tensor = state.input[self.input_key].detach().cpu() + + # Flatten + outputs = outputs.view(outputs.size(0), outputs.size(1), -1).permute(0, 2, 1).contiguous() + targets = targets.view(targets.size(0), targets.size(1), -1).permute(0, 2, 1).contiguous() + targets = targets.type_as(outputs) + + for class_index in range(self.num_classes): + outputs_i = outputs[class_index].view(-1) + targets_i = targets[class_index].view(-1) + + if self.ignore_index is not None: + mask = targets_i != self.ignore_index + outputs_i = outputs_i[mask] + targets_i = targets_i[mask] + + self.confusion_matrix.add(predicted=outputs_i, target=targets_i) + + def on_loader_end(self, state): + if self.class_names is None: + class_names = [str(i) for i in range(self.num_classes)] + else: + class_names = self.class_names + + num_classes = len(class_names) + cm = self.confusion_matrix.value() + + fig = plot_confusion_matrix( + cm, + figsize=(6 + num_classes // 3, 6 + num_classes // 3), + class_names=class_names, + normalize=True, + noshow=True, + ) + fig = render_figure_to_tensor(fig) + + logger = get_tensorboard_logger(state) + logger.add_image(f"{self.prefix}/epoch", fig, global_step=state.step) diff --git a/xview/model_wrapper.py b/xview/model_wrapper.py new file mode 100644 index 0000000..cd0e2c1 --- /dev/null +++ b/xview/model_wrapper.py @@ -0,0 +1,103 @@ +from typing import List, Union, Dict, Any + +import torch +from catalyst.dl import CallbackOrder, logger, RunnerState, Callback +from catalyst.dl.callbacks.criterion import _add_loss_to_state, CriterionCallback +from torch import nn, Tensor + +from xview.dataset import INPUT_IMAGE_KEY + + +class ModelTrainer(nn.Module): + """ + Adapter class that computes loss on each GPU independently and returns only computed losses + """ + + def __init__( + self, + model: nn.Module, + losses: List[nn.Module], + loss_input_keys: List[str], + loss_output_keys: List[str], + loss_key="losses", + model_input_key=INPUT_IMAGE_KEY, + ): + """ + + :param model: + :param loss_output_keys: List of keys to get outputs for each loss function + :param losses: List of loss functions + """ + super().__init__() + self.model = model + self.input_key = model_input_key + self.output_keys = loss_output_keys + self.losses = nn.ModuleList(losses) + self.loss_key = loss_key + self.loss_input_keys = loss_input_keys + + def forward(self, **input): + model_output = self.model(input[self.input_key]) + + losses = [] + for input_key, output_key, loss_fn in zip(self.loss_input_keys, self.output_keys, self.losses): + target = input[input_key] + output = model_output[output_key] + loss = loss_fn(output, target) + losses.append(loss) + + model_output[self.loss_key] = losses + return model_output + + +class PassthroughCriterionCallback(CriterionCallback): + """ + This callback allows you to aggregate the values of the loss + (with different aggregation strategies) + and put the value back into ``state.loss``. + """ + + def __init__( + self, + prefix: str, + output_key="losses", + loss_keys: Union[str, List[str], Dict[str, float]] = None, + loss_aggregate_fn: str = "sum", + ) -> None: + """ + Args: + prefix (str): new key for aggregated loss. + loss_keys (Union[str, List[str], Dict[str, float]]): If not empty, + it aggregates only the values from the loss by these keys. + for ``weighted_sum`` aggregation it must be a Dict[str, float]. + loss_aggregate_fn (str): function for aggregation. + Must be either ``sum``, ``mean`` or ``weighted_sum``. + """ + super().__init__(prefix=prefix) + if prefix is None or not isinstance(prefix, str): + raise ValueError("prefix must be str") + self.prefix = prefix + + if isinstance(loss_keys, str): + loss_keys = [loss_keys] + self.loss_keys = loss_keys + self.output_key = output_key + self.loss_aggregate_name = loss_aggregate_fn + + def on_stage_start(self, state: RunnerState): + pass + + def on_batch_end(self, state: RunnerState) -> None: + """ + Computes the loss and add it to the metrics + """ + losses = state.output[self.output_key] + losses = [torch.sum(x) for x in losses] # Sum losses from all devices + + for loss_name, loss in zip(self.loss_keys, losses): + state.metrics.add_batch_value(metrics_dict={loss_name: loss.item()}) + + loss = torch.sum(torch.stack(losses)) + _add_loss_to_state(self.prefix, state, loss) + + state.metrics.add_batch_value(metrics_dict={self.prefix: loss.item()}) diff --git a/xview/optim.py b/xview/optim.py new file mode 100644 index 0000000..35b9828 --- /dev/null +++ b/xview/optim.py @@ -0,0 +1,172 @@ +from catalyst.contrib.optimizers import RAdam, Lamb +from torch.optim import SGD, Adam, RMSprop, AdamW + +import math +import torch +from torch.optim.optimizer import Optimizer +import numpy as np +import torch.nn as nn + + +# import torch.optim as Optimizer + +# Original source: https://github.com/shivram1987/diffGrad/blob/master/diffGrad.py + +# modifications: @lessw2020 + + +class DiffGrad(Optimizer): + r"""Implements diffGrad algorithm. It is modified from the pytorch implementation of Adam. + It has been proposed in `diffGrad: An Optimization Method for Convolutional Neural Networks`_. + Arguments: + params (iterable): iterable of parameters to optimize or dicts defining + parameter groups + lr (float, optional): learning rate (default: 1e-3) + betas (Tuple[float, float], optional): coefficients used for computing + running averages of gradient and its square (default: (0.9, 0.999)) + eps (float, optional): term added to the denominator to improve + numerical stability (default: 1e-8) + weight_decay (float, optional): weight decay (L2 penalty) (default: 0) + amsgrad (boolean, optional): whether to use the AMSGrad variant of this + algorithm from the paper `On the Convergence of Adam and Beyond`_ + (default: False) + .. _diffGrad: An Optimization Method for Convolutional Neural Networks: + https://arxiv.org/abs/1909.11015 + .. _Adam\: A Method for Stochastic Optimization: + https://arxiv.org/abs/1412.6980 + .. _On the Convergence of Adam and Beyond: + https://openreview.net/forum?id=ryQu7f-RZ + """ + + def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, version=0, weight_decay=0): + if not 0.0 <= lr: + raise ValueError("Invalid learning rate: {}".format(lr)) + if not 0.0 <= eps: + raise ValueError("Invalid epsilon value: {}".format(eps)) + if not 0.0 <= betas[0] < 1.0: + raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) + if not 0.0 <= betas[1] < 1.0: + raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) + + defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) + + super().__init__(params, defaults) + + # save version + self.version = version + + def __setstate__(self, state): + super().__setstate__(state) + + def step(self, closure=None): + """Performs a single optimization step. + Arguments: + closure (callable, optional): A closure that reevaluates the model + and returns the loss. + """ + loss = None + if closure is not None: + loss = closure() + + for group in self.param_groups: + for p in group['params']: + if p.grad is None: + continue + grad = p.grad.data + if grad.is_sparse: + raise RuntimeError('diffGrad does not support sparse gradients, please consider SparseAdam instead') + + state = self.state[p] + + # State initialization + if len(state) == 0: + state['step'] = 0 + # Exponential moving average of gradient values + state['exp_avg'] = torch.zeros_like(p.data) + # Exponential moving average of squared gradient values + state['exp_avg_sq'] = torch.zeros_like(p.data) + # Previous gradient + state['previous_grad'] = torch.zeros_like(p.data) + + exp_avg, exp_avg_sq, previous_grad = state['exp_avg'], state['exp_avg_sq'], state['previous_grad'] + beta1, beta2 = group['betas'] + + state['step'] += 1 + + if group['weight_decay'] != 0: + grad.add_(group['weight_decay'], p.data) + + # Decay the first and second moment running average coefficient + exp_avg.mul_(beta1).add_(1 - beta1, grad) + exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) + denom = exp_avg_sq.sqrt().add_(group['eps']) + + bias_correction1 = 1 - beta1 ** state['step'] + bias_correction2 = 1 - beta2 ** state['step'] + + # compute diffgrad coefficient (dfc) + + if self.version == 0: + diff = abs(previous_grad - grad) + elif self.version == 1: + diff = previous_grad - grad + elif self.version == 2: + diff = .5 * abs(previous_grad - grad) + + if self.version == 0 or self.version == 1: + dfc = 1. / (1. + torch.exp(-diff)) + elif self.version == 2: + dfc = 9. / (1. + torch.exp(-diff)) - 4 # DFC2 = 9/(1+e-(.5/g/)-4 #range .5,5 + + state['previous_grad'] = grad + + # update momentum with dfc + exp_avg1 = exp_avg * dfc + + step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1 + + p.data.addcdiv_(-step_size, exp_avg1, denom) + + return loss + +def get_optimizer(optimizer_name: str, parameters, learning_rate: float, weight_decay=1e-5, **kwargs): + if optimizer_name.lower() == "sgd": + return SGD(parameters, learning_rate, momentum=0.9, nesterov=True, weight_decay=weight_decay, **kwargs) + + if optimizer_name.lower() == "adam": + return Adam(parameters, learning_rate, weight_decay=weight_decay, eps=1e-5, **kwargs) # As Jeremy suggests + + if optimizer_name.lower() == "rms": + return RMSprop(parameters, learning_rate, weight_decay=weight_decay, **kwargs) + + if optimizer_name.lower() == "adamw": + return AdamW(parameters, learning_rate, weight_decay=weight_decay, eps=1e-5, **kwargs) + + if optimizer_name.lower() == "radam": + return RAdam(parameters, learning_rate, weight_decay=weight_decay, eps=1e-5, **kwargs) # As Jeremy suggests + + # if optimizer_name.lower() == "ranger": + # return Ranger(parameters, learning_rate, weight_decay=weight_decay, + # **kwargs) + + # if optimizer_name.lower() == "qhadamw": + # return QHAdamW(parameters, learning_rate, weight_decay=weight_decay, + # **kwargs) + # + if optimizer_name.lower() == "lamb": + return Lamb(parameters, learning_rate, weight_decay=weight_decay, **kwargs) + + if optimizer_name.lower() == "fused_lamb": + from apex.optimizers import FusedLAMB + + return FusedLAMB(parameters, learning_rate, weight_decay=weight_decay, **kwargs) + + if optimizer_name.lower() == "fused_adam": + from apex.optimizers import FusedAdam + + return FusedAdam(parameters, learning_rate, eps=1e-5, weight_decay=weight_decay, adam_w_mode=True, **kwargs) + + if optimizer_name.lower() == "diffgrad": + return DiffGrad(parameters, learning_rate, eps=1e-5, weight_decay=weight_decay, **kwargs) + + raise ValueError("Unsupported optimizer name " + optimizer_name) diff --git a/xview/postprocessing.py b/xview/postprocessing.py new file mode 100644 index 0000000..0c6e3a9 --- /dev/null +++ b/xview/postprocessing.py @@ -0,0 +1,236 @@ +import cv2 +import numpy as np +from skimage.measure import label, regionprops +import matplotlib.pyplot as plt + +__all__ = ["make_predictions_dominant", "make_predictions_naive", "make_predictions_floodfill"] + +from skimage.morphology import remove_small_objects +from skimage.segmentation import relabel_sequential + +from xview.dataset import UNLABELED_SAMPLE + + +def make_pseudolabeling_target(damage_probs:np.ndarray, ratio_threshold=1.5): + damage_probs = damage_probs.copy() + class_index = np.argmax(damage_probs, axis=0) + + sorted_probs = np.sort(-damage_probs, axis=0) + + ratio = sorted_probs[0] / sorted_probs[1] + + confident_classes = ratio > ratio_threshold + class_index[~confident_classes] = UNLABELED_SAMPLE + return class_index + + +def make_predictions_naive(damage_probs: np.ndarray): + loc_pred = np.stack((damage_probs[0, ...], np.sum(damage_probs[1:, ...], axis=0))) + loc_cls = np.argmax(loc_pred, axis=0) + + # After we have 'fixed' localization predictions, we must zero-out probabilities for damage probs + damage_probs = damage_probs.copy() + damage_probs[0, loc_cls > 0] = 0 + dmg_cls = np.argmax(damage_probs, axis=0) + + dmg_cls[dmg_cls == 0] = 1 # Fill remaining with damage type 1 (no damage) + return loc_cls.astype(np.uint8), dmg_cls.astype(np.uint8) + + + +def make_predictions_dominant( + damage_probs: np.ndarray, min_size=32, assign_dominant=True, max_building_area=2048, min_solidity=0.75 +): + loc_pred = np.stack((damage_probs[0, ...], np.sum(damage_probs[1:, ...], axis=0))) + loc_cls = np.argmax(loc_pred, axis=0) + + # After we have 'fixed' localization predictions, we must zero-out probabilities for damage probs + damage_probs = damage_probs.copy() + damage_probs[0, loc_cls > 0] = 0 + dmg_cls = np.argmax(damage_probs, axis=0) + + buildings = label(loc_cls) + + if min_size is not None: + # If there are any objects at all + if buildings.max() > 0: + buildings = remove_small_objects(buildings, min_size=min_size) + buildings, _, _ = relabel_sequential(buildings) + loc_cls = buildings > 0 + dmg_cls[~loc_cls] = 0 + + if assign_dominant: + building_props = regionprops(buildings) + classes = list(range(1, 5)) + for index, region in enumerate(building_props): + region_label, area, solidity = region["label"], region["area"], region["solidity"] + + region_mask = buildings == region_label + + if area < max_building_area or solidity > min_solidity: + label_counts = [np.sum(dmg_cls[region_mask] == cls_indxex) for cls_indxex in classes] + max_label = np.argmax(label_counts) + 1 + dmg_cls[region_mask] = max_label + + # print(region_label, area, solidity) + + dmg_cls[dmg_cls == 0] = 1 # Fill remaining with damage type 1 (no damage) + return loc_cls.astype(np.uint8), dmg_cls.astype(np.uint8) + + +def make_predictions_most_severe(damage_probs: np.ndarray, min_size=32, assign_severe=True): + loc_pred = np.stack((damage_probs[0, ...], np.sum(damage_probs[1:, ...], axis=0))) + loc_cls = np.argmax(loc_pred, axis=0) + + # After we have 'fixed' localization predictions, we must zero-out probabilities for damage probs + damage_probs = damage_probs.copy() + damage_probs[0, loc_cls > 0] = 0 + + dmg_cls = np.argmax(damage_probs, axis=0) + + buildings = label(loc_cls) + + if min_size is not None: + # If there are any objects at all + if buildings.max() > 0: + buildings = remove_small_objects(buildings, min_size=min_size) + buildings, _, _ = relabel_sequential(buildings) + loc_cls = buildings > 0 + dmg_cls[~loc_cls] = 0 + + if assign_severe: + building_props = regionprops(buildings) + classes = np.arange(1, 5) + for index, region in enumerate(building_props): + region_label, area, solidity = region["label"], region["area"], region["solidity"] + + region_mask = buildings == region_label + + if area < 2048 or solidity > 0.75: + label_counts = np.array([np.sum(dmg_cls[region_mask] == cls_indxex) for cls_indxex in classes]) + if label_counts.sum() == 0: + import matplotlib.pyplot as plt + + plt.figure() + plt.imshow(buildings) + plt.show() + + plt.figure() + plt.imshow(region_mask) + plt.show() + + plt.figure() + plt.imshow(dmg_cls) + plt.show() + + breakpoint() + + min_count = max(1, label_counts[label_counts > 0].mean() - 3 * label_counts[label_counts > 0].std()) + + labels = classes[label_counts >= min_count] + max_label = labels.max() + + if len(labels) > 1: + print(label_counts, min_count, labels, max_label) + # label_counts > 0 + # max_label = np.argmax(label_counts) + 1 + dmg_cls[region_mask] = max_label + + # print(region_label, area, solidity) + + dmg_cls[dmg_cls == 0] = 1 # Fill remaining with damage type 1 (no damage) + return loc_cls.astype(np.uint8), dmg_cls.astype(np.uint8) + + +def make_predictions_floodfill(damage_probs: np.ndarray): + loc_pred = np.stack((damage_probs[0, ...], np.sum(damage_probs[1:, ...], axis=0))) + loc_cls = np.argmax(loc_pred, axis=0) + + # After we have 'fixed' localization predictions, we must zero-out probabilities for damage probs + damage_probs = damage_probs.copy() + damage_probs[0, loc_cls > 0] = 0 + + seed = np.argmax(damage_probs, axis=0) + + dist = cv2.distanceTransform((1 - loc_cls).astype(np.uint8), distanceType=cv2.DIST_L2, maskSize=3, dstType=cv2.CV_8U) + dist = np.clip(dist, a_min=0, a_max=255).astype(np.uint8) + + # plt.figure() + # plt.imshow(dist) + # plt.show() + + img = np.dstack([dist, dist, dist]) + + dmg_cls = cv2.watershed(img, seed.astype(int)) + if not isinstance(dmg_cls, np.ndarray): + dmg_cls = dmg_cls.get() + dmg_cls[dmg_cls < 1] = 1 + + return loc_cls.astype(np.uint8), dmg_cls.astype(np.uint8) + + +def make_predictions_floodfill_with_image(damage_probs: np.ndarray, image): + loc_pred = np.stack((damage_probs[0, ...], np.sum(damage_probs[1:, ...], axis=0))) + loc_cls = np.argmax(loc_pred, axis=0) + + # After we have 'fixed' localization predictions, we must zero-out probabilities for damage probs + damage_probs = damage_probs.copy() + damage_probs[0, loc_cls > 0] = 0 + + seed = np.argmax(damage_probs, axis=0) + + dmg_cls = cv2.watershed(image, seed.astype(int)) + if not isinstance(dmg_cls, np.ndarray): + dmg_cls = dmg_cls.get() + dmg_cls[dmg_cls < 1] = 1 + + return loc_cls.astype(np.uint8), dmg_cls.astype(np.uint8) + + +def make_predictions_dominant_v2( + damage_probs: np.ndarray, min_size=32, assign_dominant=True, max_building_area=4096, min_solidity=0.9 +): + """ + Combines floodfill and dominant postprocessing + :param damage_probs: + :param min_size: + :param assign_dominant: + :param max_building_area: + :param min_solidity: + :return: + """ + loc_pred = np.stack((damage_probs[0, ...], np.sum(damage_probs[1:, ...], axis=0))) + loc_cls = np.argmax(loc_pred, axis=0) + + # After we have 'fixed' localization predictions, we must zero-out probabilities for damage probs + damage_probs = damage_probs.copy() + damage_probs[0, loc_cls > 0] = 0 + dmg_cls = np.argmax(damage_probs, axis=0) + + buildings = label(loc_cls) + + if min_size is not None: + # If there are any objects at all + if buildings.max() > 0: + buildings = remove_small_objects(buildings, min_size=min_size) + buildings, _, _ = relabel_sequential(buildings) + loc_cls = buildings > 0 + dmg_cls[~loc_cls] = 0 + + if assign_dominant: + building_props = regionprops(buildings) + classes = list(range(1, 5)) + for index, region in enumerate(building_props): + region_label, area, solidity = region["label"], region["area"], region["solidity"] + + region_mask = buildings == region_label + + if area < max_building_area and solidity > min_solidity: + label_counts = [np.sum(dmg_cls[region_mask] == cls_indxex) for cls_indxex in classes] + max_label = np.argmax(label_counts) + 1 + dmg_cls[region_mask] = max_label + + # print(region_label, area, solidity) + + dmg_cls[dmg_cls == 0] = 1 # Fill remaining with damage type 1 (no damage) + return loc_cls.astype(np.uint8), dmg_cls.astype(np.uint8) diff --git a/xview/pseudo.py b/xview/pseudo.py new file mode 100644 index 0000000..c8800ce --- /dev/null +++ b/xview/pseudo.py @@ -0,0 +1,93 @@ +import numpy as np +import torch.nn.functional as F +from catalyst.dl import Callback, CallbackOrder, RunnerState +from pytorch_toolbelt.utils.catalyst import PseudolabelDatasetMixin +from pytorch_toolbelt.utils.torch_utils import to_numpy + + +class CEOnlinePseudolabelingCallback2d(Callback): + """ + Online pseudo-labeling callback for multi-class problem. + + >>> unlabeled_train = get_test_dataset( + >>> data_dir, image_size=image_size, augmentation=augmentations + >>> ) + >>> unlabeled_eval = get_test_dataset( + >>> data_dir, image_size=image_size + >>> ) + >>> + >>> callbacks += [ + >>> CEOnlinePseudolabelingCallback2d( + >>> unlabeled_train.targets, + >>> pseudolabel_loader="label", + >>> prob_threshold=0.9) + >>> ] + >>> train_ds = train_ds + unlabeled_train + >>> + >>> loaders = collections.OrderedDict() + >>> loaders["train"] = DataLoader(train_ds) + >>> loaders["valid"] = DataLoader(valid_ds) + >>> loaders["label"] = DataLoader(unlabeled_eval, shuffle=False) # ! shuffle=False is important ! + """ + + def __init__( + self, + unlabeled_ds: PseudolabelDatasetMixin, + pseudolabel_loader="label", + prob_threshold=0.9, + sample_index_key="index", + output_key="logits", + unlabeled_class=-100, + label_smoothing=0.0, + label_frequency=1, + ): + assert 1.0 > prob_threshold > 0.5 + + super().__init__(CallbackOrder.Other) + self.unlabeled_ds = unlabeled_ds + self.pseudolabel_loader = pseudolabel_loader + self.prob_threshold = prob_threshold + self.sample_index_key = sample_index_key + self.output_key = output_key + self.unlabeled_class = unlabeled_class + self.label_smoothing = label_smoothing + self.label_frequency = label_frequency + self.last_labeled_epoch = None + self.should_relabel = None + + def on_epoch_start(self, state: RunnerState): + self.should_relabel = ( + self.last_labeled_epoch is None or (state.epoch - self.last_labeled_epoch) % self.label_frequency == 0 + ) + + def on_epoch_end(self, state: RunnerState): + if self.should_relabel: + self.last_labeled_epoch = state.epoch + + def get_probabilities(self, state: RunnerState): + probs = state.output[self.output_key].detach().softmax(dim=1) + indexes = state.input[self.sample_index_key] + + if probs.size(2) != 1024 or probs.size(3) != 1024: + probs = F.interpolate(probs, size=(1024, 1024), mode="bilinear", align_corners=False) + + return to_numpy(probs), to_numpy(indexes) + + def on_batch_end(self, state: RunnerState): + if state.loader_name != self.pseudolabel_loader: + return + + if not self.should_relabel: + return + + # Get predictions for batch + probs, indexes = self.get_probabilities(state) + + for p, sample_index in zip(probs, indexes): + max_prob = np.max(p, axis=0) + class_index = np.argmax(p, axis=0) + + confident_classes = max_prob > self.prob_threshold + class_index[~confident_classes] = self.unlabeled_class + + self.unlabeled_ds.set_target(sample_index, class_index) diff --git a/xview/rounder.py b/xview/rounder.py new file mode 100644 index 0000000..b8b4e3f --- /dev/null +++ b/xview/rounder.py @@ -0,0 +1,85 @@ +from functools import partial +from multiprocessing.pool import Pool + +import cv2 +import numpy as np +import scipy as sp +import torch +from pytorch_toolbelt.utils.torch_utils import to_numpy + +from xview.dataset import read_mask +from xview.metric import CompetitionMetricCallback +from xview.postprocessing import make_predictions_naive + + +@torch.no_grad() +def _compute_fn(args, coef_exp): + xi, dmg_true = args + dmg_pred = xi.astype(np.float32) * coef_exp + loc_pred, dmg_pred = make_predictions_naive(dmg_pred) + + if loc_pred.shape[0] != 1024: + loc_pred = cv2.resize(loc_pred, dsize=(1024, 1024), interpolation=cv2.INTER_NEAREST) + dmg_pred = cv2.resize(dmg_pred, dsize=(1024, 1024), interpolation=cv2.INTER_NEAREST) + + row = CompetitionMetricCallback.get_row_pair(loc_pred, dmg_pred, dmg_true, dmg_true) + return row + + +class OptimizedRounder(object): + def __init__(self, apply_softmax, workers=0): + self.coef_ = 0 + self.workers = workers + self.apply_softmax = apply_softmax + + def _target_metric_loss(self, coef, X, y): + coef_exp = np.expand_dims(np.expand_dims(coef, -1), -1) + + all_rows = [] + + proc_fn = partial(_compute_fn, coef_exp=coef_exp) + + with Pool(self.workers) as wp: + for row in wp.imap_unordered(proc_fn, zip(X, y)): + all_rows.append(row) + + score, localization_f1, damage_f1, damage_f1s = CompetitionMetricCallback.compute_metrics(all_rows) + print(score, localization_f1, damage_f1, damage_f1s, "coeffs", coef) + return 1.0 - score + + def _prepare_data(self, X, y): + assert self.apply_softmax == "pre" + X_data = [to_numpy(torch.from_numpy(np.load(xi)).float().softmax(dim=0)).astype(np.float16) for xi in X] + Y_data = [read_mask(yi) for yi in y] + print("Loaded data into memory") + return X_data, Y_data + + def fit(self, X, y): + X_data, Y_data = self._prepare_data(X, y) + + loss_partial = partial(self._target_metric_loss, X=X_data, y=Y_data) + initial_coef = [0.5, 1.1, 1.1, 1.1, 1.1] + self.coef_ = sp.optimize.minimize( + loss_partial, initial_coef, method="nelder-mead", options={"maxiter": 100, "xatol": 0.001} + ) + + del X_data, Y_data + return self.coefficients() + + def predict(self, X, y, coef: np.ndarray): + X_data, Y_data = self._prepare_data(X, y) + + coef_exp = np.expand_dims(np.expand_dims(coef, -1), -1) + all_rows = [] + proc_fn = partial(_compute_fn, coef_exp=coef_exp) + + with Pool(self.workers) as wp: + for row in wp.imap_unordered(proc_fn, zip(X_data, Y_data)): + all_rows.append(row) + + score, localization_f1, damage_f1, damage_f1s = CompetitionMetricCallback.compute_metrics(all_rows) + del X_data, Y_data + return score, localization_f1, damage_f1, damage_f1s + + def coefficients(self): + return self.coef_["x"] diff --git a/xview/scheduler.py b/xview/scheduler.py new file mode 100644 index 0000000..225198b --- /dev/null +++ b/xview/scheduler.py @@ -0,0 +1,52 @@ +import torch +from pytorch_toolbelt.optimization.lr_schedules import PolyLR +from torch.optim.lr_scheduler import ( + OneCycleLR, + ExponentialLR, + CyclicLR, + MultiStepLR, + CosineAnnealingLR, + CosineAnnealingWarmRestarts, +) + + +def get_scheduler(scheduler_name: str, optimizer, lr, num_epochs, batches_in_epoch=None): + if scheduler_name is None or scheduler_name.lower() == "none": + return None + + if scheduler_name.lower() == "poly": + return PolyLR(optimizer, num_epochs, gamma=0.9) + + if scheduler_name.lower() == "cos": + return CosineAnnealingLR(optimizer, num_epochs, eta_min=1e-5) + + if scheduler_name.lower() == "cosr": + return CosineAnnealingWarmRestarts(optimizer, T_0=max(2, num_epochs // 4), eta_min=1e-5) + + if scheduler_name.lower() in {"1cycle", "one_cycle"}: + return OneCycleLR( + optimizer, lr_range=(lr, 1e-6, 1e-5), num_steps=batches_in_epoch, warmup_fraction=0.05, decay_fraction=0.1 + ) + + if scheduler_name.lower() == "exp": + return ExponentialLR(optimizer, gamma=0.95) + + if scheduler_name.lower() == "clr": + return CyclicLR( + optimizer, + base_lr=1e-6, + max_lr=lr, + step_size_up=batches_in_epoch // 4, + # mode='exp_range', + gamma=0.99, + ) + + if scheduler_name.lower() == "multistep": + return MultiStepLR( + optimizer, milestones=[int(num_epochs * 0.5), int(num_epochs * 0.7), int(num_epochs * 0.9)], gamma=0.3 + ) + + if scheduler_name.lower() == "simple": + return MultiStepLR(optimizer, milestones=[int(num_epochs * 0.4), int(num_epochs * 0.7)], gamma=0.4) + + raise KeyError(scheduler_name) diff --git a/xview/ssim_loss.py b/xview/ssim_loss.py new file mode 100644 index 0000000..7ff3ca4 --- /dev/null +++ b/xview/ssim_loss.py @@ -0,0 +1,58 @@ +import torch +import torch.nn.functional as F +from torch.autograd import Variable +import numpy as np +from math import exp + + +def gaussian(window_size, sigma): + gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)]) + return gauss / gauss.sum() + + +def create_window(window_size, channel): + _1D_window = gaussian(window_size, 1.5).unsqueeze(1) + _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0) + window = _2D_window.expand(channel, 1, window_size, window_size).contiguous() + return window + + +def _ssim(img1, img2, window, window_size, channel, size_average=True): + mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel) + mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel) + + mu1_sq = mu1.pow(2) + mu2_sq = mu2.pow(2) + mu1_mu2 = mu1 * mu2 + + sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq + sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq + sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2 + + C1 = 0.01 ** 2 + C2 = 0.03 ** 2 + + ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)) + + if size_average: + return 1 - ssim_map.mean() + else: + return 1 - ssim_map.mean(1).mean(1).mean(1) + + +class SSIM(torch.nn.Module): + def __init__(self, channels, window_size=11, size_average=True): + super(SSIM, self).__init__() + self.window_size = window_size + self.size_average = size_average + self.register_buffer("window", create_window(window_size, channels)) + + def forward(self, input: torch.Tensor, target: torch.Tensor): + (_, channel, _, _) = input.size() + + input = input.softmax(dim=1) + + target = F.one_hot(target, channel).permute(0, 3, 1, 2).type_as(input) + + window = self.window.type_as(input) + return _ssim(input, target, window, self.window_size, channel, self.size_average) diff --git a/xview/train_utils.py b/xview/train_utils.py new file mode 100644 index 0000000..cbd83c2 --- /dev/null +++ b/xview/train_utils.py @@ -0,0 +1,63 @@ +import torch + +__all__ = ["report_checkpoint", "clean_checkpoint"] + +from catalyst.dl import CriterionCallback + +from .dataset import UNLABELED_SAMPLE +from .losses import get_loss + + +def report_checkpoint(checkpoint): + print("Epoch :", checkpoint["epoch"]) + + # {'mask_pre/bce': 0.011939526008819881, 'mask_post/ce': 0.039905175798535336, 'loss': 0.05184470175895215, 'jaccard': 0.6682964469961886, '_base/lr': 0.001, + # '_base/momentum': 0.9, '_timers/data_time': 0.2810825881448131, '_timers/model_time': 0.025946252149632927, '_timers/batch_time': 0.3070834094035581, '_timers/_fps': 121.48878000184467, + # 'localization_f1': 0.7123450379603988, 'damage_f1': 0.021565931686082063, 'weighted_f1': 0.22879966356837708, 'jaccard_no-damage': 0.4595737876547124, 'jaccard_minor-damage': 0.7845541293707017, 'jaccard_major-damage': 0.7821522489229876, 'jaccard_destroyed': 0.6469056220363518} + skip_fields = [ + "_base/lr", + "_base/momentum", + "_timers/data_time", + "_timers/model_time", + "_timers/batch_time", + "_timers/_fps", + ] + print( + "Metrics (Train):", [(k, v) for k, v, in checkpoint["epoch_metrics"]["train"].items() if k not in skip_fields] + ) + print( + "Metrics (Valid):", [(k, v) for k, v, in checkpoint["epoch_metrics"]["valid"].items() if k not in skip_fields] + ) + + +def clean_checkpoint(src_fname, dst_fname): + checkpoint = torch.load(src_fname) + + keys = ["criterion_state_dict", "optimizer_state_dict", "scheduler_state_dict"] + + for key in keys: + if key in checkpoint: + del checkpoint[key] + + torch.save(checkpoint, dst_fname) + + +def get_criterion_callback(loss_name, input_key, output_key, prefix=None, loss_weight=1.0, ignore_index=UNLABELED_SAMPLE): + criterions_dict = {f"{prefix}/{loss_name}": get_loss(loss_name, ignore_index=ignore_index)} + if prefix is None: + prefix = input_key + + criterion_callback = CriterionCallback( + prefix=f"{prefix}/{loss_name}", + input_key=input_key, + output_key=output_key, + criterion_key=f"{prefix}/{loss_name}", + multiplier=float(loss_weight), + ) + + return criterions_dict, criterion_callback, criterion_callback.prefix + +def get_criterion(loss_name, prefix=None, ignore_index=UNLABELED_SAMPLE): + loss = get_loss(loss_name, ignore_index=ignore_index) + prefix = f"{prefix}/{loss_name}" + return loss, prefix diff --git a/xview/utils/combine_jsons.py b/xview/utils/combine_jsons.py new file mode 100644 index 0000000..5271772 --- /dev/null +++ b/xview/utils/combine_jsons.py @@ -0,0 +1,98 @@ +##################################################################################################################################################################### +# xView2 # +# Copyright 2019 Carnegie Mellon University. # +# NO WARRANTY. THIS CARNEGIE MELLON UNIVERSITY AND SOFTWARE ENGINEERING INSTITUTE MATERIAL IS FURNISHED ON AN "AS-IS" BASIS. CARNEGIE MELLON UNIVERSITY MAKES NO # +# WARRANTIES OF ANY KIND, EITHER EXPRESSED OR IMPLIED, AS TO ANY MATTER INCLUDING, BUT NOT LIMITED TO, WARRANTY OF FITNESS FOR PURPOSE OR MERCHANTABILITY, # +# EXCLUSIVITY, OR RESULTS OBTAINED FROM USE OF THE MATERIAL. CARNEGIE MELLON UNIVERSITY DOES NOT MAKE ANY WARRANTY OF ANY KIND WITH RESPECT TO FREEDOM FROM PATENT, # +# TRADEMARK, OR COPYRIGHT INFRINGEMENT. # +# Released under a MIT (SEI)-style license, please see LICENSE.md or contact permission@sei.cmu.edu for full terms. # +# [DISTRIBUTION STATEMENT A] This material has been approved for public release and unlimited distribution. Please see Copyright notice for non-US Government use # +# and distribution. # +# This Software includes and/or makes use of the following Third-Party Software subject to its own license: # +# 1. SpaceNet (https://github.com/motokimura/spacenet_building_detection/blob/master/LICENSE) Copyright 2017 Motoki Kimura. # +# DM19-0988 # +##################################################################################################################################################################### + +import json + + +def combine_output(pred_polygons, pred_classification, output_file): + """ + :param pred_polygons: the file path to the localization inference output json + :param pre_classification: the file path to the classification inference output json + :param output_file: the file path to store the combined json file + """ + + # Skeleton of the json with null values + output_json = { + "features": {"lng_lat": [], "xy": []}, + "metadata": { + "sensor": "", + "provider_asset_type": "", + "gsd": 0, + "capture_date": "", + "off_nadir_angle": 0, + "pan_resolution": 0, + "sun_azimuth": 0, + "sun_elevation": 0, + "target_azimuth": 0, + "disaster": "", + "disaster_type": "", + "catalog_id": "", + "original_width": 0, + "original_height": 0, + "width": 0, + "height": 0, + "id": "", + "img_name": "", + }, + } + + # Open the classification json + with open(pred_classification) as labels: + label_json = json.load(labels) + + # Open the localization json + with open(pred_polygons) as polys: + poly_json = json.load(polys) + + # Match UUIDs from the two jsons and combine in output_json skeleton + for p in poly_json["features"]["xy"]: + p["properties"]["subtype"] = label_json[p["properties"]["uid"]] + output_json["features"]["xy"].append(p) + + # Finally save out the combined json file + with open(output_file, "w") as out: + json.dump(output_json, out) + + +if __name__ == "__main__": + import argparse + + # Parse command line arguments + parser = argparse.ArgumentParser( + description="""combine_jsons.py: combines the outputs of localization and classification inference into a single output json""" + ) + parser.add_argument( + "--polys", + required=True, + metavar="/path/to/input/polygons.json", + help="Full path to the json from polygonize.py", + ) + parser.add_argument( + "--classes", + required=True, + metavar="/path/to/classifications.json", + help="Full path to the json from tensor_inf.py", + ) + parser.add_argument( + "--output", + required=True, + metavar="/path/to/pred.json", + help="Full path to save the final single output file to", + ) + + args = parser.parse_args() + + # Combining the json based off the uuid assigned at the polygonize stage + combine_output(args.polys, args.classes, args.output) diff --git a/xview/utils/data_finalize.sh b/xview/utils/data_finalize.sh new file mode 100644 index 0000000..58ee049 --- /dev/null +++ b/xview/utils/data_finalize.sh @@ -0,0 +1,105 @@ +#!/bin/bash + +##################################################################################################################################################################### +# xView2 # +# Copyright 2019 Carnegie Mellon University. # +# NO WARRANTY. THIS CARNEGIE MELLON UNIVERSITY AND SOFTWARE ENGINEERING INSTITUTE MATERIAL IS FURNISHED ON AN "AS-IS" BASIS. CARNEGIE MELLON UNIVERSITY MAKES NO # +# WARRANTIES OF ANY KIND, EITHER EXPRESSED OR IMPLIED, AS TO ANY MATTER INCLUDING, BUT NOT LIMITED TO, WARRANTY OF FITNESS FOR PURPOSE OR MERCHANTABILITY, # +# EXCLUSIVITY, OR RESULTS OBTAINED FROM USE OF THE MATERIAL. CARNEGIE MELLON UNIVERSITY DOES NOT MAKE ANY WARRANTY OF ANY KIND WITH RESPECT TO FREEDOM FROM PATENT, # +# TRADEMARK, OR COPYRIGHT INFRINGEMENT. # +# Released under a MIT (SEI)-style license, please see LICENSE.md or contact permission@sei.cmu.edu for full terms. # +# [DISTRIBUTION STATEMENT A] This material has been approved for public release and unlimited distribution. Please see Copyright notice for non-US Government use # +# and distribution. # +# This Software includes and/or makes use of the following Third-Party Software subject to its own license: # +# 1. SpaceNet (https://github.com/motokimura/spacenet_building_detection/blob/master/LICENSE) Copyright 2017 Motoki Kimura. # +# DM19-0988 # +##################################################################################################################################################################### + +set -euo pipefail + +# this function is called when Ctrl-C is sent +function trap_ctrlc () +{ + # perform cleanup here + echo "Ctrl-C or Error caught...performing clean up" + + if [ -d "$input"/spacenet_gt ]; then + rm -rf "$input"/spacenet_gt + fi + + exit 99 +} + +# initialise trap to call trap_ctrlc function +# when signal 2 (SIGINT) is received +trap "trap_ctrlc" 2 9 13 3 + +help_message () { + printf "${0}: Moves files around for the spacenet model to train\n\t-i /path/to/xBD/ \n\t-s split percentage to go to train\n\t-x /path/to/xview-2/repository/\n\t(Note: this script expects mask_polygons.py to have ran first to create labels)\n\n" +} + +# Checking for `bc` first (users reported that wasn't installed on some systems) +if ! [ -x "$(command -v bc)" ]; then + echo 'Error: bc is not installed, please install before continuing.' >&2 + exit 98 +fi + +if [ $# -lt 3 ]; then + help_message + exit 1 +fi + +while getopts "i:s:x:h" OPTION +do + case $OPTION in + h) + help_message + exit 1 + ;; + i) + input="$OPTARG" + ;; + s) + split="$OPTARG" + ;; + x) + XBDIR="$OPTARG" + ;; + esac +done + + +# Get list of disasters to iterate over +disasters=`/bin/ls -1 "$input"` + +# Making the spacenet training directory +mkdir -p "$input"/spacenet_gt/images +mkdir -p "$input"/spacenet_gt/labels +mkdir -p "$input"/spacenet_gt/dataSet + +# for each disaster, copy the pre images and labels to the spacenet training directory +for disaster in $disasters; do + masks=`/bin/ls -1 "$input"/"$disaster"/masks` + for mask in $masks; do + cp "$input"/"$disaster"/masks/$mask "$input"/spacenet_gt/labels + cp "$input"/"$disaster"/images/$mask "$input"/spacenet_gt/images + done +done + +# Listing all files to do the split +cd "$input"/spacenet_gt/dataSet/ +touch all_images.txt +/bin/ls -1 "$input"/spacenet_gt/images > all_images.txt + +line_count=`cat all_images.txt | wc -l` +lines_to_split=$(bc -l <<< "$line_count"*"$split") +split -l `awk -F. '{print $1}' <<< $lines_to_split` all_images.txt + +mv ./xaa train.txt +mv ./xab val.txt +rm all_images.txt + +# Running the mean creation code over the images +python "$XBDIR"/spacenet/src/features/compute_mean.py "$input"/spacenet_gt/dataSet/train.txt --root "$input"/spacenet_gt/images/ --output "$input"/spacenet_gt/dataSet/mean.npy + +echo "Done!" diff --git a/xview/utils/inference.sh b/xview/utils/inference.sh new file mode 100644 index 0000000..bbe6b2c --- /dev/null +++ b/xview/utils/inference.sh @@ -0,0 +1,175 @@ +#!/bin/bash + +##################################################################################################################################################################### +# xView2 # +# Copyright 2019 Carnegie Mellon University. # +# NO WARRANTY. THIS CARNEGIE MELLON UNIVERSITY AND SOFTWARE ENGINEERING INSTITUTE MATERIAL IS FURNISHED ON AN "AS-IS" BASIS. CARNEGIE MELLON UNIVERSITY MAKES NO # +# WARRANTIES OF ANY KIND, EITHER EXPRESSED OR IMPLIED, AS TO ANY MATTER INCLUDING, BUT NOT LIMITED TO, WARRANTY OF FITNESS FOR PURPOSE OR MERCHANTABILITY, # +# EXCLUSIVITY, OR RESULTS OBTAINED FROM USE OF THE MATERIAL. CARNEGIE MELLON UNIVERSITY DOES NOT MAKE ANY WARRANTY OF ANY KIND WITH RESPECT TO FREEDOM FROM PATENT, # +# TRADEMARK, OR COPYRIGHT INFRINGEMENT. # +# Released under a MIT (SEI)-style license, please see LICENSE.md or contact permission@sei.cmu.edu for full terms. # +# [DISTRIBUTION STATEMENT A] This material has been approved for public release and unlimited distribution. Please see Copyright notice for non-US Government use # +# and distribution. # +# This Software includes and/or makes use of the following Third-Party Software subject to its own license: # +# 1. SpaceNet (https://github.com/motokimura/spacenet_building_detection/blob/master/LICENSE) Copyright 2017 Motoki Kimura. # +# DM19-0988 # +##################################################################################################################################################################### + +set -euo pipefail + +# this function is called when Ctrl-C is sent +function trap_ctrlc () +{ + # perform cleanup here + echo "Ctrl-C or Error caught...performing clean up check /tmp/inference.log" + + if [ -d /tmp/inference ]; then + rm -rf /tmp/inference + fi + + exit 99 +} + +# initialise trap to call trap_ctrlc function +# when signal 2 (SIGINT) is received +trap "trap_ctrlc" 2 9 13 3 + +help_message () { + printf "${0}: Runs the polygonization in inference mode\n\t-x: path to xview-2 repository\n\t-i: /full/path/to/input/pre-disaster/image.png\n\t-p: /full/path/to/input/post-disaster/image.png\n\t-o: /path/to/output.png\n\t-l: path/to/localization_weights\n\t-c: path/to/classification_weights\n\t-e /path/to/virtual/env/activate\n\t-y continue with local environment and without interactive prompt\n\n" +} + +input="" +input_post="" +inference_base="/tmp/inference" +LOGFILE="/tmp/inference_log" +XBDIR="" +virtual_env="" +localization_weights="" +classification_weights="" +continue_answer="n" + +if [ "$#" -lt 13 ]; then + help_message + exit 1 +fi + +while getopts "i:p:o:x:l:e:c:hy" OPTION +do + case $OPTION in + h) + help_message + exit 0 + ;; + y) + continue_answer="y" + ;; + o) + output_file="$OPTARG" + ;; + x) + XBDIR="$OPTARG" + virtual_env="$XBDIR/bin/activate" + ;; + i) + input="$OPTARG" + ;; + p) + input_post="$OPTARG" + ;; + l) + localization_weights="$OPTARG" + ;; + c) + classification_weights="$OPTARG" + ;; + e) + virtual_env="$OPTARG" + ;; + ?) + help_message + exit 0 + ;; + esac +done + +# Create the output directory if it doesn't exist +mkdir -p "$inference_base" + +if ! [ -f "$LOGFILE" ]; then + touch "$LOGFILE" +fi + +printf "==========\n" >> "$LOGFILE" +echo `date +%Y%m%dT%H%M%S` >> "$LOGFILE" +printf "\n" >> "$LOGFILE" + +input_image=${input##*/} + +label_temp="$inference_base"/"${input_image%.*}"/labels +mkdir -p "$label_temp" + +printf "\n" + +printf "\n" + +# Run in inference mode +# Because of the models _have_ to be in the correct directory, they use relative paths to find the source (e.g. "../src") +# sourcing the virtual environment packages if they exist +# this is *necessary* or all packages must be installed globally +if [ -f "$virtual_env" ]; then + source "$virtual_env" +else + if [ "$continue_answer" = "n" ]; then + printf "Error: cannot source virtual environment \n\tDo you have all the dependencies installed and want to continue? [Y/N]: " + read continue_answer + if [ "$continue_answer" == "N" ]; then + exit 2 + fi + fi +fi + +cd "$XBDIR"/spacenet/inference/ + +# Quietly running the localization inference to output a json with the predicted polygons from the supplied input image +printf "Running localization\n" +python3 ./inference.py --input "$input" --weights "$localization_weights" --mean "$XBDIR"/weights/mean.npy --output "$label_temp"/"${input_image%.*}".json >> "$LOGFILE" 2>&1 + +printf "\n" >> "$LOGFILE" + +# Classification inferences start below +cd "$XBDIR"/model + +# Replace the pre image here with the post +# We need to do this so the classification inference pulls the images from the post +# Since post is where the damage occurs +printf "Grabbing post image file for classification\n" +disaster_post_file="$input_post" + +mkdir -p "$inference_base"/output_polygons + +printf "Running classification\n" + +# Extracting polygons from post image +python3 ./process_data_inference.py --input_img "$disaster_post_file" --label_path "$label_temp"/"${input_image%.*}".json --output_dir "$inference_base"/output_polygons --output_csv "$inference_base"/output.csv >> "$LOGFILE" 2>&1 + +# Classifying extracted polygons +python3 ./damage_inference.py --test_data "$inference_base"/output_polygons --test_csv "$inference_base"/output.csv --model_weights "$classification_weights" --output_json /tmp/inference/classification_inference.json >> "$LOGFILE" 2>&1 + +printf "\n" >> "$LOGFILE" + +# Combining the predicted polygons with the predicted labels, based off a UUID generated during the localization inference stage +printf "Formatting json and scoring image\n" +python3 "$XBDIR"/utils/combine_jsons.py --polys "$label_temp"/"${input_image%.*}".json --classes /tmp/inference/classification_inference.json --output "$inference_base/inference.json" >> "$LOGFILE" 2>&1 +printf "\n" >> "$LOGFILE" + +# Transforming the inference json file to the image required for scoring +printf "Finalizing output file" +python3 "$XBDIR"/utils/inference_image_output.py --input "$inference_base"/inference.json --output "$output_file" >> "$LOGFILE" 2>&1 + +#Cleaning up by removing the temporary working directory we created +printf "Cleaning up\n" +rm -rf "$inference_base" + +printf "==========\n" >> "$LOGFILE" +printf "Done!\n" + diff --git a/xview/utils/inference_image_output.py b/xview/utils/inference_image_output.py new file mode 100644 index 0000000..36cf785 --- /dev/null +++ b/xview/utils/inference_image_output.py @@ -0,0 +1,187 @@ +##################################################################################################################################################################### +# xView2 # +# Copyright 2019 Carnegie Mellon University. # +# NO WARRANTY. THIS CARNEGIE MELLON UNIVERSITY AND SOFTWARE ENGINEERING INSTITUTE MATERIAL IS FURNISHED ON AN "AS-IS" BASIS. CARNEGIE MELLON UNIVERSITY MAKES NO # +# WARRANTIES OF ANY KIND, EITHER EXPRESSED OR IMPLIED, AS TO ANY MATTER INCLUDING, BUT NOT LIMITED TO, WARRANTY OF FITNESS FOR PURPOSE OR MERCHANTABILITY, # +# EXCLUSIVITY, OR RESULTS OBTAINED FROM USE OF THE MATERIAL. CARNEGIE MELLON UNIVERSITY DOES NOT MAKE ANY WARRANTY OF ANY KIND WITH RESPECT TO FREEDOM FROM PATENT, # +# TRADEMARK, OR COPYRIGHT INFRINGEMENT. # +# Released under a MIT (SEI)-style license, please see LICENSE.md or contact permission@sei.cmu.edu for full terms. # +# [DISTRIBUTION STATEMENT A] This material has been approved for public release and unlimited distribution. Please see Copyright notice for non-US Government use # +# and distribution. # +# This Software includes and/or makes use of the following Third-Party Software subject to its own license: # +# 1. SpaceNet (https://github.com/motokimura/spacenet_building_detection/blob/master/LICENSE) Copyright 2017 Motoki Kimura. # +# DM19-0988 # +##################################################################################################################################################################### + + +import json +from typing import List, Tuple + +from shapely import wkt +from shapely.geometry import Polygon +import numpy as np +from cv2 import fillPoly, imwrite + +from PIL import Image + + +def open_json(json_file_path): + """ + :param json_file_path: path to open inference json file + :returns: the json data dictionary of localized polygon and their classifications + """ + + with open(json_file_path) as jf: + json_data = json.load(jf) + inference_data = json_data["features"]["xy"] + return inference_data + + +def resize_mask_one_hot(mask, size): + import albumentations as A + import cv2 + + mask = np.squeeze(mask,-1) + one_hot_mask = (np.arange(mask.max() + 1) == mask[..., None]).astype(np.float32) + + resize_op = A.Resize(size[0], size[1], interpolation=cv2.INTER_LINEAR) + image_resized = resize_op(image=one_hot_mask)["image"] + + mask = np.argmax(image_resized, axis=2).astype(mask.dtype) + return mask + + +def create_image(inference_data) -> np.ndarray: + """ + :params inference_data: json data dictionary of localized polygon and their classifications + :returns: an numpy array of 8-bit grey scale image with polygons filled in according to the key provided + """ + + damage_key = {"un-classified": 1, "no-damage": 1, "minor-damage": 2, "major-damage": 3, "destroyed": 4} + + mask_img = np.zeros((1024, 1024, 1), np.uint8) + + for poly in inference_data: + damage = poly["properties"].get("subtype", "no-damage") + coords = wkt.loads(poly["wkt"]) + poly_np = np.array(coords.exterior.coords, np.int32) + + fillPoly(mask_img, [poly_np], damage_key[damage]) + + return mask_img + + +def create_instance_image(inference_data) -> Tuple[np.ndarray, List[int]]: + """ + :params inference_data: json data dictionary of localized polygon and their classifications + :returns: an numpy array of 16-bit grey scale image with polygons filled in according to the key provided + """ + + damage_key = {"un-classified": 1, "no-damage": 1, "minor-damage": 2, "major-damage": 3, "destroyed": 4} + + mask_img = np.zeros((1024, 1024), np.uint16) + + damage_labels = [] + for poly_index, poly in enumerate(inference_data): + damage = poly["properties"].get("subtype", "no-damage") + damage_label = damage_key[damage] + damage_labels.append(damage_label) + + coords = wkt.loads(poly["wkt"]) + poly_np = np.array(coords.exterior.coords, np.int32) + + fillPoly(mask_img, [poly_np], poly_index + 1) + + return mask_img, damage_labels + + +def colorize_mask(mask, color_map=None): + """ + Attaches a color palette to a PIL image. So long as the image is saved as a PNG, it will render visibly using the + provided color map. + :param mask: PIL image whose values are only 0 to 4 inclusive + :param color_map: np.ndarray or list of 3-tuples with 5 rows + :return: + """ + if len(mask.shape) == 3: + mask = np.squeeze(mask, -1) + mask = Image.fromarray(mask, "L") + + color_map = color_map or np.array( + [ + (0, 0, 0), # 0=background + (0, 255, 0), # no damage (or just 'building' for localization) (green) + (255, 255, 0), # minor damage (yellow) + (255, 128, 0), # major damage (red) + (255, 0, 0), # destroyed (red) + (127, 127, 127), # Unlabeled + ] + ) + assert color_map.shape == (6, 3) + mask.putpalette(color_map.astype(np.uint8)) + return mask + + +def make_rgb_image(mask): + + color_map = np.array( + [ + (0, 0, 0), # 0=background + (0, 255, 0), # no damage (or just 'building' for localization) (green) + (255, 255, 0), # minor damage (yellow) + (255, 128, 0), # major damage (red) + (255, 0, 0), # destroyed (red) + (127, 127, 127), # Unlabeled + ], dtype=np.uint8 + ) + + mask_rgb = color_map[mask] + return mask_rgb + +def save_image(polygons, output_path): + """ + :param polygons: np array with filled in polygons from create_image() + :param output_path: path to save the final output inference image + """ + mask = colorize_mask(polygons) + mask.save(output_path) + + # Output the filled in polygons to an image file + # imwrite(output_path, polygons) + + +def create_inference_image(json_input_path, image_output_path): + """ + :param json_input_path: Path to output inference json file + :param image_outut_pat: Path to save the final inference image + """ + + # Getting the inference data from the localization and classification + inference_data = open_json(json_input_path) + + # Filling in the polygons and readying the image format + polygon_array = create_image(inference_data) + + # Saving the image to the desired location + save_image(polygon_array, image_output_path) + return polygon_array + + +if __name__ == "__main__": + import argparse + + # Parse command line arguments + parser = argparse.ArgumentParser( + description="""inference_image_output.py: Takes the inference localization and classification final outputs in json from and outputs an image ready to be scored based off the challenge parameters""" + ) + parser.add_argument( + "--input", required=True, metavar="/path/to/final/inference.json", help="Full path to the final inference json" + ) + parser.add_argument( + "--output", required=True, metavar="/path/to/inference.png", help="Full path to save the image to" + ) + + args = parser.parse_args() + + # Creating the scoring image + create_inference_image(args.input, args.output) diff --git a/xview/utils/mask_polygons.py b/xview/utils/mask_polygons.py new file mode 100644 index 0000000..dcd0429 --- /dev/null +++ b/xview/utils/mask_polygons.py @@ -0,0 +1,275 @@ +##################################################################################################################################################################### +# xView2 # +# Copyright 2019 Carnegie Mellon University. # +# NO WARRANTY. THIS CARNEGIE MELLON UNIVERSITY AND SOFTWARE ENGINEERING INSTITUTE MATERIAL IS FURNISHED ON AN "AS-IS" BASIS. CARNEGIE MELLON UNIVERSITY MAKES NO # +# WARRANTIES OF ANY KIND, EITHER EXPRESSED OR IMPLIED, AS TO ANY MATTER INCLUDING, BUT NOT LIMITED TO, WARRANTY OF FITNESS FOR PURPOSE OR MERCHANTABILITY, # +# EXCLUSIVITY, OR RESULTS OBTAINED FROM USE OF THE MATERIAL. CARNEGIE MELLON UNIVERSITY DOES NOT MAKE ANY WARRANTY OF ANY KIND WITH RESPECT TO FREEDOM FROM PATENT, # +# TRADEMARK, OR COPYRIGHT INFRINGEMENT. # +# Released under a MIT (SEI)-style license, please see LICENSE.md or contact permission@sei.cmu.edu for full terms. # +# [DISTRIBUTION STATEMENT A] This material has been approved for public release and unlimited distribution. Please see Copyright notice for non-US Government use # +# and distribution. # +# This Software includes and/or makes use of the following Third-Party Software subject to its own license: # +# 1. SpaceNet (https://github.com/motokimura/spacenet_building_detection/blob/master/LICENSE) Copyright 2017 Motoki Kimura. # +# DM19-0988 # +##################################################################################################################################################################### + + +import json +from os import path, walk, makedirs +from sys import exit, stderr + +from cv2 import fillPoly, imwrite +import numpy as np +from shapely import wkt +from shapely.geometry import mapping, Polygon +from skimage.io import imread +from tqdm import tqdm + +# import imantics + +# This removes the massive amount of scikit warnings of "low contrast images" +import warnings + +warnings.filterwarnings("ignore", category=UserWarning) + + +def get_dimensions(file_path): + """ + :param file_path: The path of the file + :return: returns (width,height,channels) + """ + # Open the image we are going to mask + pil_img = imread(file_path) + img = np.array(pil_img) + w, h, c = img.shape + return (w, h, c) + + +def mask_polygons_separately(size, shapes): + """ + :param size: A tuple of the (width,height,channels) + :param shapes: A list of points in the polygon from get_feature_info + :returns: a dict of masked polygons with the shapes filled in from cv2.fillPoly + """ + # For each WKT polygon, read the WKT format and fill the polygon as an image + masked_polys = {} + + for u in shapes: + sh = shapes[u] + mask_img = np.zeros(size, np.uint8) + i = fillPoly(mask_img, [sh], (255, 255, 255)) + masked_polys[u] = i + + return masked_polys + + +def mask_polygons_together(size, shapes): + """ + :param size: A tuple of the (width,height,channels) + :param shapes: A list of points in the polygon from get_feature_info + :returns: A numpy array with the polygons filled 255s where there's a building and 0 where not + """ + # For each WKT polygon, read the WKT format and fill the polygon as an image + mask_img = np.zeros(size, np.uint8) + + for u in shapes: + blank = np.zeros(size, np.uint8) + poly = shapes[u] + fillPoly(blank, [poly], (1, 1, 1)) + mask_img += blank + + # Here we are taking the overlap (+=) and squashing it back to 0 + mask_img[mask_img > 1] = 0 + + # Finally we are taking all 1s and making it pure white (255) + mask_img[mask_img == 1] = 255 + + return mask_img + + +def mask_polygons_together_with_border(size, shapes, border): + """ + :param size: A tuple of the (width,height,channels) + :param shapes: A list of points in the polygon from get_feature_info + :returns: a dict of masked polygons with the shapes filled in from cv2.fillPoly + """ + + # For each WKT polygon, read the WKT format and fill the polygon as an image + mask_img = np.zeros(size, np.uint8) + + for u in shapes: + blank = np.zeros(size, np.uint8) + # Each polygon stored in shapes is a np.ndarray + poly = shapes[u] + + # Creating a shapely polygon object out of the numpy array + polygon = Polygon(poly) + + # Getting the center points from the polygon and the polygon points + (poly_center_x, poly_center_y) = polygon.centroid.coords[0] + polygon_points = polygon.exterior.coords + + # Setting a new polygon with each X,Y manipulated based off the center point + shrunk_polygon = [] + for (x, y) in polygon_points: + if x < poly_center_x: + x += border + elif x > poly_center_x: + x -= border + + if y < poly_center_y: + y += border + elif y > poly_center_y: + y -= border + + shrunk_polygon.append([x, y]) + + # Transforming the polygon back to a np.ndarray + ns_poly = np.array(shrunk_polygon, np.int32) + + # Filling the shrunken polygon to add a border between close polygons + fillPoly(blank, [ns_poly], (1, 1, 1)) + mask_img += blank + + mask_img[mask_img > 1] = 0 + mask_img[mask_img == 1] = 255 + return mask_img + + +def save_masks(masks, output_path, mask_file_name): + """ + :param masks: dictionary of UID:masked polygons from mask_polygons_separately() + :param output_path: path to save the masks + :param mask_file_name: the file name the masks should have + """ + # For each filled polygon, write out a separate file, increasing the name + for m in masks: + final_out = path.join(output_path, mask_file_name + "_{}.png".format(m)) + imwrite(final_out, masks[m]) + + +def save_one_mask(masks, output_path, mask_file_name): + """ + :param masks: list of masked polygons from the mask_polygons_separately function + :param output_path: path to save the masks + :param mask_file_name: the file name the masks should have + """ + # For each filled polygon, write the mask shape out to the file per image + mask_file_name = path.join(output_path, mask_file_name + ".png") + imwrite(mask_file_name, masks) + + +def read_json(json_path): + """ + :param json_path: path to load json from + :returns: a python dictionary of json features + """ + annotations = json.load(open(json_path)) + return annotations + + +def get_feature_info(feature): + """ + :param feature: a python dictionary of json labels + :returns: a list mapping of polygons contained in the image + """ + # Getting each polygon points from the json file and adding it to a dictionary of uid:polygons + props = {} + + for feat in feature["features"]["xy"]: + feat_shape = wkt.loads(feat["wkt"]) + coords = list(mapping(feat_shape)["coordinates"][0]) + props[feat["properties"]["uid"]] = np.array(coords, np.int32) + + return props + + +def mask_chips(json_path, images_directory, output_directory, single_file, border): + """ + :param json_path: path to find multiple json files for the chips + :param images_directory: path to the directory containing the images to be masked + :param output_directory: path to the directory where masks are to be saved + :param single_file: a boolean value to see if masks should be saved a single file or multiple + """ + # For each feature in the json we will create a separate mask + # Getting all files in the directory provided for jsons + jsons = [j for j in next(walk(json_path))[2] if "_pre" in j] + + # After removing non-json items in dir (if any) + for j in tqdm([j for j in jsons if j.endswith("json")], unit="poly", leave=False): + # Our chips start off in life as PNGs + chip_image_id = path.splitext(j)[0] + ".png" + mask_file = path.splitext(j)[0] + + # Loading the per chip json + j_full_path = path.join(json_path, j) + chip_json = read_json(j_full_path) + + # Getting the full chip path, and loading the size dimensions + chip_file = path.join(images_directory, chip_image_id) + chip_size = get_dimensions(chip_file) + + # Reading in the polygons from the json file + polys = get_feature_info(chip_json) + + # Getting a list of the polygons and saving masks as separate or single image files + if len(polys) > 0: + if single_file: + if border > 0: + masked_polys = mask_polygons_together_with_border(chip_size, polys, border) + else: + masked_polys = mask_polygons_together(chip_size, polys) + save_one_mask(masked_polys, output_directory, mask_file) + else: + masked_polys = mask_polygons_separately(chip_size, polys) + save_masks(masked_polys, output_directory, mask_file) + + +if __name__ == "__main__": + import argparse + + # Parse command line arguments + parser = argparse.ArgumentParser( + description="""mask_polygons.py: Takes in xBD dataset and masks polygons in the image (make sure you've ran chip_masks.py first)\n\n + WARNING: This could lead to hundreds of output images per input\n""" + ) + + parser.add_argument( + "--input", required=True, metavar="/path/to/xBD/", help='Path to parent dataset directory "xBD"' + ) + parser.add_argument( + "--single-file", + action="store_true", + help="use to save all masked polygon instances to a single file rather than one polygon per mask file", + ) + parser.add_argument( + "--border", + default=0, + type=int, + metavar="positive integer for pixel border (e.g. 1)", + help="Positive integer used to shrink the polygon by", + ) + + args = parser.parse_args() + + # Getting the list of the disaster types under the xBD directory + disasters = next(walk(args.input))[1] + + for disaster in tqdm(disasters, desc="Masking", unit="disaster"): + # Create the full path to the images, labels, and mask output directories + image_dir = path.join(args.input, disaster, "images") + json_dir = path.join(args.input, disaster, "labels") + output_dir = path.join(args.input, disaster, "masks") + + if not path.isdir(image_dir): + print("Error, could not find image files in {}.\n\n".format(image_dir), file=stderr) + exit(2) + + if not path.isdir(json_dir): + print("Error, could not find labels in {}.\n\n".format(json_dir), file=stderr) + exit(3) + + if not path.isdir(output_dir): + makedirs(output_dir) + + mask_chips(json_dir, image_dir, output_dir, args.single_file, args.border) diff --git a/xview/utils/split_into_disasters.py b/xview/utils/split_into_disasters.py new file mode 100644 index 0000000..d53c59c --- /dev/null +++ b/xview/utils/split_into_disasters.py @@ -0,0 +1,80 @@ +##################################################################################################################################################################### +# xView2 # +# Copyright 2019 Carnegie Mellon University. # +# NO WARRANTY. THIS CARNEGIE MELLON UNIVERSITY AND SOFTWARE ENGINEERING INSTITUTE MATERIAL IS FURNISHED ON AN "AS-IS" BASIS. CARNEGIE MELLON UNIVERSITY MAKES NO # +# WARRANTIES OF ANY KIND, EITHER EXPRESSED OR IMPLIED, AS TO ANY MATTER INCLUDING, BUT NOT LIMITED TO, WARRANTY OF FITNESS FOR PURPOSE OR MERCHANTABILITY, # +# EXCLUSIVITY, OR RESULTS OBTAINED FROM USE OF THE MATERIAL. CARNEGIE MELLON UNIVERSITY DOES NOT MAKE ANY WARRANTY OF ANY KIND WITH RESPECT TO FREEDOM FROM PATENT, # +# TRADEMARK, OR COPYRIGHT INFRINGEMENT. # +# Released under a MIT (SEI)-style license, please see LICENSE.md or contact permission@sei.cmu.edu for full terms. # +# [DISTRIBUTION STATEMENT A] This material has been approved for public release and unlimited distribution. Please see Copyright notice for non-US Government use # +# and distribution. # +# This Software includes and/or makes use of the following Third-Party Software subject to its own license: # +# 1. SpaceNet (https://github.com/motokimura/spacenet_building_detection/blob/master/LICENSE) Copyright 2017 Motoki Kimura. # +# DM19-0988 # +##################################################################################################################################################################### + +from os import walk, path, makedirs +from shutil import copy2 as cp + + +def get_files(base_dir): + # Minmizing (halfing) list to just pre image files + base_dir = path.join(base_dir, "images") + files = [f for f in next(walk(base_dir))[2] if "pre" in f] + + return files + + +def move_files(files, base_dir, output_dir): + for filename in files: + disaster = filename.split("_")[0] + + # If the output directory and disater name do not exist make the directory + if not path.isdir(path.join(output_dir, disaster)): + makedirs(path.join(output_dir, disaster)) + + # Check if the images directory exists + if not path.isdir(path.join(output_dir, disaster, "images")): + # If not create it + makedirs(path.join(output_dir, disaster, "images")) + + # Move the pre and post image to the images directory under the disaster name + cp(path.join(base_dir, "images", filename), path.join(output_dir, disaster, "images", filename)) + post_file = filename.replace("_pre_", "_post_") + cp(path.join(base_dir, "images", post_file), path.join(output_dir, disaster, "images", post_file)) + + # Check if the label directory exists + if not path.isdir(path.join(output_dir, disaster, "labels")): + # If not create it + makedirs(path.join(output_dir, disaster, "labels")) + + pre_label_file = filename.replace("png", "json") + # Move the pre and post label files to the labels directory under the disaster name + cp(path.join(base_dir, "labels", pre_label_file), path.join(output_dir, disaster, "labels", pre_label_file)) + post_label_file = pre_label_file.replace("_pre_", "_post_") + cp(path.join(base_dir, "labels", post_label_file), path.join(output_dir, disaster, "labels", post_label_file)) + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser( + description="split_into_disasters.py: Splits files under a single directory (with images/ and labels/ into directory of disasters/images|labels for the base submission pipeline (copies files)" + ) + parser.add_argument( + "--input", + required=True, + metavar="/path/to/dataset/train", + help="Full path to the train (or any other directory) with /images and /labels", + ) + parser.add_argument( + "--output", + required=True, + metavar="/path/to/output/xBD", + help="Full path to the output root dataset directory, will create disaster/images|labels under this directory", + ) + + args = parser.parse_args() + + files = get_files(args.input) + move_files(files, args.input, args.output) diff --git a/xview/utils/view_polygons.ipynb b/xview/utils/view_polygons.ipynb new file mode 100644 index 0000000..25b9098 --- /dev/null +++ b/xview/utils/view_polygons.ipynb @@ -0,0 +1,367 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#####################################################################################################################################################################\n", + "# xView2 #\n", + "# Copyright 2019 Carnegie Mellon University. #\n", + "# NO WARRANTY. THIS CARNEGIE MELLON UNIVERSITY AND SOFTWARE ENGINEERING INSTITUTE MATERIAL IS FURNISHED ON AN \"AS-IS\" BASIS. CARNEGIE MELLON UNIVERSITY MAKES NO #\n", + "# WARRANTIES OF ANY KIND, EITHER EXPRESSED OR IMPLIED, AS TO ANY MATTER INCLUDING, BUT NOT LIMITED TO, WARRANTY OF FITNESS FOR PURPOSE OR MERCHANTABILITY, # \n", + "# EXCLUSIVITY, OR RESULTS OBTAINED FROM USE OF THE MATERIAL. CARNEGIE MELLON UNIVERSITY DOES NOT MAKE ANY WARRANTY OF ANY KIND WITH RESPECT TO FREEDOM FROM PATENT, # \n", + "# TRADEMARK, OR COPYRIGHT INFRINGEMENT. #\n", + "# Released under a MIT (SEI)-style license, please see LICENSE.md or contact permission@sei.cmu.edu for full terms. #\n", + "# [DISTRIBUTION STATEMENT A] This material has been approved for public release and unlimited distribution. Please see Copyright notice for non-US Government use #\n", + "# and distribution. #\n", + "# This Software includes and/or makes use of the following Third-Party Software subject to its own license: #\n", + "# 1. SpaceNet (https://github.com/motokimura/spacenet_building_detection/blob/master/LICENSE) Copyright 2017 Motoki Kimura. #\n", + "# DM19-0988 #\n", + "#####################################################################################################################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# To Run this notebook, start at the first cell with the license information and click run 4 times to show \n", + "# the field blocks, then input the *full path* to the label, and image. Finally, click \"Create next input\", \n", + "# you'll then see a full sized image with labels overlaid, you will also get different color labels if the\n", + "# label file as damage labels under ['features']['xy'][i]['properties']['subtype'] where i is the polygon \n", + "# in the ['xy'] list " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "from ipywidgets import Layout\n", + "from IPython.display import Javascript, HTML\n", + "import ipywidgets as widgets\n", + "\n", + "def run_all(ev):\n", + " display(Javascript('IPython.notebook.execute_cells_below()'))\n", + "\n", + "path_to_label = widgets.Text(\n", + " placeholder='Label path here',\n", + " description='Label:',\n", + " disabled=False,\n", + " layout=Layout(width='100%')\n", + ")\n", + "path_to_image = widgets.Text(\n", + " placeholder='Image path here',\n", + " description='Image:',\n", + " disabled=False,\n", + " layout=Layout(width='100%')\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "The raw code for this IPython notebook is by default hidden for easier reading.\n", + "To toggle on/off the raw code, click here." + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "HTML('''\n", + "The raw code for this IPython notebook is by default hidden for easier reading.\n", + "To toggle on/off the raw code, click here.''')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "IPython.notebook.execute_cells_below()" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d2be852d23c1454ca6d8b7871ecf5eb5", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Text(value='/tmp/inference/inference.json', description='Label:', layout=Layout(width='100%'), placeholder='La…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a4df1b7f08c6433eaba706e160b7956b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Text(value='/Users/rthosfelt/Downloads/xBD/tuscaloosa-tornado/images/tuscaloosa-tornado_00000027_pre_disaster.…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1bcd23b318194309bcee1c297b83c4c2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Button(description='Create next input', style=ButtonStyle())" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4df43ae86c7847c595ef8deab722b358", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Text(value='', description='Label:', layout=Layout(width='100%'), placeholder='Label path here')" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6d55945ff4664d3bb3341993e0df13f7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Text(value='', description='Image:', layout=Layout(width='100%'), placeholder='Image path here')" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9fa49996441e4368bb3c477207577b08", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Button(description='Create next input', style=ButtonStyle())" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(path_to_label, path_to_image)\n", + "button = widgets.Button(description=\"Create next input\")\n", + "button.on_click(run_all)\n", + "display(button)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import json \n", + "from PIL import Image, ImageDraw\n", + "from IPython.display import display\n", + "from shapely import wkt" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: ''", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mpath_to_image_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpath_to_image\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_to_label_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mimage_json_file\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mimage_json\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_json_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: ''" + ] + } + ], + "source": [ + "# Opening and loading polygons from label json \n", + "path_to_label_value = path_to_label.value\n", + "path_to_image_value = path_to_image.value\n", + "\n", + "with open(path_to_label_value, 'rb') as image_json_file:\n", + " image_json = json.load(image_json_file)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "coords = image_json['features']['xy']\n", + "wkt_polygons = []\n", + "\n", + "for coord in coords:\n", + " if 'subtype' in coord['properties']:\n", + " damage = coord['properties']['subtype']\n", + " else:\n", + " damage = 'no-damage'\n", + " wkt_polygons.append((damage, coord['wkt']))\n", + " \n", + "polygons = []\n", + "\n", + "for damage, swkt in wkt_polygons:\n", + " polygons.append((damage, wkt.loads(swkt)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Loading image\n", + "img = Image.open(path_to_image_value)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "draw = ImageDraw.Draw(img, 'RGBA')\n", + "\n", + "damage_dict = {\n", + " \"no-damage\": (0, 255, 0, 100),\n", + " \"minor-damage\": (0, 0, 255, 125),\n", + " \"major-damage\": (255, 69, 0, 125),\n", + " \"destroyed\": (255, 0, 0, 125),\n", + " \"un-classified\": (255, 255, 255, 125)\n", + "}\n", + "\n", + "for damage, polygon in polygons:\n", + " x,y = polygon.exterior.coords.xy\n", + " coords = list(zip(x,y))\n", + " draw.polygon(coords, damage_dict[damage])\n", + "\n", + "del draw\n", + "\n", + "display(img)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/xview/visualization.py b/xview/visualization.py new file mode 100644 index 0000000..1f7d31e --- /dev/null +++ b/xview/visualization.py @@ -0,0 +1,134 @@ +import cv2 +import torch +from albumentations.augmentations.functional import longest_max_size +from pytorch_toolbelt.utils.torch_utils import rgb_image_from_tensor, to_numpy + +from xview.dataset import ( + INPUT_IMAGE_PRE_KEY, + INPUT_IMAGE_POST_KEY, + INPUT_MASK_PRE_KEY, + OUTPUT_MASK_PRE_KEY, + INPUT_MASK_POST_KEY, + OUTPUT_MASK_POST_KEY, + INPUT_IMAGE_KEY, + INPUT_MASK_KEY, + INPUT_IMAGE_ID_KEY) + +import numpy as np + + +def overlay_image_and_mask(image, mask, class_colors, alpha=0.5): + overlay = image.copy() + for class_index, class_color in enumerate(class_colors): + if class_index == 0: + continue # Skip background + overlay[mask == class_index, :] = class_color + + return cv2.addWeighted(overlay, alpha, image, 1 - alpha, 0) + + +@torch.no_grad() +def draw_predictions( + input: dict, + output: dict, + image_id_key="image_id", + mean=(0.485, 0.456, 0.406), + std=(0.229, 0.224, 0.225), + class_colors=[ + (0, 0, 0), # 0=background + (0, 255, 0), # no damage (or just 'building' for localization) (green) + (255, 255, 0), # minor damage (yellow) + (255, 128, 0), # major damage (red) + (255, 0, 0), # destroyed (red) + (127, 127, 127) + ], + max_images=32 +): + images = [] + + num_images = len(input[image_id_key]) + for i in range(num_images): + image_id = input[INPUT_IMAGE_ID_KEY][i] + image_pre = rgb_image_from_tensor(input[INPUT_IMAGE_KEY][i, 0:3, ...], mean, std) + image_pre = cv2.cvtColor(image_pre, cv2.COLOR_RGB2BGR) + + image_post = rgb_image_from_tensor(input[INPUT_IMAGE_KEY][i, 3:6, ...], mean, std) + image_post = cv2.cvtColor(image_post, cv2.COLOR_RGB2BGR) + + image_pre_gt = image_pre.copy() + image_post_gt = image_post.copy() + + damage_target = to_numpy(input[INPUT_MASK_KEY][i]) + + image_pre_gt = overlay_image_and_mask(image_pre_gt, damage_target, class_colors) + image_post_gt = overlay_image_and_mask(image_post_gt, damage_target, class_colors) + + damage_predictions = to_numpy(output[INPUT_MASK_KEY][i]).argmax(axis=0) + + image_pre = overlay_image_and_mask(image_pre, damage_predictions, class_colors) + image_post = overlay_image_and_mask(image_post, damage_predictions, class_colors) + + overlay_gt = np.column_stack([image_pre_gt, image_post_gt]) + overlay = np.column_stack([image_pre, image_post]) + overlay = np.row_stack([overlay_gt, overlay]) + + overlay = longest_max_size(overlay, 1024, cv2.INTER_LINEAR) + + cv2.putText(overlay, str(image_id), (10, 15), cv2.FONT_HERSHEY_PLAIN, 1, (250, 250, 250)) + images.append(overlay) + if len(images) >= max_images: + break + + return images + + +@torch.no_grad() +def draw_predictions_dual( + input: dict, + output: dict, + image_id_key="image_id", + mean=(0.485, 0.456, 0.406), + std=(0.229, 0.224, 0.225), + class_colors=[ + (0, 0, 0), # 0=background + (0, 255, 0), # no damage (or just 'building' for localization) (green) + (255, 255, 0), # minor damage (yellow) + (255, 128, 0), # major damage (red) + (255, 0, 0), # destroyed (red) + ], +): + images = [] + + num_images = len(input[image_id_key]) + for i, image_id in enumerate(range(num_images)): + image_pre = rgb_image_from_tensor(input[INPUT_IMAGE_PRE_KEY][i], mean, std) + image_pre = cv2.cvtColor(image_pre, cv2.COLOR_RGB2BGR) + + image_post = rgb_image_from_tensor(input[INPUT_IMAGE_POST_KEY][i], mean, std) + image_post = cv2.cvtColor(image_post, cv2.COLOR_RGB2BGR) + + image_pre_gt = image_pre.copy() + image_post_gt = image_post.copy() + + localization_target = to_numpy(input[INPUT_MASK_PRE_KEY][i].squeeze(0)) + damage_target = to_numpy(input[INPUT_MASK_POST_KEY][i]) + + image_pre_gt = overlay_image_and_mask(image_pre_gt, localization_target, class_colors) + image_post_gt = overlay_image_and_mask(image_post_gt, damage_target, class_colors) + + localization_predictions = to_numpy(output[OUTPUT_MASK_PRE_KEY][i].squeeze(0).sigmoid() > 0.5).astype(np.uint8) + damage_predictions = to_numpy(output[OUTPUT_MASK_POST_KEY][i]).argmax(axis=0) + + image_pre = overlay_image_and_mask(image_pre, localization_predictions, class_colors) + image_post = overlay_image_and_mask(image_post, damage_predictions, class_colors) + + overlay_gt = np.column_stack([image_pre_gt, image_post_gt]) + overlay = np.column_stack([image_pre, image_post]) + overlay = np.row_stack([overlay_gt, overlay]) + + overlay = longest_max_size(overlay, 1024, cv2.INTER_LINEAR) + + cv2.putText(overlay, str(image_id), (10, 15), cv2.FONT_HERSHEY_PLAIN, 1, (250, 250, 250)) + images.append(overlay) + + return images diff --git a/xview/xview2_metrics.py b/xview/xview2_metrics.py new file mode 100644 index 0000000..4dc3ef3 --- /dev/null +++ b/xview/xview2_metrics.py @@ -0,0 +1,297 @@ +# xview2 metrics +# +# the total score is calculated a weighted average of the localization f1 score (lf1) and the damage f1 score (df1) +# score = .3 * lf1 + .7 * df1 +# +# the df1 is calculated by taking the harmonic mean of the 4 damage f1 scores (no damage, minor damage, major damage, and destroyed) +# df1 = 4 / sum((f1+epsilon)**-1 for f1 in [no_damage_f1, minor_damage_f1, major_damage_f1, destroyed_f1]), where epsilon = 1e-6 +# +# Abbreviations used in this file: +# l: localization +# d: damage +# p: prediction +# t: target (ground truth) +# x: usually a numpy array + +import os, json + +import numpy as np +import pandas as pd + +from multiprocessing import Pool, cpu_count +from pathlib import Path +from PIL import Image +from typing import Union, List + + +class PathHandler: + def __init__(self, pred_dir: Path, targ_dir: Path, img_id: str, test_hold: str): + """ + Args: + pred_dir (Path): directory of localization and damage predictions + targ_dir (Path): directory of localization and damage targets + img_id (str) : 5 digit string of image id + test_hold (str) : either 'test' or 'hold'. Most likely 'test' unless you have access to holdout set + """ + assert isinstance(pred_dir, Path), f"pred_dir should be of type Path, got {type(pred_dir)}" + assert pred_dir.is_dir(), f"Directory '{pred_dir}' does not exist or is not a directory" + + assert isinstance(targ_dir, Path), f"targ_dir '{targ_dir}' should be of type Path, got {type(pred_dir)}" + assert targ_dir.is_dir(), f"Directory '{targ_dir}' does not exist or is not a directory" + + assert test_hold in ["test", "hold"], f"test_hold '{test_hold}' was not one of 'test' or 'hold'" + + self.lp = pred_dir / f"{test_hold}_localization_{img_id}_prediction.png" # localization prediction + self.dp = pred_dir / f"{test_hold}_damage_{img_id}_prediction.png" # damage prediction + self.lt = targ_dir / f"{test_hold}_localization_{img_id}_target.png" # localization target + self.dt = targ_dir / f"{test_hold}_damage_{img_id}_target.png" # damage target + self.paths = (self.lp, self.dp, self.lt, self.dt) + + def load_and_validate_image(self, path): + assert path.is_file(), f"file '{path}' does not exist or is not a file" + img = np.array(Image.open(path)) + assert img.dtype == np.uint8, f"{path.name} is of wrong format {img.dtype} - should be np.uint8" + assert set(np.unique(img)) <= {0, 1, 2, 3, 4}, f"values must ints 0-4, found {np.unique(img)}, path: {path}" + assert img.shape == (1024, 1024), f"{path} must be a 1024x1024 image" + return img + + def load_images(self): + return [self.load_and_validate_image(path) for path in self.paths] + + +class RowPairCalculator: + """ + Contains all the information and functions necessary to calculate the true positives (TPs), + false negatives (FNs), and false positives (FPs), for a pair of localization/damage predictions + """ + + @staticmethod + def extract_buildings(x: np.ndarray): + """ Returns a mask of the buildings in x """ + buildings = x.copy() + buildings[x > 0] = 1 + return buildings + + @staticmethod + def compute_tp_fn_fp(pred: np.ndarray, targ: np.ndarray, c: int) -> List[int]: + """ + Computes the number of TPs, FNs, FPs, between a prediction (x) and a target (y) for the desired class (c) + + Args: + pred (np.ndarray): prediction + targ (np.ndarray): target + c (int): positive class + """ + TP = np.logical_and(pred == c, targ == c).sum() + FN = np.logical_and(pred != c, targ == c).sum() + FP = np.logical_and(pred == c, targ != c).sum() + return [TP, FN, FP] + + @classmethod + def get_row_pair(cls, ph: PathHandler): + """ + Builds a row of TPs, FNs, and FPs for both the localization dataframe and the damage dataframe. + This pair of rows are built in the same function as damages are only assessed where buildings are predicted. + + Args: + ph (PathHandler): used to load the required prediction and target images + """ + lp, dp, lt, dt = ph.load_images() + lp_b, lt_b, dt_b = map(cls.extract_buildings, (lp, lt, dt)) # convert all damage scores 1-4 to 1 + + dp = dp * lp_b # only give credit to damages where buildings are predicted + dp, dt = dp[dt_b == 1], dt[dt_b == 1] # only score damage where there exist buildings in target damage + + lrow = cls.compute_tp_fn_fp(lp_b, lt_b, 1) + drow = [] + for i in range(1, 5): + drow += cls.compute_tp_fn_fp(dp, dt, i) + return lrow, drow + + +class F1Recorder: + """ + Records the precision and recall when calculating the f1 score. + Read about the f1 score here: https://en.wikipedia.org/wiki/F1_score + """ + + def __init__(self, TP, FP, FN, name=""): + """ + Args: + TP (int): true positives + FP (int): false positives + FN (int): false negatives + name (str): optional name when printing + """ + self.TP, self.FN, self.FP, self.name = TP, FN, FP, name + self.P = self.precision() + self.R = self.recall() + self.f1 = self.f1() + + def __repr__(self): + return f"{self.name} | f1: {self.f1:.4f}, precision: {self.P:.4f}, recall: {self.R:.4f}" + + def precision(self): + """ calculates the precision using the true positives (self.TP) and false positives (self.FP)""" + assert self.TP >= 0 and self.FP >= 0 + if self.TP == 0: + return 0 + else: + return self.TP / (self.TP + self.FP) + + def recall(self): + """ calculates recall using the true positives (self.TP) and false negatives (self.FN) """ + assert self.TP >= 0 and self.FN >= 0 + if self.TP == 0: + return 0 + return self.TP / (self.TP + self.FN) + + def f1(self): + """ calculates the f1 score using precision (self.P) and recall (self.R) """ + assert 0 <= self.P <= 1 and 0 <= self.R <= 1 + if self.P == 0 or self.R == 0: + return 0 + return (2 * self.P * self.R) / (self.P + self.R) + + +class XviewMetrics: + """ + Calculates the xview2 metrics given a directory of predictions and a directory of targets + + Directory of predictions and directory of targets must be two separate directories. These + could be structured as followed: + . + ├── predictions + │ ├── test_damage_00000_prediction.png + │ ├── test_damage_00001_prediction.png + │ ├── test_localization_00000_prediction.png + │ ├── test_localization_00001_prediction.png + │ └── ... + └── targets + ├── test_damage_00000_target.png + ├── test_damage_00001_target.png + ├── test_localization_00000_target.png + ├── test_localization_00001_target.png + └── ... + """ + + def __init__(self, pred_dir, targ_dir): + self.pred_dir, self.targ_dir = Path(pred_dir), Path(targ_dir) + assert self.pred_dir.is_dir(), f"Could not find prediction directory: '{pred_dir}'" + assert self.targ_dir.is_dir(), f"Could not find target directory: '{targ_dir}'" + + self.dmg2str = { + 1: f"No damage (1) ", + 2: f"Minor damage (2) ", + 3: f"Major damage (3) ", + 4: f"Destroyed (4) ", + } + + self.get_path_handlers() + self.get_dfs() + self.get_lf1r() + self.get_df1rs() + + def __repr__(self): + s = "Localization:\n" + s += f" {self.lf1r}\n" + + s += "\nDamage:\n" + for F1Rec in self.df1rs: + s += f" {F1Rec}\n" + s += f" Harmonic mean dmgs | f1: {self.df1:.4f}\n" + + s += "\nScore:\n" + s += f" Score | f1: {self.score:.4f}\n" + return s.rstrip() + + def get_path_handlers(self): + self.path_handlers = [] + for path in self.targ_dir.glob("*.png"): + test_hold, loc_dmg, img_id, target = path.name.rstrip(".png").split("_") + assert loc_dmg in [ + "localization", + "damage", + ], f"target filenames must have 'localization' or 'damage' in filename, got {path}" + assert target == "target", f"{target} should equal 'target' when getting path handlers" + if loc_dmg == "localization": # localization or damage is fine here + self.path_handlers.append(PathHandler(self.pred_dir, self.targ_dir, img_id, test_hold)) + + def get_dfs(self): + """ + builds the localization dataframe (self.ldf) and damage dataframe (self.ddf) from + path handlers (self.path_handlers) + """ + with Pool() as p: + all_rows = p.map(RowPairCalculator.get_row_pair, self.path_handlers) + + lcolumns = ["lTP", "lFN", "lFP"] + self.ldf = pd.DataFrame([lrow for lrow, drow in all_rows], columns=lcolumns) + + dcolumns = ["dTP1", "dFN1", "dFP1", "dTP2", "dFN2", "dFP2", "dTP3", "dFN3", "dFP3", "dTP4", "dFN4", "dFP4"] + self.ddf = pd.DataFrame([drow for lrow, drow in all_rows], columns=dcolumns) + + def get_lf1r(self): + """ localization f1 recorder """ + TP = self.ldf["lTP"].sum() + FP = self.ldf["lFP"].sum() + FN = self.ldf["lFN"].sum() + self.lf1r = F1Recorder(TP, FP, FN, "Buildings") + + @property + def lf1(self): + """ localization f1 """ + return self.lf1r.f1 + + def get_df1rs(self): + """ damage f1 recorders """ + self.df1rs = [] + for i in range(1, 5): + TP = self.ddf[f"dTP{i}"].sum() + FP = self.ddf[f"dFP{i}"].sum() + FN = self.ddf[f"dFN{i}"].sum() + self.df1rs.append(F1Recorder(TP, FP, FN, self.dmg2str[i])) + + @property + def df1s(self): + """ damage f1s """ + return [F1.f1 for F1 in self.df1rs] + + @property + def df1(self): + """ damage f1. Computed using harmonic mean of damage f1s """ + harmonic_mean = lambda xs: len(xs) / sum((x + 1e-6) ** -1 for x in xs) + return harmonic_mean(self.df1s) + + @property + def score(self): + """ xview2 score computed as a weighted average of the localization f1 and damage f1 """ + return 0.3 * self.lf1 + 0.7 * self.df1 + + @classmethod + def compute_score(cls, pred_dir, targ_dir, out_fp): + """ + Args: + pred_dir (str): directory of localization and damage predictions + targ_dir (str): directory of localization and damage targets + out_fp (str): output json - folder must already exist + """ + print(f"Calculating metrics using {cpu_count()} cpus...") + + self = cls(pred_dir, targ_dir) + + d = {"score": self.score, "damage_f1": self.df1, "localization_f1": self.lf1} + d["damage_f1_no_damage"] = self.df1s[0] + d["damage_f1_minor_damage"] = self.df1s[1] + d["damage_f1_major_damage"] = self.df1s[2] + d["damage_f1_destroyed"] = self.df1s[3] + + with open(out_fp, "w") as f: + json.dump(d, f) + print(f"Wrote metrics to {out_fp}") + + +if __name__ == "__main__": + import fire + + fire.Fire(XviewMetrics.compute_score)