predict154cls.py

import os

from os import path, makedirs, listdir
import sys
import numpy as np
np.random.seed(1)
import random
random.seed(1)

import torch
from torch import nn
from torch.backends import cudnn
from torch.autograd import Variable

import pandas as pd
from tqdm import tqdm
import timeit
import cv2

from zoo.models import SeNet154_Unet_Double

from utils import *

cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False)

test_dir = 'test/images/pre'
models_folder = 'weights'

if __name__ == '__main__':
    t0 = timeit.default_timer()

    seed = int(sys.argv[1])
    # vis_dev = sys.argv[2]

    # os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
    # os.environ["CUDA_VISIBLE_DEVICES"] = vis_dev

    pred_folder = 'se154cls_{}_tuned'.format(seed)
    makedirs(pred_folder, exist_ok=True)

    # cudnn.benchmark = True

    models = []

    snap_to_load = 'se154_cls_cce_{}_tuned_best'.format(seed)

    model = SeNet154_Unet_Double().cuda()

    model = nn.DataParallel(model).cuda()
    
    print("=> loading checkpoint '{}'".format(snap_to_load))
    checkpoint = torch.load(path.join(models_folder, snap_to_load), map_location='cpu')
    loaded_dict = checkpoint['state_dict']
    sd = model.state_dict()
    for k in model.state_dict():
        if k in loaded_dict and sd[k].size() == loaded_dict[k].size():
            sd[k] = loaded_dict[k]
    loaded_dict = sd
    model.load_state_dict(loaded_dict)
    print("loaded checkpoint '{}' (epoch {}, best_score {})"
            .format(snap_to_load, checkpoint['epoch'], checkpoint['best_score']))

    model.eval()
    models.append(model)

    print("Getting chips...")
    pre_chips = [os.path.join(test_dir, a) for a in os.listdir(test_dir)]
    post_chips = [os.path.join(test_dir.replace('pre','post'), a) for a in os.listdir(test_dir.replace('pre','post'))]
    
    with torch.no_grad():
        for i in tqdm(range(len(pre_chips))):
            if True:
                img = cv2.imread(pre_chips[i], cv2.IMREAD_COLOR)
                img2 = cv2.imread(post_chips[i], cv2.IMREAD_COLOR)
                img = np.concatenate([img, img2], axis=2)
                img = preprocess_inputs(img)

                inp = []
                inp.append(img)
                inp.append(img[::-1, ...])
                inp.append(img[:, ::-1, ...])
                inp.append(img[::-1, ::-1, ...])
                inp = np.asarray(inp, dtype='float')
                inp = torch.from_numpy(inp.transpose((0, 3, 1, 2))).float()
                inp = Variable(inp).cuda()

                pred = []
                for model in models:
                    msk = model(inp)
                    msk = torch.sigmoid(msk)
                    msk = msk.cpu().numpy()
                    
                    pred.append(msk[0, ...])
                    pred.append(msk[1, :, ::-1, :])
                    pred.append(msk[2, :, :, ::-1])
                    pred.append(msk[3, :, ::-1, ::-1])

                pred_full = np.asarray(pred).mean(axis=0)
                
                msk = pred_full * 255
                msk = msk.astype('uint8').transpose(1, 2, 0)
                rt = pre_chips[i].split('/')[-1]
                cv2.imwrite(path.join(pred_folder, '{0}'.format(rt.replace('.tif', '_part1.png'))), msk[..., :3], [cv2.IMWRITE_PNG_COMPRESSION, 9])
                cv2.imwrite(path.join(pred_folder, '{0}'.format(rt.replace('.tif', '_part2.png'))), msk[..., 2:], [cv2.IMWRITE_PNG_COMPRESSION, 9])

    elapsed = timeit.default_timer() - t0
    print('Time: {:.3f} min'.format(elapsed / 60))