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validate_and_forward_pass.py
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validate_and_forward_pass.py
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# Copyright (c) 2019-2023 Mitsubishi Electric Research Laboratories (MERL)
#
# SPDX-License-Identifier: AGPL-3.0-or-later
"""
Sample Run:
python validate_and_forward_pass.py --exp_dir abhinav_model_dir/ --exp_id run_496_eval --pp "relu" --saved_wt_file abhinav_model_dir/run_496/lr-0.0000025-29.pth.tar --bs 4 --gpu_id 1 --val_json dataset/all_300Wtest_val.json
srun --gres gpu:1 --cpus-per-task 4 -X python validate_and_forward_pass.py --exp_dir abhinav_model_dir/ --exp_id run_462_test --pp "relu" --saved_wt_file abhinav_model_dir/run_462/lr-0.0000025-29.pth.tar| tee -a abhinav_model_dir/run_462_test/val.log
srun --gres gpu:1 --cpus-per-task 4 -X python validate_and_forward_pass.py --exp_dir abhinav_model_dir/ --exp_id run_207_test --pp "" --smax --saved_wt_file abhinav_model_dir/run_207/lr-0.0000025-29.pth.tar
srun --gres gpu:1 --cpus-per-task 4 -X python validate_and_forward_pass.py --exp_dir abhinav_model_dir/ --exp_id run_272_300W_test --pp "" --smax --saved_wt_file abhinav_model_dir/run_272/lr-0.0000025-29.pth.tar --val_json dataset/all_300Wtest_val.json | tee abhinav_model_dir/run_272_300W_test/val.log
srun --gres gpu:1 --cpus-per-task 3 -X python validate_and_forward_pass.py --exp_dir abhinav_model_dir/ --exp_id run_272_menpo --pp "" --smax --saved_wt_file abhinav_model_dir/run_272/lr-0.0000025-29.pth.tar --val_json dataset/menpo_val.json | tee abhinav_model_dir/run_272_menpo/val.log
srun --gres gpu:1 --cpus-per-task 3 -X python validate_and_forward_pass.py --exp_dir abhinav_model_dir/ --exp_id run_276_menpo --pp "" --smax --saved_wt_file abhinav_model_dir/run_276/lr-0.0000025-29.pth.tar --val_json dataset/menpo_val.json | tee abhinav_model_dir/run_276_menpo/val.log
Saves the corresponding images, heatmaps and everything by doing a forward pass
"""
import os, time, sys
import cv2
import numpy as np
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.autograd import Variable
from torch.nn.parameter import Parameter
from data.face_bbx import FACE
from loss.gaussian_loss import *
from loss.gaussian_regularization_loss import *
from models.cu_net_prev_version_cholesky_common_for_HG import create_cu_net
from options.train_options import TrainOptions
from pylib import FaceAcc, Evaluation, HumanAug
from pylib.HeatmapStats import get_spatial_mean_and_covariance
from utils.checkpoint import Checkpoint
from utils.logger import Logger
from utils.util import *
from utils.visualizer import Visualizer
cudnn.benchmark = True
flip_index = np.array([[0, 16], [1, 15], [2, 14], [3, 13], [4, 12], [5, 11], [6, 10], [7, 9], # outline
[17, 26], [18, 25], [19, 24], [20, 23], [21, 22], # eyebrow
[36, 45], [37, 44], [38, 43], [39, 42], [40, 47], [41, 46], # eye
[31, 35], [32, 34], # nose
[48, 54], [49, 53], [50, 52], [59, 57], [58, 56], # outer mouth
[60, 64], [61, 63], [67, 65]]) # inner mouth
images_save_path = "images"
gt_save_path = "ground_truth"
means_save_path = "means"
covar_save_path = "covar"
cholesky_save_path = "cholesky"
heatmaps_save_path = "heatmaps"
vis_gt_save_path = "vis_gt"
vis_estimated_save_path = "vis_estimated"
nme_new_per_image_path = "nme_new_per_image"
nme_new_box_per_image_path = "nme_new_box_per_image"
nme_new_per_image_per_landmark_path = "nme_new_per_image_per_landmark"
nme_new_box_per_image_per_landmark_path = "nme_new_box_per_image_per_landmark"
# The below variables are assigned values in the main function
f_path = ""
weights_HG = [0, 0, 0, 0, 0, 0, 0, 1.0]
def main():
opt = TrainOptions().parse()
train_history = TrainHistoryFace()
checkpoint = Checkpoint()
visualizer = Visualizer(opt)
exp_dir = os.path.join(opt.exp_dir, opt.exp_id)
log_name = opt.vis_env + '_val_log.txt'
visualizer.log_name = os.path.join(exp_dir, log_name)
num_classes = opt.class_num
if not opt.slurm:
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu_id
layer_num = opt.layer_num
order = opt.order
net = create_cu_net(neck_size= 4, growth_rate= 32, init_chan_num= 128,
class_num= num_classes, layer_num= layer_num, order= order,
loss_num= layer_num, use_spatial_transformer= opt.stn,
mlp_tot_layers= opt.mlp_tot_layers, mlp_hidden_units= opt.mlp_hidden_units,
get_mean_from_mlp= opt.get_mean_from_mlp)
# Load the pre-trained model
saved_wt_file = opt.saved_wt_file
print("Loading weights from " + saved_wt_file)
checkpoint_t = torch.load(saved_wt_file)
state_dict = checkpoint_t['state_dict']
for name, param in state_dict.items():
name = name[7:]
if name not in net.state_dict():
print("=> not load weights '{}'".format(name))
continue
if isinstance(param, Parameter):
param = param.data
net.state_dict()[name].copy_(param)
net = torch.nn.DataParallel(net).cuda() # use multiple GPUs
# Optimizer
if opt.optimizer == "rmsprop":
optimizer = torch.optim.RMSprop(filter(lambda p: p.requires_grad, net.parameters()), lr=opt.lr, alpha=0.99,
eps=1e-8, momentum=0, weight_decay=0)
elif opt.optimizer == "adam":
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, net.parameters()), lr=opt.lr)
else:
print("Unknown Optimizer. Aborting!!!")
sys.exit(0)
print(type(optimizer))
# Optionally resume from a checkpoint
if opt.resume_prefix != '':
# if 'pth' in opt.resume_prefix:
# trunc_index = opt.resume_prefix.index('pth')
# opt.resume_prefix = opt.resume_prefix[0:trunc_index - 1]
checkpoint.save_prefix = os.path.join(exp_dir, opt.resume_prefix)
checkpoint.load_prefix = os.path.join(exp_dir, opt.resume_prefix)[0:-1]
checkpoint.load_checkpoint(net, optimizer, train_history)
else:
checkpoint.save_prefix = exp_dir + '/'
print("Save prefix = {}".format(checkpoint.save_prefix))
# Load data
json_path = opt.json_path
train_json = opt.train_json
val_json = opt.val_json
print("Path added to each image path in JSON = {}".format(json_path))
print("Train JSON path = {}".format(train_json))
print("Val JSON path = {}".format(val_json))
# This train loader is useless
train_loader = torch.utils.data.DataLoader(
FACE(train_json, json_path, is_train= True),
batch_size= opt.bs, shuffle= True,
num_workers= opt.nThreads, pin_memory= True)
val_loader = torch.utils.data.DataLoader(
FACE(val_json, json_path, is_train= False),
batch_size= opt.bs, shuffle= False,
num_workers= opt.nThreads, pin_memory= True)
if not opt.is_train:
visualizer.log_path = os.path.join(opt.exp_dir, opt.exp_id, 'val_log.txt')
val_loss, val_rmse, predictions = validate(val_loader, net,
train_history.epoch[-1]['epoch'], visualizer, num_classes, flip_index)
checkpoint.save_preds(predictions)
return
global f_path
global weights_HG
f_path = exp_dir
weights_HG = [float(x) for x in opt.hg_wt.split(",")]
print ("Postprocessing applied = {}".format(opt.pp))
if (opt.smax):
print("Scaled softmax used with tau = {}".format(opt.tau))
else:
print("No softmax used")
if opt.is_covariance:
print("Covariance used from the heatmap")
else:
print("Covariance calculated from MLP")
print("Individual Hourglass loss weights")
print(weights_HG)
print("wt_MSE (tradeoff between GLL and MSE in each hourglass)= " + str(opt.wt_mse))
print("wt_gauss_regln (tradeoff between GLL and Gaussian Regularisation in each hourglass)= " + str(opt.wt_gauss_regln))
# Optionally resume from a checkpoint
start_epoch = 0
if opt.resume_prefix != '':
start_epoch = train_history.epoch[-1]['epoch'] + 1
# Training and validation
start_epoch = 0
if opt.resume_prefix != '':
start_epoch = train_history.epoch[-1]['epoch'] + 1
train_loss_orig_epoch = []
train_loss_gau_t1_epoch = []
train_loss_gau_t2_epoch = []
train_nme_orig_epoch = []
train_nme_gau_epoch = []
train_nme_new_epoch = []
val_loss_orig_epoch = []
val_loss_gau_t1_epoch = []
val_loss_gau_t2_epoch = []
val_nme_orig_epoch = []
val_nme_gau_epoch = []
val_nme_new_epoch = []
for epoch in range(1):
# Evaluate on validation set
val_loss, val_loss_mse, val_loss_gau_t1, val_loss_gau_t2 , val_rmse_orig, val_rmse_gau, val_rmse_new_box, predictions= validate(val_loader, net, epoch, visualizer, opt, num_classes, flip_index)
val_loss_orig_epoch.append(val_loss_mse)
val_loss_gau_t1_epoch.append(val_loss_gau_t1)
val_loss_gau_t2_epoch.append(val_loss_gau_t2)
val_nme_orig_epoch.append(val_rmse_orig)
val_nme_gau_epoch.append(val_rmse_gau)
def validate(val_loader, net, epoch, visualizer, opt, num_classes, flip_index):
batch_time = AverageMeter()
rmses0 = AverageMeter()
rmses1 = AverageMeter()
rmses2 = AverageMeter()
rmses_orig = AverageMeter()
rmses_gau = AverageMeter()
rmses_new = AverageMeter()
rmses_new_box = AverageMeter()
rmses_new_meta_box = AverageMeter()
rmses_new_0 = AverageMeter()
rmses_new_1 = AverageMeter()
rmses_new_2 = AverageMeter()
img_batch_list = []
pts_batch_list = []
# Objects which keep track of the loss across the entire epoch
losses = AverageMeter()
losses_gau = AverageMeter()
losses_gau_t1 = AverageMeter()
losses_gau_t2 = AverageMeter()
losses_mse = AverageMeter()
losses_regln = AverageMeter()
losses_vis = AverageMeter()
# Default downsampling is 4 times.
downsample = 4.
downsample_save = 1.
img_dim = int(256/downsample_save)
# Variables for saving Image, groundtruths, means, covariance and heatmaps
num_val_images = len(val_loader.dataset)
if opt.save_image_heatmaps:
img_save = np.zeros((num_val_images, 3, img_dim, img_dim))
#heatmaps_save = np.zeros((8, num_val_images, 68, img_dim, img_dim))
gt_save = np.zeros((num_val_images, num_classes, 2))
means_save = np.zeros((num_val_images, num_classes, 2))
cholesky_save = np.zeros((num_val_images, num_classes, 3))
covar_save = np.zeros((num_val_images, num_classes, 4))
vis_gt_save = np.zeros((num_val_images, num_classes))
vis_estimated_save = np.zeros((num_val_images, num_classes))
rmse_new_per_image_save = np.zeros((num_val_images,))
rmse_new_box_per_image_save = np.zeros((num_val_images,))
rmse_new_per_image_per_landmark_save = np.zeros((num_val_images, num_classes))
rmse_new_box_per_image_per_landmark_save = np.zeros((num_val_images, num_classes))
img_cnt = 0
# Loss functions
mse_loss = nn.MSELoss()
bce_loss = nn.BCELoss()
if not opt.use_heatmaps:
loss_fn = FaceAlignLoss(laplacian= opt.laplacian, form= opt.laplacian_form)
gauss_regln_loss = GaussianRegularizationLoss()
# Switch to evaluate mode
net.eval()
end = time.time()
wt_gau_new = opt.wt_gau
wt_gauss_regln_new = opt.wt_gauss_regln
wt_mse_new = opt.wt_mse
if opt.use_heatmaps:
wt_mse_new = 1.
wt_gau_new = 0.
print("weight_MSE= {} weight_GLL= {} weight_GR= {}".format(wt_mse_new, wt_gau_new, wt_gauss_regln_new))
for i, (img, heatmap, pts, _, _, _, htp_mask, _, visible_multiclass, meta_box_input_res) in enumerate(val_loader):
# Input and Groundtruth
img_var = Variable(img).cuda()
vis = visible_multiclass.clone()
vis[vis > 1] = 1
vis = Variable(vis[:,:, None].float()).cuda()
# vis with zero points was producing weird error. Add a small constant
# to the invisibile points
vis[vis<1] = constants.EPSILON
# pts contain the invalid points in the center. If you use pts to calculate
# MSE it is going to be bad. Make a masked version of points for MSE
pts_masked = pts.float() * vis.data.cpu().float()
pts_var = Variable(pts.float()/4.0).cuda() * vis
heatmap = heatmap.cuda(async=True)
target_heatmap = Variable(heatmap)
# Tensor at the output and neck of every hourglass
output, out_y = net(img_var) # output batch_size x 68 x 64 x 64
batch_size = pts.shape[0]
num_points = pts.shape[1]
# Scalar loss values
loss = 0.
loss_term1 = 0.
loss_term2 = 0.
loss_mse = 0.
loss_regln = 0.
loss_vis = 0.
hg_cnt = 0
for per_out in output:
# Weight of this Hourglass
weight_hg = weights_HG[hg_cnt]
if opt.use_heatmaps:
# Calculate MSE between the heatmaps
tmp_loss = (per_out - target_heatmap) ** 2
loss_t = tmp_loss.sum() / tmp_loss.numel()
# All other loss as zeros
loss_gau = Variable(torch.zeros(1,).float()).cuda()
loss_gauss_regln = loss_gau.clone()
loss_term1_temp = loss_gau.clone()
loss_term2_temp = loss_gau.clone()
loss_vis_hg = loss_gau.clone()
else:
if isinstance(out_y, list):
if len(out_y) > 1:
cholesky = out_y[-1]
else:
cholesky = out_y[0]
else:
cholesky = out_y
pred_pts_new, covar, normalized_heatmaps = get_spatial_mean_and_covariance(per_out, use_softmax= opt.smax, tau= opt.tau, postprocess= opt.pp)
pred_pts_new = pred_pts_new * vis
covar = covar.view(covar.shape[0], covar.shape[1], 4)
vis_estimated = cholesky[:,:, 3]
# Concat the calculations for each image and each landmark.
# pred_pts_new: batch_size x 68 x 2
# out_y : batch_size x 68 x 3
# covar : batch_size x 68 x 4
if opt.is_covariance:
# pre_pts : batch_size x 68 x 6
pre_pts = torch.cat((pred_pts_new, covar) ,2)
else:
if opt.get_mean_from_mlp:
pred_pts_new = cholesky[:, :, 4:6]
cholesky = cholesky[:, :, 0:3]
# pre_pts : batch_size x 68 x 5
pre_pts = torch.cat((pred_pts_new,cholesky),2)
# Gaussian_loss, Gaussian Loss in stages, Loss_term1, Loss_term2, Loss_term1 in stages, Loss_term2 in stages
loss_gau, loss_stages, loss_term1_temp, loss_term2_temp, loss_term1_stages, loss_term2_stages = loss_fn([pre_pts], pts_var, is_covariance=opt.is_covariance)
# Loss used is MSE but error metric used is NME
loss_t = mse_loss(pred_pts_new, pts_var)
# Regularization to force heatmaps to be gaussian
loss_gauss_regln = gauss_regln_loss(normalized_heatmaps, pred_pts_new, covar)
if opt.use_visibility:
loss_vis_hg = bce_loss(vis_estimated, vis.squeeze())
else:
loss_vis_hg = Variable(torch.zeros(1,).float()).cuda()
loss += weight_hg * (wt_gau_new*loss_gau + wt_mse_new*loss_t + wt_gauss_regln_new*loss_gauss_regln + loss_vis_hg)
loss_term1 += weight_hg * wt_gau_new*loss_term1_temp
loss_term2 += weight_hg * wt_gau_new*loss_term2_temp
loss_mse += weight_hg * wt_mse_new*loss_t
loss_regln += weight_hg * wt_gauss_regln_new * loss_gauss_regln
loss_vis += weight_hg * loss_vis_hg
#if opt.save_image_heatmaps:
# heatmaps_save[hg_cnt, img_cnt:img_cnt + img.shape[0] ] = normalized_heatmaps.clone().cpu().data.numpy() # Clone so that the original Variable doesnot get detached from the graph
hg_cnt += 1
# Measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
# Update the losses
N = img.shape[0]
losses.update (loss.data[0] , N)
losses_gau_t1.update(loss_term1.data[0], N)
losses_gau_t2.update(loss_term2.data[0], N)
losses_mse.update (loss_mse.data[0] , N)
losses_regln.update (loss_regln.data[0], N)
losses_vis.update (loss_vis.data[0] , N)
# pred_pts_0: Integer coordinates where heatmap is maximum
# pred_pts_1: pred_pts_0 + 0.25*sign(gradient of pred_pts_0 at pt shifted by 1 pixel)
# pred_pts_2: pred_pts_0 + 0.5 + 0.25*sign(gradient of pred_pts_0 at pt shifted by 1 pixel)
# Each coords is batch_size x 68 x 2
pred_pts_0, pred_pts_1, pred_pts_2 = FaceAcc.heatmap2pts(output[-1].data.cpu(), flag=0)
pred_pts_2 -= 1
pred_pts_2 = Variable(pred_pts_2).cuda()*vis
pred_pts_2 = pred_pts_2.data.cpu()
# NME in reality since the code calculate norm-2 values using numpy
# rmse_orig = NME(pred_pts_0 - 0.5 + 0.25*sign(gradient of pred_pts_0 at pt shifted by 1 pixel), ground_truth)
rmse_orig = np.sum(FaceAcc.per_image_rmse(pred_pts_2.numpy() * 4., pts_masked.numpy())) / img.size(0)
rmses_orig.update(rmse_orig, img.size(0))
if opt.use_heatmaps:
# We will use the prediction from the heatmap since there is no spatial mean
# available
pred_pts_new = Variable(torch.Tensor(pred_pts_2)).cuda() * vis
# rmse_new = NME(centroid, ground_truth)
rmse_new_per_image_per_landmark = FaceAcc.per_image_rmse(pred_pts_new.data.cpu().numpy() * downsample, pts_masked.numpy(), per_landmark=True)
rmse_new_per_image = FaceAcc.per_image_rmse(pred_pts_new.data.cpu().numpy() * downsample, pts_masked.numpy())
rmse_new = np.sum(rmse_new_per_image) / img.size(0)
rmses_new.update(rmse_new, img.size(0))
# rmse_new_box = NME(centroid, ground_truth, ground_bounding_box)
# For box calculation remember to use pts and not pts_masked
# since min of pts_masked is always zero
max_box, _ = torch.max(pts, 1)
min_box, _ = torch.min(pts, 1)
width_height_gd = max_box - min_box
rmse_new_box_per_image_per_landmark = FaceAcc.per_image_rmse_with_bounding_box(pred_pts_new.data.cpu().numpy() * downsample, pts_masked.numpy(), width_height_gd.numpy(), per_landmark=True)
rmse_new_box_per_image = FaceAcc.per_image_rmse_with_bounding_box(pred_pts_new.data.cpu().numpy() * downsample, pts_masked.numpy(), width_height_gd.numpy())
rmse_new_box = np.sum(rmse_new_box_per_image) / img.size(0)
rmses_new_box.update(rmse_new_box, img.size(0))
# rmse_new_meta_box = NME(centroid, ground_truth, meta_bounding_box)
rmse_new_meta_box = np.sum(FaceAcc.per_image_rmse_with_bounding_box(pred_pts_new.data.cpu().numpy() * 4., pts_masked.numpy(), meta_box_input_res.numpy(), is_scale= True)) / img.size(0)
rmses_new_meta_box.update(rmse_new_meta_box, img.size(0))
# Applies rounding to the coordinates of the predicted heatmap. The next
# 'new' variables with underscore study effect of quantization of maximum
# value of predictions
# pred_pts_new_0: Rounds them to integer and then adds 1
# pred_pts_new_1: pred_pts_new_0 + 0.25*sign(gradient of pred_pts_new_0 at pt shifted by 1 pixel)
# pred_pts_new_2: pred_pts_new_0 + 0.5 + 0.25*sign(gradient of pred_pts_new_0 at pt shifted by 1 pixel)
pred_pts_new_0, pred_pts_new_1, pred_pts_new_2 = FaceAcc.pts_trans(output[-1].data.cpu(),pred_pts_new.data.cpu())
# rmse_new_0 = NME(pred_pts_new_0, ground_truth)
# rmse_new_1 = NME(pred_pts_new_1, ground_truth)
# rmse_new_2 = NME(pred_pts_new_2, ground_truth)
rmse_new_0 = np.sum(FaceAcc.per_image_rmse(pred_pts_new_0.numpy() * downsample, pts_masked.numpy())) / img.size(0)
rmse_new_1 = np.sum(FaceAcc.per_image_rmse(pred_pts_new_1.numpy() * downsample, pts_masked.numpy())) / img.size(0)
rmse_new_2 = np.sum(FaceAcc.per_image_rmse(pred_pts_new_2.numpy() * downsample, pts_masked.numpy())) / img.size(0)
rmses_new_0.update(rmse_new_0, img.size(0))
rmses_new_1.update(rmse_new_1, img.size(0))
rmses_new_2.update(rmse_new_2, img.size(0))
loss_dict = OrderedDict([('loss_val', losses.avg), ('loss_vis', losses_vis.avg), ('loss_val_t1', losses_gau_t1.avg), ('loss_val_t2', losses_gau_t2.avg), ('loss_mse', losses_mse.avg), ('loss_regln', losses_regln.avg), ('rmse_orig', rmses_orig.avg), ('rmse_new', rmses_new.avg), ('rmse_new_box', rmses_new_box.avg), ('rmse_new_meta_box', rmses_new_meta_box.avg), ('rmse_new_0', rmses_new_0.avg), ('rmse_new_1', rmses_new_1.avg), ('rmse_new_2', rmses_new_2.avg) ])
if i % opt.print_freq == 0 or i==len(val_loader)-1:
visualizer.print_log(epoch, i, len(val_loader), value1=loss_dict)
if i == 0:
tt = pred_pts_2
if i>0:
tt = torch.cat((tt,pred_pts_2),0)
predictions = tt
# Save the values the numpy arrays.
if opt.save_image_heatmaps:
img_save [img_cnt:img_cnt + img.shape[0]] = resize_4d_numpy(img.cpu().numpy(), downsample= downsample_save)
gt_save [img_cnt:img_cnt + img.shape[0]] = pts.float().numpy() / downsample_save
means_save [img_cnt:img_cnt + img.shape[0]] = pred_pts_new.data.cpu().numpy() * 4./ downsample_save
vis_gt_save [img_cnt:img_cnt + img.shape[0]] = visible_multiclass.cpu().numpy()
if not opt.use_heatmaps:
covar_save [img_cnt:img_cnt + img.shape[0]] = covar.data.cpu().numpy() * (4./ downsample_save) * (4./ downsample_save)
cholesky_save [img_cnt:img_cnt + img.shape[0]] = cholesky.data.cpu().numpy() * (4./ downsample_save) # Cholesky are similar to standard deviations. So multiplied by one time only
vis_estimated_save [img_cnt:img_cnt + img.shape[0]] = vis_estimated.data.cpu().numpy()
rmse_new_per_image_save [img_cnt:img_cnt + img.shape[0]] = rmse_new_per_image
rmse_new_box_per_image_save [img_cnt:img_cnt + img.shape[0]] = rmse_new_box_per_image
rmse_new_per_image_per_landmark_save [img_cnt:img_cnt + img.shape[0]] = rmse_new_per_image_per_landmark
rmse_new_box_per_image_per_landmark_save[img_cnt:img_cnt + img.shape[0]] = rmse_new_box_per_image_per_landmark
img_cnt += img.shape[0]
# Write numpy arrays to file
print("Started saving everything...")
if opt.save_image_heatmaps:
print("Saving images and heatmaps as well")
np.save(os.path.join(f_path, images_save_path) , img_save)
#np.save(os.path.join(f_path, heatmaps_save_path) , heatmaps_save)
np.save(os.path.join(f_path, gt_save_path) , gt_save)
np.save(os.path.join(f_path, means_save_path) , means_save)
np.save(os.path.join(f_path, vis_gt_save_path) , vis_gt_save)
if not opt.use_heatmaps:
np.save(os.path.join(f_path, vis_estimated_save_path) , vis_estimated_save)
if opt.is_covariance:
np.save(os.path.join(f_path, covar_save_path) , covar_save)
else:
np.save(os.path.join(f_path, cholesky_save_path) , cholesky_save)
np.save(os.path.join(f_path, nme_new_per_image_path) , rmse_new_per_image_save)
np.save(os.path.join(f_path, nme_new_box_per_image_path) , rmse_new_box_per_image_save)
np.save(os.path.join(f_path, nme_new_per_image_per_landmark_path) , rmse_new_per_image_per_landmark_save)
np.save(os.path.join(f_path, nme_new_box_per_image_per_landmark_path), rmse_new_box_per_image_per_landmark_save)
print("Saving done.")
return losses.avg, losses_mse.avg,losses_gau_t1.avg, losses_gau_t2.avg, rmses_orig.avg, rmses_new.avg, rmses_new_box.avg, predictions
def resize_4d_numpy(input, downsample= 4):
new_dim = int(256/downsample)
output = np.zeros((input.shape[0], input.shape[1], new_dim, new_dim)) # b x c x h x w
input = input.transpose(0, 2, 3, 1) # b x h x w x c
for i in range(input.shape[0]):
temp = cv2.resize(input[i], dsize=(new_dim, new_dim), interpolation=cv2.INTER_CUBIC) # h x w x c
output[i] = temp.transpose(2, 0, 1) # c x h x w
return output
if __name__ == '__main__':
main()