def visualize_images_to_folder(output_folder, batch_idx, image, target,
output, dataset):
for i in range(image.shape[0]):
outpath = os.path.join(output_folder, '{0}_{1:04d}_{2:02d}.png')
scipy.misc.imsave(outpath.format('img', batch_idx, i),
np.transpose(image[i, :, :, :], axes=[1, 2, 0]))
scipy.misc.imsave(
outpath.format('tgt', batch_idx, i),
map_segmentation_to_colors(target[i, :, :], dataset))
scipy.misc.imsave(
outpath.format('prd', batch_idx, i),
map_segmentation_to_colors(output[i, :, :], dataset))
def test_gradients():
from utils.loss import SegmentationLosses
import numpy as np
from dataloaders import make_dataloader
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
from dataloaders.utils import map_segmentation_to_colors
import sys
kwargs = {'pin_memory': True, 'init_set': 'set_dummy.txt'}
_, train_loader, _, _, num_classes = make_dataloader('active_cityscapes_region', 513, 513, 1, True, **kwargs)
model = DeepLab(backbone='mobilenet', output_stride=16, mc_dropout=False)
train_params = [{'params': model.get_1x_lr_params(), 'lr': 0.001},
{'params': model.get_10x_lr_params(), 'lr': 0.001 * 10}]
optimizer = torch.optim.SGD(train_params, momentum=0.9, weight_decay=5e-4, nesterov=False)
criterion = SegmentationLosses(weight=None, cuda=True).build_loss(mode='ce')
model = model.cuda()
model.eval()
for name, param in model.named_parameters():
if param.requires_grad:
print(name)
print(param)
break
display = False
for i, sample in enumerate(train_loader):
image, target = sample['image'], sample['label']
image, target = image.cuda(), target.cuda()
if display:
gt_colored = map_segmentation_to_colors(np.array(target[0].cpu().numpy()).astype(np.uint8), 'cityscapes')
image_unnormalized = ((np.transpose(image[0].cpu().numpy(), axes=[1, 2, 0]) *
(0.229, 0.224, 0.225) + (0.485, 0.456, 0.406)) * 255).astype(np.uint8)
plt.figure()
plt.title('display')
plt.subplot(211)
plt.imshow(image_unnormalized)
plt.subplot(212)
plt.imshow(gt_colored)
plt.show()
optimizer.zero_grad()
output = model(image)
loss = criterion(output, target)
loss.backward()
for name, param in model.named_parameters():
if param.requires_grad:
print(name)
print(param.grad)
input()
optimizer.step()
sys.exit(0)
def _visualize_entropy(image_normalized, entropy_map, prediction):
import matplotlib
import matplotlib.pyplot as plt
image_unnormalized = ((np.transpose(image_normalized, axes=[1, 2, 0]) *
(0.229, 0.224, 0.225) + (0.485, 0.456, 0.406)) *
255).astype(np.uint8)
prediction_mapped = map_segmentation_to_colors(
prediction.astype(np.uint8), 'cityscapes')
norm = matplotlib.colors.Normalize(vmin=np.min(entropy_map),
vmax=np.max(entropy_map),
clip=False)
plt.figure()
plt.title('display')
plt.subplot(1, 3, 1)
plt.imshow(image_unnormalized)
plt.subplot(1, 3, 2)
plt.imshow(prediction_mapped)
plt.subplot(1, 3, 3)
plt.imshow(entropy_map, norm=norm, cmap='gray')
plt.show(block=True)
num_workers=0)
active_selector = ActiveSelectionMCDropout(19, cityscapes_train.env,
crop_size, 2)
print('Before Expansion', len(dataloader))
cityscapes_train.expand_training_set(
active_selector.get_random_uncertainity(
cityscapes_train.remaining_image_paths, 10))
print('After Expansion', len(dataloader))
for i, sample in enumerate(dataloader, 0):
for j in range(sample['image'].size()[0]):
image = sample['image'].numpy()
gt = sample['label'].numpy()
print(gt.shape)
gt_colored = map_segmentation_to_colors(
np.array(gt[j]).astype(np.uint8), 'cityscapes')
image_unnormalized = ((np.transpose(image[j], axes=[1, 2, 0]) *
(0.229, 0.224, 0.225) +
(0.485, 0.456, 0.406)) * 255).astype(
np.uint8)
plt.figure()
plt.title('display')
plt.subplot(211)
plt.imshow(image_unnormalized)
plt.subplot(212)
plt.imshow(gt_colored)
if i == 1:
break
plt.show(block=True)
if __name__ == "__main__":
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
from dataloaders.utils import map_segmentation_to_colors
path = os.path.join(constants.DATASET_ROOT, 'pascal')
crop_size = -1
base_size = 512
split = 'train'
pascal_train = ActivePascalRegion(path, base_size, crop_size, split, 'set_dummy.txt')
pascal_train.expand_training_set({pascal_train.image_paths[299]: [(36, 100, 127, 127)]}, 0)
dataloader = DataLoader(pascal_train, batch_size=1, shuffle=False, num_workers=0)
for i, sample in enumerate(dataloader):
for j in range(sample['image'].size()[0]):
image = sample['image'].numpy()
gt = sample['label'].numpy()
gt_colored = map_segmentation_to_colors(np.array(gt[j]).astype(np.uint8), 'pascal')
image_unnormalized = ((np.transpose(image[j], axes=[1, 2, 0]) * (0.229, 0.224, 0.225) + (0.485, 0.456, 0.406)) * 255).astype(np.uint8)
plt.figure()
plt.title('display')
plt.subplot(211)
plt.imshow(image_unnormalized)
plt.subplot(212)
plt.imshow(gt_colored)
plt.show(block=True)