def demo4(image_paths, arch, topk, stride, n_batches, output_dir, cuda):
"""
Generate occlusion sensitivity maps
"""
device = get_device(cuda)
# Synset words
classes = get_classtable()
# Model from torchvision
model = models.__dict__[arch](pretrained=True)
model = torch.nn.DataParallel(model)
model.to(device)
model.eval()
# Images
images = []
raw_images = []
print("Images:")
for i, image_path in enumerate(image_paths):
print("\t#{}: {}".format(i, image_path))
image, raw_image = preprocess(image_path)
images.append(image)
raw_images.append(raw_image)
images = torch.stack(images).to(device)
print("Occlusion Sensitivity:")
patche_sizes = [10, 15, 25, 35, 45, 90]
logits = model(images)
probs = F.softmax(logits, dim=1)
probs, ids = probs.sort(dim=1, descending=True)
for i in range(topk):
for p in patche_sizes:
print("Patch:", p)
sensitivity = occlusion_sensitivity(model,
images,
ids[:, [i]],
patch=p,
stride=stride,
n_batches=n_batches)
# Save results as image files
for j in range(len(images)):
print("\t#{}: {} ({:.5f})".format(j, classes[ids[j, i]],
probs[j, i]))
save_sensitivity(
filename=osp.join(
output_dir,
"{}-{}-sensitivity-{}-{}.png".format(
j, arch, p, classes[ids[j, i]]),
),
maps=sensitivity[j],
)
def demo4(image_paths, arch, topk, stride, n_batches, output_dir, cuda):
"""
Generate occlusion sensitivity maps
"""
device = get_device(cuda)
# Synset words
classes = get_classtable()
# Model from torchvision
model = models.__dict__[arch](pretrained=True)
model = torch.nn.DataParallel(model)
model.to(device)
model.eval()
# Images
images = []
raw_images = []
print("Images:")
for i, image_path in enumerate(image_paths):
print("\t#{}: {}".format(i, image_path))
image, raw_image = preprocess(image_path)
images.append(image)
raw_images.append(raw_image)
images = torch.stack(images).to(device)
print("Occlusion Sensitivity:")
patche_sizes = [10, 15, 25, 35, 45, 90]
logits = model(images)
probs = F.softmax(logits, dim=1)
probs, ids = probs.sort(dim=1, descending=True)
for i in range(topk):
for p in patche_sizes:
print("Patch:", p)
sensitivity = occlusion_sensitivity(
model, images, ids[:, [i]], patch=p, stride=stride, n_batches=n_batches
)
# Save results as image files
for j in range(len(images)):
print("\t#{}: {} ({:.5f})".format(j, classes[ids[j, i]], probs[j, i]))
save_sensitivity(
filename=osp.join(
output_dir,
"{}-{}-sensitivity-{}-{}.png".format(
j, arch, p, classes[ids[j, i]]
),
),
maps=sensitivity[j],
)
def demo4(image_paths, arch, topk, stride, n_batches, output_dir, cuda):
"""
Generate occlusion sensitivity maps
"""
device = get_device(cuda)
# Synset words
classes = get_classtable()
# Model from torchvision
# check if CUDA is available
train_on_gpu = torch.cuda.is_available()
if not train_on_gpu:
print('CUDA is not available. Training on CPU ...')
else:
print('CUDA is available! Training on GPU ...')
device = get_device(cuda)
# Synset words
classes = get_classtable()
# Model from torchvision
PRE_MODEL_DIR = '/content/gdrive/My Drive/UnB/TCC-1/TCC1-1-dataset-final/restnet_model152_trained_exp7.pt'
model_name = 'resnet'
num_classes = 9
feature_extract = False
model, input_size = initialize_model(model_name,
num_classes,
feature_extract,
use_pretrained=True)
if train_on_gpu:
state = torch.load(PRE_MODEL_DIR)
else:
state = torch.load(PRE_MODEL_DIR, map_location='cpu')
# Loading weights in restnet architecture
model.load_state_dict(state['state_dict'])
model = torch.nn.DataParallel(model)
model.to(device)
model.eval()
# Images
images = []
raw_images = []
print("Images:")
for i, image_path in enumerate(image_paths):
print("\t#{}: {}".format(i, image_path))
image, raw_image = preprocess(image_path)
images.append(image)
raw_images.append(raw_image)
images = torch.stack(images).to(device)
print("Occlusion Sensitivity:")
patche_sizes = [10, 15, 25, 35, 45, 90]
logits = model(images)
probs = F.softmax(logits, dim=1)
probs, ids = probs.sort(dim=1, descending=True)
for i in range(topk):
for p in patche_sizes:
print("Patch:", p)
sensitivity = occlusion_sensitivity(model,
images,
ids[:, [i]],
patch=p,
stride=stride,
n_batches=n_batches)
# Save results as image files
for j in range(len(images)):
print("\t#{}: {} ({:.5f})".format(j, classes[ids[j, i]],
probs[j, i]))
save_sensitivity(
filename=osp.join(
output_dir,
"{}-{}-sensitivity-{}-{}.png".format(
j, arch, p, classes[ids[j, i]]),
),
maps=sensitivity[j],
)
