def process_a_batch(image_paths, target_layer, arch, topk, output_dir, cuda):
device = get_device(cuda)
# Synset words
classes = get_classtable()
# Model from torchvision
model = models.__dict__[arch](pretrained=True)
model.to(device)
model.eval()
# Images
images = load_images_only(image_paths)
images = torch.stack(images).to(device)
bp = BackPropagation(model=model)
probs, ids = bp.forward(images) # sorted
# =========================================================================
print("Grad-CAM:")
gcam = GradCAM(model=model)
_ = gcam.forward(images)
for i in range(topk):
# Grad-CAM
gcam.backward(ids=ids[:, [i]])
regions = gcam.generate(target_layer=target_layer)
regions = regions.cpu().numpy()
for j in range(len(images)):
print("\t#{}: {} ({:.5f})".format(image_paths[j],
classes[ids[j, i]], probs[j, i]))
# Grad-CAM
save_cam(filename=os.path.join(
output_dir,
"{}-{}-{}-{:.3}.png".format(os.path.basename(image_paths[j]),
classes[ids[j, i]], ids[j, i],
probs[j, i]),
),
gcam=regions[j, 0])
bp.remove_hook()
gcam.remove_hook()
del bp
del images
del gcam
del model
del regions
del probs
del ids
del _
torch.cuda.empty_cache()
def get_grad_cam(device, model, classes, images, labels, target_layers):
# move the model to device
model.to(device)
# set the model in evaluation mode
model.eval()
# get the grad cam
gcam = GradCAM(model=model, candidate_layers=target_layers)
# images = torch.stack(images).to(device)
# predicted probabilities and class ids
pred_probs, pred_ids = gcam.forward(images)
# actual class ids
# target_ids = torch.LongTensor(labels).view(len(images), -1).to(device)
target_ids = labels.view(len(images), -1).to(device)
# backward pass wrt to the actual ids
gcam.backward(ids=target_ids)
# we will store the layers and correspondings images activations here
layers_region = {}
# fetch the grad cam layers of all the images
for target_layer in target_layers:
# logger.info(f'generating Grad-CAM for {target_layer}')
# Grad-CAM
regions = gcam.generate(target_layer=target_layer)
layers_region[target_layer] = regions
# we are done here, remove the hooks
gcam.remove_hook()
return layers_region, pred_probs, pred_ids
def demo1(target_layer, arch, topk, output_dir, cuda):
"""
Visualize model responses given multiple images
"""
device = get_device(cuda)
# Synset words
classes = get_classtable()
# Model from torchvision
model = models.__dict__[arch](pretrained=True)
num_ftrs = model.classifier[6].in_features
model.classifier[6] = nn.Linear(in_features=num_ftrs, out_features=10)
model.load_state_dict(torch.load('model_imageclass10bn.pth'))
#print(model)
model.to(device)
model.eval()
#normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
#trans = transforms.Compose([transforms.ToTensor(), normalize])
dsets = Data()
dset_loaders = torch.utils.data.DataLoader(dsets,
batch_size=10,
shuffle=False,
num_workers=2)
# =========================================================================
print("Grad-CAM/Guided Backpropagation/Guided Grad-CAM:")
#bp = BackPropagation(model=model)
for batch, inp in enumerate(tqdm(dset_loaders), 1):
#print(images)
i = inp[0]
raw_images = inp[1].numpy()
#print(raw_images)
paths = inp[2]
images = i.cuda()
#raw_images = r.cuda()
#probs, ids = bp.forward(images)
gcam = GradCAM(model=model)
probs, ids = gcam.forward(images)
#_ = gcam.forward(images)
#gbp = GuidedBackPropagation(model=model)
#_ = gbp.forward(images)
for i in range(topk):
# Guided Backpropagation
#gbp.backward(ids=ids[:, [i]])
#gradients = gbp.generate()
# Grad-CAM
gcam.backward(ids=ids[:, [i]], image=images)
regions = gcam.generate(target_layer=target_layer)
for j in range(len(images)):
print("\t#{}: {} ({:.5f})".format(j, classes[ids[j, i]],
probs[j, i]))
# Grad-CAM
save_path = os.path.join(output_dir, paths[j].split('/')[6],
paths[j].split('/')[7].split(".")[0])
#save_path = os.path.join(output_dir, paths[j].split('/')[6],paths[j].split('/')[7].split(".")[0])
create_path(
os.path.join(
save_path.split('/')[0],
save_path.split('/')[1]))
save_gradcam(
filename1=save_path +
"gradcam.png".format(classes[ids[j, i]]),
filename2=save_path + ".png",
gcam=regions[j, 0],
raw_image=raw_images[j],
)
gcam.remove_hook()
gcam.model.zero_grad()
torch.cuda.empty_cache()