def main(args):
# Load the synset words
idx2cls = list()
with open('samples/synset_words.txt') as lines:
for line in lines:
line = line.strip().split(' ', 1)[1]
line = line.split(', ', 1)[0].replace(' ', '_')
idx2cls.append(line)
print('Loading a model...')
model = torchvision.models.resnet152(pretrained=True)
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
print('\nGrad-CAM')
gcam = GradCAM(model=model, target_layer='layer4.2', cuda=args.cuda)
gcam.load_image(args.image, transform)
gcam.forward()
for i in range(0, 3):
gcam.backward(idx=gcam.idx[i])
cls_name = idx2cls[gcam.idx[i]]
output = gcam.generate()
print('\t{:.5f}\t{}'.format(gcam.prob[i], cls_name))
gcam.save('results/{}_gcam.png'.format(cls_name), output)
print('\nVanilla Backpropagation')
bp = BackPropagation(model=model, target_layer='conv1', cuda=args.cuda)
bp.load_image(args.image, transform)
bp.forward()
for i in range(0, 3):
bp.backward(idx=bp.idx[i])
cls_name = idx2cls[bp.idx[i]]
output = bp.generate()
print('\t{:.5f}\t{}'.format(bp.prob[i], cls_name))
bp.save('results/{}_bp.png'.format(cls_name), output)
print('\nGuided Backpropagation')
gbp = GuidedBackPropagation(model=model,
target_layer='conv1',
cuda=args.cuda)
gbp.load_image(args.image, transform)
gbp.forward()
for i in range(0, 3):
cls_idx = gcam.idx[i]
cls_name = idx2cls[cls_idx]
gcam.backward(idx=cls_idx)
output_gcam = gcam.generate()
gbp.backward(idx=cls_idx)
output_gbp = gbp.generate()
output_gcam -= output_gcam.min()
output_gcam /= output_gcam.max()
output_gcam = cv2.resize(output_gcam, (224, 224))
output_gcam = cv2.cvtColor(output_gcam, cv2.COLOR_GRAY2BGR)
output = output_gbp * output_gcam
print('\t{:.5f}\t{}'.format(gbp.prob[i], cls_name))
gbp.save('results/{}_gbp.png'.format(cls_name), output_gbp)
gbp.save('results/{}_ggcam.png'.format(cls_name), output)
def main(image_path, arch, topk, cuda):
CONFIG = {
'resnet152': {
'target_layer': 'layer4.2',
'input_size': 224
},
'vgg19': {
'target_layer': 'features.35',
'input_size': 224
},
'inception_v3': {
'target_layer': 'Mixed_7c',
'input_size': 299
},
}.get(arch)
cuda = cuda and torch.cuda.is_available()
# Synset words
classes = list()
with open('samples/synset_words.txt') as lines:
for line in lines:
line = line.strip().split(' ', 1)[1]
line = line.split(', ', 1)[0].replace(' ', '_')
classes.append(line)
# Model
model = models.__dict__[arch](pretrained=True)
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
# Image
raw_image = cv2.imread(image_path)[:, :, ::-1]
raw_image = cv2.resize(raw_image, (CONFIG['input_size'], ) * 2)
image = transform(raw_image).unsqueeze(0)
image = Variable(image, volatile=False, requires_grad=True)
if cuda:
model.cuda()
image = image.cuda()
print('1. Grad-CAM')
gcam = GradCAM(model=model, cuda=cuda)
probs, idx = gcam.forward(image)
for i in range(0, topk):
gcam.backward(idx=idx[i])
output = gcam.generate(target_layer=CONFIG['target_layer'])
gcam.save('results/{}_gcam_{}.png'.format(classes[idx[i]], arch), output, raw_image) # NOQA
print('\t{:.5f}\t{}'.format(probs[i], classes[idx[i]]))
print('2. Vanilla Backpropagation')
bp = BackPropagation(model=model, cuda=cuda)
probs, idx = bp.forward(image)
for i in range(0, topk):
bp.backward(idx=idx[i])
output = bp.generate()
bp.save('results/{}_bp_{}.png'.format(classes[idx[i]], arch), output)
print('\t{:.5f}\t{}'.format(probs[i], classes[idx[i]]))
print('3. Guided Backpropagation/Grad-CAM')
gbp = GuidedBackPropagation(model=model, cuda=cuda)
probs, idx = gbp.forward(image)
for i in range(0, topk):
gcam.backward(idx=idx[i])
region = gcam.generate(target_layer=CONFIG['target_layer'])
gbp.backward(idx=idx[i])
feature = gbp.generate()
output = feature * region[:, :, np.newaxis]
gbp.save('results/{}_gbp_{}.png'.format(classes[idx[i]], arch), feature) # NOQA
gbp.save('results/{}_ggcam_{}.png'.format(classes[idx[i]], arch), output) # NOQA
print('\t{:.5f}\t{}'.format(probs[i], classes[idx[i]]))