def demo1(image_paths, target_layer, arch, topk, output_dir, cuda, model):
"""
Visualize model responses given multiple images
"""
device = get_device(cuda)
gradcam_list = []
# Synset words
classes = get_classtable()
# Model from torchvision
if model is None:
model = models.__dict__[arch](pretrained=True)
model.to(device)
model.eval()
# Images
#print(image_paths)
images, raw_images = load_images(image_paths)
images = torch.stack(images).to(device)
"""
Common usage:
1. Wrap your model with visualization classes defined in grad_cam.py
2. Run forward() with images
3. Run backward() with a list of specific classes
4. Run generate() to export results
"""
# =========================================================================
print("Grad-CAM/Guided Backpropagation/Guided Grad-CAM:")
bp = BackPropagation(model=model)
probs, ids = bp.forward(images)
gcam = GradCAM(model=model)
_ = 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]])
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
filename = osp.join(
output_dir,
"{}-{}-gradcam-{}.png".format(j, arch, target_layer))
gradcam_list.append(filename)
save_gradcam(filename, gcam=regions[j, 0], raw_image=raw_images[j])
return gradcam_list
def guided_backprop(model, device, raw_image, image, CONFIG, topk):
# =========================================================================
print('Guided Backpropagation')
# =========================================================================
gbp = GuidedBackPropagation(model=model)
probs, idx = gbp.forward(image.to(device))
for i in range(0, topk):
gbp.backward(idx=idx[i])
feature = gbp.generate()
results.append(find_gradient(feature))
print('[{:.5f}] {}'.format(probs[i], idx[i].cpu().numpy()))
return (results, probs, idx)
def guided_backprop_eye(image, name, net):
img = torch.stack([image[name]])
bp = BackPropagation(model=net)
probs, ids = bp.forward(img)
gcam = GradCAM(model=net)
_ = gcam.forward(img)
gbp = GuidedBackPropagation(model=net)
_ = gbp.forward(img)
# Guided Backpropagation
actual_status = ids[:, 0]
gbp.backward(ids=actual_status.reshape(1, 1))
gradients = gbp.generate()
# Grad-CAM
gcam.backward(ids=actual_status.reshape(1, 1))
regions = gcam.generate(target_layer='last_conv')
# Get Images
prob = probs.data[:, 0]
if actual_status == 0:
prob = probs.data[:, 1]
prob_image = np.zeros((shape[0], 60, 3), np.uint8)
cv2.putText(prob_image, '%.1f%%' % (prob * 100), (5, 15),
cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255), 1, cv2.LINE_AA)
guided_bpg_image = get_gradient_image(gradients[0])
guided_bpg_image = cv2.merge(
(guided_bpg_image, guided_bpg_image, guided_bpg_image))
grad_cam_image = get_gradcam_image(gcam=regions[0, 0],
raw_image=image[name + '_raw'])
guided_gradcam_image = get_gradient_image(torch.mul(regions, gradients)[0])
guided_gradcam_image = cv2.merge(
(guided_gradcam_image, guided_gradcam_image, guided_gradcam_image))
#print(image['path'],classes[actual_status.data], probs.data[:,0] * 100)
print(classes[actual_status.data], probs.data[:, 0] * 100)
return cv2.hconcat([
image[name + '_raw'], prob_image, guided_bpg_image, grad_cam_image,
guided_gradcam_image
])
def guided_gradcam(model, device, raw_image, image, CONFIG, topk):
# =========================================================================
print('Guided Grad-CAM')
# =========================================================================
gcam = GradCAM(model=model)
gbp = GuidedBackPropagation(model=model)
probs, idx = gbp.forward(image.to(device))
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()
h, w, _ = feature.shape
region = cv2.resize(region, (w, h))[..., np.newaxis]
output = feature * region
results.append(find_gradient(output))
print('[{:.5f}] {}'.format(probs[i], idx[i].cpu().numpy()))
return (results, probs, idx)
def main():
# Dataset
print('Creating dataset...')
transform_val= transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(MEAN, STD)
])
valset = torchvision.datasets.CIFAR100(root='./data', train=False, download=True, transform=transform_val)
val_loader = DataLoader(valset, batch_size=100,shuffle=False, num_workers=4, pin_memory=True)
# Model
checkpoint = os.path.join(args.checkpoint, args.model + "_" + args.attention)
model_path = os.path.join(checkpoint, args.attention + '_' + 'best_model.pt')
print('Loading model...')
model = get_model(args.model,'bn',args.attention)
if os.path.exists(model_path):
model.load_state_dict(torch.load(model_path))
else:
raise Exception('Cannot find model', model_path)
# if torch.cuda.device_count() > 1:
# print("Using", torch.cuda.device_count(), "GPUs!")
# model = nn.DataParallel(model)
model.cuda()
cudnn.benchmark = True
print('\tModel loaded: ' + args.model)
print('\tAttention type: ' + args.attention)
print("\tNumber of parameters: ", sum([param.nelement() for param in model.parameters()]))
result_path = os.path.join('results', args.model + "_" + args.attention)
if not os.path.exists(result_path):
os.makedirs(result_path)
if True:
image_paths = get_image_links()
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("cuda")
model.eval()
# if False:
# summary(model, (3, 32, 32))
# return
# Get sample for evaluate
GET_SAMPLE = False
if GET_SAMPLE:
for i, (inputs, labels) in enumerate(val_loader):
inputs, labels = (Variable(inputs.cuda()),
Variable(labels.cuda()))
outputs = model(inputs)
_, preds = outputs.topk(1, 1, True, True)
for sample in SAMPLES:
image = get_image(inputs[sample])
save_image(result_path, image, sample, preds[sample], labels[sample])
break
print("Vanilla Backpropagation:")
topk = 1
target_layer = "layer4.2"
bp = BackPropagation(model=model)
probs, ids = bp.forward(images)
for i in range(topk):
# In this example, we specify the high confidence classes
bp.backward(ids=ids[:, [i]])
gradients = bp.generate()
# Save results as image files
for j in range(len(images)):
print("\t#{}: {} ({:.5f})".format(j, LABELS[ids[j, i]], probs[j, i]))
# Remove all the hook function in the "model"
bp.remove_hook()
# =========================================================================
print("Deconvolution:")
deconv = Deconvnet(model=model)
_ = deconv.forward(images)
for i in range(topk):
deconv.backward(ids=ids[:, [i]])
gradients = deconv.generate()
for j in range(len(images)):
print("\t#{}: {} ({:.5f})".format(j, LABELS[ids[j, i]], probs[j, i]))
deconv.remove_hook()
# =========================================================================
print("Grad-CAM/Guided Backpropagation/Guided Grad-CAM:")
gcam = GradCAM(model=model)
_ = 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]])
regions = gcam.generate(target_layer=target_layer)
for j in range(len(images)):
print("\t#{}: {} ({:.5f})".format(j, LABELS[ids[j, i]], probs[j, i]))
# Grad-CAM
save_gradcam(
filename=osp.join(
result_path,
"{}-gradcam-{}-{}.png".format(
j, target_layer, LABELS[ids[j, i]]
),
),
gcam=regions[j, 0],
raw_image=raw_images[j],
)
print('Finish!!!')
def fulltest(image_paths, target_layer, arch, topk, output_dir, cuda):
"""
Visualize model responses given multiple images
"""
fold = 0
device = get_device(cuda)
# Synset words
#classes = {0:'normal',1:'covid'}
classes = get_classtable()
# Model
#model = models.resnet34(pretrained=False,num_classes=config.num_classes)
best_model = torch.load(config.weights + 'ct-cn/' + 'model_best.pth.tar')
print(best_model["state_dict"].keys())
model = make_model(arch, num_classes=config.num_classes, pretrained=True)
#best_model = torch.load(config.weights +'model_best.pth.tar')
model.load_state_dict(best_model["state_dict"])
print(best_model["state_dict"].keys())
model.to(device)
model.eval()
# Images
images, raw_images = load_images(image_paths)
images = torch.stack(images).to(device)
"""
Common usage:
1. Wrap your model with visualization classes defined in grad_cam.py
2. Run forward() with images
3. Run backward() with a list of specific classes
4. Run generate() to export results
"""
# =========================================================================
print("Vanilla Backpropagation:")
bp = BackPropagation(model=model)
probs, ids = bp.forward(images) # sorted
print(probs)
print(ids)
for i in range(topk):
bp.backward(ids=ids[:, [i]])
gradients = bp.generate()
# Save results as image files
for j in range(len(images)):
print("\t#{}: {} ({:.5f})".format(j, classes[ids[j, i]], probs[j,
i]))
save_gradient(
filename=osp.join(
output_dir,
"{}-{}-vanilla-{}.png".format(j, arch, classes[ids[j, i]]),
),
gradient=gradients[j],
)
# Remove all the hook function in the "model"
bp.remove_hook()
# =========================================================================
print("Deconvolution:")
deconv = Deconvnet(model=model)
_ = deconv.forward(images)
for i in range(topk):
deconv.backward(ids=ids[:, [i]])
gradients = deconv.generate()
for j in range(len(images)):
print("\t#{}: {} ({:.5f})".format(j, classes[ids[j, i]], probs[j,
i]))
save_gradient(
filename=osp.join(
output_dir,
"{}-{}-deconvnet-{}.png".format(j, arch, classes[ids[j,
i]]),
),
gradient=gradients[j],
)
deconv.remove_hook()
# =========================================================================
print("Grad-CAM/Guided Backpropagation/Guided Grad-CAM:")
gcam = GradCAM(model=model)
_ = 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]])
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]))
# Guided Backpropagation
save_gradient(
filename=osp.join(
output_dir,
"{}-{}-guided-{}.png".format(j, arch, classes[ids[j, i]]),
),
gradient=gradients[j],
)
# Grad-CAM
save_gradcam(
filename=osp.join(
output_dir,
"{}-{}-gradcam-{}-{}.png".format(j, arch, target_layer,
classes[ids[j, i]]),
),
gcam=regions[j, 0],
raw_image=raw_images[j],
)
# Guided Grad-CAM
save_gradient(
filename=osp.join(
output_dir,
"{}-{}-guided_gradcam-{}-{}.png".format(
j, arch, target_layer, classes[ids[j, i]]),
),
gradient=torch.mul(regions, gradients)[j],
)
def main():
parser = make_parser() #creates the parser
args = parser.parse_args()
CONFIG = {
'resnet152': {
'target_layer': 'layer4.2',
'input_size': 224
},
'vgg19': {
'target_layer': 'features.36',
'input_size': 224
},
'vgg19_bn': {
'target_layer': 'features.52',
'input_size': 224
},
'inception_v3': {
'target_layer': 'Mixed_7c',
'input_size': 299
},
'densenet201': {
'target_layer': 'features.denseblock4',
'input_size': 224
},
'resnet50': {
'target_layer': 'layer4',
'input_size': 224
},
'resnet18': {
'target_layer': 'layer4',
'input_size': 224
},
# Add your model
}.get(args.arch)
device = torch.device(
'cuda' if args.cuda and torch.cuda.is_available() else 'cpu')
if args.cuda:
current_device = torch.cuda.current_device()
print('Running on the GPU:',
torch.cuda.get_device_name(current_device))
else:
print('Running on the CPU')
# 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
print("1")
class_names = ['no',
'yes'] #important to define the classes for prediction
model_ft = get_cnn(len(class_names),
args) #retrieves the cnn - architecture to be used
print("2")
criterion = nn.CrossEntropyLoss(
) #creates the criterion (used in training and testing)
optimizer_ft = get_optimizer(
model_ft, args
) #changes the weights based on error (using Stochastic Gradient Descent)
exp_lr_scheduler = lr_scheduler.StepLR(
optimizer_ft, step_size=7, gamma=0.1
) #helps with the learning rate, to be zigzagging to get into the right function
model = load_model(model_ft, args.weights) #load the model with weights
print("3")
model = model.to(device)
model.eval()
# Image
print("image path: " + str(args.image_path))
raw_image = cv2.imread(args.image_path)[..., ::-1]
raw_image = cv2.resize(raw_image, (CONFIG['input_size'], ) * 2)
image = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
)
])(raw_image).unsqueeze(0)
# =========================================================================
print('Grad-CAM')
# =========================================================================
gcam = GradCAM(model=model)
probs, idx = gcam.forward(image.to(device))
for i in range(0, args.topk):
print("idx is: " + str(idx))
print("idx is: " + str(probs))
print("i is: " + str(i))
gcam.backward(idx=idx[i])
print("idx AFTER is: " + str(idx))
print("idx[i]: " + str(idx[i]))
output = gcam.generate(target_layer=CONFIG['target_layer'])
print("classes[idx[i]]: " + str(classes[idx[i]]))
save_gradcam(
'results/{}_gcam_{}.png'.format(classes[idx[i]], args.arch),
output, raw_image)
print('[{:.5f}] {}'.format(probs[i], classes[idx[i]]))
# =========================================================================
print('Vanilla Backpropagation')
# =========================================================================
bp = BackPropagation(model=model)
probs, idx = bp.forward(image.to(device))
for i in range(0, args.topk):
bp.backward(idx=idx[i])
output = bp.generate()
save_gradient(
'results/{}_bp_{}.png'.format(classes[idx[i]], args.arch), output)
print('[{:.5f}] {}'.format(probs[i], classes[idx[i]]))
# =========================================================================
print('Deconvolution')
# =========================================================================
deconv = Deconvolution(
model=copy.deepcopy(model)) # TODO: remove hook func in advance
probs, idx = deconv.forward(image.to(device))
for i in range(0, args.topk):
deconv.backward(idx=idx[i])
output = deconv.generate()
save_gradient(
'results/{}_deconv_{}.png'.format(classes[idx[i]], args.arch),
output)
print('[{:.5f}] {}'.format(probs[i], classes[idx[i]]))
# =========================================================================
print('Guided Backpropagation/Guided Grad-CAM')
# =========================================================================
gbp = GuidedBackPropagation(model=model)
probs, idx = gbp.forward(image.to(device))
for i in range(0, args.topk):
gcam.backward(idx=idx[i])
region = gcam.generate(target_layer=CONFIG['target_layer'])
gbp.backward(idx=idx[i])
feature = gbp.generate()
h, w, _ = feature.shape
region = cv2.resize(region, (w, h))[..., np.newaxis]
output = feature * region
save_gradient(
'results/{}_gbp_{}.png'.format(classes[idx[i]], args.arch),
feature)
save_gradient(
'results/{}_ggcam_{}.png'.format(classes[idx[i]], args.arch),
output)
print('[{:.5f}] {}'.format(probs[i], classes[idx[i]]))
def cam(config):
## prepare data
classes = {}
classes[0] = 'non-merger'
classes[1] = 'merger'
dsets = {}
# dset_loaders = {}
#sampling WOR, i guess we leave the 10 in the middle to validate?
pristine_indices = torch.randperm(len(pristine_x))
pristine_x_test = pristine_x[
pristine_indices[int(np.floor(.8 * len(pristine_x))):]]
pristine_y_test = pristine_y[
pristine_indices[int(np.floor(.8 * len(pristine_x))):]]
noisy_indices = torch.randperm(len(noisy_x))
noisy_x_test = noisy_x[noisy_indices[int(np.floor(.8 * len(noisy_x))):]]
noisy_y_test = noisy_y[noisy_indices[int(np.floor(.8 * len(noisy_x))):]]
dsets["source"] = pristine_x_test
dsets["target"] = noisy_x_test
class_num = config["network"]["params"]["class_num"]
# load checkpoint
print('load model from {}'.format(config['ckpt_path']))
ckpt = torch.load(config['ckpt_path'] + '/best_model.pth.tar')
## set base network
net_config = config["network"]
base_network = net_config["name"](**net_config["params"])
base_network.load_state_dict(ckpt['base_network'])
centroids = None
if 'center_criterion' in ckpt.keys():
centroids = ckpt['center_criterion']['centers'].cpu()
target_centroids = None
if 'target_center_criterion' in ckpt.keys():
target_centroids = ckpt['target_center_criterion']['centers'].cpu()
use_gpu = torch.cuda.is_available()
if use_gpu:
base_network = base_network.cuda()
#might not get to tstack this?
source_images = torch.stack(list(dsets["source"])).cuda()
target_images = torch.stack(list(dsets["source"])).cuda()
base_network.train(False)
model_name = config["network"]
target_layer = config[
"target_layer"] #no idea... maybe i can print this out? layer4 for resnet, relu for deepemerge
if config["class"] == 'non-merger':
target_class = 0 #non-merger
elif config["class"] == 'merger':
target_class = 1 #non-merger
else:
print("incorrect class choice")
sys.exit()
save_where = osp.join(
config["ckpt_path"],
'guided ' + str(config["which"]) + '-' + str(config["class"]))
output_dir = save_where
if not osp.exists(save_where):
os.makedirs(save_where)
else:
os.chdir(save_where)
gcam = GradCAM(model=base_network)
gbp = GuidedBackPropagation(model=base_network)
if config["which"] == 'source':
print("start source test: ")
probs, ids = gcam.forward(source_images) #see how they load in images
_ = gbp.forward(source_images) #see if you need to catch anything
#not sure what to do about this
if use_gpu:
ids_ = torch.LongTensor([[target_class]] *
len(source_images)).cuda()
else:
ids_ = torch.LongTensor([[target_class]] * len(source_images))
gcam.backward(ids=ids_)
gbp.backward(ids=ids_)
gradients = gbp.generate()
regions = gcam.generate(target_layer=target_layer)
# print("ids")
# print(ids)
# print("probs")
# print(probs)
# # print("argmax")
# # print(torch.argmax(ids, dim = 1))
# # print("equality")
# # print(torch.argmax(ids, dim = 1).cpu() == target_class)
# # print(probs)
# # print(probs[(torch.argmax(ids, dim = 1).cpu() == target_class).cpu()])
# # print(probs.cpu()[(torch.argmax(ids, dim = 1).cpu() == target_class).cpu()])
# #print(torch.argmax(probs, dim = 1).cpu() == target_class)
# print("try 1")
# print(probs[[torch.tensor(torch.argmax(ids, dim = 1).cpu() == target_class)]])
# print("try 2")
# a = probs[[torch.tensor(torch.argmax(ids, dim = 1).cpu() == target_class)]]
# print(a[:, target_class])
# a = probs[[torch.tensor(torch.argmax(ids, dim = 1).cpu() == target_class)]]
# a = a[:, target_class]
# print(len(regions))
# print(len(source_images)-1)
for j in range(0, len(source_images) - 1):
# print(
# "\t#{}: {} ({:.5f})".format(
# j, classes[target_class], float(probs[:, target_class][j])
# )
# )
# save_gradcam(
# filename=osp.join(
# output_dir,
# "{}-{}-gradcam-{}-{}.png".format(
# j, model_name, target_layer, classes[target_class]
# ),
# ),
# gcam=regions[j, 0],
# #raw_image=raw_images[j],
# raw_image=source_images[j],
# )
save_gradient(
filename=osp.join(
output_dir,
"{}-{}-guided-{}.png".format(j, net_config,
classes[target_class]),
),
gradient=torch.mul(regions, gradients)
[j], #gradients[j], #torch.mul(regions, gradients)[j],
)
elif config["which"] == 'target':
print("start target test: ")
probs, ids = gcam.forward(target_images) #see how they load in images
_ = gbp.forward(target_images) #see if you need to catch anything
#not sure what to do about this
if use_gpu:
ids_ = torch.LongTensor([[target_class]] *
len(target_images)).cuda()
else:
ids_ = torch.LongTensor([[target_class]] * len(target_images))
gcam.backward(ids=ids_)
gbp.backward(ids=ids_)
gradients = gbp.generate()
regions = gcam.generate(target_layer=target_layer)
for j in range(0, len(target_images) - 1):
# print(
# "\t#{}: {} ({:.5f})".format(
# j, classes[target_class], float(probs[:, target_class][j])
# )
# )
# save_gradcam(
# filename=osp.join(
# output_dir,
# "{}-{}-gradcam-{}-{}.png".format(
# j, model_name, target_layer, classes[target_class]
# ),
# ),
# gcam=regions[j, 0],
# #raw_image=raw_images[j],
# raw_image= target_images[j],
# )
save_gradient(
filename=osp.join(
output_dir,
"{}-{}-guided-{}.png".format(j, net_config,
classes[target_class]),
),
gradient=torch.mul(regions, gradients)[j], #gradients[j],
)
else:
print("incorrect domain choice")
sys.exit()
return ()
def demo1(image_paths, target_layer, arch, topk, output_dir, cuda):
"""
Visualize model responses given multiple images
"""
# 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.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)
"""
Common usage:
1. Wrap your model with visualization classes defined in grad_cam.py
2. Run forward() with images
3. Run backward() with a list of specific classes
4. Run generate() to export results
"""
# =========================================================================
print("Vanilla Backpropagation:")
bp = BackPropagation(model=model)
probs, ids = bp.forward(images)
for i in range(topk):
# In this example, we specify the high confidence classes
bp.backward(ids=ids[:, [i]])
gradients = bp.generate()
# Save results as image files
for j in range(len(images)):
print("\t#{}: {} ({:.5f})".format(j, classes[ids[j, i]], probs[j,
i]))
save_gradient(
filename=osp.join(
output_dir,
"{}-{}-vanilla-{}.png".format(j, arch, classes[ids[j, i]]),
),
gradient=gradients[j],
)
# Remove all the hook function in the "model"
bp.remove_hook()
# =========================================================================
print("Deconvolution:")
deconv = Deconvnet(model=model)
_ = deconv.forward(images)
for i in range(topk):
deconv.backward(ids=ids[:, [i]])
gradients = deconv.generate()
for j in range(len(images)):
print("\t#{}: {} ({:.5f})".format(j, classes[ids[j, i]], probs[j,
i]))
save_gradient(
filename=osp.join(
output_dir,
"{}-{}-deconvnet-{}.png".format(j, arch, classes[ids[j,
i]]),
),
gradient=gradients[j],
)
deconv.remove_hook()
# =========================================================================
print("Grad-CAM/Guided Backpropagation/Guided Grad-CAM:")
gcam = GradCAM(model=model)
_ = 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]])
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]))
# Guided Backpropagation
save_gradient(
filename=osp.join(
output_dir,
"{}-{}-guided-{}.png".format(j, arch, classes[ids[j, i]]),
),
gradient=gradients[j],
)
# Grad-CAM
save_gradcam(
filename=osp.join(
output_dir,
"{}-{}-gradcam-{}-{}.png".format(j, arch, target_layer,
classes[ids[j, i]]),
),
gcam=regions[j, 0],
raw_image=raw_images[j],
)
# Guided Grad-CAM
save_gradient(
filename=osp.join(
output_dir,
"{}-{}-guided_gradcam-{}-{}.png".format(
j, arch, target_layer, classes[ids[j, i]]),
),
gradient=torch.mul(regions, gradients)[j],
)
def main(image_path, target_layer, arch, topk, cuda):
device = torch.device(
"cuda" if cuda and torch.cuda.is_available() else "cpu")
if cuda:
current_device = torch.cuda.current_device()
print("Running on the GPU:",
torch.cuda.get_device_name(current_device))
else:
print("Running on the CPU")
# Synset words
classes = list()
# Model from torchvision
model = MGN()
"""
pickle.load = partial(pickle.load, encoding="latin1")
pickle.Unpickler = partial(pickle.Unpickler, encoding="latin1")
checkpoint = torch.load("hacnn_market_xent.pth.tar", pickle_module=pickle)
#checkpoint = torch.load(args.load_weights)
#checkpoint = torch.load("hacnn_market_xent.pth.tar")
pretrain_dict = checkpoint['state_dict']
model_dict = model.state_dict()
pretrain_dict = {k: v for k, v in pretrain_dict.items() if k in model_dict and model_dict[k].size() == v.size()}
model_dict.update(pretrain_dict)
model.load_state_dict(model_dict)
"""
pretrain_dict = torch.load("model_700.pt")
model_dict = model.state_dict()
pretrain_dict = {
k: v
for k, v in pretrain_dict.items()
if k in model_dict and model_dict[k].size() == v.size()
}
model_dict.update(pretrain_dict)
model.load_state_dict(model_dict)
#model = models.__dict__[arch](pretrained=True)
model.to(device)
model.eval()
place = []
with open("samples/new.txt") as lines:
for line in lines:
line = line.strip().split(" ", 1)[1]
line = line.split(", ", 1)[0].replace(" ", "_")
classes.append(line)
# Image preprocessing
with open("new.txt") as lines:
for line in lines:
line = line.strip()
line = "/mnt/SSD/jzwang/market1501/query/" + line
place.append(line)
for line in place:
image_path = line
raw_image = cv2.imread(image_path)[..., ::-1]
raw_image = cv2.resize(raw_image, (128, 384))
image = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])(raw_image).unsqueeze(0)
image = image.to(device)
"""
Common usage:
1. Wrap your model with visualization classes defined in grad_cam.py
2. Run forward() with an image
3. Run backward() with a specific class
4. Run generate() to export result
"""
# =========================================================================
print("Vanilla Backpropagation")
bp = BackPropagation(model=model)
predictions = bp.forward(image)
for i in range(topk):
print("[{:.5f}] {}".format(predictions[i][0],
classes[predictions[i][1]]))
bp.backward(idx=predictions[i][1])
gradient = bp.generate()
# Remove all the hook function in the "model"
bp.remove_hook()
# =========================================================================
print("Deconvolution")
deconv = Deconvnet(model=model)
_ = deconv.forward(image)
for i in range(topk):
print("[{:.5f}] {}".format(predictions[i][0],
classes[predictions[i][1]]))
deconv.backward(idx=predictions[i][1])
gradient = deconv.generate()
deconv.remove_hook()
# =========================================================================
print("Grad-CAM/Guided Backpropagation/Guided Grad-CAM")
gcam = GradCAM(model=model)
_ = gcam.forward(image)
gbp = GuidedBackPropagation(model=model)
_ = gbp.forward(image)
t = ['p1', 'p2', 'p3']
for i in range(topk):
print("[{:.5f}] {}".format(predictions[i][0],
classes[predictions[i][1]]))
# Grad-CAM
for target_layer in t:
gcam.backward(idx=predictions[i][1])
#print("1")
region = gcam.generate(target_layer=target_layer)
#print(2)
line = line.strip().split("/")[-1]
save_gradcam(
"results/{}-gradcam-{}.png".format(line, target_layer),
region,
raw_image,
)
#print(3)
# Guided Backpropagation
gbp.backward(idx=predictions[i][1])
gradient = gbp.generate()
# Guided Grad-CAM
h, w, _ = gradient.shape
region = cv2.resize(region, (w, h))[..., np.newaxis]
output = gradient * region
def main(image_path, arch, topk, cuda):
CONFIG = {
'resnet18': {
'target_layer': 'layer4.1',
'input_size': 224
},
'resnet152': {
'target_layer': 'layer4.2',
'input_size': 224
},
'vgg19': {
'target_layer': 'features.36',
'input_size': 224
},
'vgg19_bn': {
'target_layer': 'features.52',
'input_size': 224
},
'inception_v3': {
'target_layer': 'Mixed_7c',
'input_size': 299
},
'densenet201': {
'target_layer': 'features.denseblock4',
'input_size': 224
},
# Add your model
}.get(arch)
device = torch.device(
'cuda' if cuda and torch.cuda.is_available() else 'cpu')
if cuda:
current_device = torch.cuda.current_device()
print('Running on the GPU:',
torch.cuda.get_device_name(current_device))
else:
print('Running on the CPU')
# Synset words
classes = ["other", "rori"]
# 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)
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, 200) #これにより512->2の層に変わった
model.add_module('relu_fc', nn.ReLU())
model.add_module('fc2', nn.Linear(200, 2))
param = torch.load('weight_resnet18_3.pth')
model.load_state_dict(param)
model.to(device)
model.eval()
# Image
raw_image = cv2.imread(image_path)[..., ::-1]
raw_image = cv2.resize(raw_image, (CONFIG['input_size'], ) * 2)
image = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
)
])(raw_image).unsqueeze(0)
# =========================================================================
print('Grad-CAM')
# =========================================================================
gcam = GradCAM(model=model)
probs, idx = gcam.forward(image.to(device))
for i in range(0, topk):
gcam.backward(idx=idx[i])
output = gcam.generate(target_layer=CONFIG['target_layer'])
save_gradcam('results/{}_gcam_{}.png'.format(classes[idx[i]], arch),
output, raw_image)
print('[{:.5f}] {}'.format(probs[i], classes[idx[i]]))
# =========================================================================
print('Vanilla Backpropagation')
# =========================================================================
bp = BackPropagation(model=model)
probs, idx = bp.forward(image.to(device))
for i in range(0, topk):
bp.backward(idx=idx[i])
output = bp.generate()
save_gradient('results/{}_bp_{}.png'.format(classes[idx[i]], arch),
output)
print('[{:.5f}] {}'.format(probs[i], classes[idx[i]]))
# =========================================================================
print('Deconvolution')
# =========================================================================
deconv = Deconvolution(
model=copy.deepcopy(model)) # TODO: remove hook func in advance
probs, idx = deconv.forward(image.to(device))
for i in range(0, topk):
deconv.backward(idx=idx[i])
output = deconv.generate()
save_gradient('results/{}_deconv_{}.png'.format(classes[idx[i]], arch),
output)
print('[{:.5f}] {}'.format(probs[i], classes[idx[i]]))
# =========================================================================
print('Guided Backpropagation/Guided Grad-CAM')
# =========================================================================
gbp = GuidedBackPropagation(model=model)
probs, idx = gbp.forward(image.to(device))
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()
h, w, _ = feature.shape
region = cv2.resize(region, (w, h))[..., np.newaxis]
output = feature * region
save_gradient('results/{}_gbp_{}.png'.format(classes[idx[i]], arch),
feature)
save_gradient('results/{}_ggcam_{}.png'.format(classes[idx[i]], arch),
output)
print('[{:.5f}] {}'.format(probs[i], classes[idx[i]]))
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]]))
def demo1(image_paths, target_layer, arch, topk, output_dir, cuda, checkpoint,
distribute):
"""
Visualize model responses given multiple images
"""
device = get_device(cuda)
# Synset words
classes = get_classtable()
# Model from torchvision
model = models.__dict__[arch]()
model = model.cuda()
# print(model)
checkpoint = checkpoint + arch + '/model_best.pth.tar'
# print(checkpoint)
check_point = torch.load(checkpoint,
map_location=lambda storage, loc: storage.cuda(0))
distributed_model = (distribute > 0.5)
only_CAM = True
if distributed_model == True:
# create new OrderedDict that does not contain `module.`
from collections import OrderedDict
new_check_point = OrderedDict()
for k, v in check_point['state_dict'].items():
# name = k[7:] # remove `module.`
# name = k[9:] # remove `module.1.`
if k.startswith('module.1.'):
name = k[9:]
else:
name = k[7:]
new_check_point[name] = v
# load params
model.load_state_dict(new_check_point)
else:
model.load_state_dict(check_point['state_dict'])
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)
"""
Common usage:
1. Wrap your model with visualization classes defined in grad_cam.py
2. Run forward() with images
3. Run backward() with a list of specific classes
4. Run generate() to export results
"""
# =========================================================================
print("Vanilla Backpropagation:")
bp = BackPropagation(model=model)
probs, ids = bp.forward(images)
for i in range(topk):
# In this example, we specify the high confidence classes
bp.backward(ids=ids[:, [i]])
gradients = bp.generate()
# Save results as image files
for j in range(len(images)):
print("\t#{}: {} ({:.5f})".format(j, classes[ids[j, i]], probs[j,
i]))
if not only_CAM:
save_gradient(
filename=osp.join(
output_dir,
"{}-{}-vanilla-{}.png".format(j, arch,
classes[ids[j, i]]),
),
gradient=gradients[j],
)
# Remove all the hook function in the "model"
bp.remove_hook()
# =========================================================================
print("Deconvolution:")
deconv = Deconvnet(model=model)
_ = deconv.forward(images)
for i in range(topk):
deconv.backward(ids=ids[:, [i]])
gradients = deconv.generate()
for j in range(len(images)):
print("\t#{}: {} ({:.5f})".format(j, classes[ids[j, i]], probs[j,
i]))
if not only_CAM:
save_gradient(
filename=osp.join(
output_dir,
"{}-{}-deconvnet-{}.png".format(
j, arch, classes[ids[j, i]]),
),
gradient=gradients[j],
)
deconv.remove_hook()
# =========================================================================
print("Grad-CAM/Guided Backpropagation/Guided Grad-CAM:")
gcam = GradCAM(model=model)
_ = 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]])
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]))
# Guided Backpropagation
if not only_CAM:
save_gradient(
filename=osp.join(
output_dir,
"{}-{}-guided-{}.png".format(j, arch, classes[ids[j,
i]]),
),
gradient=gradients[j],
)
# Grad-CAM
save_gradcam(
filename=osp.join(
output_dir,
"{}-{}-{}.png".format(
# j, arch, target_layer, classes[ids[j, i]] image_path
osp.splitext(image_paths[j])[0],
arch,
target_layer),
),
gcam=regions[j, 0],
raw_image=raw_images[j],
)
# Guided Grad-CAM
if not only_CAM:
save_gradient(
filename=osp.join(
output_dir,
"{}-{}-guided_gradcam-{}-{}.png".format(
j, arch, target_layer, classes[ids[j, i]]),
),
gradient=torch.mul(regions, gradients)[j],
)
def demo1(image_paths, 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)
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)
"""
Common usage:
1. Wrap your model with visualization classes defined in grad_cam.py
2. Run forward() with images
3. Run backward() with a list of specific classes
4. Run generate() to export results
"""
# =========================================================================
print("Vanilla Backpropagation:")
bp = BackPropagation(model=model)
probs, ids = bp.forward(images)
for i in range(topk):
# In this example, we specify the high confidence classes
bp.backward(ids=ids[:, [i]])
gradients = bp.generate()
# Save results as image files
for j in range(len(images)):
print("\t#{}: {} ({:.5f})".format(j, classes[ids[j, i]], probs[j, i]))
save_gradient(
filename=osp.join(
output_dir,
"{}-{}-vanilla-{}.png".format(j, arch, classes[ids[j, i]]),
),
gradient=gradients[j],
)
# Remove all the hook function in the "model"
bp.remove_hook()
# =========================================================================
print("Deconvolution:")
deconv = Deconvnet(model=model)
_ = deconv.forward(images)
for i in range(topk):
deconv.backward(ids=ids[:, [i]])
gradients = deconv.generate()
for j in range(len(images)):
print("\t#{}: {} ({:.5f})".format(j, classes[ids[j, i]], probs[j, i]))
save_gradient(
filename=osp.join(
output_dir,
"{}-{}-deconvnet-{}.png".format(j, arch, classes[ids[j, i]]),
),
gradient=gradients[j],
)
deconv.remove_hook()
# =========================================================================
print("Grad-CAM/Guided Backpropagation/Guided Grad-CAM:")
gcam = GradCAM(model=model)
_ = 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]])
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]))
# Guided Backpropagation
save_gradient(
filename=osp.join(
output_dir,
"{}-{}-guided-{}.png".format(j, arch, classes[ids[j, i]]),
),
gradient=gradients[j],
)
# Grad-CAM
save_gradcam(
filename=osp.join(
output_dir,
"{}-{}-gradcam-{}-{}.png".format(
j, arch, target_layer, classes[ids[j, i]]
),
),
gcam=regions[j, 0],
raw_image=raw_images[j],
)
# Guided Grad-CAM
save_gradient(
filename=osp.join(
output_dir,
"{}-{}-guided_gradcam-{}-{}.png".format(
j, arch, target_layer, classes[ids[j, i]]
),
),
gradient=torch.mul(regions, gradients)[j],
)
def main(image_path, target_layer, arch, topk, cuda):
device = torch.device(
"cuda" if cuda and torch.cuda.is_available() else "cpu")
if cuda:
current_device = torch.cuda.current_device()
print("Running on the GPU:",
torch.cuda.get_device_name(current_device))
else:
print("Running on the CPU")
# 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 from torchvision
model = models.__dict__[arch](pretrained=True)
model.to(device)
model.eval()
# Image preprocessing
raw_image = cv2.imread(image_path)[..., ::-1]
raw_image = cv2.resize(raw_image, (224, ) * 2)
image = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])(raw_image).unsqueeze(0)
image = image.to(device)
"""
Common usage:
1. Wrap your model with visualization classes defined in grad_cam.py
2. Run forward() with an image
3. Run backward() with a specific class
4. Run generate() to export result
"""
# =========================================================================
print("Vanilla Backpropagation")
bp = BackPropagation(model=model)
predictions = bp.forward(image)
for i in range(topk):
print("[{:.5f}] {}".format(predictions[i][0],
classes[predictions[i][1]]))
bp.backward(idx=predictions[i][1])
gradient = bp.generate()
save_gradient(
"results/{}-vanilla-{}.png".format(arch,
classes[predictions[i][1]]),
gradient,
)
# Remove all the hook function in the "model"
bp.remove_hook()
# =========================================================================
print("Deconvolution")
deconv = Deconvnet(model=model)
_ = deconv.forward(image)
for i in range(topk):
print("[{:.5f}] {}".format(predictions[i][0],
classes[predictions[i][1]]))
deconv.backward(idx=predictions[i][1])
gradient = deconv.generate()
save_gradient(
"results/{}-deconvnet-{}.png".format(arch,
classes[predictions[i][1]]),
gradient,
)
deconv.remove_hook()
# =========================================================================
print("Grad-CAM/Guided Backpropagation/Guided Grad-CAM")
gcam = GradCAM(model=model)
_ = gcam.forward(image)
gbp = GuidedBackPropagation(model=model)
_ = gbp.forward(image)
for i in range(topk):
print("[{:.5f}] {}".format(predictions[i][0],
classes[predictions[i][1]]))
# Grad-CAM
gcam.backward(idx=predictions[i][1])
region = gcam.generate(target_layer=target_layer)
save_gradcam(
"results/{}-gradcam-{}-{}.png".format(arch, target_layer,
classes[predictions[i][1]]),
region,
raw_image,
)
# Guided Backpropagation
gbp.backward(idx=predictions[i][1])
gradient = gbp.generate()
# Guided Grad-CAM
h, w, _ = gradient.shape
region = cv2.resize(region, (w, h))[..., np.newaxis]
output = gradient * region
save_gradient(
"results/{}-guided-{}.png".format(arch,
classes[predictions[i][1]]),
gradient,
)
save_gradient(
"results/{}-guided_gradcam-{}-{}.png".format(
arch, target_layer, classes[predictions[i][1]]),
output,
)
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.36',
'input_size': 224
},
'vgg19_bn': {
'target_layer': 'features.52',
'input_size': 224
},
'inception_v3': {
'target_layer': 'Mixed_7c',
'input_size': 299
},
'densenet201': {
'target_layer': 'features.denseblock4',
'input_size': 224
},
# Add your model
}.get(arch)
cuda = cuda and torch.cuda.is_available()
if cuda:
current_device = torch.cuda.current_device()
print('Running on the GPU:', torch.cuda.get_device_name(current_device))
else:
print('Running on the CPU')
# 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)
# Image
raw_image = cv2.imread(image_path)[..., ::-1]
raw_image = cv2.resize(raw_image, (CONFIG['input_size'], ) * 2)
image = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])(raw_image)
if cuda:
model.cuda()
image = image.cuda()
# =========================================================================
print('Grad-CAM')
# =========================================================================
gcam = GradCAM(model=model)
probs, idx = gcam.forward(to_var(image))
for i in range(0, topk):
gcam.backward(idx=idx[i])
output = gcam.generate(target_layer=CONFIG['target_layer'])
save_gradcam('results/{}_gcam_{}.png'.format(classes[idx[i]], arch), output, raw_image) # NOQA
print('[{:.5f}] {}'.format(probs[i], classes[idx[i]]))
# =========================================================================
print('Vanilla Backpropagation')
# =========================================================================
bp = BackPropagation(model=model)
probs, idx = bp.forward(to_var(image))
for i in range(0, topk):
bp.backward(idx=idx[i])
output = bp.generate()
save_gradient('results/{}_bp_{}.png'.format(classes[idx[i]], arch), output) # NOQA
print('[{:.5f}] {}'.format(probs[i], classes[idx[i]]))
# =========================================================================
print('Deconvolution')
# =========================================================================
deconv = Deconvolution(model=copy.deepcopy(model)) # TODO: remove hook func in advance
probs, idx = deconv.forward(to_var(image))
for i in range(0, topk):
deconv.backward(idx=idx[i])
output = deconv.generate()
save_gradient('results/{}_deconv_{}.png'.format(classes[idx[i]], arch), output) # NOQA
print('[{:.5f}] {}'.format(probs[i], classes[idx[i]]))
# =========================================================================
print('Guided Backpropagation/Guided Grad-CAM')
# =========================================================================
gbp = GuidedBackPropagation(model=model)
probs, idx = gbp.forward(to_var(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()
h, w, _ = feature.shape
region = cv2.resize(region, (w, h))[..., np.newaxis]
output = feature * region
save_gradient('results/{}_gbp_{}.png'.format(classes[idx[i]], arch), feature) # NOQA
save_gradient('results/{}_ggcam_{}.png'.format(classes[idx[i]], arch), output) # NOQA
print('[{:.5f}] {}'.format(probs[i], classes[idx[i]]))
def main():
root_path = '/media/palm/Unimportant/pdr2018/typesep_validate/Tomato/'
image_name = 'c9ebc74c2177ce60a8230855333fb9e7.jpg'
folder_name = '14_Tomato_Spider_Mite_Damage_Serious'
# image_path = root_path+'/14_Tomato_Spider_Mite_Damage_Serious/1c0f1ae1374d01c2933069232735a331.jpg'
image_path = os.path.join(root_path, folder_name, image_name)
topk = 1
cuda = 'cuda'
arch = 'densenet201'
CONFIG = {
'resnet152': {
'target_layer': 'layer4.2',
'input_size': 224
},
'vgg19': {
'target_layer': 'features.36',
'input_size': 224
},
'vgg19_bn': {
'target_layer': 'features.52',
'input_size': 224
},
'inception_v3': {
'target_layer': 'Mixed_7c',
'input_size': 299
},
'densenet201': {
'target_layer': 'features.denseblock4',
'input_size': 224
},
# Add your model
}.get(arch)
a, b, c = getlookup()
device = torch.device(
'cuda' if cuda and torch.cuda.is_available() else 'cpu')
if cuda:
current_device = torch.cuda.current_device()
print('Running on the GPU:',
torch.cuda.get_device_name(current_device))
else:
print('Running on the CPU')
# Synset words
classes = c['Tomato']
# Model
model = getmodel(20)
checkpoint = torch.load('checkpoint/try_4_densesep-Tomatotemp.t7')
model.load_state_dict(checkpoint['net'])
model.to('cuda')
model.eval()
# Image
raw_image = cv2.imread(image_path)[..., ::-1]
raw_image = cv2.resize(raw_image, (CONFIG['input_size'], ) * 2)
image = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
)
])(raw_image).unsqueeze(0)
# =========================================================================
print('Grad-CAM')
# =========================================================================
gcam = GradCAM(model=model)
probs, idx = gcam.forward(image.to(device))
for i in range(0, topk):
gcam.backward(idx=idx[i])
output = gcam.generate(target_layer=CONFIG['target_layer'])
save_gradcam(
'results/{}_{}_gcam_{}.png'.format(image_name, classes[idx[i]],
arch), output, raw_image)
print('[{:.5f}] {}'.format(probs[i], classes[idx[i]]))
# =========================================================================
print('Vanilla Backpropagation')
# =========================================================================
bp = BackPropagation(model=model)
probs, idx = bp.forward(image.to(device))
for i in range(0, topk):
bp.backward(idx=idx[i])
output = bp.generate()
save_gradient(
'results/{}_{}_bp_{}.png'.format(image_name, classes[idx[i]],
arch), output)
print('[{:.5f}] {}'.format(probs[i], classes[idx[i]]))
# =========================================================================
print('Deconvolution')
# =========================================================================
deconv = Deconvolution(
model=copy.deepcopy(model)) # TODO: remove hook func in advance
probs, idx = deconv.forward(image.to(device))
for i in range(0, topk):
deconv.backward(idx=idx[i])
output = deconv.generate()
save_gradient(
'results/{}_{}_deconv_{}.png'.format(image_name, classes[idx[i]],
arch), output)
print('[{:.5f}] {}'.format(probs[i], classes[idx[i]]))
# =========================================================================
print('Guided Backpropagation/Guided Grad-CAM')
# =========================================================================
gbp = GuidedBackPropagation(model=model)
probs, idx = gbp.forward(image.to(device))
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()
h, w, _ = feature.shape
region = cv2.resize(region, (w, h))[..., np.newaxis]
output = feature * region
save_gradient(
'results/{}_{}_gbp_{}.png'.format(image_name, classes[idx[i]],
arch), feature)
save_gradient(
'results/{}_{}_ggcam_{}.png'.format(image_name, classes[idx[i]],
arch), output)
print('[{:.5f}] {}'.format(probs[i], classes[idx[i]]))
def visualization(image_paths, model_name, model_path, target_layer, arch,
topk, output_dir, cuda):
"""
Visualize model responses given multiple images
"""
device = get_device(cuda)
# Synset words
classes = get_classtable()
# Model
kwargs = {}
module = import_module(model_name)
model = module.make_model().to(device)
model.load_state_dict(torch.load(model_path, **kwargs), strict=False)
#print(model)
model.to(device)
model.eval()
# Images
images = []
raw_images = []
print("Images:")
path = gb.glob(image_paths[0] + '/*.jpg')
index = 0
for img in path:
print("\t#{}: {}".format(index, img))
image, raw_image = preprocess(img)
images.append(image)
raw_images.append(raw_image)
index = index + 1
images = torch.stack(images).to(device)
"""
Common usage:
1. Wrap your model with visualization classes defined in grad_cam.py
2. Run forward() with images
3. Run backward() with a list of specific classes
4. Run generate() to export results
"""
# =========================================================================
print("Vanilla Backpropagation:")
bp = BackPropagation(model=model)
probs, ids = bp.forward(images)
#print(probs, ids)
for i in range(topk):
# In this example, we specify the high confidence classes
bp.backward(ids=ids[:, [i]])
gradients = bp.generate()
# Save results as image files
for j in range(len(images)):
print("\t#{}: {} ({:.5f})".format(j, ids[j, i], probs[j, i]))
save_gradient(
filename=osp.join(
output_dir,
#"{}-{}-vanilla-{}.png".format(j, arch, classes[ids[j, i]]),
"{}-{}-vanilla-{}.png".format(j, arch, ids[j, i]),
),
gradient=gradients[j],
)
# Remove all the hook function in the "model"
bp.remove_hook()
# =========================================================================
print("Deconvolution:")
deconv = Deconvnet(model=model)
_ = deconv.forward(images)
for i in range(topk):
deconv.backward(ids=ids[:, [i]])
gradients = deconv.generate()
for j in range(len(images)):
print("\t#{}: {} ({:.5f})".format(j, ids[j, i], probs[j, i]))
save_gradient(
filename=osp.join(
output_dir,
#"{}-{}-deconvnet-{}.png".format(j, arch, classes[ids[j, i]]),
"{}-{}-deconvnet-{}.png".format(j, arch, ids[j, i]),
),
gradient=gradients[j],
)
deconv.remove_hook()
# =========================================================================
print("Grad-CAM/Guided Backpropagation/Guided Grad-CAM:")
gcam = GradCAM(model=model)
_ = gcam.forward(images)
gbp = GuidedBackPropagation(model=model)
_ = gbp.forward(images)
for i in range(topk):
# Guided Backpropagation
gbp.backward(ids=ids[:, [i]])
#print(ids[:, [i]])
gradients = gbp.generate()
# Grad-CAM
gcam.backward(ids=ids[:, [i]])
regions = gcam.generate(target_layer=target_layer)
#print(regions)
for j in range(len(images)):
print("\t#{}: {} ({:.5f})".format(j, ids[j, i], probs[j, i]))
# Guided Backpropagation
save_gradient(
filename=osp.join(
output_dir,
#"{}-{}-guided-{}.png".format(j, arch, classes[ids[j, i]]),
"{}-{}-guided-{}.png".format(j, arch, ids[j, i]),
),
gradient=gradients[j],
)
# Grad-CAM
save_gradcam(
filename=osp.join(
output_dir,
"{}-{}-gradcam-{}-{}.png".format(
#j, arch, target_layer, classes[ids[j, i]]
j,
arch,
target_layer,
ids[j, i]),
),
gcam=regions[j, 0],
raw_image=raw_images[j],
)
#print(regions.shape)
# Guided Grad-CAM
save_gradient(
filename=osp.join(
output_dir,
"{}-{}-guided_gradcam-{}-{}.png".format(
#j, arch, target_layer, classes[ids[j, i]]
j,
arch,
target_layer,
ids[j, i]),
),
gradient=torch.mul(regions, gradients)[j],
)
# =========================================================================
print("Channel Visialization:")
gcam = GradCAM(model=model)
_ = gcam.forward(images)
feature_map = gcam.channel_visualization(target_layer=target_layer)
draw_features(4, 4, feature_map, output_dir, target_layer)
def demo1(image_paths, 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)
model.to(device)
model.eval()
# Images
images, raw_images = load_images(image_paths)
images = torch.stack(images).to(device)
"""
Common usage:
1. Wrap your model with visualization classes defined in grad_cam.py
2. Run forward() with images
3. Run backward() with a list of specific classes
4. Run generate() to export results
"""
# =========================================================================
print("Vanilla Backpropagation:")
bp = BackPropagation(model=model)
probs, ids = bp.forward(images) # sorted
for i in range(topk):
bp.backward(ids=ids[:, [i]])
gradients = bp.generate()
# Save results as image files
for j in range(len(images)):
print("\t#{}: {} ({:.5f})".format(j, classes[ids[j, i]], probs[j,
i]))
save_gradient(
filename=osp.join(
output_dir,
"{}-{}-vanilla-{}.png".format(j, arch, classes[ids[j, i]]),
),
gradient=gradients[j],
)
# Remove all the hook function in the "model"
bp.remove_hook()
# =========================================================================
print("Deconvolution:")
deconv = Deconvnet(model=model)
_ = deconv.forward(images)
for i in range(topk):
deconv.backward(ids=ids[:, [i]])
gradients = deconv.generate()
for j in range(len(images)):
print("\t#{}: {} ({:.5f})".format(j, classes[ids[j, i]], probs[j,
i]))
save_gradient(
filename=osp.join(
output_dir,
"{}-{}-deconvnet-{}.png".format(j, arch, classes[ids[j,
i]]),
),
gradient=gradients[j],
)
deconv.remove_hook()
# =========================================================================
print("Grad-CAM/Guided Backpropagation/Guided Grad-CAM:")
gcam = GradCAM(model=model)
_ = 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]])
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]))
# Guided Backpropagation
save_gradient(
filename=osp.join(
output_dir,
"{}-{}-guided-{}.png".format(j, arch, classes[ids[j, i]]),
),
gradient=gradients[j],
)
# Grad-CAM
save_gradcam(
filename=osp.join(
output_dir,
"{}-{}-gradcam-{}-{}.png".format(j, arch, target_layer,
classes[ids[j, i]]),
),
gcam=regions[j, 0],
raw_image=raw_images[j],
)
# Guided Grad-CAM
save_gradient(
filename=osp.join(
output_dir,
"{}-{}-guided_gradcam-{}-{}.png".format(
j, arch, target_layer, classes[ids[j, i]]),
),
gradient=torch.mul(regions, gradients)[j],
)
def calc_cam(output_path, image_path, model, transform, arch, topk, cuda):
results = []
CONFIG = {
'resnet152': {
'target_layer': 'layer4.2',
'input_size': 224
},
'vgg19': {
'target_layer': 'features.36',
'input_size': 224
},
'vgg19_bn': {
'target_layer': 'features.52',
'input_size': 224
},
'inception_v3': {
'target_layer': 'Mixed_7c',
'input_size': 299
},
'densenet201': {
'target_layer': 'features.denseblock4',
'input_size': 224
},
# Add your model
}.get(arch)
device = torch.device(
'cuda' if cuda and torch.cuda.is_available() else 'cpu')
if cuda:
current_device = torch.cuda.current_device()
print('Running on the GPU:',
torch.cuda.get_device_name(current_device))
else:
print('Running on the CPU')
# Model
model.to(device)
model.eval()
# Image
pil_image = Image.open(image_path)
#raw_image = cv2.imread(image_path)[..., ::-1]
#raw_image = cv2.resize(raw_image, (CONFIG['input_size'], ) * 2)
raw_image = transform(pil_image)
image = raw_image.unsqueeze(0)
raw_image = raw_image.numpy().transpose(1, 2, 0)
# =========================================================================
print('Grad-CAM')
# =========================================================================
gcam = GradCAM(model=model)
probs, idx = gcam.forward(image.to(device))
for i in range(0, topk):
gcam.backward(idx=idx[i])
output = gcam.generate(target_layer=CONFIG['target_layer'])
result_name = '{}_gcam_{}.png'.format(idx[i].cpu().numpy(), arch)
save_gradcam(os.path.join(output_path, result_name), output, raw_image)
results.append(os.path.join(output_path, result_name))
print('[{:.5f}] {}'.format(probs[i], idx[i].cpu().numpy()))
# =========================================================================
print('Vanilla Backpropagation')
# =========================================================================
bp = BackPropagation(model=model)
probs, idx = bp.forward(image.to(device))
for i in range(0, topk):
bp.backward(idx=idx[i])
output = bp.generate()
result_name = '{}_bp_{}.png'.format(idx[i].cpu().numpy(), arch)
save_gradient(os.path.join(output_path, result_name), output)
results.append(os.path.join(output_path, result_name))
print('[{:.5f}] {}'.format(probs[i], idx[i].cpu().numpy()))
# # =========================================================================
# print('Deconvolution')
# # =========================================================================
# deconv = Deconvolution(model=copy.deepcopy(model)) # TODO: remove hook func in advance
# probs, idx = deconv.forward(image.to(device))
#
# for i in range(0, topk):
# deconv.backward(idx=idx[i])
# output = deconv.generate()
#
# result_name = '{}_deconv_{}.png'.format(idx[i].cpu().numpy(), arch)
# save_gradient(os.path.join(output_path, result_name), output)
# results.append(os.path.join(output_path, result_name))
# print('[{:.5f}] {}'.format(probs[i], idx[i].cpu().numpy()))
# =========================================================================
print('Guided Backpropagation/Guided Grad-CAM')
# =========================================================================
gbp = GuidedBackPropagation(model=model)
probs, idx = gbp.forward(image.to(device))
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()
h, w, _ = feature.shape
region = cv2.resize(region, (w, h))[..., np.newaxis]
output = feature * region
result_name = '{}_gbp_{}.png'.format(idx[i].cpu().numpy(), arch)
save_gradient(os.path.join(output_path, result_name), feature)
results.append(os.path.join(output_path, result_name))
result_name = '{}_ggcam_{}.png'.format(idx[i].cpu().numpy(), arch)
save_gradient(os.path.join(output_path, result_name), output)
results.append(os.path.join(output_path, result_name))
print('[{:.5f}] {}'.format(probs[i], idx[i].cpu().numpy()))
return (results, probs)