def multiple_saliency_maps(model, tensors, start, num, k=0, guide = True):
backprop = Backprop(model)
for i in range(start,start+num):
cur_image = tensors[i,:,:,:].unsqueeze(0).requires_grad_(requires_grad=True)
backprop.visualize(cur_image, k, guided=guide)
plt.show()
print("Range: ", start, " - ", start+num)
start += num
return start
def test_visualize_calls_calculate_gradients_twice(mocker, model):
backprop = Backprop(model)
mocker.spy(backprop, 'calculate_gradients')
top_class = 5
target_class = 5
input_ = torch.zeros([1, 3, 224, 224])
make_expected_gradient_target(top_class)
make_mock_output(mocker, model, target_class)
backprop.visualize(input_, target_class, use_gpu=True)
assert backprop.calculate_gradients.call_count == 2
def di_saliency():
hockey_path_violence = '/media/david/datos/Violence DATA/HockeyFights/frames/violence'
hockey_path_noviolence = '/media/david/datos/Violence DATA/HockeyFights/frames/nonviolence'
datasetAll, labelsAll, numFramesAll = createDataset(hockey_path_violence, hockey_path_noviolence) #ordered
print(len(datasetAll), len(labelsAll), len(numFramesAll))
input_size = 224
data_transforms = createTransforms(input_size)
dataset_source = "frames"
debugg_mode = False
avgmaxDuration = 1.66
interval_duration = 0.3
numDiPerVideos = 1
batch_size = 1
num_workers = 1
# model = torch.load('models/alexnet-frames-Finetuned:True-1di-tempMaxPool-OnPlateau.tar')
path = os.path.join('models','alexnet-frames-Finetuned:True-1di-tempMaxPool-OnPlateau.tar')
# model, input_size = initialize_model( model_name='alexnet', num_classes=2, feature_extract=False, numDiPerVideos=1, joinType='OnPlateau', use_pretrained=True)
# model = load_checkpoint(model,'models/alexnet-frames-Finetuned:True-1di-tempMaxPool-OnPlateau.tar')
# model.load_state_dict(torch.load(path))
model = torch.load(path)
model.cuda()
backprop = Backprop(model)
image_datasets = {
"train": ViolenceDatasetVideos( dataset=datasetAll, labels=labelsAll, spatial_transform=data_transforms["train"], source=dataset_source,
interval_duration=interval_duration,difference=3, maxDuration=avgmaxDuration, nDynamicImages=numDiPerVideos, debugg_mode=debugg_mode, ),
"test": ViolenceDatasetVideos( dataset=datasetAll, labels=labelsAll, spatial_transform=data_transforms["test"], source=dataset_source,
interval_duration=interval_duration, difference=3, maxDuration=avgmaxDuration, nDynamicImages=numDiPerVideos, debugg_mode=debugg_mode, )
}
dataloaders_dict = {
"train": torch.utils.data.DataLoader( image_datasets["train"], batch_size=batch_size, shuffle=False, num_workers=num_workers, ),
"test": torch.utils.data.DataLoader( image_datasets["test"], batch_size=batch_size, shuffle=False, num_workers=num_workers, ),
}
count = 0
max_plots = 5
for inputs, labels in dataloaders_dict["test"]:
count += 1
if count > max_plots:
break
print('*' * 12)
print('inputs size: ',inputs.size())
# inputs = inputs.permute(1, 0, 2, 3, 4)
inputs = inputs.cuda()
# labels = labels.to(self.device)
backprop.visualize(inputs, target_class=None, guided=False, use_gpu=True, di=True)
def test_visualize_passes_gpu_flag(mocker, model):
backprop = Backprop(model)
mocker.spy(backprop, 'calculate_gradients')
top_class = 5
target_class = 5
input_ = torch.zeros([1, 3, 224, 224])
make_expected_gradient_target(top_class)
make_mock_output(mocker, model, target_class)
backprop.visualize(input_, target_class, use_gpu=True)
_, _, kwargs = backprop.calculate_gradients.mock_calls[0]
assert kwargs['use_gpu']
def visualise_cnn(model_name, image, class_label, model_path=None, title=None):
if model_path is None:
model = get_model(model_name)
else:
model = get_model(model_name)
model = load_model(model, model_path)
model.eval()
backprop = Backprop(model)
# Transform the input image to a tensor
img = apply_transforms(image)
# Set a target class from ImageNet task: 24 in case of great gray owl
imagenet = ImageNetIndex()
target_class = imagenet[class_label]
# Ready to roll!
backprop.visualize(img, target_class, guided=True, title=title)
def saliency():
"""### 1. Load an image"""
buho = 'images/great_grey_owl.jpg'
di1 = 'images/1.png'
image = load_image(buho)
# image = load_image(buho)
# plt.imshow(image)
# plt.title('Original image'+str(type(image)))
# plt.axis('off');
# plt.show()
"""### 2. Load a pre-trained Model"""
model = models.alexnet(pretrained=True)
# model = torch.load('/content/alexnet-frames-Finetuned:False-1di-tempMaxPool-OnPlateau.tar')
"""### 3. Create an instance of Backprop with the model"""
backprop = Backprop(model)
"""### 4. Visualize saliency maps"""
# Transform the input image to a tensor
owl = apply_transforms(image)
# print(owl.size()) #torch.Size([/1, 3, 224, 224])
# input_size = 224
# data_transforms = createTransforms(input_size)
# owl = data_transforms['test'](image)
# owl = owl.unsqueeze(dim=0)
# owl = owl.unsqueeze(dim=0)
# owl = owl.permute(1, 0, 2, 3, 4)
# print(owl.size())
# Set a target class from ImageNet task: 24 in case of great gray owl
target_class = 24
# Ready to roll!
backprop.visualize(owl, target_class=target_class, guided=True, use_gpu=True)
NIH_CXR_BASE = CXR_BASE.joinpath("nih/v1").resolve()
test_df = pd.read_csv("~/cxr-jingyi/Age/NIH_test_2500.csv")
path1 = test_df.iloc[0]['path']
path1 = NIH_CXR_BASE.joinpath(path1).resolve()
import matplotlib.pyplot as plt
from flashtorch.utils import apply_transforms, load_image
from flashtorch.saliency import Backprop
image = load_image(str(path1))
plt.imshow(image)
model = MobileNet(16)
checkpoint = torch.load(
'/home/jingyi/cxr-jingyi/Age/result/supervised/model_best.pth.tar')
model.load_state_dict(checkpoint['state_dict'])
backprop = Backprop(model)
# Transform the input image to a tensor
owl = apply_transforms(image)
# Set a target class from ImageNet task: 24 in case of great gray owl
target_class = 16
# Ready to roll!
backprop.visualize(owl, target_class, guided=True)
def visualize_helper(model_module, tensor=img, k=854):
model = model_module(pretrained=True).float()
backprop = Backprop(model)
backprop.visualize(tensor, k, guided=True)
import matplotlib.pyplot as plt
import torchvision.models as models
from flashtorch.utils import load_image
from flashtorch.saliency import Backprop
from flashtorch.utils import apply_transforms
image = load_image("G:\EEGNet/test/test.jpg")
plt.imshow(image)
net = models.vgg16(pretrained=1)
backprop = Backprop(net)
input_ = apply_transforms(image)
target_class = 24
backprop.visualize(input_, target_class, guided=True)
import os # NOQA: E402
os.environ["CUDA_VISIBLE_DEVICES"] = "1" # NOQA: E402
import matplotlib.pyplot as plt
import torchvision.models as models
from flashtorch.utils import apply_transforms, load_image
from flashtorch.saliency import Backprop
### 1. Load a pre-trained Model
# model = models.alexnet(pretrained=True)
model = models.vgg16(pretrained=True)
### 2. Create an instance of Backprop with the model
backprop = Backprop(model)
path = './figure'
path_names = os.listdir(path)
for i in range(len(path_names)):
peacock = apply_transforms(load_image(os.path.join(path, path_names[i])))
backprop.visualize(peacock, None, guided=True, use_gpu=True)
plt.show()
def visualize_saliency(model, tensor, k=1, guide=True):
backprop = Backprop(model)
backprop.visualize(tensor, k, guided=guide)
plt.show()
