def main():
normalize = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
model = get_pretrained_model()
visualizer = AttributionVisualizer(
models=[model],
score_func=lambda o: torch.nn.functional.softmax(o, 1),
classes=get_classes(),
features=[
ImageFeature(
"Photo",
baseline_transforms=[baseline_func],
input_transforms=[normalize],
)
],
dataset=formatted_data_iter(),
)
visualizer.serve(debug=True)
download=True,
transform=transforms.ToTensor())
dataloader = iter(
torch.utils.data.DataLoader(dataset,
batch_size=4,
shuffle=False,
num_workers=2))
while True:
images, labels = next(dataloader)
yield Batch(inputs=images, labels=labels)
if __name__ == "__main__":
normalize = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
model = get_pretrained_model()
visualizer = AttributionVisualizer(
models=[model],
score_func=lambda o: torch.nn.functional.softmax(o, 1),
classes=get_classes(),
features=[
ImageFeature(
"Photo",
baseline_transforms=[baseline_func],
input_transforms=[normalize],
)
],
dataset=formatted_data_iter(),
)
visualizer.serve()
download=True,
transform=transforms.ToTensor())
dataloader = iter(
torch.utils.data.DataLoader(dataset,
batch_size=4,
shuffle=False,
num_workers=2))
while True:
images, labels = next(dataloader)
yield Batch(inputs=images, labels=labels)
if __name__ == "__main__":
normalize = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
model = get_pretrained_model()
visualizer = AttributionVisualizer(
models=[model],
score_func=lambda o: torch.nn.functional.softmax(o, 1),
classes=get_classes(),
features=[
ImageFeature(
"Photo",
baseline_transforms=[baseline_func],
input_transforms=[normalize],
)
],
dataset=formatted_data_iter(),
)
visualizer.serve(debug=True)
dataset_sizes = {x: len(image_datasets[x]) for x in stages}
class_names = image_datasets[stages[0]].classes
# Setup the device to run the computations
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print('Device::', device)
# Load the best model from file
model_ = torch.load(model_file)
_ = model_.to(device).eval()
visualizer = AttributionVisualizer(
models=[model_],
score_func=lambda o: torch.nn.functional.softmax(o, 1),
classes=class_names,
features=[
ImageFeature("Photo",
baseline_transforms=[baseline_func],
input_transforms=[
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
])
],
dataset=formatted_data_iter(dataloaders['test']),
)
visualizer.serve(port=8600)