def insights(x: CaptumInterpretation, inp_data, debug=True):
_baseline_func = lambda o: o * 0
_get_vocab = lambda vocab: list(map(str, vocab)) if isinstance(
vocab[0], bool) else vocab
dl = x.dls.test_dl(L(inp_data), with_labels=True, bs=4)
normalize_func = next(
(func for func in dl.after_batch if type(func) == Normalize), noop)
# captum v0.3 expects tensors without the batch dimension.
if hasattr(normalize_func, 'mean'):
if normalize_func.mean.ndim == 4: normalize_func.mean.squeeze_(0)
if hasattr(normalize_func, 'std'):
if normalize_func.std.ndim == 4: normalize_func.std.squeeze_(0)
visualizer = AttributionVisualizer(
models=[x.model],
score_func=lambda o: torch.nn.functional.softmax(o, 1),
classes=_get_vocab(dl.vocab),
features=[
ImageFeature(
"Image",
baseline_transforms=[_baseline_func],
input_transforms=[normalize_func],
)
],
dataset=x._formatted_data_iter(dl, normalize_func))
visualizer.render(debug=debug)
def visualize(self,inp_data,debug=True):
_baseline_func= lambda o: o*0
_get_vocab = lambda vocab: list(map(str,vocab)) if isinstance(vocab[0],bool) else vocab
dl = self.dls.test_dl(L(inp_data),with_labels=True, bs=4)
normalize_func= next((func for func in dl.after_batch if type(func)==Normalize),noop)
visualizer = AttributionVisualizer(
models=[self.model],
score_func=lambda o: torch.nn.functional.softmax(o, 1),
classes=_get_vocab(dl.vocab),
features=[
ImageFeature(
"Image",
baseline_transforms=[_baseline_func],
input_transforms=[normalize_func],
)
],
dataset=self._formatted_data_iter(dl,normalize_func)
)
visualizer.render(debug=debug)
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.render()
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.render(debug=True)