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import torch
def fasterrcnn_reshape_transform(x):
target_size = x['pool'].size()[-2 : ]
activations = []
for key, value in x.items():
activations.append(torch.nn.functional.interpolate(torch.abs(value), target_size, mode='bilinear'))
activations = torch.cat(activations, axis=1)
return activations
def swinT_reshape_transform(tensor, height=7, width=7):
result = tensor.reshape(tensor.size(0),
height, width, tensor.size(2))
# Bring the channels to the first dimension,
# like in CNNs.
result = result.transpose(2, 3).transpose(1, 2)
return result
def vit_reshape_transform(tensor, height=14, width=14):
result = tensor[:, 1:, :].reshape(tensor.size(0),
height, width, tensor.size(2))
# Bring the channels to the first dimension,
# like in CNNs.
result = result.transpose(2, 3).transpose(1, 2)
return result
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