input
torch.Size([64, 3, 224, 224])
target
torch.Size([64])
inputforward167
after expand
torch.Size([1, 64, 3, 224, 224])
input185after permute
torch.Size([64, 3, 1, 224, 224])
this is the code
def forward(self, input):
print("inputforward167")
print (input.shape)
#print(input)
#sample_len = (3 if self.modality == "RGB" else 2) * self.new_length
if self.modality == 'RGBDiff':
sample_len = 3 * self.new_length
input = self._get_diff(input)
print("input157")
print(input.shape)
if not self.slow_testing:#self.test_mode:
input=input.view((-1,self.new_length,3)+ input.size()[-2:])
print("input181")
print(input.shape)
input=input.permute(1,2,0,3,4).contiguous()
print("input185")
print(input.shape)
else:
input=input.view((-1,3,self.new_length)+ input.size()[-2:])
print("inputbase")
print(input.shape)
# input = input.view(input.size(0), -1)
# print (input.shape)
base_out = self.base_model(input)
print("base193")
print(base_out.shape)
#base_out=F.dropout(base_out,p=0.5)
if self.gtsn:
base_out=base_out.view(base_out.shape[0]/self.num_segments,-1)
print("base198")
print(base_out.shape)
if self.dropout > 0:
base_out = self.new_fc(base_out)
print("base205")
print(base_out.shape)
if not self.gtsn:
if self.reshape:
base_out = base_out.view((-1, self.num_segments) + base_out.size()[1:])
print("base213")
print(base_out.shape)
base_out = self.consensus(base_out).squeeze(1)
print("base220")
print(base_out.shape)
if self.slow_testing:#apply_softmax:
base_out = self.softmax(base_out)
print("base_out")
print(base_out.shape)
return base_out#.squeeze(1)
else:
return base_out
and i’m using resnet50 as base model
self.base_model= I3ResNet(resnet50, self.new_length,num_class,test_mode=test_mode,num_segments=self.num_segments,fast_implementation=fast_implementation)
self.new_fc1 = nn.Linear(3224224, 2048)
self.new_fc=nn.Linear(2048,num_class)
