def forward(self, input: Tensor) -> Generator[pop, Tensor, Tensor]: # type: ignore
skipped_input = yield pop('skip')
skip_shape = skipped_input.shape[2:]
input_shape = input.shape[2:]
if input_shape != skip_shape:
pad = [d2 - d1 for d1, d2 in zip(input_shape, skip_shape)]
pad = sum([[0, p] for p in pad[::-1]], [])
input = F.pad(input, pad=pad)
output = torch.cat((input, skipped_input), dim=1)
return output
def forward(self, input: Tensor) -> Tensor: # type: ignore
identity = yield pop('identity')
if self.downsample is not None:
identity = self.downsample(identity)
return input + identity
def forward(self, input):
foo = yield pop('foo')
return foo
def forward(self, input):
yield pop('foo')
def forward(self, input):
skip = yield pop('skip')
return input + skip
def forward(self, input):
bar = yield pop('bar')
return input + bar
def forward(self, input):
none = yield pop('none')
assert none is None
return input
def forward(self, input):
skip_1to3 = yield pop('1to3')
output = self.conv(input) + skip_1to3
return output
def forward(self, input):
identity = yield pop('identity')
out = input
if self.stride == 1:
out = input + self.shortcut(identity)
return out
def forward(self, input: torch.Tensor):
skip = yield pop("skip")
if skip is not None:
input = torch.cat([input, skip], dim=1)
return input
def forward(self, input):
identity = yield pop('identity')
return input + self.shortcut(identity)