def forward(self, v, v2=None):
"""v: tower to build. v2: image of tower we have built so far"""
# insert batch if it is not already there
if len(v.shape) == 3:
v = np.expand_dims(v, 0)
inserted_batch = True
if v2 is not None:
assert len(v2.shape) == 3
v2 = np.expand_dims(v2, 0)
elif len(v.shape) == 4:
inserted_batch = False
pass
else:
assert False, "v has the shape %s" % (str(v.shape))
if v2 is None: v2 = np.zeros(v.shape)
v = np.concatenate((v, v2), axis=3)
v = np.transpose(v, (0, 3, 1, 2))
assert v.shape == (v.shape[0], 6, self.inputImageDimension,
self.inputImageDimension)
v = variable(v, cuda=self.CUDA).float()
window = int(self.inputImageDimension / self.resizedDimension)
v = F.avg_pool2d(v, (window, window))
#showArrayAsImage(np.transpose(v.data.numpy()[0,:3,:,:],[1,2,0]))
v = self.encoder(v)
if inserted_batch:
return v.view(-1)
else:
return v
def forward(self, v):
assert len(v) == self.inputImageDimension*self.inputImageDimension
floatOnlyTask = list(map(float, v))
reshaped = [floatOnlyTask[i:i + self.inputImageDimension]
for i in range(0, len(floatOnlyTask), self.inputImageDimension)]
v = variable(reshaped).float()
# insert channel and batch
v = torch.unsqueeze(v, 0)
v = torch.unsqueeze(v, 0)
v = maybe_cuda(v, next(self.parameters()).is_cuda)/256.
window = int(self.inputImageDimension/self.resizedDimension)
v = F.avg_pool2d(v, (window,window))
v = self.encoder(v)
return v.view(-1)