def classify_many(self, network: Network, points: torch.tensor, out: torch.tensor): tus.check_tensors(points=(points, (('batch', None), ('input_dim', network.input_dim)), torch.float32), out=(out, (('batch', points.shape[0]), ('output_dim', network.output_dim)), torch.float32)) network.eval() with torch.no_grad(): result = network(points) out.copy_(result)
def classify_many(self, network: Network, points: torch.tensor, out: torch.tensor): network.eval() with torch.no_grad(): result = network(points, self.recurrent_times, None, self.input_times) out.copy_(result)
def forward(self, input: Tensor) -> Tensor: #input = rotate.forward(input) w, h = input.shape[-1], input.shape[-2] im_num = input.detach().numpy() for i in range(4): img = im_num[i][0] for j in range(w): for k in range(h): img[j][k] = int(img[j][k]*self.margin) # pyplot.imshow(im_num[0][0]) # pyplot.show() with torch.no_grad(): input.copy_(torch.tensor(im_num)) x = self._conv_forward(input, self.weight, self.bias) return x