def forward_feature(self, batch, **kwargs): data = list_to_variable(batch['image_batch'], volatile=kwargs.get('volatile', False)) labels = tensor_to_var(torch.Tensor(np.array(batch['label_batch']))) features = self.model.features(data) return features, labels
def forward(self, batch, **kwargs): """ :param batch: :param kwargs: :return: """ # print(batch['image_batch'][0].min()) # print(batch['image_batch'][0].max()) data = list_to_variable(batch['image_batch'], volatile=kwargs.get('volatile', False)) lbls = [img / 255.0 for img in batch['image_batch']] labels = list_to_variable(lbls, volatile=kwargs.get('volatile', False)) pred = self.model(data) return pred, labels
def forward(self, batch, **kwargs): """ :param batch: :param kwargs: :return: """ data = list_to_variable(batch['image_batch'], volatile=kwargs.get('volatile', False)) labels = tensor_to_var(torch.Tensor(np.array(batch['label_batch']))) pred = self.model(data) return pred, labels
def forward(self, batch, **kwargs): """ :param batch: :param kwargs: :return: """ data = list_to_variable(batch['image_batch'], volatile=kwargs.get('volatile', False)) labels_lesion = tensor_to_var( torch.Tensor(np.array(batch['label_batch']))) labels_row = tensor_to_var( torch.Tensor(np.array(batch['center_row_batch']))) labels_col = tensor_to_var( torch.Tensor(np.array(batch['center_col_batch']))) pred = self.model(data) return pred[:, 0], pred[:, 1], pred[:, 2], labels_lesion, labels_row, labels_col
def test_resnet_auto(): model = get_feature_model('auto', 'auto', load_pretrained=False, opti=None, lr=None, mom=None, checkpoint_pretrained=None, learn_pos=False, force_on_cpu=False) val_loader = get_valloader_only(False, False, 1, 0, False) for batch_idx, batch in enumerate(val_loader): if len(batch['image_batch']) > 0: # print(len(batch['image_batch'])) # print(batch['image_batch'][0].size()) data = list_to_variable(batch['image_batch']) labels = tensor_to_var(torch.Tensor(np.array( batch['label_batch'])), async=True) pred = model(data) break