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| # coding=utf-8 | |
| # Copyright 2023 HuggingFace Inc. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import unittest | |
| from typing import Tuple | |
| import torch | |
| from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device | |
| from diffusers.utils.testing_utils import require_torch | |
| class UNetBlockTesterMixin: | |
| def dummy_input(self): | |
| return self.get_dummy_input() | |
| def output_shape(self): | |
| if self.block_type == "down": | |
| return (4, 32, 16, 16) | |
| elif self.block_type == "mid": | |
| return (4, 32, 32, 32) | |
| elif self.block_type == "up": | |
| return (4, 32, 64, 64) | |
| raise ValueError(f"'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'.") | |
| def get_dummy_input( | |
| self, | |
| include_temb=True, | |
| include_res_hidden_states_tuple=False, | |
| include_encoder_hidden_states=False, | |
| include_skip_sample=False, | |
| ): | |
| batch_size = 4 | |
| num_channels = 32 | |
| sizes = (32, 32) | |
| generator = torch.manual_seed(0) | |
| device = torch.device(torch_device) | |
| shape = (batch_size, num_channels) + sizes | |
| hidden_states = randn_tensor(shape, generator=generator, device=device) | |
| dummy_input = {"hidden_states": hidden_states} | |
| if include_temb: | |
| temb_channels = 128 | |
| dummy_input["temb"] = randn_tensor((batch_size, temb_channels), generator=generator, device=device) | |
| if include_res_hidden_states_tuple: | |
| generator_1 = torch.manual_seed(1) | |
| dummy_input["res_hidden_states_tuple"] = (randn_tensor(shape, generator=generator_1, device=device),) | |
| if include_encoder_hidden_states: | |
| dummy_input["encoder_hidden_states"] = floats_tensor((batch_size, 32, 32)).to(torch_device) | |
| if include_skip_sample: | |
| dummy_input["skip_sample"] = randn_tensor(((batch_size, 3) + sizes), generator=generator, device=device) | |
| return dummy_input | |
| def prepare_init_args_and_inputs_for_common(self): | |
| init_dict = { | |
| "in_channels": 32, | |
| "out_channels": 32, | |
| "temb_channels": 128, | |
| } | |
| if self.block_type == "up": | |
| init_dict["prev_output_channel"] = 32 | |
| if self.block_type == "mid": | |
| init_dict.pop("out_channels") | |
| inputs_dict = self.dummy_input | |
| return init_dict, inputs_dict | |
| def test_output(self, expected_slice): | |
| init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() | |
| unet_block = self.block_class(**init_dict) | |
| unet_block.to(torch_device) | |
| unet_block.eval() | |
| with torch.no_grad(): | |
| output = unet_block(**inputs_dict) | |
| if isinstance(output, Tuple): | |
| output = output[0] | |
| self.assertEqual(output.shape, self.output_shape) | |
| output_slice = output[0, -1, -3:, -3:] | |
| expected_slice = torch.tensor(expected_slice).to(torch_device) | |
| assert torch_all_close(output_slice.flatten(), expected_slice, atol=5e-3) | |
| def test_training(self): | |
| init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() | |
| model = self.block_class(**init_dict) | |
| model.to(torch_device) | |
| model.train() | |
| output = model(**inputs_dict) | |
| if isinstance(output, Tuple): | |
| output = output[0] | |
| device = torch.device(torch_device) | |
| noise = randn_tensor(output.shape, device=device) | |
| loss = torch.nn.functional.mse_loss(output, noise) | |
| loss.backward() | |