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