122 lines
4.1 KiB
Python
122 lines
4.1 KiB
Python
# coding=utf-8
|
|
# Copyright 2022 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
|
|
|
|
|
|
@require_torch
|
|
class UNetBlockTesterMixin:
|
|
@property
|
|
def dummy_input(self):
|
|
return self.get_dummy_input()
|
|
|
|
@property
|
|
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)
|
|
|
|
@unittest.skipIf(torch_device == "mps", "Training is not supported in mps")
|
|
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()
|