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Running
on
Zero
# coding=utf-8 | |
# Copyright 2024 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 | |
import torch | |
from diffusers import VQModel | |
from diffusers.utils.testing_utils import ( | |
backend_manual_seed, | |
enable_full_determinism, | |
floats_tensor, | |
torch_device, | |
) | |
from ..test_modeling_common import ModelTesterMixin, UNetTesterMixin | |
enable_full_determinism() | |
class VQModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase): | |
model_class = VQModel | |
main_input_name = "sample" | |
def dummy_input(self, sizes=(32, 32)): | |
batch_size = 4 | |
num_channels = 3 | |
image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device) | |
return {"sample": image} | |
def input_shape(self): | |
return (3, 32, 32) | |
def output_shape(self): | |
return (3, 32, 32) | |
def prepare_init_args_and_inputs_for_common(self): | |
init_dict = { | |
"block_out_channels": [8, 16], | |
"norm_num_groups": 8, | |
"in_channels": 3, | |
"out_channels": 3, | |
"down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"], | |
"up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"], | |
"latent_channels": 3, | |
} | |
inputs_dict = self.dummy_input | |
return init_dict, inputs_dict | |
def test_forward_signature(self): | |
pass | |
def test_training(self): | |
pass | |
def test_from_pretrained_hub(self): | |
model, loading_info = VQModel.from_pretrained("fusing/vqgan-dummy", output_loading_info=True) | |
self.assertIsNotNone(model) | |
self.assertEqual(len(loading_info["missing_keys"]), 0) | |
model.to(torch_device) | |
image = model(**self.dummy_input) | |
assert image is not None, "Make sure output is not None" | |
def test_output_pretrained(self): | |
model = VQModel.from_pretrained("fusing/vqgan-dummy") | |
model.to(torch_device).eval() | |
torch.manual_seed(0) | |
backend_manual_seed(torch_device, 0) | |
image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size) | |
image = image.to(torch_device) | |
with torch.no_grad(): | |
output = model(image).sample | |
output_slice = output[0, -1, -3:, -3:].flatten().cpu() | |
# fmt: off | |
expected_output_slice = torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]) | |
# fmt: on | |
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3)) | |
def test_loss_pretrained(self): | |
model = VQModel.from_pretrained("fusing/vqgan-dummy") | |
model.to(torch_device).eval() | |
torch.manual_seed(0) | |
backend_manual_seed(torch_device, 0) | |
image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size) | |
image = image.to(torch_device) | |
with torch.no_grad(): | |
output = model(image).commit_loss.cpu() | |
# fmt: off | |
expected_output = torch.tensor([0.1936]) | |
# fmt: on | |
self.assertTrue(torch.allclose(output, expected_output, atol=1e-3)) | |