From db7ec72dd84ffe9c3b290b6bbf6d583a701cd2cf Mon Sep 17 00:00:00 2001 From: Patrick von Platen Date: Thu, 30 Jun 2022 22:29:18 +0000 Subject: [PATCH] up --- test_modeling_utils.py | 1181 ---------------------------------- tests/test_modeling_utils.py | 4 +- 2 files changed, 2 insertions(+), 1183 deletions(-) delete mode 100755 test_modeling_utils.py diff --git a/test_modeling_utils.py b/test_modeling_utils.py deleted file mode 100755 index 94f88a6a..00000000 --- a/test_modeling_utils.py +++ /dev/null @@ -1,1181 +0,0 @@ -# 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 inspect -import tempfile -import unittest - -import numpy as np -import torch - -from diffusers import ( - AutoencoderKL, - BDDMPipeline, - DDIMPipeline, - DDIMScheduler, - DDPMPipeline, - DDPMScheduler, - GlidePipeline, - GlideSuperResUNetModel, - GlideTextToImageUNetModel, - GradTTSPipeline, - GradTTSScheduler, - LatentDiffusionPipeline, - LatentDiffusionUncondPipeline, - NCSNpp, - PNDMPipeline, - PNDMScheduler, - ScoreSdeVePipeline, - ScoreSdeVeScheduler, - ScoreSdeVpPipeline, - ScoreSdeVpScheduler, - TemporalUNet, - UNetGradTTSModel, - UNetLDMModel, - UNetModel, - VQModel, -) -from diffusers.configuration_utils import ConfigMixin -from diffusers.pipeline_utils import DiffusionPipeline -from diffusers.pipelines.bddm.pipeline_bddm import DiffWave -from diffusers.testing_utils import floats_tensor, slow, torch_device - - -torch.backends.cuda.matmul.allow_tf32 = False - - -class ConfigTester(unittest.TestCase): - def test_load_not_from_mixin(self): - with self.assertRaises(ValueError): - ConfigMixin.from_config("dummy_path") - - def test_save_load(self): - class SampleObject(ConfigMixin): - config_name = "config.json" - - def __init__( - self, - a=2, - b=5, - c=(2, 5), - d="for diffusion", - e=[1, 3], - ): - self.register_to_config(a=a, b=b, c=c, d=d, e=e) - - obj = SampleObject() - config = obj.config - - assert config["a"] == 2 - assert config["b"] == 5 - assert config["c"] == (2, 5) - assert config["d"] == "for diffusion" - assert config["e"] == [1, 3] - - with tempfile.TemporaryDirectory() as tmpdirname: - obj.save_config(tmpdirname) - new_obj = SampleObject.from_config(tmpdirname) - new_config = new_obj.config - - # unfreeze configs - config = dict(config) - new_config = dict(new_config) - - assert config.pop("c") == (2, 5) # instantiated as tuple - assert new_config.pop("c") == [2, 5] # saved & loaded as list because of json - assert config == new_config - - -class ModelTesterMixin: - def test_from_pretrained_save_pretrained(self): - init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() - - model = self.model_class(**init_dict) - model.to(torch_device) - model.eval() - - with tempfile.TemporaryDirectory() as tmpdirname: - model.save_pretrained(tmpdirname) - new_model = self.model_class.from_pretrained(tmpdirname) - new_model.to(torch_device) - - with torch.no_grad(): - image = model(**inputs_dict) - new_image = new_model(**inputs_dict) - - max_diff = (image - new_image).abs().sum().item() - self.assertLessEqual(max_diff, 5e-5, "Models give different forward passes") - - def test_determinism(self): - init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() - model = self.model_class(**init_dict) - model.to(torch_device) - model.eval() - with torch.no_grad(): - first = model(**inputs_dict) - second = model(**inputs_dict) - - out_1 = first.cpu().numpy() - out_2 = second.cpu().numpy() - out_1 = out_1[~np.isnan(out_1)] - out_2 = out_2[~np.isnan(out_2)] - max_diff = np.amax(np.abs(out_1 - out_2)) - self.assertLessEqual(max_diff, 1e-5) - - def test_output(self): - init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() - model = self.model_class(**init_dict) - model.to(torch_device) - model.eval() - - with torch.no_grad(): - output = model(**inputs_dict) - - self.assertIsNotNone(output) - expected_shape = inputs_dict["x"].shape - self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match") - - def test_forward_signature(self): - init_dict, _ = self.prepare_init_args_and_inputs_for_common() - - model = self.model_class(**init_dict) - signature = inspect.signature(model.forward) - # signature.parameters is an OrderedDict => so arg_names order is deterministic - arg_names = [*signature.parameters.keys()] - - expected_arg_names = ["x", "timesteps"] - self.assertListEqual(arg_names[:2], expected_arg_names) - - def test_model_from_config(self): - init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() - - model = self.model_class(**init_dict) - model.to(torch_device) - model.eval() - - # test if the model can be loaded from the config - # and has all the expected shape - with tempfile.TemporaryDirectory() as tmpdirname: - model.save_config(tmpdirname) - new_model = self.model_class.from_config(tmpdirname) - new_model.to(torch_device) - new_model.eval() - - # check if all paramters shape are the same - for param_name in model.state_dict().keys(): - param_1 = model.state_dict()[param_name] - param_2 = new_model.state_dict()[param_name] - self.assertEqual(param_1.shape, param_2.shape) - - with torch.no_grad(): - output_1 = model(**inputs_dict) - output_2 = new_model(**inputs_dict) - - self.assertEqual(output_1.shape, output_2.shape) - - def test_training(self): - init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() - - model = self.model_class(**init_dict) - model.to(torch_device) - model.train() - output = model(**inputs_dict) - noise = torch.randn((inputs_dict["x"].shape[0],) + self.output_shape).to(torch_device) - loss = torch.nn.functional.mse_loss(output, noise) - loss.backward() - - -class UnetModelTests(ModelTesterMixin, unittest.TestCase): - model_class = UNetModel - - @property - def dummy_input(self): - batch_size = 4 - num_channels = 3 - sizes = (32, 32) - - noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device) - time_step = torch.tensor([10]).to(torch_device) - - return {"x": noise, "timesteps": time_step} - - @property - def input_shape(self): - return (3, 32, 32) - - @property - def output_shape(self): - return (3, 32, 32) - - def prepare_init_args_and_inputs_for_common(self): - init_dict = { - "ch": 32, - "ch_mult": (1, 2), - "num_res_blocks": 2, - "attn_resolutions": (16,), - "resolution": 32, - } - inputs_dict = self.dummy_input - return init_dict, inputs_dict - - def test_from_pretrained_hub(self): - model, loading_info = UNetModel.from_pretrained("fusing/ddpm_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 = UNetModel.from_pretrained("fusing/ddpm_dummy") - model.eval() - - torch.manual_seed(0) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(0) - - noise = torch.randn(1, model.config.in_channels, model.config.resolution, model.config.resolution) - time_step = torch.tensor([10]) - - with torch.no_grad(): - output = model(noise, time_step) - - output_slice = output[0, -1, -3:, -3:].flatten() - # fmt: off - expected_output_slice = torch.tensor([0.2891, -0.1899, 0.2595, -0.6214, 0.0968, -0.2622, 0.4688, 0.1311, 0.0053]) - # fmt: on - self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2)) - - -class GlideSuperResUNetTests(ModelTesterMixin, unittest.TestCase): - model_class = GlideSuperResUNetModel - - @property - def dummy_input(self): - batch_size = 4 - num_channels = 6 - sizes = (32, 32) - low_res_size = (4, 4) - - noise = torch.randn((batch_size, num_channels // 2) + sizes).to(torch_device) - low_res = torch.randn((batch_size, 3) + low_res_size).to(torch_device) - time_step = torch.tensor([10] * noise.shape[0], device=torch_device) - - return {"x": noise, "timesteps": time_step, "low_res": low_res} - - @property - def input_shape(self): - return (3, 32, 32) - - @property - def output_shape(self): - return (6, 32, 32) - - def prepare_init_args_and_inputs_for_common(self): - init_dict = { - "attention_resolutions": (2,), - "channel_mult": (1, 2), - "in_channels": 6, - "out_channels": 6, - "model_channels": 32, - "num_head_channels": 8, - "num_heads_upsample": 1, - "num_res_blocks": 2, - "resblock_updown": True, - "resolution": 32, - "use_scale_shift_norm": True, - } - inputs_dict = self.dummy_input - return init_dict, inputs_dict - - def test_output(self): - init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() - model = self.model_class(**init_dict) - model.to(torch_device) - model.eval() - - with torch.no_grad(): - output = model(**inputs_dict) - - output, _ = torch.split(output, 3, dim=1) - - self.assertIsNotNone(output) - expected_shape = inputs_dict["x"].shape - self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match") - - def test_from_pretrained_hub(self): - model, loading_info = GlideSuperResUNetModel.from_pretrained( - "fusing/glide-super-res-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 = GlideSuperResUNetModel.from_pretrained("fusing/glide-super-res-dummy") - - torch.manual_seed(0) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(0) - - noise = torch.randn(1, 3, 64, 64) - low_res = torch.randn(1, 3, 4, 4) - time_step = torch.tensor([42] * noise.shape[0]) - - with torch.no_grad(): - output = model(noise, time_step, low_res) - - output, _ = torch.split(output, 3, dim=1) - output_slice = output[0, -1, -3:, -3:].flatten() - # fmt: off - expected_output_slice = torch.tensor([-22.8782, -23.2652, -15.3966, -22.8034, -23.3159, -15.5640, -15.3970, -15.4614, - 10.4370]) - # fmt: on - self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3)) - - -class GlideTextToImageUNetModelTests(ModelTesterMixin, unittest.TestCase): - model_class = GlideTextToImageUNetModel - - @property - def dummy_input(self): - batch_size = 4 - num_channels = 3 - sizes = (32, 32) - transformer_dim = 32 - seq_len = 16 - - noise = torch.randn((batch_size, num_channels) + sizes).to(torch_device) - emb = torch.randn((batch_size, seq_len, transformer_dim)).to(torch_device) - time_step = torch.tensor([10] * noise.shape[0], device=torch_device) - - return {"x": noise, "timesteps": time_step, "transformer_out": emb} - - @property - def input_shape(self): - return (3, 32, 32) - - @property - def output_shape(self): - return (6, 32, 32) - - def prepare_init_args_and_inputs_for_common(self): - init_dict = { - "attention_resolutions": (2,), - "channel_mult": (1, 2), - "in_channels": 3, - "out_channels": 6, - "model_channels": 32, - "num_head_channels": 8, - "num_heads_upsample": 1, - "num_res_blocks": 2, - "resblock_updown": True, - "resolution": 32, - "use_scale_shift_norm": True, - "transformer_dim": 32, - } - inputs_dict = self.dummy_input - return init_dict, inputs_dict - - def test_output(self): - init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() - model = self.model_class(**init_dict) - model.to(torch_device) - model.eval() - - with torch.no_grad(): - output = model(**inputs_dict) - - output, _ = torch.split(output, 3, dim=1) - - self.assertIsNotNone(output) - expected_shape = inputs_dict["x"].shape - self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match") - - def test_from_pretrained_hub(self): - model, loading_info = GlideTextToImageUNetModel.from_pretrained( - "fusing/unet-glide-text2im-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 = GlideTextToImageUNetModel.from_pretrained("fusing/unet-glide-text2im-dummy") - - torch.manual_seed(0) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(0) - - noise = torch.randn((1, model.config.in_channels, model.config.resolution, model.config.resolution)).to( - torch_device - ) - emb = torch.randn((1, 16, model.config.transformer_dim)).to(torch_device) - time_step = torch.tensor([10] * noise.shape[0], device=torch_device) - - model.to(torch_device) - with torch.no_grad(): - output = model(noise, time_step, emb) - - output, _ = torch.split(output, 3, dim=1) - output_slice = output[0, -1, -3:, -3:].cpu().flatten() - # fmt: off - expected_output_slice = torch.tensor([2.7766, -10.3558, -14.9149, -0.9376, -14.9175, -17.7679, -5.5565, -12.9521, -12.9845]) - # fmt: on - self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3)) - - -class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase): - model_class = UNetLDMModel - - @property - def dummy_input(self): - batch_size = 4 - num_channels = 4 - sizes = (32, 32) - - noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device) - time_step = torch.tensor([10]).to(torch_device) - - return {"x": noise, "timesteps": time_step} - - @property - def input_shape(self): - return (4, 32, 32) - - @property - def output_shape(self): - return (4, 32, 32) - - def prepare_init_args_and_inputs_for_common(self): - init_dict = { - "image_size": 32, - "in_channels": 4, - "out_channels": 4, - "model_channels": 32, - "num_res_blocks": 2, - "attention_resolutions": (16,), - "channel_mult": (1, 2), - "num_heads": 2, - "conv_resample": True, - } - inputs_dict = self.dummy_input - return init_dict, inputs_dict - - def test_from_pretrained_hub(self): - model, loading_info = UNetLDMModel.from_pretrained("fusing/unet-ldm-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 = UNetLDMModel.from_pretrained("fusing/unet-ldm-dummy") - model.eval() - - torch.manual_seed(0) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(0) - - noise = torch.randn(1, model.config.in_channels, model.config.image_size, model.config.image_size) - time_step = torch.tensor([10] * noise.shape[0]) - - with torch.no_grad(): - output = model(noise, time_step) - - output_slice = output[0, -1, -3:, -3:].flatten() - # fmt: off - expected_output_slice = torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800]) - # fmt: on - - self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3)) - - def test_output_pretrained_spatial_transformer(self): - model = UNetLDMModel.from_pretrained("fusing/unet-ldm-dummy-spatial") - model.eval() - - torch.manual_seed(0) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(0) - - noise = torch.randn(1, model.config.in_channels, model.config.image_size, model.config.image_size) - context = torch.ones((1, 16, 64), dtype=torch.float32) - time_step = torch.tensor([10] * noise.shape[0]) - - with torch.no_grad(): - output = model(noise, time_step, context=context) - - output_slice = output[0, -1, -3:, -3:].flatten() - # fmt: off - expected_output_slice = torch.tensor([61.3445, 56.9005, 29.4339, 59.5497, 60.7375, 34.1719, 48.1951, 42.6569, 25.0890]) - # fmt: on - - self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3)) - - -class UNetGradTTSModelTests(ModelTesterMixin, unittest.TestCase): - model_class = UNetGradTTSModel - - @property - def dummy_input(self): - batch_size = 4 - num_features = 32 - seq_len = 16 - - noise = floats_tensor((batch_size, num_features, seq_len)).to(torch_device) - condition = floats_tensor((batch_size, num_features, seq_len)).to(torch_device) - mask = floats_tensor((batch_size, 1, seq_len)).to(torch_device) - time_step = torch.tensor([10] * batch_size).to(torch_device) - - return {"x": noise, "timesteps": time_step, "mu": condition, "mask": mask} - - @property - def input_shape(self): - return (4, 32, 16) - - @property - def output_shape(self): - return (4, 32, 16) - - def prepare_init_args_and_inputs_for_common(self): - init_dict = { - "dim": 64, - "groups": 4, - "dim_mults": (1, 2), - "n_feats": 32, - "pe_scale": 1000, - "n_spks": 1, - } - inputs_dict = self.dummy_input - return init_dict, inputs_dict - - def test_from_pretrained_hub(self): - model, loading_info = UNetGradTTSModel.from_pretrained("fusing/unet-grad-tts-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 = UNetGradTTSModel.from_pretrained("fusing/unet-grad-tts-dummy") - model.eval() - - torch.manual_seed(0) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(0) - - num_features = model.config.n_feats - seq_len = 16 - noise = torch.randn((1, num_features, seq_len)) - condition = torch.randn((1, num_features, seq_len)) - mask = torch.randn((1, 1, seq_len)) - time_step = torch.tensor([10]) - - with torch.no_grad(): - output = model(noise, time_step, condition, mask) - - output_slice = output[0, -3:, -3:].flatten() - # fmt: off - expected_output_slice = torch.tensor([-0.0690, -0.0531, 0.0633, -0.0660, -0.0541, 0.0650, -0.0656, -0.0555, 0.0617]) - # fmt: on - - self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3)) - - -class TemporalUNetModelTests(ModelTesterMixin, unittest.TestCase): - model_class = TemporalUNet - - @property - def dummy_input(self): - batch_size = 4 - num_features = 14 - seq_len = 16 - - noise = floats_tensor((batch_size, seq_len, num_features)).to(torch_device) - time_step = torch.tensor([10] * batch_size).to(torch_device) - - return {"x": noise, "timesteps": time_step} - - @property - def input_shape(self): - return (4, 16, 14) - - @property - def output_shape(self): - return (4, 16, 14) - - def prepare_init_args_and_inputs_for_common(self): - init_dict = { - "training_horizon": 128, - "dim": 32, - "dim_mults": [1, 4, 8], - "predict_epsilon": False, - "clip_denoised": True, - "transition_dim": 14, - "cond_dim": 3, - } - inputs_dict = self.dummy_input - return init_dict, inputs_dict - - def test_from_pretrained_hub(self): - model, loading_info = TemporalUNet.from_pretrained( - "fusing/ddpm-unet-rl-hopper-hor128", 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 = TemporalUNet.from_pretrained("fusing/ddpm-unet-rl-hopper-hor128") - model.eval() - - torch.manual_seed(0) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(0) - - num_features = model.transition_dim - seq_len = 16 - noise = torch.randn((1, seq_len, num_features)) - time_step = torch.full((num_features,), 0) - - with torch.no_grad(): - output = model(noise, time_step) - - output_slice = output[0, -3:, -3:].flatten() - # fmt: off - expected_output_slice = torch.tensor([-0.2714, 0.1042, -0.0794, -0.2820, 0.0803, -0.0811, -0.2345, 0.0580, -0.0584]) - # fmt: on - - self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3)) - - -class NCSNppModelTests(ModelTesterMixin, unittest.TestCase): - model_class = NCSNpp - - @property - def dummy_input(self): - batch_size = 4 - num_channels = 3 - sizes = (32, 32) - - noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device) - time_step = torch.tensor(batch_size * [10]).to(torch_device) - - return {"x": noise, "timesteps": time_step} - - @property - def input_shape(self): - return (3, 32, 32) - - @property - def output_shape(self): - return (3, 32, 32) - - def prepare_init_args_and_inputs_for_common(self): - init_dict = { - "image_size": 32, - "ch_mult": [1, 2, 2, 2], - "nf": 32, - "fir": True, - "progressive": "output_skip", - "progressive_combine": "sum", - "progressive_input": "input_skip", - "scale_by_sigma": True, - "skip_rescale": True, - "embedding_type": "fourier", - } - inputs_dict = self.dummy_input - return init_dict, inputs_dict - - def test_from_pretrained_hub(self): - model, loading_info = NCSNpp.from_pretrained("fusing/cifar10-ncsnpp-ve", 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_ve_small(self): - model = NCSNpp.from_pretrained("fusing/ncsnpp-cifar10-ve-dummy") - model.eval() - model.to(torch_device) - - torch.manual_seed(0) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(0) - - batch_size = 4 - num_channels = 3 - sizes = (32, 32) - - noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device) - time_step = torch.tensor(batch_size * [1e-4]).to(torch_device) - - with torch.no_grad(): - output = model(noise, time_step) - - output_slice = output[0, -3:, -3:, -1].flatten().cpu() - # fmt: off - expected_output_slice = torch.tensor([0.1315, 0.0741, 0.0393, 0.0455, 0.0556, 0.0180, -0.0832, -0.0644, -0.0856]) - # fmt: on - - self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2)) - - def test_output_pretrained_ve_large(self): - model = NCSNpp.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy") - model.eval() - model.to(torch_device) - - torch.manual_seed(0) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(0) - - batch_size = 4 - num_channels = 3 - sizes = (32, 32) - - noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device) - time_step = torch.tensor(batch_size * [1e-4]).to(torch_device) - - with torch.no_grad(): - output = model(noise, time_step) - - output_slice = output[0, -3:, -3:, -1].flatten().cpu() - # fmt: off - expected_output_slice = torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256]) - # fmt: on - - self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2)) - - def test_output_pretrained_vp(self): - model = NCSNpp.from_pretrained("fusing/cifar10-ddpmpp-vp") - model.eval() - model.to(torch_device) - - torch.manual_seed(0) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(0) - - batch_size = 4 - num_channels = 3 - sizes = (32, 32) - - noise = torch.randn((batch_size, num_channels) + sizes).to(torch_device) - time_step = torch.tensor(batch_size * [9.0]).to(torch_device) - - with torch.no_grad(): - output = model(noise, time_step) - - output_slice = output[0, -3:, -3:, -1].flatten().cpu() - # fmt: off - expected_output_slice = torch.tensor([0.3303, -0.2275, -2.8872, -0.1309, -1.2861, 3.4567, -1.0083, 2.5325, -1.3866]) - # fmt: on - - self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2)) - - -class VQModelTests(ModelTesterMixin, unittest.TestCase): - model_class = VQModel - - @property - def dummy_input(self): - batch_size = 4 - num_channels = 3 - sizes = (32, 32) - - image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device) - - return {"x": image} - - @property - def input_shape(self): - return (3, 32, 32) - - @property - def output_shape(self): - return (3, 32, 32) - - def prepare_init_args_and_inputs_for_common(self): - init_dict = { - "ch": 64, - "out_ch": 3, - "num_res_blocks": 1, - "attn_resolutions": [], - "in_channels": 3, - "resolution": 32, - "z_channels": 3, - "n_embed": 256, - "embed_dim": 3, - "sane_index_shape": False, - "ch_mult": (1,), - "dropout": 0.0, - "double_z": False, - } - 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.eval() - - torch.manual_seed(0) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(0) - - image = torch.randn(1, model.config.in_channels, model.config.resolution, model.config.resolution) - with torch.no_grad(): - output = model(image) - - output_slice = output[0, -1, -3:, -3:].flatten() - # fmt: off - expected_output_slice = torch.tensor([-1.1321, 0.1056, 0.3505, -0.6461, -0.2014, 0.0419, -0.5763, -0.8462, -0.4218]) - # fmt: on - self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2)) - - -class AutoEncoderKLTests(ModelTesterMixin, unittest.TestCase): - model_class = AutoencoderKL - - @property - def dummy_input(self): - batch_size = 4 - num_channels = 3 - sizes = (32, 32) - - image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device) - - return {"x": image} - - @property - def input_shape(self): - return (3, 32, 32) - - @property - def output_shape(self): - return (3, 32, 32) - - def prepare_init_args_and_inputs_for_common(self): - init_dict = { - "ch": 64, - "ch_mult": (1,), - "embed_dim": 4, - "in_channels": 3, - "num_res_blocks": 1, - "out_ch": 3, - "resolution": 32, - "z_channels": 4, - "attn_resolutions": [], - } - 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 = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-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 = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy") - model.eval() - - torch.manual_seed(0) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(0) - - image = torch.randn(1, model.config.in_channels, model.config.resolution, model.config.resolution) - with torch.no_grad(): - output = model(image, sample_posterior=True) - - output_slice = output[0, -1, -3:, -3:].flatten() - # fmt: off - expected_output_slice = torch.tensor([-0.0814, -0.0229, -0.1320, -0.4123, -0.0366, -0.3473, 0.0438, -0.1662, 0.1750]) - # fmt: on - self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2)) - - -class PipelineTesterMixin(unittest.TestCase): - def test_from_pretrained_save_pretrained(self): - # 1. Load models - model = UNetModel(ch=32, ch_mult=(1, 2), num_res_blocks=2, attn_resolutions=(16,), resolution=32) - schedular = DDPMScheduler(timesteps=10) - - ddpm = DDPMPipeline(model, schedular) - - with tempfile.TemporaryDirectory() as tmpdirname: - ddpm.save_pretrained(tmpdirname) - new_ddpm = DDPMPipeline.from_pretrained(tmpdirname) - - generator = torch.manual_seed(0) - - image = ddpm(generator=generator) - generator = generator.manual_seed(0) - new_image = new_ddpm(generator=generator) - - assert (image - new_image).abs().sum() < 1e-5, "Models don't give the same forward pass" - - @slow - def test_from_pretrained_hub(self): - model_path = "fusing/ddpm-cifar10" - - ddpm = DDPMPipeline.from_pretrained(model_path) - ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path) - - ddpm.noise_scheduler.num_timesteps = 10 - ddpm_from_hub.noise_scheduler.num_timesteps = 10 - - generator = torch.manual_seed(0) - - image = ddpm(generator=generator) - generator = generator.manual_seed(0) - new_image = ddpm_from_hub(generator=generator) - - assert (image - new_image).abs().sum() < 1e-5, "Models don't give the same forward pass" - - @slow - def test_ddpm_cifar10(self): - model_id = "fusing/ddpm-cifar10" - - unet = UNetModel.from_pretrained(model_id) - noise_scheduler = DDPMScheduler.from_config(model_id) - noise_scheduler = noise_scheduler.set_format("pt") - - ddpm = DDPMPipeline(unet=unet, noise_scheduler=noise_scheduler) - - generator = torch.manual_seed(0) - image = ddpm(generator=generator) - - image_slice = image[0, -1, -3:, -3:].cpu() - - assert image.shape == (1, 3, 32, 32) - expected_slice = torch.tensor( - [-0.5712, -0.6215, -0.5953, -0.5438, -0.4775, -0.4539, -0.5172, -0.4872, -0.5105] - ) - assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2 - - @slow - def test_ddim_cifar10(self): - model_id = "fusing/ddpm-cifar10" - - unet = UNetModel.from_pretrained(model_id) - noise_scheduler = DDIMScheduler(tensor_format="pt") - - ddim = DDIMPipeline(unet=unet, noise_scheduler=noise_scheduler) - - generator = torch.manual_seed(0) - image = ddim(generator=generator, eta=0.0) - - image_slice = image[0, -1, -3:, -3:].cpu() - - assert image.shape == (1, 3, 32, 32) - expected_slice = torch.tensor( - [-0.6553, -0.6765, -0.6799, -0.6749, -0.7006, -0.6974, -0.6991, -0.7116, -0.7094] - ) - assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2 - - @slow - def test_pndm_cifar10(self): - model_id = "fusing/ddpm-cifar10" - - unet = UNetModel.from_pretrained(model_id) - noise_scheduler = PNDMScheduler(tensor_format="pt") - - pndm = PNDMPipeline(unet=unet, noise_scheduler=noise_scheduler) - generator = torch.manual_seed(0) - image = pndm(generator=generator) - - image_slice = image[0, -1, -3:, -3:].cpu() - - assert image.shape == (1, 3, 32, 32) - expected_slice = torch.tensor( - [-0.6872, -0.7071, -0.7188, -0.7057, -0.7515, -0.7191, -0.7377, -0.7565, -0.7500] - ) - assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2 - - @slow - @unittest.skip("Skipping for now as it takes too long") - def test_ldm_text2img(self): - model_id = "fusing/latent-diffusion-text2im-large" - ldm = LatentDiffusionPipeline.from_pretrained(model_id) - - prompt = "A painting of a squirrel eating a burger" - generator = torch.manual_seed(0) - image = ldm([prompt], generator=generator, num_inference_steps=20) - - image_slice = image[0, -1, -3:, -3:].cpu() - - assert image.shape == (1, 3, 256, 256) - expected_slice = torch.tensor([0.7295, 0.7358, 0.7256, 0.7435, 0.7095, 0.6884, 0.7325, 0.6921, 0.6458]) - assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2 - - @slow - def test_ldm_text2img_fast(self): - model_id = "fusing/latent-diffusion-text2im-large" - ldm = LatentDiffusionPipeline.from_pretrained(model_id) - - prompt = "A painting of a squirrel eating a burger" - generator = torch.manual_seed(0) - image = ldm([prompt], generator=generator, num_inference_steps=1) - - image_slice = image[0, -1, -3:, -3:].cpu() - - assert image.shape == (1, 3, 256, 256) - expected_slice = torch.tensor([0.3163, 0.8670, 0.6465, 0.1865, 0.6291, 0.5139, 0.2824, 0.3723, 0.4344]) - assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2 - - @slow - def test_glide_text2img(self): - model_id = "fusing/glide-base" - glide = GlidePipeline.from_pretrained(model_id) - - prompt = "a pencil sketch of a corgi" - generator = torch.manual_seed(0) - image = glide(prompt, generator=generator, num_inference_steps_upscale=20) - - image_slice = image[0, :3, :3, -1].cpu() - - assert image.shape == (1, 256, 256, 3) - expected_slice = torch.tensor([0.7119, 0.7073, 0.6460, 0.7780, 0.7423, 0.6926, 0.7378, 0.7189, 0.7784]) - assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2 - - @slow - def test_grad_tts(self): - model_id = "fusing/grad-tts-libri-tts" - grad_tts = GradTTSPipeline.from_pretrained(model_id) - noise_scheduler = GradTTSScheduler() - grad_tts.noise_scheduler = noise_scheduler - - text = "Hello world, I missed you so much." - generator = torch.manual_seed(0) - - # generate mel spectograms using text - mel_spec = grad_tts(text, generator=generator) - - assert mel_spec.shape == (1, 80, 143) - expected_slice = torch.tensor( - [-6.7584, -6.8347, -6.3293, -6.6437, -6.7233, -6.4684, -6.1187, -6.3172, -6.6890] - ) - assert (mel_spec[0, :3, :3].cpu().flatten() - expected_slice).abs().max() < 1e-2 - - @slow - def test_score_sde_ve_pipeline(self): - model = NCSNpp.from_pretrained("fusing/ffhq_ncsnpp") - scheduler = ScoreSdeVeScheduler.from_config("fusing/ffhq_ncsnpp") - - sde_ve = ScoreSdeVePipeline(model=model, scheduler=scheduler) - - torch.manual_seed(0) - image = sde_ve(num_inference_steps=2) - - expected_image_sum = 3382849024.0 - expected_image_mean = 1075.3788 - - assert (image.abs().sum() - expected_image_sum).abs().cpu().item() < 1e-2 - assert (image.abs().mean() - expected_image_mean).abs().cpu().item() < 1e-4 - - @slow - def test_score_sde_vp_pipeline(self): - model = NCSNpp.from_pretrained("fusing/cifar10-ddpmpp-vp") - scheduler = ScoreSdeVpScheduler.from_config("fusing/cifar10-ddpmpp-vp") - - sde_vp = ScoreSdeVpPipeline(model=model, scheduler=scheduler) - - torch.manual_seed(0) - image = sde_vp(num_inference_steps=10) - - expected_image_sum = 4183.2012 - expected_image_mean = 1.3617 - - assert (image.abs().sum() - expected_image_sum).abs().cpu().item() < 1e-2 - assert (image.abs().mean() - expected_image_mean).abs().cpu().item() < 1e-4 - - @slow - def test_ldm_uncond(self): - ldm = LatentDiffusionUncondPipeline.from_pretrained("fusing/latent-diffusion-celeba-256") - - generator = torch.manual_seed(0) - image = ldm(generator=generator, num_inference_steps=5) - - image_slice = image[0, -1, -3:, -3:].cpu() - - assert image.shape == (1, 3, 256, 256) - expected_slice = torch.tensor([0.5025, 0.4121, 0.3851, 0.4806, 0.3996, 0.3745, 0.4839, 0.4559, 0.4293]) - assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2 - - def test_module_from_pipeline(self): - model = DiffWave(num_res_layers=4) - noise_scheduler = DDPMScheduler(timesteps=12) - - bddm = BDDMPipeline(model, noise_scheduler) - - # check if the library name for the diffwave moduel is set to pipeline module - self.assertTrue(bddm.config["diffwave"][0] == "bddm") - - # check if we can save and load the pipeline - with tempfile.TemporaryDirectory() as tmpdirname: - bddm.save_pretrained(tmpdirname) - _ = BDDMPipeline.from_pretrained(tmpdirname) - # check if the same works using the DifusionPipeline class - bddm = DiffusionPipeline.from_pretrained(tmpdirname) - - self.assertTrue(bddm.config["diffwave"][0] == "bddm") diff --git a/tests/test_modeling_utils.py b/tests/test_modeling_utils.py index 1a410b93..94f88a6a 100755 --- a/tests/test_modeling_utils.py +++ b/tests/test_modeling_utils.py @@ -880,7 +880,7 @@ class VQModelTests(ModelTesterMixin, unittest.TestCase): # fmt: off expected_output_slice = torch.tensor([-1.1321, 0.1056, 0.3505, -0.6461, -0.2014, 0.0419, -0.5763, -0.8462, -0.4218]) # fmt: on - self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3)) + self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2)) class AutoEncoderKLTests(ModelTesterMixin, unittest.TestCase): @@ -951,7 +951,7 @@ class AutoEncoderKLTests(ModelTesterMixin, unittest.TestCase): # fmt: off expected_output_slice = torch.tensor([-0.0814, -0.0229, -0.1320, -0.4123, -0.0366, -0.3473, 0.0438, -0.1662, 0.1750]) # fmt: on - self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3)) + self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2)) class PipelineTesterMixin(unittest.TestCase):