# Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # 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. """Testing suite for the PyTorch Swin2SR model.""" import unittest from transformers import Swin2SRConfig from transformers.testing_utils import Expectations, require_torch, require_vision, slow, torch_device from transformers.utils import is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import Swin2SRForImageSuperResolution, Swin2SRModel if is_vision_available(): from PIL import Image from transformers import Swin2SRImageProcessor class Swin2SRModelTester: def __init__( self, parent, batch_size=13, image_size=32, patch_size=1, num_channels=3, num_channels_out=1, embed_dim=16, depths=[1, 2, 1], num_heads=[2, 2, 4], window_size=2, mlp_ratio=2.0, qkv_bias=True, hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, drop_path_rate=0.1, hidden_act="gelu", use_absolute_embeddings=False, patch_norm=True, initializer_range=0.02, layer_norm_eps=1e-5, is_training=True, scope=None, use_labels=False, upscale=2, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.num_channels_out = num_channels_out self.embed_dim = embed_dim self.depths = depths self.num_heads = num_heads self.window_size = window_size self.mlp_ratio = mlp_ratio self.qkv_bias = qkv_bias self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.drop_path_rate = drop_path_rate self.hidden_act = hidden_act self.use_absolute_embeddings = use_absolute_embeddings self.patch_norm = patch_norm self.layer_norm_eps = layer_norm_eps self.initializer_range = initializer_range self.is_training = is_training self.scope = scope self.use_labels = use_labels self.upscale = upscale # here we set some attributes to make tests pass self.num_hidden_layers = len(depths) self.hidden_size = embed_dim self.seq_length = (image_size // patch_size) ** 2 def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) labels = None if self.use_labels: labels = ids_tensor([self.batch_size], self.type_sequence_label_size) config = self.get_config() return config, pixel_values, labels def get_config(self): return Swin2SRConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, num_channels_out=self.num_channels_out, embed_dim=self.embed_dim, depths=self.depths, num_heads=self.num_heads, window_size=self.window_size, mlp_ratio=self.mlp_ratio, qkv_bias=self.qkv_bias, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, drop_path_rate=self.drop_path_rate, hidden_act=self.hidden_act, use_absolute_embeddings=self.use_absolute_embeddings, path_norm=self.patch_norm, layer_norm_eps=self.layer_norm_eps, initializer_range=self.initializer_range, upscale=self.upscale, ) def create_and_check_model(self, config, pixel_values, labels): model = Swin2SRModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.embed_dim, self.image_size, self.image_size) ) def create_and_check_for_image_super_resolution(self, config, pixel_values, labels): model = Swin2SRForImageSuperResolution(config) model.to(torch_device) model.eval() result = model(pixel_values) expected_image_size = self.image_size * self.upscale self.parent.assertEqual( result.reconstruction.shape, (self.batch_size, self.num_channels_out, expected_image_size, expected_image_size), ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, labels = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class Swin2SRModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (Swin2SRModel, Swin2SRForImageSuperResolution) if is_torch_available() else () pipeline_model_mapping = ( {"image-feature-extraction": Swin2SRModel, "image-to-image": Swin2SRForImageSuperResolution} if is_torch_available() else {} ) test_resize_embeddings = False test_torch_exportable = True def setUp(self): self.model_tester = Swin2SRModelTester(self) self.config_tester = ConfigTester( self, config_class=Swin2SRConfig, embed_dim=37, has_text_modality=False, common_properties=["image_size", "patch_size", "num_channels"], ) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_model_for_image_super_resolution(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_super_resolution(*config_and_inputs) # TODO: check if this works again for PyTorch 2.x.y @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0.") def test_multi_gpu_data_parallel_forward(self): pass @unittest.skip(reason="Swin2SR does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Swin2SR does not support training yet") def test_training(self): pass @unittest.skip(reason="Swin2SR does not support training yet") def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass def test_model_get_set_embeddings(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), (nn.Module)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) @slow def test_model_from_pretrained(self): model_name = "caidas/swin2SR-classical-sr-x2-64" model = Swin2SRModel.from_pretrained(model_name) self.assertIsNotNone(model) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class._from_config(config, attn_implementation="eager") config = model.config model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions expected_num_attentions = len(self.model_tester.depths) self.assertEqual(len(attentions), expected_num_attentions) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True window_size_squared = config.window_size**2 model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions self.assertEqual(len(attentions), expected_num_attentions) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_heads[0], window_size_squared, window_size_squared], ) out_len = len(outputs) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) self.assertEqual(out_len + 1, len(outputs)) self_attentions = outputs.attentions self.assertEqual(len(self_attentions), expected_num_attentions) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_heads[0], window_size_squared, window_size_squared], ) @require_vision @require_torch @slow class Swin2SRModelIntegrationTest(unittest.TestCase): def test_inference_image_super_resolution_head(self): processor = Swin2SRImageProcessor() model = Swin2SRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64").to(torch_device) image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") inputs = processor(images=image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the logits expected_shape = torch.Size([1, 3, 976, 1296]) self.assertEqual(outputs.reconstruction.shape, expected_shape) expected_slice = torch.tensor( [[0.5458, 0.5546, 0.5638], [0.5526, 0.5565, 0.5651], [0.5396, 0.5426, 0.5621]] ).to(torch_device) torch.testing.assert_close(outputs.reconstruction[0, 0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4) def test_inference_fp16(self): processor = Swin2SRImageProcessor() model = Swin2SRForImageSuperResolution.from_pretrained( "caidas/swin2SR-classical-sr-x2-64", dtype=torch.float16 ).to(torch_device) image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") inputs = processor(images=image, return_tensors="pt").to(model.dtype).to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the logits expected_shape = torch.Size([1, 3, 976, 1296]) self.assertEqual(outputs.reconstruction.shape, expected_shape) expectations = Expectations( { (None, None): [[0.5454, 0.5542, 0.5640], [0.5518, 0.5562, 0.5649], [0.5391, 0.5425, 0.5620]], ("cuda", 8): [[0.5454, 0.5547, 0.5640], [0.5522, 0.5562, 0.5649], [0.5391, 0.5425, 0.5620]], } ) expected_slice = torch.tensor(expectations.get_expectation()).to(torch_device, dtype=model.dtype) torch.testing.assert_close(outputs.reconstruction[0, 0, :3, :3], expected_slice, rtol=2e-4, atol=2e-4)