# Copyright 2023 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 SwiftFormer model.""" import unittest from functools import cached_property from transformers import SwiftFormerConfig from transformers.testing_utils import ( 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 SwiftFormerForImageClassification, SwiftFormerModel if is_vision_available(): from PIL import Image from transformers import ViTImageProcessor class SwiftFormerModelTester: def __init__( self, parent, batch_size=13, num_channels=3, is_training=True, use_labels=True, hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, image_size=224, num_labels=3, layer_depths=[1, 1, 1, 1], embed_dims=[16, 16, 32, 32], ): self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.is_training = is_training self.use_labels = use_labels self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.num_labels = num_labels self.image_size = image_size self.layer_depths = layer_depths self.embed_dims = embed_dims 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.num_labels) config = self.get_config() return config, pixel_values, labels def get_config(self): return SwiftFormerConfig( depths=self.layer_depths, embed_dims=self.embed_dims, mlp_ratio=4, downsamples=[True, True, True, True], hidden_act="gelu", num_labels=self.num_labels, down_patch_size=3, down_stride=2, down_pad=1, drop_rate=0.0, drop_path_rate=0.0, use_layer_scale=True, layer_scale_init_value=1e-5, ) def create_and_check_model(self, config, pixel_values, labels): model = SwiftFormerModel(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_dims[-1], 7, 7)) def create_and_check_for_image_classification(self, config, pixel_values, labels): config.num_labels = self.num_labels model = SwiftFormerForImageClassification(config) model.to(torch_device) model.eval() result = model(pixel_values, labels=labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) model = SwiftFormerForImageClassification(config) model.to(torch_device) model.eval() pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) result = model(pixel_values) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def prepare_config_and_inputs_for_common(self): (config, pixel_values, labels) = self.prepare_config_and_inputs() inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class SwiftFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as SwiftFormer does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else () pipeline_model_mapping = ( {"image-feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification} if is_torch_available() else {} ) test_resize_embeddings = False has_attentions = False test_torch_exportable = True def setUp(self): self.model_tester = SwiftFormerModelTester(self) self.config_tester = ConfigTester( self, config_class=SwiftFormerConfig, has_text_modality=False, hidden_size=37, num_attention_heads=12, num_hidden_layers=12, ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="SwiftFormer does not use inputs_embeds") def test_inputs_embeds(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) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) 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_for_image_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): model_name = "MBZUAI/swiftformer-xs" model = SwiftFormerModel.from_pretrained(model_name) self.assertIsNotNone(model) @unittest.skip(reason="SwiftFormer does not output attentions") def test_attention_outputs(self): pass def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.hidden_states expected_num_stages = 8 self.assertEqual(len(hidden_states), expected_num_stages) # TODO # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width) # with the width and height being successively divided by 2, after every 2 blocks for i in range(len(hidden_states)): self.assertEqual( hidden_states[i].shape, torch.Size( [ self.model_tester.batch_size, self.model_tester.embed_dims[i // 2], (self.model_tester.image_size // 4) // 2 ** (i // 2), (self.model_tester.image_size // 4) // 2 ** (i // 2), ] ), ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_torch @require_vision class SwiftFormerModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs") if is_vision_available() else None @slow def test_inference_image_classification_head(self): model = SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs").to(torch_device) image_processor = self.default_image_processor image = prepare_img() inputs = image_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, 1000)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]]).to(torch_device) torch.testing.assert_close(outputs.logits[0, :3], expected_slice, rtol=1e-4, atol=1e-4)