# coding=utf-8 # 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 Dinat model. """ import collections import inspect import unittest from transformers import DinatConfig from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_backbone_common import BackboneTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import DinatBackbone, DinatForImageClassification, DinatModel from transformers.models.dinat.modeling_dinat import DINAT_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class DinatModelTester: def __init__( self, parent, batch_size=13, image_size=64, patch_size=4, num_channels=3, embed_dim=16, depths=[1, 2, 1], num_heads=[2, 4, 8], kernel_size=3, dilations=[[3], [1, 2], [1]], 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", patch_norm=True, initializer_range=0.02, layer_norm_eps=1e-5, is_training=True, scope=None, use_labels=True, num_labels=10, out_features=["stage1", "stage2"], out_indices=[1, 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.embed_dim = embed_dim self.depths = depths self.num_heads = num_heads self.kernel_size = kernel_size self.dilations = dilations 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.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.num_labels = num_labels self.out_features = out_features self.out_indices = out_indices 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 DinatConfig( num_labels=self.num_labels, image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, embed_dim=self.embed_dim, depths=self.depths, num_heads=self.num_heads, kernel_size=self.kernel_size, dilations=self.dilations, 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, patch_norm=self.patch_norm, layer_norm_eps=self.layer_norm_eps, initializer_range=self.initializer_range, out_features=self.out_features, out_indices=self.out_indices, ) def create_and_check_model(self, config, pixel_values, labels): model = DinatModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) expected_height = expected_width = (config.image_size // config.patch_size) // (2 ** (len(config.depths) - 1)) expected_dim = int(config.embed_dim * 2 ** (len(config.depths) - 1)) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, expected_height, expected_width, expected_dim) ) def create_and_check_for_image_classification(self, config, pixel_values, labels): model = DinatForImageClassification(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)) # test greyscale images config.num_channels = 1 model = DinatForImageClassification(config) model.to(torch_device) model.eval() pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]) result = model(pixel_values) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_backbone(self, config, pixel_values, labels): model = DinatBackbone(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # verify hidden states self.parent.assertEqual(len(result.feature_maps), len(config.out_features)) self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, model.channels[0], 16, 16]) # verify channels self.parent.assertEqual(len(model.channels), len(config.out_features)) # verify backbone works with out_features=None config.out_features = None model = DinatBackbone(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # verify feature maps self.parent.assertEqual(len(result.feature_maps), 1) self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, model.channels[-1], 4, 4]) # verify channels self.parent.assertEqual(len(model.channels), 1) 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_natten @require_torch class DinatModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( DinatModel, DinatForImageClassification, DinatBackbone, ) if is_torch_available() else () ) pipeline_model_mapping = ( {"feature-extraction": DinatModel, "image-classification": DinatForImageClassification} if is_torch_available() else {} ) fx_compatible = False test_torchscript = False test_pruning = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = DinatModelTester(self) self.config_tester = ConfigTester(self, config_class=DinatConfig, embed_dim=37) def test_config(self): self.create_and_test_config_common_properties() self.config_tester.create_and_test_config_to_json_string() self.config_tester.create_and_test_config_to_json_file() self.config_tester.create_and_test_config_from_and_save_pretrained() self.config_tester.create_and_test_config_with_num_labels() self.config_tester.check_config_can_be_init_without_params() self.config_tester.check_config_arguments_init() def create_and_test_config_common_properties(self): return 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) def test_backbone(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_backbone(*config_and_inputs) @unittest.skip(reason="Dinat does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Dinat does not use feedforward chunking") def test_feed_forward_chunking(self): pass def test_model_common_attributes(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)) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_attention_outputs(self): self.skipTest("Dinat's attention operation is handled entirely by NATTEN.") def check_hidden_states_output(self, inputs_dict, config, model_class, image_size): 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_layers = getattr( self.model_tester, "expected_num_hidden_layers", len(self.model_tester.depths) + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) # Dinat has a different seq_length patch_size = ( config.patch_size if isinstance(config.patch_size, collections.abc.Iterable) else (config.patch_size, config.patch_size) ) height = image_size[0] // patch_size[0] width = image_size[1] // patch_size[1] self.assertListEqual( list(hidden_states[0].shape[-3:]), [height, width, self.model_tester.embed_dim], ) if model_class.__name__ != "DinatBackbone": reshaped_hidden_states = outputs.reshaped_hidden_states self.assertEqual(len(reshaped_hidden_states), expected_num_layers) batch_size, num_channels, height, width = reshaped_hidden_states[0].shape reshaped_hidden_states = ( reshaped_hidden_states[0].view(batch_size, num_channels, height, width).permute(0, 2, 3, 1) ) self.assertListEqual( list(reshaped_hidden_states.shape[-3:]), [height, width, self.model_tester.embed_dim], ) def test_hidden_states_output(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() image_size = ( self.model_tester.image_size if isinstance(self.model_tester.image_size, collections.abc.Iterable) else (self.model_tester.image_size, self.model_tester.image_size) ) for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True self.check_hidden_states_output(inputs_dict, config, model_class, image_size) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True self.check_hidden_states_output(inputs_dict, config, model_class, image_size) @slow def test_model_from_pretrained(self): for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = DinatModel.from_pretrained(model_name) self.assertIsNotNone(model) def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): if "embeddings" not in name and param.requires_grad: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) @require_natten @require_vision @require_torch class DinatModelIntegrationTest(unittest.TestCase): @cached_property def default_feature_extractor(self): return AutoImageProcessor.from_pretrained("shi-labs/dinat-mini-in1k-224") if is_vision_available() else None @slow def test_inference_image_classification_head(self): model = DinatForImageClassification.from_pretrained("shi-labs/dinat-mini-in1k-224").to(torch_device) feature_extractor = self.default_feature_extractor image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") inputs = feature_extractor(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([-0.1545, -0.7667, 0.4642]).to(torch_device) self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4)) @require_torch @require_natten class DinatBackboneTest(unittest.TestCase, BackboneTesterMixin): all_model_classes = (DinatBackbone,) if is_torch_available() else () config_class = DinatConfig def setUp(self): self.model_tester = DinatModelTester(self)