# 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 Van model. """ import inspect import math import unittest from transformers import VanConfig from transformers.testing_utils import require_scipy, require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_scipy_available, is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor if is_scipy_available(): from scipy import stats if is_torch_available(): import torch from torch import nn from transformers import VanForImageClassification, VanModel from transformers.models.van.modeling_van import VAN_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import AutoFeatureExtractor class VanModelTester: def __init__( self, parent, batch_size=2, image_size=224, num_channels=3, hidden_sizes=[16, 32, 64, 128], depths=[1, 1, 1, 1], is_training=True, use_labels=True, num_labels=3, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.num_channels = num_channels self.hidden_sizes = hidden_sizes self.depths = depths self.is_training = is_training self.use_labels = use_labels self.num_labels = num_labels self.type_sequence_label_size = num_labels 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 VanConfig( num_channels=self.num_channels, hidden_sizes=self.hidden_sizes, depths=self.depths, num_labels=self.num_labels, is_decoder=False, ) def create_and_check_model(self, config, pixel_values, labels): model = VanModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # expected last hidden states: B, C, H // 32, W // 32 self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32), ) def create_and_check_for_image_classification(self, config, pixel_values, labels): model = VanForImageClassification(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)) 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 VanModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as Van does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (VanModel, VanForImageClassification) if is_torch_available() else () test_pruning = False test_resize_embeddings = False test_head_masking = False has_attentions = False def setUp(self): self.model_tester = VanModelTester(self) self.config_tester = ConfigTester(self, config_class=VanConfig, has_text_modality=False, hidden_size=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 @unittest.skip(reason="Van does not output attentions") def test_attention_outputs(self): pass @unittest.skip(reason="Van does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Van does not support input and output embeddings") def test_model_common_attributes(self): pass 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_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) @require_scipy 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, module in model.named_modules(): if isinstance(module, (nn.BatchNorm2d, nn.GroupNorm, nn.LayerNorm)): self.assertTrue( torch.all(module.weight == 1), msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) self.assertTrue( torch.all(module.bias == 0), msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) elif isinstance(module, nn.Conv2d): fan_out = module.kernel_size[0] * module.kernel_size[1] * module.out_channels fan_out //= module.groups std = math.sqrt(2.0 / fan_out) # divide by std -> mean = 0, std = 1 data = module.weight.data.cpu().flatten().numpy() / std test = stats.anderson(data) self.assertTrue(test.statistic > 0.05) 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.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_stages = len(self.model_tester.hidden_sizes) # van has no embeddings self.assertEqual(len(hidden_states), expected_num_stages) # Van's feature maps are of shape (batch_size, num_channels, height, width) self.assertListEqual( list(hidden_states[0].shape[-2:]), [self.model_tester.image_size // 4, self.model_tester.image_size // 4], ) 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) 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): for model_name in VAN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = VanModel.from_pretrained(model_name) self.assertIsNotNone(model) # 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 VanModelIntegrationTest(unittest.TestCase): @cached_property def default_feature_extractor(self): return AutoFeatureExtractor.from_pretrained(VAN_PRETRAINED_MODEL_ARCHIVE_LIST[0]) @slow def test_inference_image_classification_head(self): model = VanForImageClassification.from_pretrained(VAN_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(torch_device) feature_extractor = self.default_feature_extractor image = prepare_img() 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([0.1029, -0.0904, -0.6365]).to(torch_device) self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))