# coding=utf-8 # 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 Pvt model. """ import inspect import unittest from transformers import is_torch_available, is_vision_available from transformers.models.auto import get_values from transformers.testing_utils import ( require_accelerate, require_torch, require_torch_gpu, slow, torch_device, ) 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 transformers import MODEL_MAPPING, PvtConfig, PvtForImageClassification, PvtImageProcessor, PvtModel from transformers.models.pvt.modeling_pvt import PVT_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image class PvtConfigTester(ConfigTester): def run_common_tests(self): config = self.config_class(**self.inputs_dict) self.parent.assertTrue(hasattr(config, "hidden_sizes")) self.parent.assertTrue(hasattr(config, "num_encoder_blocks")) class PvtModelTester: def __init__( self, parent, batch_size=13, image_size=64, num_channels=3, num_encoder_blocks=4, depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1], hidden_sizes=[16, 32, 64, 128], downsampling_rates=[1, 4, 8, 16], num_attention_heads=[1, 2, 4, 8], is_training=True, use_labels=True, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, initializer_range=0.02, num_labels=3, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.num_channels = num_channels self.num_encoder_blocks = num_encoder_blocks self.sr_ratios = sr_ratios self.depths = depths self.hidden_sizes = hidden_sizes self.downsampling_rates = downsampling_rates self.num_attention_heads = num_attention_heads self.is_training = is_training self.use_labels = use_labels self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.initializer_range = initializer_range self.num_labels = num_labels self.scope = scope 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.image_size, self.image_size], self.num_labels) config = self.get_config() return config, pixel_values, labels def get_config(self): return PvtConfig( image_size=self.image_size, num_channels=self.num_channels, num_encoder_blocks=self.num_encoder_blocks, depths=self.depths, hidden_sizes=self.hidden_sizes, num_attention_heads=self.num_attention_heads, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, pixel_values, labels): model = PvtModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) self.parent.assertIsNotNone(result.last_hidden_state) def create_and_check_for_image_classification(self, config, pixel_values, labels): config.num_labels = self.type_sequence_label_size model = PvtForImageClassification(config) model.to(torch_device) model.eval() result = model(pixel_values, labels=labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)) # test greyscale images config.num_channels = 1 model = PvtForImageClassification(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.type_sequence_label_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 # 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 class PvtModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (PvtModel, PvtForImageClassification) if is_torch_available() else () pipeline_model_mapping = ( {"feature-extraction": PvtModel, "image-classification": PvtForImageClassification} if is_torch_available() else {} ) test_head_masking = False test_pruning = False test_resize_embeddings = False test_torchscript = False has_attentions = False def setUp(self): self.model_tester = PvtModelTester(self) self.config_tester = PvtConfigTester(self, config_class=PvtConfig) 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) @unittest.skip("Pvt does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip("Pvt does not have get_input_embeddings method and get_output_embeddings methods") def test_model_common_attributes(self): pass def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config=config) for name, param in model.named_parameters(): self.assertTrue( -1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) 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_layers = sum(self.model_tester.depths) + 1 self.assertEqual(len(hidden_states), expected_num_layers) # verify the first hidden states (first block) self.assertListEqual( list(hidden_states[0].shape[-3:]), [ self.model_tester.batch_size, (self.model_tester.image_size // 4) ** 2, 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_training(self): if not self.model_tester.is_training: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True for model_class in self.all_model_classes: if model_class in get_values(MODEL_MAPPING): continue model = model_class(config) model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) loss = model(**inputs).loss loss.backward() 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) @slow def test_model_from_pretrained(self): for model_name in PVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = PvtModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_torch class PvtModelIntegrationTest(unittest.TestCase): @slow def test_inference_image_classification(self): # only resize + normalize image_processor = PvtImageProcessor.from_pretrained("Zetatech/pvt-tiny-224") model = PvtForImageClassification.from_pretrained("Zetatech/pvt-tiny-224").to(torch_device).eval() image = prepare_img() encoded_inputs = image_processor(images=image, return_tensors="pt") pixel_values = encoded_inputs.pixel_values.to(torch_device) with torch.no_grad(): outputs = model(pixel_values) expected_shape = torch.Size((1, model.config.num_labels)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = torch.tensor([-1.4192, -1.9158, -0.9702]).to(torch_device) self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4)) @slow def test_inference_model(self): model = PvtModel.from_pretrained("Zetatech/pvt-tiny-224").to(torch_device).eval() image_processor = PvtImageProcessor.from_pretrained("Zetatech/pvt-tiny-224") image = prepare_img() inputs = image_processor(images=image, return_tensors="pt") pixel_values = inputs.pixel_values.to(torch_device) # forward pass with torch.no_grad(): outputs = model(pixel_values) # verify the logits expected_shape = torch.Size((1, 50, 512)) self.assertEqual(outputs.last_hidden_state.shape, expected_shape) expected_slice = torch.tensor( [[-0.3086, 1.0402, 1.1816], [-0.2880, 0.5781, 0.6124], [0.1480, 0.6129, -0.0590]] ).to(torch_device) self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4)) @slow @require_accelerate @require_torch_gpu def test_inference_fp16(self): r""" A small test to make sure that inference work in half precision without any problem. """ model = PvtForImageClassification.from_pretrained("Zetatech/pvt-tiny-224", torch_dtype=torch.float16) model.to(torch_device) image_processor = PvtImageProcessor(size=224) image = prepare_img() inputs = image_processor(images=image, return_tensors="pt") pixel_values = inputs.pixel_values.to(torch_device, dtype=torch.float16) # forward pass to make sure inference works in fp16 with torch.no_grad(): _ = model(pixel_values)