# 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 ConvNextV2 model.""" import unittest from functools import cached_property from transformers import ConvNextV2Config from transformers.models.auto import get_values from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES 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 transformers import ConvNextV2Backbone, ConvNextV2ForImageClassification, ConvNextV2Model if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class ConvNextV2ModelTester: def __init__( self, parent, batch_size=13, image_size=32, num_channels=3, num_stages=4, hidden_sizes=[10, 20, 30, 40], depths=[2, 2, 3, 2], is_training=True, use_labels=True, intermediate_size=37, hidden_act="gelu", num_labels=10, initializer_range=0.02, out_features=["stage2", "stage3", "stage4"], out_indices=[2, 3, 4], scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.num_channels = num_channels self.num_stages = num_stages self.hidden_sizes = hidden_sizes self.depths = depths self.is_training = is_training self.use_labels = use_labels self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.num_labels = num_labels self.initializer_range = initializer_range self.out_features = out_features self.out_indices = out_indices 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.num_labels) config = self.get_config() return config, pixel_values, labels def get_config(self): return ConvNextV2Config( num_channels=self.num_channels, hidden_sizes=self.hidden_sizes, depths=self.depths, num_stages=self.num_stages, hidden_act=self.hidden_act, is_decoder=False, initializer_range=self.initializer_range, out_features=self.out_features, out_indices=self.out_indices, num_labels=self.num_labels, ) def create_and_check_model(self, config, pixel_values, labels): model = ConvNextV2Model(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 = ConvNextV2ForImageClassification(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 def prepare_config_and_inputs_with_labels(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, labels = config_and_inputs inputs_dict = {"pixel_values": pixel_values, "labels": labels} return config, inputs_dict @require_torch class ConvNextV2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as ConvNextV2 does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = ( ( ConvNextV2Model, ConvNextV2ForImageClassification, ConvNextV2Backbone, ) if is_torch_available() else () ) pipeline_model_mapping = ( {"image-feature-extraction": ConvNextV2Model, "image-classification": ConvNextV2ForImageClassification} if is_torch_available() else {} ) test_resize_embeddings = False has_attentions = False test_torch_exportable = True def setUp(self): self.model_tester = ConvNextV2ModelTester(self) self.config_tester = ConfigTester( self, config_class=ConvNextV2Config, has_text_modality=False, hidden_size=37, common_properties=["hidden_sizes", "num_channels"], ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="ConvNextV2 does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="ConvNextV2 does not support input and output embeddings") def test_model_get_set_embeddings(self): pass @unittest.skip(reason="ConvNextV2 does not use feedforward chunking") def test_feed_forward_chunking(self): pass def test_training(self): if not self.model_tester.is_training: self.skipTest(reason="ModelTester is not set to test training") for model_class in self.all_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_with_labels() config.return_dict = True if model_class.__name__ in [ *get_values(MODEL_MAPPING_NAMES), *get_values(MODEL_FOR_BACKBONE_MAPPING_NAMES), ]: 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_training_gradient_checkpointing(self): if not self.model_tester.is_training: self.skipTest(reason="ModelTester is not set to test training") for model_class in self.all_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_with_labels() config.use_cache = False config.return_dict = True if ( model_class.__name__ in [*get_values(MODEL_MAPPING_NAMES), *get_values(MODEL_FOR_BACKBONE_MAPPING_NAMES)] or not model_class.supports_gradient_checkpointing ): continue model = model_class(config) model.to(torch_device) model.gradient_checkpointing_enable() model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) loss = model(**inputs).loss loss.backward() 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_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 = self.model_tester.num_stages self.assertEqual(len(hidden_states), expected_num_stages + 1) # ConvNextV2'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): model_name = "facebook/convnextv2-tiny-1k-224" model = ConvNextV2Model.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 ConvNextV2ModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224") if is_vision_available() else None @slow def test_inference_image_classification_head(self): model = ConvNextV2ForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224").to(torch_device) preprocessor = self.default_image_processor image = prepare_img() inputs = preprocessor(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([0.9996, 0.1966, -0.4386]).to(torch_device) torch.testing.assert_close(outputs.logits[0, :3], expected_slice, rtol=1e-4, atol=1e-4)