# 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 AIMv2 model.""" import unittest from transformers import AIMv2Config from transformers.testing_utils import ( is_flaky, require_torch, 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(): from torch import nn from transformers import AIMv2ForImageClassification, AIMv2Model if is_vision_available(): pass class AIMv2ModelTester: def __init__( self, parent, batch_size=13, image_size=30, patch_size=2, num_channels=3, is_training=True, use_labels=True, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="silu", type_sequence_label_size=10, initializer_range=0.02, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.is_training = is_training self.use_labels = use_labels self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.scope = scope num_patches = (image_size // patch_size) ** 2 self.seq_length = num_patches 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.type_sequence_label_size) config = self.get_config() return config, pixel_values, labels def get_config(self): return AIMv2Config( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, is_decoder=False, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, pixel_values, labels): model = AIMv2Model(config=config) model.to(torch_device) model.eval() result = model(pixel_values) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_for_image_classification(self, config, pixel_values, labels): config.num_labels = self.type_sequence_label_size model = AIMv2ForImageClassification(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 = AIMv2ForImageClassification(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 @require_torch class Dinov2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as Dinov2 does not use input_ids, inputs_embeds, attention_mask and seq_length. """ test_torch_exportable = True all_model_classes = ( ( AIMv2Model, AIMv2ForImageClassification, ) if is_torch_available() else () ) pipeline_model_mapping = ( {"image-feature-extraction": AIMv2Model, "image-classification": AIMv2ForImageClassification} if is_torch_available() else {} ) fx_compatible = True test_pruning = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = AIMv2ModelTester(self) self.config_tester = ConfigTester(self, config_class=AIMv2Config, has_text_modality=False, hidden_size=37) @is_flaky(max_attempts=3, description="`torch.nn.init.trunc_normal_` is flaky.") def test_initialization(self): super().test_initialization() def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="Dinov2 does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(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) 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_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) @unittest.skip(reason="Dinov2 does not support feedforward chunking yet") def test_feed_forward_chunking(self): pass # 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 Dinov2ModelIntegrationTest(unittest.TestCase): # @cached_property # def default_image_processor(self): # return AutoImageProcessor.from_pretrained("facebook/dinov2-base") if is_vision_available() else None # @slow # def test_inference_no_head(self): # model = Dinov2Model.from_pretrained("facebook/dinov2-base").to(torch_device) # image_processor = self.default_image_processor # image = prepare_img() # inputs = image_processor(image, return_tensors="pt").to(torch_device) # # forward pass # with torch.no_grad(): # outputs = model(**inputs) # # verify the last hidden states # expected_shape = torch.Size((1, 257, 768)) # self.assertEqual(outputs.last_hidden_state.shape, expected_shape) # expected_slice = torch.tensor( # [[-2.2005, -0.4495, 1.0964], [-3.3959, -0.8942, -1.0315], [-2.9355, 1.1564, -0.7656]], # device=torch_device, # ) # torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=1e-3, atol=1e-3)