# Copyright 2024 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 Dinov2WithRegisters model.""" import unittest from functools import cached_property from transformers import Dinov2WithRegistersConfig from transformers.testing_utils import ( require_torch, require_vision, slow, torch_device, ) from transformers.utils import is_torch_available, is_vision_available from ...test_backbone_common import BackboneTesterMixin 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 torch import nn from transformers import ( Dinov2WithRegistersBackbone, Dinov2WithRegistersForImageClassification, Dinov2WithRegistersModel, ) if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class Dinov2WithRegistersModelTester: 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="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, type_sequence_label_size=10, initializer_range=0.02, num_register_tokens=2, mask_ratio=0.5, 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.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_register_tokens = num_register_tokens self.scope = scope # in DINOv2 with Registers, the seq length equals the number of patches + 1 + num_register_tokens (we add 1 for the [CLS] token) num_patches = (image_size // patch_size) ** 2 self.seq_length = num_patches + 1 + self.num_register_tokens self.mask_ratio = mask_ratio self.num_masks = int(mask_ratio * self.seq_length) self.mask_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 Dinov2WithRegistersConfig( 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, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, is_decoder=False, initializer_range=self.initializer_range, num_register_tokens=self.num_register_tokens, ) def create_and_check_model(self, config, pixel_values, labels): model = Dinov2WithRegistersModel(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_backbone(self, config, pixel_values, labels): model = Dinov2WithRegistersBackbone(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)) expected_size = self.image_size // config.patch_size self.parent.assertListEqual( list(result.feature_maps[0].shape), [self.batch_size, model.channels[0], expected_size, expected_size] ) # verify channels self.parent.assertEqual(len(model.channels), len(config.out_features)) # verify backbone works with out_features=None config.out_features = None model = Dinov2WithRegistersBackbone(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[0], expected_size, expected_size] ) # verify channels self.parent.assertEqual(len(model.channels), 1) # verify backbone works with apply_layernorm=False and reshape_hidden_states=False config.apply_layernorm = False config.reshape_hidden_states = False model = Dinov2WithRegistersBackbone(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, 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 = Dinov2WithRegistersForImageClassification(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 = Dinov2WithRegistersForImageClassification(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 Dinov2WithRegistersModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as Dinov2WithRegisters does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = ( ( Dinov2WithRegistersModel, Dinov2WithRegistersForImageClassification, Dinov2WithRegistersBackbone, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "image-feature-extraction": Dinov2WithRegistersModel, "image-classification": Dinov2WithRegistersForImageClassification, } if is_torch_available() else {} ) test_resize_embeddings = False test_torch_exportable = True def setUp(self): self.model_tester = Dinov2WithRegistersModelTester(self) self.config_tester = ConfigTester( self, config_class=Dinov2WithRegistersConfig, has_text_modality=False, hidden_size=37 ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="Dinov2WithRegisters 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_backbone(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_backbone(*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="Dinov2WithRegisters does not support feedforward chunking yet") def test_feed_forward_chunking(self): pass @slow def test_model_from_pretrained(self): model_name = "facebook/dinov2-with-registers-base" model = Dinov2WithRegistersModel.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 Dinov2WithRegistersModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return ( AutoImageProcessor.from_pretrained("facebook/dinov2-with-registers-base") if is_vision_available() else None ) @slow def test_inference_no_head(self): model = Dinov2WithRegistersModel.from_pretrained("facebook/dinov2-with-registers-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 # in DINOv2 with Registers, the seq length equals the number of patches + 1 + num_register_tokens (we add 1 for the [CLS] token) num_patches = (image_processor.crop_size["height"] // model.config.patch_size) ** 2 expected_seq_length = num_patches + 1 + model.config.num_register_tokens expected_shape = torch.Size((1, expected_seq_length, model.config.hidden_size)) self.assertEqual(outputs.last_hidden_state.shape, expected_shape) expected_slice = torch.tensor( [[-0.4636, -1.4582, -0.0274], [-1.4738, -0.8858, 0.3002], [0.0714, -0.2407, -1.5940]], device=torch_device, ) torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4) @require_torch class Dinov2WithRegistersBackboneTest(unittest.TestCase, BackboneTesterMixin): all_model_classes = (Dinov2WithRegistersBackbone,) if is_torch_available() else () config_class = Dinov2WithRegistersConfig has_attentions = False def setUp(self): self.model_tester = Dinov2WithRegistersModelTester(self)