# 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 ConvNext model.""" import unittest from functools import cached_property from transformers import DINOv3ConvNextConfig 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 transformers import DINOv3ConvNextBackbone, DINOv3ConvNextModel if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class DINOv3ConvNextModelTester: def __init__( self, parent, batch_size=13, image_size=32, num_channels=3, hidden_sizes=[10, 20, 30, 40], depths=[2, 2, 3, 2], is_training=False, use_labels=True, intermediate_size=37, hidden_act="gelu", num_labels=10, initializer_range=0.02, 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.intermediate_size = intermediate_size self.hidden_act = hidden_act self.num_labels = num_labels self.initializer_range = initializer_range 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 DINOv3ConvNextConfig( num_channels=self.num_channels, hidden_sizes=self.hidden_sizes, depths=self.depths, hidden_act=self.hidden_act, is_decoder=False, initializer_range=self.initializer_range, num_labels=self.num_labels, ) def create_and_check_model(self, config, pixel_values, labels): model = DINOv3ConvNextModel(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, 1 + self.image_size // 32 * self.image_size // 32, self.hidden_sizes[-1], ), ) def create_and_check_backbone(self, config, pixel_values, labels): model = DINOv3ConvNextBackbone(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 // (4 * (2 ** (len(config.depths) - 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), len(config.out_features)) # verify backbone works with out_features=None config.out_features = None model = DINOv3ConvNextBackbone(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) model = DINOv3ConvNextBackbone(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] ) 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 DINOv3ConvNextModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as ConvNext does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (DINOv3ConvNextModel,) if is_torch_available() else () pipeline_model_mapping = {"image-feature-extraction": DINOv3ConvNextModel} if is_torch_available() else {} test_resize_embeddings = False has_attentions = False test_torch_exportable = True def setUp(self): self.model_tester = DINOv3ConvNextModelTester(self) self.config_tester = ConfigTester( self, config_class=DINOv3ConvNextConfig, has_text_modality=False, hidden_size=37, common_properties=["num_channels", "hidden_sizes"], ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="DINOv3ConvNext does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="DINOv3ConvNext does not support input and output embeddings") def test_model_get_set_embeddings(self): pass @unittest.skip(reason="DINOv3ConvNext does not use feedforward chunking") def test_feed_forward_chunking(self): pass 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_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 self.assertEqual(len(hidden_states), 5) # DINOv3ConvNext's feature maps are of shape (batch_size, num_channels, height, width) self.assertListEqual( list(hidden_states[1].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) @slow def test_model_from_pretrained(self): model_name = "facebook/dinov3-convnext-tiny-pretrain-lvd1689m" model = DINOv3ConvNextModel.from_pretrained(model_name) self.assertIsNotNone(model) @unittest.skip(reason="DINOv3ConvNext does not retain grads for first hidden state (original pixel_values)") def test_retain_grad_hidden_states_attentions(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 DINOv3ConvNextModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return ( AutoImageProcessor.from_pretrained("facebook/dinov3-convnext-tiny-pretrain-lvd1689m") if is_vision_available() else None ) @slow def test_inference_no_head(self): model = DINOv3ConvNextModel.from_pretrained("facebook/dinov3-convnext-tiny-pretrain-lvd1689m").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 _, _, height, width = inputs["pixel_values"].shape expected_seq_length = (height * width) // 4 ** (model.config.num_stages + 1) + 1 # +1 for the "CLS" token expected_shape = torch.Size((1, expected_seq_length, model.config.hidden_sizes[-1])) self.assertEqual(outputs.last_hidden_state.shape, expected_shape) last_layer_cls_token = outputs.pooler_output expected_slice = torch.tensor([-6.3721, 1.3008, 2.0743, -0.0800, 0.6072], device=torch_device) torch.testing.assert_close(last_layer_cls_token[0, :5], expected_slice, rtol=1e-4, atol=1e-4) last_layer_patch_tokens = outputs.last_hidden_state[:, 1:] expected_slice = torch.tensor([0.4905, -3.7135, 1.8485, -1.0403, -1.0908], device=torch_device) torch.testing.assert_close(last_layer_patch_tokens[0, 0, :5], expected_slice, rtol=1e-4, atol=1e-4) @require_torch class DINOv3ConvNextBackboneTest(unittest.TestCase, BackboneTesterMixin): all_model_classes = (DINOv3ConvNextBackbone,) if is_torch_available() else () config_class = DINOv3ConvNextConfig has_attentions = False def setUp(self): self.model_tester = DINOv3ConvNextModelTester(self)