# 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 Owlv2 model. """ import inspect import os import tempfile import unittest import numpy as np import requests from transformers import Owlv2Config, Owlv2TextConfig, Owlv2VisionConfig from transformers.testing_utils import require_torch, require_torch_gpu, 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, _config_zero_init, floats_tensor, ids_tensor, random_attention_mask, ) from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import Owlv2ForObjectDetection, Owlv2Model, Owlv2TextModel, Owlv2VisionModel from transformers.models.owlv2.modeling_owlv2 import OWLV2_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import OwlViTProcessor # Copied from tests.models.owlvit.test_modeling_owlvit.OwlViTVisionModelTester with OwlViT->Owlv2 class Owlv2VisionModelTester: def __init__( self, parent, batch_size=12, image_size=32, patch_size=2, num_channels=3, is_training=True, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, 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.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.dropout = dropout self.attention_dropout = attention_dropout self.initializer_range = initializer_range self.scope = scope # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token) num_patches = (image_size // patch_size) ** 2 self.seq_length = num_patches + 1 def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) config = self.get_config() return config, pixel_values def get_config(self): return Owlv2VisionConfig( 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, dropout=self.dropout, attention_dropout=self.attention_dropout, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, pixel_values): model = Owlv2VisionModel(config=config).to(torch_device) model.eval() pixel_values = pixel_values.to(torch.float32) with torch.no_grad(): result = model(pixel_values) # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) num_patches = (self.image_size // self.patch_size) ** 2 self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch # Copied from tests.models.owlvit.test_modeling_owlvit.OwlViTVisionModelTest with OwlViT->Owlv2, OWL-ViT->OwlV2, OWLVIT->OWLV2 class Owlv2VisionModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as OWLV2 does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (Owlv2VisionModel,) if is_torch_available() else () fx_compatible = False test_pruning = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = Owlv2VisionModelTester(self) self.config_tester = ConfigTester( self, config_class=Owlv2VisionConfig, has_text_modality=False, hidden_size=37 ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="OWLV2 does not use inputs_embeds") def test_inputs_embeds(self): pass def test_model_common_attributes(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_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) 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(reason="OwlV2 does not support training yet") def test_training(self): pass @unittest.skip(reason="OwlV2 does not support training yet") def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="Owlv2VisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="Owlv2VisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): for model_name in OWLV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = Owlv2VisionModel.from_pretrained(model_name) self.assertIsNotNone(model) # Copied from tests.models.owlvit.test_modeling_owlvit.OwlViTTextModelTester with OwlViT->Owlv2 class Owlv2TextModelTester: def __init__( self, parent, batch_size=12, num_queries=4, seq_length=16, is_training=True, use_input_mask=True, use_labels=True, vocab_size=99, hidden_size=64, num_hidden_layers=12, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, max_position_embeddings=16, initializer_range=0.02, scope=None, ): self.parent = parent self.batch_size = batch_size self.num_queries = num_queries self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_labels = use_labels self.vocab_size = vocab_size 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.dropout = dropout self.attention_dropout = attention_dropout self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.scope = scope def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size * self.num_queries, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size * self.num_queries, self.seq_length]) if input_mask is not None: num_text, seq_length = input_mask.shape rnd_start_indices = np.random.randint(1, seq_length - 1, size=(num_text,)) for idx, start_index in enumerate(rnd_start_indices): input_mask[idx, :start_index] = 1 input_mask[idx, start_index:] = 0 config = self.get_config() return config, input_ids, input_mask def get_config(self): return Owlv2TextConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, dropout=self.dropout, attention_dropout=self.attention_dropout, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, input_ids, input_mask): model = Owlv2TextModel(config=config).to(torch_device) model.eval() with torch.no_grad(): result = model(input_ids=input_ids, attention_mask=input_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size * self.num_queries, self.seq_length, self.hidden_size) ) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size * self.num_queries, self.hidden_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, input_mask = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch # Copied from tests.models.owlvit.test_modeling_owlvit.OwlViTTextModelTest with OwlViT->Owlv2, OWL-ViT->OwlV2, OWLVIT->OWLV2 class Owlv2TextModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (Owlv2TextModel,) if is_torch_available() else () fx_compatible = False test_pruning = False test_head_masking = False def setUp(self): self.model_tester = Owlv2TextModelTester(self) self.config_tester = ConfigTester(self, config_class=Owlv2TextConfig, hidden_size=37) 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(reason="OwlV2 does not support training yet") def test_training(self): pass @unittest.skip(reason="OwlV2 does not support training yet") def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="OWLV2 does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Owlv2TextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="Owlv2TextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): for model_name in OWLV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = Owlv2TextModel.from_pretrained(model_name) self.assertIsNotNone(model) class Owlv2ModelTester: def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True): if text_kwargs is None: text_kwargs = {} if vision_kwargs is None: vision_kwargs = {} self.parent = parent self.text_model_tester = Owlv2TextModelTester(parent, **text_kwargs) self.vision_model_tester = Owlv2VisionModelTester(parent, **vision_kwargs) self.is_training = is_training self.text_config = self.text_model_tester.get_config().to_dict() self.vision_config = self.vision_model_tester.get_config().to_dict() def prepare_config_and_inputs(self): text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs() vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs() config = self.get_config() return config, input_ids, attention_mask, pixel_values def get_config(self): return Owlv2Config.from_text_vision_configs(self.text_config, self.vision_config, projection_dim=64) def create_and_check_model(self, config, input_ids, attention_mask, pixel_values): model = Owlv2Model(config).to(torch_device).eval() with torch.no_grad(): result = model( input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, ) image_logits_size = ( self.vision_model_tester.batch_size, self.text_model_tester.batch_size * self.text_model_tester.num_queries, ) text_logits_size = ( self.text_model_tester.batch_size * self.text_model_tester.num_queries, self.vision_model_tester.batch_size, ) self.parent.assertEqual(result.logits_per_image.shape, image_logits_size) self.parent.assertEqual(result.logits_per_text.shape, text_logits_size) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, attention_mask, pixel_values = config_and_inputs inputs_dict = { "pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask, "return_loss": False, } return config, inputs_dict @require_torch # Copied from tests.models.owlvit.test_modeling_owlvit.OwlViTModelTest with OwlViT->Owlv2, OWL-ViT->OwlV2, OWLVIT->OWLV2 class Owlv2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (Owlv2Model,) if is_torch_available() else () pipeline_model_mapping = ( {"feature-extraction": Owlv2Model, "zero-shot-object-detection": Owlv2ForObjectDetection} if is_torch_available() else {} ) fx_compatible = False test_head_masking = False test_pruning = False test_resize_embeddings = False test_attention_outputs = False def setUp(self): self.model_tester = Owlv2ModelTester(self) 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(reason="Hidden_states is tested in individual model tests") def test_hidden_states_output(self): pass @unittest.skip(reason="Inputs_embeds is tested in individual model tests") def test_inputs_embeds(self): pass @unittest.skip(reason="Retain_grad is tested in individual model tests") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="Owlv2Model does not have input/output embeddings") def test_model_common_attributes(self): pass # override as the `logit_scale` parameter initilization is different for OWLV2 def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): if param.requires_grad: # check if `logit_scale` is initilized as per the original implementation if name == "logit_scale": self.assertAlmostEqual( param.data.item(), np.log(1 / 0.07), delta=1e-3, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) else: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) def _create_and_check_torchscript(self, config, inputs_dict): if not self.test_torchscript: return configs_no_init = _config_zero_init(config) # To be sure we have no Nan configs_no_init.torchscript = True configs_no_init.return_dict = False for model_class in self.all_model_classes: model = model_class(config=configs_no_init).to(torch_device) model.eval() try: input_ids = inputs_dict["input_ids"] pixel_values = inputs_dict["pixel_values"] # OWLV2 needs pixel_values traced_model = torch.jit.trace(model, (input_ids, pixel_values)) except RuntimeError: self.fail("Couldn't trace module.") with tempfile.TemporaryDirectory() as tmp_dir_name: pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt") try: torch.jit.save(traced_model, pt_file_name) except Exception: self.fail("Couldn't save module.") try: loaded_model = torch.jit.load(pt_file_name) except Exception: self.fail("Couldn't load module.") loaded_model = loaded_model.to(torch_device) loaded_model.eval() model_state_dict = model.state_dict() loaded_model_state_dict = loaded_model.state_dict() non_persistent_buffers = {} for key in loaded_model_state_dict.keys(): if key not in model_state_dict.keys(): non_persistent_buffers[key] = loaded_model_state_dict[key] loaded_model_state_dict = { key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers } self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys())) model_buffers = list(model.buffers()) for non_persistent_buffer in non_persistent_buffers.values(): found_buffer = False for i, model_buffer in enumerate(model_buffers): if torch.equal(non_persistent_buffer, model_buffer): found_buffer = True break self.assertTrue(found_buffer) model_buffers.pop(i) models_equal = True for layer_name, p1 in model_state_dict.items(): p2 = loaded_model_state_dict[layer_name] if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) def test_load_vision_text_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Save Owlv2Config and check if we can load Owlv2VisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = Owlv2VisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save Owlv2Config and check if we can load Owlv2TextConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) text_config = Owlv2TextConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict()) @slow def test_model_from_pretrained(self): for model_name in OWLV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = Owlv2Model.from_pretrained(model_name) self.assertIsNotNone(model) # Copied from tests.models.owlvit.test_modeling_owlvit.OwlViTForObjectDetectionTester with OwlViT->Owlv2, OWL-ViT->OwlV2, OWLVIT->OWLV2 class Owlv2ForObjectDetectionTester: def __init__(self, parent, is_training=True): self.parent = parent self.text_model_tester = Owlv2TextModelTester(parent) self.vision_model_tester = Owlv2VisionModelTester(parent) self.is_training = is_training self.text_config = self.text_model_tester.get_config().to_dict() self.vision_config = self.vision_model_tester.get_config().to_dict() def prepare_config_and_inputs(self): text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs() vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs() config = self.get_config() return config, pixel_values, input_ids, attention_mask def get_config(self): return Owlv2Config.from_text_vision_configs(self.text_config, self.vision_config, projection_dim=64) def create_and_check_model(self, config, pixel_values, input_ids, attention_mask): model = Owlv2ForObjectDetection(config).to(torch_device).eval() with torch.no_grad(): result = model( pixel_values=pixel_values, input_ids=input_ids, attention_mask=attention_mask, return_dict=True, ) pred_boxes_size = ( self.vision_model_tester.batch_size, (self.vision_model_tester.image_size // self.vision_model_tester.patch_size) ** 2, 4, ) pred_logits_size = ( self.vision_model_tester.batch_size, (self.vision_model_tester.image_size // self.vision_model_tester.patch_size) ** 2, 4, ) pred_class_embeds_size = ( self.vision_model_tester.batch_size, (self.vision_model_tester.image_size // self.vision_model_tester.patch_size) ** 2, self.text_model_tester.hidden_size, ) self.parent.assertEqual(result.pred_boxes.shape, pred_boxes_size) self.parent.assertEqual(result.logits.shape, pred_logits_size) self.parent.assertEqual(result.class_embeds.shape, pred_class_embeds_size) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, input_ids, attention_mask = config_and_inputs inputs_dict = { "pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask, } return config, inputs_dict @require_torch # Copied from tests.models.owlvit.test_modeling_owlvit.OwlViTForObjectDetectionTest with OwlViT->Owlv2, OWL-ViT->OwlV2, OWLVIT->OWLV2 class Owlv2ForObjectDetectionTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (Owlv2ForObjectDetection,) if is_torch_available() else () fx_compatible = False test_head_masking = False test_pruning = False test_resize_embeddings = False test_attention_outputs = False def setUp(self): self.model_tester = Owlv2ForObjectDetectionTester(self) 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(reason="Hidden_states is tested in individual model tests") def test_hidden_states_output(self): pass @unittest.skip(reason="Inputs_embeds is tested in individual model tests") def test_inputs_embeds(self): pass @unittest.skip(reason="Retain_grad is tested in individual model tests") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="Owlv2Model does not have input/output embeddings") def test_model_common_attributes(self): pass @unittest.skip(reason="Test_initialization is tested in individual model tests") def test_initialization(self): pass @unittest.skip(reason="Test_forward_signature is tested in individual model tests") def test_forward_signature(self): pass @unittest.skip(reason="Test_save_load_fast_init_from_base is tested in individual model tests") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="OwlV2 does not support training yet") def test_training(self): pass @unittest.skip(reason="OwlV2 does not support training yet") def test_training_gradient_checkpointing(self): pass def _create_and_check_torchscript(self, config, inputs_dict): if not self.test_torchscript: return configs_no_init = _config_zero_init(config) # To be sure we have no Nan configs_no_init.torchscript = True configs_no_init.return_dict = False for model_class in self.all_model_classes: model = model_class(config=configs_no_init).to(torch_device) model.eval() try: input_ids = inputs_dict["input_ids"] pixel_values = inputs_dict["pixel_values"] # OWLV2 needs pixel_values traced_model = torch.jit.trace(model, (input_ids, pixel_values)) except RuntimeError: self.fail("Couldn't trace module.") with tempfile.TemporaryDirectory() as tmp_dir_name: pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt") try: torch.jit.save(traced_model, pt_file_name) except Exception: self.fail("Couldn't save module.") try: loaded_model = torch.jit.load(pt_file_name) except Exception: self.fail("Couldn't load module.") loaded_model = loaded_model.to(torch_device) loaded_model.eval() model_state_dict = model.state_dict() loaded_model_state_dict = loaded_model.state_dict() non_persistent_buffers = {} for key in loaded_model_state_dict.keys(): if key not in model_state_dict.keys(): non_persistent_buffers[key] = loaded_model_state_dict[key] loaded_model_state_dict = { key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers } self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys())) model_buffers = list(model.buffers()) for non_persistent_buffer in non_persistent_buffers.values(): found_buffer = False for i, model_buffer in enumerate(model_buffers): if torch.equal(non_persistent_buffer, model_buffer): found_buffer = True break self.assertTrue(found_buffer) model_buffers.pop(i) models_equal = True for layer_name, p1 in model_state_dict.items(): p2 = loaded_model_state_dict[layer_name] if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) @slow def test_model_from_pretrained(self): for model_name in OWLV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = Owlv2ForObjectDetection.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): url = "http://images.cocodataset.org/val2017/000000039769.jpg" im = Image.open(requests.get(url, stream=True).raw) return im @require_vision @require_torch class Owlv2ModelIntegrationTest(unittest.TestCase): @slow def test_inference(self): model_name = "google/owlv2-base-patch16" model = Owlv2Model.from_pretrained(model_name).to(torch_device) processor = OwlViTProcessor.from_pretrained(model_name) image = prepare_img() inputs = processor( text=[["a photo of a cat", "a photo of a dog"]], images=image, max_length=16, padding="max_length", return_tensors="pt", ).to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the logits self.assertEqual( outputs.logits_per_image.shape, torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])), ) self.assertEqual( outputs.logits_per_text.shape, torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])), ) expected_logits = torch.tensor([[-6.2229, -8.2601]], device=torch_device) self.assertTrue(torch.allclose(outputs.logits_per_image, expected_logits, atol=1e-3)) @slow def test_inference_object_detection(self): model_name = "google/owlv2-base-patch16" model = Owlv2ForObjectDetection.from_pretrained(model_name).to(torch_device) processor = OwlViTProcessor.from_pretrained(model_name) image = prepare_img() inputs = processor( text=[["a photo of a cat", "a photo of a dog"]], images=image, max_length=16, padding="max_length", return_tensors="pt", ).to(torch_device) with torch.no_grad(): outputs = model(**inputs) num_queries = int((model.config.vision_config.image_size / model.config.vision_config.patch_size) ** 2) self.assertEqual(outputs.pred_boxes.shape, torch.Size((1, num_queries, 4))) expected_slice_logits = torch.tensor([[-21.4139, -21.6130], [-19.0084, -19.5491], [-20.9592, -21.3830]]) self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_slice_logits, atol=1e-4)) expected_slice_boxes = torch.tensor( [[0.2413, 0.0519, 0.4533], [0.1395, 0.0457, 0.2507], [0.2330, 0.0505, 0.4277]], ).to(torch_device) self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4)) @slow def test_inference_one_shot_object_detection(self): model_name = "google/owlv2-base-patch16" model = Owlv2ForObjectDetection.from_pretrained(model_name).to(torch_device) processor = OwlViTProcessor.from_pretrained(model_name) image = prepare_img() query_image = prepare_img() inputs = processor( images=image, query_images=query_image, max_length=16, padding="max_length", return_tensors="pt", ).to(torch_device) with torch.no_grad(): outputs = model.image_guided_detection(**inputs) num_queries = int((model.config.vision_config.image_size / model.config.vision_config.patch_size) ** 2) self.assertEqual(outputs.target_pred_boxes.shape, torch.Size((1, num_queries, 4))) expected_slice_boxes = torch.tensor( [[0.2413, 0.0519, 0.4533], [0.1395, 0.0457, 0.2507], [0.2330, 0.0505, 0.4277]], ).to(torch_device) self.assertTrue(torch.allclose(outputs.target_pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4)) @slow @require_torch_gpu def test_inference_one_shot_object_detection_fp16(self): model_name = "google/owlv2-base-patch16" model = Owlv2ForObjectDetection.from_pretrained(model_name, torch_dtype=torch.float16).to(torch_device) processor = OwlViTProcessor.from_pretrained(model_name) image = prepare_img() query_image = prepare_img() inputs = processor( images=image, query_images=query_image, max_length=16, padding="max_length", return_tensors="pt", ).to(torch_device) with torch.no_grad(): outputs = model.image_guided_detection(**inputs) # No need to check the logits, we just check inference runs fine. num_queries = int((model.config.vision_config.image_size / model.config.vision_config.patch_size) ** 2) self.assertEqual(outputs.target_pred_boxes.shape, torch.Size((1, num_queries, 4)))