# coding=utf-8 # 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 OwlViT model. """ import inspect import os import tempfile import unittest import numpy as np import requests from transformers import OwlViTConfig, OwlViTTextConfig, OwlViTVisionConfig from transformers.testing_utils import ( require_torch, require_torch_accelerator, require_torch_fp16, 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 OwlViTForObjectDetection, OwlViTModel, OwlViTTextModel, OwlViTVisionModel from transformers.models.owlvit.modeling_owlvit import OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import OwlViTProcessor class OwlViTVisionModelTester: 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 OwlViTVisionConfig( 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 = OwlViTVisionModel(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 class OwlViTVisionModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as OWLVIT does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (OwlViTVisionModel,) 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 = OwlViTVisionModelTester(self) self.config_tester = ConfigTester( self, config_class=OwlViTVisionConfig, has_text_modality=False, hidden_size=37 ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="OWLVIT 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="OWL-ViT does not support training yet") def test_training(self): pass @unittest.skip(reason="OWL-ViT does not support training yet") def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure 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 architecure 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 @unittest.skip(reason="OwlViTVisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="OwlViTVisionModel 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 OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = OwlViTVisionModel.from_pretrained(model_name) self.assertIsNotNone(model) class OwlViTTextModelTester: 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 OwlViTTextConfig( 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 = OwlViTTextModel(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 class OwlViTTextModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (OwlViTTextModel,) if is_torch_available() else () fx_compatible = False test_pruning = False test_head_masking = False def setUp(self): self.model_tester = OwlViTTextModelTester(self) self.config_tester = ConfigTester(self, config_class=OwlViTTextConfig, 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="OWL-ViT does not support training yet") def test_training(self): pass @unittest.skip(reason="OWL-ViT does not support training yet") def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure 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 architecure 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 @unittest.skip(reason="OWLVIT does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="OwlViTTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="OwlViTTextModel 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 OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = OwlViTTextModel.from_pretrained(model_name) self.assertIsNotNone(model) class OwlViTModelTester: 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 = OwlViTTextModelTester(parent, **text_kwargs) self.vision_model_tester = OwlViTVisionModelTester(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 OwlViTConfig.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 = OwlViTModel(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 class OwlViTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (OwlViTModel,) if is_torch_available() else () pipeline_model_mapping = ( {"feature-extraction": OwlViTModel, "zero-shot-object-detection": OwlViTForObjectDetection} 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 = OwlViTModelTester(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="OwlViTModel does not have input/output embeddings") def test_model_common_attributes(self): pass # override as the `logit_scale` parameter initilization is different for OWLVIT 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"] # OWLVIT 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 OwlViTConfig and check if we can load OwlViTVisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = OwlViTVisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save OwlViTConfig and check if we can load OwlViTTextConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) text_config = OwlViTTextConfig.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 OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = OwlViTModel.from_pretrained(model_name) self.assertIsNotNone(model) class OwlViTForObjectDetectionTester: def __init__(self, parent, is_training=True): self.parent = parent self.text_model_tester = OwlViTTextModelTester(parent) self.vision_model_tester = OwlViTVisionModelTester(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 OwlViTConfig.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 = OwlViTForObjectDetection(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 class OwlViTForObjectDetectionTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (OwlViTForObjectDetection,) 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 = OwlViTForObjectDetectionTester(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="OwlViTModel 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="OWL-ViT does not support training yet") def test_training(self): pass @unittest.skip(reason="OWL-ViT does not support training yet") def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure 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 architecure 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 _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"] # OWLVIT 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 OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = OwlViTForObjectDetection.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 OwlViTModelIntegrationTest(unittest.TestCase): @slow def test_inference(self): model_name = "google/owlvit-base-patch32" model = OwlViTModel.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([[3.4613, 0.9403]], 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/owlvit-base-patch32" model = OwlViTForObjectDetection.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_boxes = torch.tensor( [[0.0691, 0.0445, 0.1373], [0.1592, 0.0456, 0.3192], [0.1632, 0.0423, 0.2478]] ).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/owlvit-base-patch32" model = OwlViTForObjectDetection.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( [[0.0691, 0.0445, 0.1373], [0.1592, 0.0456, 0.3192], [0.1632, 0.0423, 0.2478]] ).to(torch_device) self.assertTrue(torch.allclose(outputs.target_pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4)) @slow @require_torch_accelerator @require_torch_fp16 def test_inference_one_shot_object_detection_fp16(self): model_name = "google/owlvit-base-patch32" model = OwlViTForObjectDetection.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)))