# 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 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, 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 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 () test_resize_embeddings = 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_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_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 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 @slow def test_model_from_pretrained(self): model_name = "google/owlvit-base-patch32" 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 () 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 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 @unittest.skip(reason="OWLVIT does not use inputs_embeds") def test_inputs_embeds(self): pass @slow def test_model_from_pretrained(self): model_name = "google/owlvit-base-patch32" 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() self.batch_size = self.text_model_tester.batch_size # need bs for batching_equivalence test 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( text_config=self.text_config, vision_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 {} ) test_resize_embeddings = False test_attention_outputs = False def setUp(self): self.model_tester = OwlViTModelTester(self) common_properties = ["projection_dim", "logit_scale_init_value"] self.config_tester = ConfigTester( self, config_class=OwlViTConfig, has_text_modality=False, common_properties=common_properties ) 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="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_get_set_embeddings(self): pass 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): model_name = "google/owlvit-base-patch32" 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() self.batch_size = self.text_model_tester.batch_size # need bs for batching_equivalence test 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( text_config=self.text_config, vision_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 () 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_get_set_embeddings(self): pass @unittest.skip(reason="Test_forward_signature is tested in individual model tests") def test_forward_signature(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 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 @slow def test_model_from_pretrained(self): model_name = "google/owlvit-base-patch32" 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) torch.testing.assert_close(outputs.logits_per_image, expected_logits, rtol=1e-3, atol=1e-3) @slow def test_inference_interpolate_pos_encoding(self): model_name = "google/owlvit-base-patch32" model = OwlViTModel.from_pretrained(model_name).to(torch_device) processor = OwlViTProcessor.from_pretrained(model_name) processor.image_processor.size = {"height": 800, "width": 800} 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, interpolate_pos_encoding=True) # 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([[3.6278, 0.8861]], device=torch_device) torch.testing.assert_close(outputs.logits_per_image, expected_logits, rtol=1e-3, atol=1e-3) expected_shape = torch.Size((1, 626, 768)) self.assertEqual(outputs.vision_model_output.last_hidden_state.shape, expected_shape) # OwlViTForObjectDetection part. model = OwlViTForObjectDetection.from_pretrained(model_name).to(torch_device) with torch.no_grad(): outputs = model(**inputs, interpolate_pos_encoding=True) num_queries = int((inputs.pixel_values.shape[-1] // model.config.vision_config.patch_size) ** 2) self.assertEqual(outputs.pred_boxes.shape, torch.Size((1, num_queries, 4))) expected_slice_boxes = torch.tensor( [[0.0680, 0.0422, 0.1347], [0.2071, 0.0450, 0.4146], [0.2000, 0.0418, 0.3476]] ).to(torch_device) torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, rtol=1e-4, atol=1e-4) model = OwlViTForObjectDetection.from_pretrained(model_name).to(torch_device) 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, interpolate_pos_encoding=True) # No need to check the logits, we just check inference runs fine. num_queries = int((inputs.pixel_values.shape[-1] / model.config.vision_config.patch_size) ** 2) self.assertEqual(outputs.target_pred_boxes.shape, torch.Size((1, num_queries, 4))) # Deactivate interpolate_pos_encoding on same model, and use default image size. # Verify the dynamic change caused by the activation/deactivation of interpolate_pos_encoding of variables: (self.sqrt_num_patch_h, self.sqrt_num_patch_w), self.box_bias from (OwlViTForObjectDetection). 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, interpolate_pos_encoding=False) num_queries = int((inputs.pixel_values.shape[-1] // model.config.vision_config.patch_size) ** 2) self.assertEqual(outputs.pred_boxes.shape, torch.Size((1, num_queries, 4))) expected_default_box_bias = torch.tensor( [ [-3.1332, -3.1332, -3.1332, -3.1332], [-2.3968, -3.1332, -3.1332, -3.1332], [-1.9452, -3.1332, -3.1332, -3.1332], ] ) torch.testing.assert_close(model.box_bias[:3, :4], expected_default_box_bias, rtol=1e-4, atol=1e-4) # Interpolate with any resolution size. processor.image_processor.size = {"height": 1264, "width": 1024} 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, interpolate_pos_encoding=True) num_queries = int( (inputs.pixel_values.shape[-2] // model.config.vision_config.patch_size) * (inputs.pixel_values.shape[-1] // model.config.vision_config.patch_size) ) self.assertEqual(outputs.pred_boxes.shape, torch.Size((1, num_queries, 4))) expected_slice_boxes = torch.tensor( [[0.0499, 0.0301, 0.0983], [0.2244, 0.0365, 0.4663], [0.1387, 0.0314, 0.1859]] ).to(torch_device) torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, rtol=1e-4, atol=1e-4) 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, interpolate_pos_encoding=True) # No need to check the logits, we just check inference runs fine. num_queries = int( (inputs.pixel_values.shape[-2] // model.config.vision_config.patch_size) * (inputs.pixel_values.shape[-1] // model.config.vision_config.patch_size) ) self.assertEqual(outputs.target_pred_boxes.shape, torch.Size((1, num_queries, 4))) @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() text_labels = [["a photo of a cat", "a photo of a dog"]] inputs = processor( text=text_labels, 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) torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, rtol=1e-4, atol=1e-4) # test post-processing post_processed_output = processor.post_process_grounded_object_detection(outputs) self.assertIsNone(post_processed_output[0]["text_labels"]) post_processed_output_with_text_labels = processor.post_process_grounded_object_detection( outputs, text_labels=text_labels ) objects_labels = post_processed_output_with_text_labels[0]["labels"].tolist() self.assertListEqual(objects_labels, [0, 0]) objects_text_labels = post_processed_output_with_text_labels[0]["text_labels"] self.assertIsNotNone(objects_text_labels) self.assertListEqual(objects_text_labels, ["a photo of a cat", "a photo of a cat"]) @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) torch.testing.assert_close(outputs.target_pred_boxes[0, :3, :3], expected_slice_boxes, rtol=1e-4, 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, 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)))