# 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 TensorFlow Blip model. """ import inspect import tempfile import unittest import numpy as np import requests from transformers import BlipConfig, BlipTextConfig, BlipVisionConfig from transformers.testing_utils import require_tf, require_vision, slow from transformers.utils import is_tf_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import ( TFBlipForConditionalGeneration, TFBlipForImageTextRetrieval, TFBlipForQuestionAnswering, TFBlipModel, TFBlipTextModel, TFBlipVisionModel, ) from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import BlipProcessor class TFBlipVisionModelTester: def __init__( self, parent, batch_size=12, image_size=30, patch_size=2, num_channels=3, is_training=True, hidden_size=32, projection_dim=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, initializer_range=1e-10, 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.projection_dim = projection_dim 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 BlipVisionConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, projection_dim=self.projection_dim, 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 = TFBlipVisionModel(config=config) result = model(pixel_values) # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) image_size = (self.image_size, self.image_size) patch_size = (self.patch_size, self.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) 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_tf class TFBlipVisionModelTest(TFModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as Blip does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (TFBlipVisionModel,) if is_tf_available() else () fx_compatible = False test_pruning = False test_resize_embeddings = False test_head_masking = False test_onnx = False def setUp(self): self.model_tester = TFBlipVisionModelTester(self) self.config_tester = ConfigTester(self, config_class=BlipVisionConfig, has_text_modality=False, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="Blip does not use inputs_embeds") def test_inputs_embeds(self): pass 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.call) # 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_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(), (tf.keras.layers.Layer)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, tf.keras.layers.Layer)) 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="BlipVisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="BlipVisionModel 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 TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFBlipVisionModel.from_pretrained(model_name) self.assertIsNotNone(model) class TFBlipTextModelTester: def __init__( self, parent, batch_size=12, seq_length=7, is_training=True, use_input_mask=True, use_labels=True, vocab_size=99, hidden_size=32, projection_dim=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, max_position_embeddings=512, initializer_range=0.02, bos_token_id=0, scope=None, ): self.parent = parent self.batch_size = batch_size 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.projection_dim = projection_dim 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 self.bos_token_id = bos_token_id def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) if input_mask is not None: input_mask = input_mask.numpy() batch_size, seq_length = input_mask.shape rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,)) for batch_idx, start_index in enumerate(rnd_start_indices): input_mask[batch_idx, :start_index] = 1 input_mask[batch_idx, start_index:] = 0 input_mask = tf.convert_to_tensor(input_mask) config = self.get_config() return config, input_ids, input_mask def get_config(self): return BlipTextConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, projection_dim=self.projection_dim, 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, bos_token_id=self.bos_token_id, ) def create_and_check_model(self, config, input_ids, input_mask): model = TFBlipTextModel(config=config) result = model(input_ids, attention_mask=input_mask, training=False) result = model(input_ids, training=False) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, 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, input_ids, input_mask = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_tf class TFBlipTextModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = (TFBlipTextModel,) if is_tf_available() else () fx_compatible = False test_pruning = False test_head_masking = False test_onnx = False def setUp(self): self.model_tester = TFBlipTextModelTester(self) self.config_tester = ConfigTester(self, config_class=BlipTextConfig, 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="Blip does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="BlipTextModel 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 TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFBlipTextModel.from_pretrained(model_name) self.assertIsNotNone(model) def test_pt_tf_model_equivalence(self, allow_missing_keys=True): super().test_pt_tf_model_equivalence(allow_missing_keys=allow_missing_keys) class TFBlipModelTester: 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 = TFBlipTextModelTester(parent, **text_kwargs) self.vision_model_tester = TFBlipVisionModelTester(parent, **vision_kwargs) self.is_training = is_training 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 BlipConfig.from_text_vision_configs( self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64 ) def create_and_check_model(self, config, input_ids, attention_mask, pixel_values): model = TFBlipModel(config) result = model(input_ids, pixel_values, attention_mask, training=False) self.parent.assertEqual( result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size) ) self.parent.assertEqual( result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_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 = { "input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values, "return_loss": True, } return config, inputs_dict @require_tf class TFBlipModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (TFBlipModel,) if is_tf_available() else () pipeline_model_mapping = ( {"feature-extraction": TFBlipModel, "image-to-text": TFBlipForConditionalGeneration} if is_tf_available() else {} ) test_head_masking = False test_pruning = False test_resize_embeddings = False test_attention_outputs = False test_onnx = False def setUp(self): self.model_tester = TFBlipModelTester(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="BlipModel does not have input/output embeddings") def test_model_common_attributes(self): pass def test_load_vision_text_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Save BlipConfig and check if we can load BlipVisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = BlipVisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save BlipConfig and check if we can load BlipTextConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) text_config = BlipTextConfig.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 TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFBlipModel.from_pretrained(model_name) self.assertIsNotNone(model) def test_pt_tf_model_equivalence(self, allow_missing_keys=True): super().test_pt_tf_model_equivalence(allow_missing_keys=allow_missing_keys) class BlipTextRetrievalModelTester: 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 = TFBlipTextModelTester(parent, **text_kwargs) self.vision_model_tester = TFBlipVisionModelTester(parent, **vision_kwargs) self.is_training = is_training 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 BlipConfig.from_text_vision_configs( self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64 ) def create_and_check_model(self, config, input_ids, attention_mask, pixel_values): model = TFBlipModel(config) result = model(input_ids, pixel_values, attention_mask, training=False) self.parent.assertEqual( result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size) ) self.parent.assertEqual( result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_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 = { "input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values, } return config, inputs_dict class BlipTextImageModelsModelTester: 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 = TFBlipTextModelTester(parent, **text_kwargs) self.vision_model_tester = TFBlipVisionModelTester(parent, **vision_kwargs) self.is_training = is_training 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 BlipConfig.from_text_vision_configs( self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64 ) def create_and_check_model(self, config, input_ids, attention_mask, pixel_values): model = TFBlipModel(config) result = model(input_ids, pixel_values, attention_mask, training=False) self.parent.assertEqual( result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size) ) self.parent.assertEqual( result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_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 = { "input_ids": input_ids, "labels": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values, } return config, inputs_dict @require_tf @require_vision class BlipVQAModelTest(unittest.TestCase): all_model_classes = (TFBlipForQuestionAnswering,) if is_tf_available() else () def setUp(self): self.model_tester = TFBlipModelTester(self) def _prepare_inputs_for_vqa(self): _, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() inputs_dict["labels"] = inputs_dict["input_ids"] inputs_dict.pop("return_loss") return inputs_dict def test_class_name_consistency(self): """ Tests that all VQA models have a class name that ends with "ForQuestionAnswering" """ for model_class in self.all_model_classes: model = model_class(self.model_tester.get_config()) self.assertTrue( model.__class__.__name__.endswith("ForQuestionAnswering"), f"Class name should end with 'ForVisualQuestionAnswering' got {model.__class__.__name__}", ) def test_training(self): """ Tests that all VQA models can be trained on a single batch """ for model_class in self.all_model_classes: model = model_class(self.model_tester.get_config()) loss = model(**self._prepare_inputs_for_vqa(), training=True).loss self.assertIsNotNone(loss, "Loss should not be None") @require_tf class TFBlipTextRetrievalModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = (TFBlipForImageTextRetrieval,) if is_tf_available() else () test_head_masking = False test_pruning = False test_resize_embeddings = False test_attention_outputs = False test_onnx = False def setUp(self): self.model_tester = BlipTextRetrievalModelTester(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="BlipModel does not have input/output embeddings") def test_model_common_attributes(self): pass def test_training(self): if not self.model_tester.is_training: return for model_class in self.all_model_classes[:-1]: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True model = model_class(config) inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) # hardcode labels to be the same as input_ids inputs["labels"] = inputs["input_ids"] loss = model(**inputs, training=True).loss self.assertTrue(loss is not None) def test_load_vision_text_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Save BlipConfig and check if we can load BlipVisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = BlipVisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save BlipConfig and check if we can load BlipTextConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) text_config = BlipTextConfig.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 TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFBlipModel.from_pretrained(model_name) self.assertIsNotNone(model) @unittest.skip(reason="Tested in individual model tests") def test_compile_tf_model(self): pass @unittest.skip("Model doesn't have a clean loss output.") def test_keras_fit(self): pass @require_tf class TFBlipTextImageModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = (TFBlipForConditionalGeneration, TFBlipForQuestionAnswering) if is_tf_available() else () test_head_masking = False test_pruning = False test_resize_embeddings = False test_attention_outputs = False test_onnx = False def setUp(self): self.model_tester = BlipTextImageModelsModelTester(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 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.call) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] if model.config.is_encoder_decoder: expected_arg_names = [ "input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", ] expected_arg_names.extend( ["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"] if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names else ["encoder_outputs"] ) self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) else: expected_arg_names = ( ["input_ids"] if model_class != TFBlipForConditionalGeneration else ["pixel_values"] ) self.assertListEqual(arg_names[:1], expected_arg_names) @unittest.skip(reason="Tested in individual model tests") def test_compile_tf_model(self): pass @unittest.skip("Has some odd input names!") def test_keras_fit(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="BlipModel does not have input/output embeddings") def test_model_common_attributes(self): pass def test_training(self): if not self.model_tester.is_training: return for model_class in self.all_model_classes[:-1]: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True model = model_class(config) inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) # hardcode labels to be the same as input_ids inputs["labels"] = inputs["input_ids"] loss = model(**inputs, training=True).loss self.assertIsNotNone(loss) def test_load_vision_text_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Save BlipConfig and check if we can load BlipVisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = BlipVisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save BlipConfig and check if we can load BlipTextConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) text_config = BlipTextConfig.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 TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFBlipModel.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): url = "https://huggingface.co/hf-internal-testing/blip-test-image/resolve/main/demo.jpg" im = Image.open(requests.get(url, stream=True).raw) return im @require_vision @require_tf @slow class TFBlipModelIntegrationTest(unittest.TestCase): def test_inference_image_captioning(self): model = TFBlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") image = prepare_img() # image only inputs = processor(images=image, return_tensors="tf") predictions = model.generate(**inputs) # Test output self.assertEqual( predictions[0].numpy().tolist(), [30522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102] ) # image and context context = ["a picture of"] inputs = processor(images=image, text=context, return_tensors="tf") predictions = model.generate(**inputs) # Test output self.assertEqual( predictions[0].numpy().tolist(), [30522, 1037, 3861, 1997, 1037, 2450, 1998, 2014, 3899, 2006, 1996, 3509, 102], ) def test_inference_vqa(self): model = TFBlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base") processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base") image = prepare_img() text = "how many dogs are in the picture?" inputs = processor(image, text=text, return_tensors="tf") out = model.generate(**inputs) # Test output self.assertEqual(out[0].numpy().tolist(), [30522, 1015, 102]) def test_inference_itm(self): model = TFBlipForImageTextRetrieval.from_pretrained("Salesforce/blip-itm-base-coco") processor = BlipProcessor.from_pretrained("Salesforce/blip-itm-base-coco") image = prepare_img() text = "A woman and her dog sitting in a beach" inputs = processor(image, text, return_tensors="tf") out_itm = model(**inputs) out = model(**inputs, use_itm_head=False, training=False) expected_scores = tf.convert_to_tensor([[0.0029, 0.9971]]) self.assertTrue(np.allclose(tf.nn.softmax(out_itm[0]).numpy(), expected_scores, rtol=1e-3, atol=1e-3)) self.assertTrue(np.allclose(out[0], tf.convert_to_tensor([[0.5162]]), rtol=1e-3, atol=1e-3))