# 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. """ from __future__ import annotations 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=2, 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=2, 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) @unittest.skip("Matt: Re-enable this test when we have a proper export function for TF models.") def test_saved_model_creation(self): # This fails because the if return_loss: conditional can return None or a Tensor and TF hates that. # We could fix that by setting the bool to a constant when exporting, but that requires a dedicated export # function that we don't have yet. pass 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 class BlipVQAModelsModelTester: 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, "decoder_input_ids": input_ids, "labels": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values, } return config, inputs_dict @require_tf @require_vision class TFBlipVQAModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = (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 = BlipVQAModelsModelTester(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["decoder_input_ids"] = 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.model_tester.prepare_config_and_inputs_for_common()[1], training=True).loss self.assertIsNotNone(loss, "Loss should not be None") @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 @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 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,) 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))