# Copyright 2022 The HuggingFace 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. import inspect import unittest from huggingface_hub import hf_hub_download from transformers import GitConfig, GitProcessor, GitVisionConfig, is_torch_available, is_vision_available from transformers.models.auto import get_values from transformers.testing_utils import require_torch, require_vision, slow, torch_device from ...generation.test_utils import GenerationTesterMixin 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 MODEL_FOR_CAUSAL_LM_MAPPING, GitForCausalLM, GitModel, GitVisionModel if is_vision_available(): from PIL import Image class GitVisionModelTester: def __init__( self, parent, batch_size=12, image_size=32, patch_size=16, 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=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.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 GitVisionConfig( 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 = GitVisionModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): 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)) 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 GitVisionModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as GIT does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (GitVisionModel,) if is_torch_available() else () test_resize_embeddings = False def setUp(self): self.model_tester = GitVisionModelTester(self) self.config_tester = ConfigTester(self, config_class=GitVisionConfig, has_text_modality=False, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="GIT 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 def test_training(self): pass @unittest.skip 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 = "microsoft/git-base" model = GitVisionModel.from_pretrained(model_name) self.assertIsNotNone(model) class GitModelTester: def __init__( self, parent, num_channels=3, image_size=32, patch_size=16, batch_size=13, text_seq_length=7, is_training=True, use_input_mask=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, initializer_range=0.02, num_labels=3, scope=None, ): self.parent = parent self.num_channels = num_channels self.image_size = image_size self.patch_size = patch_size self.batch_size = batch_size self.text_seq_length = text_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.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.initializer_range = initializer_range self.num_labels = num_labels self.scope = scope # make sure the BOS, EOS and PAD tokens are within the vocab self.bos_token_id = vocab_size - 1 self.eos_token_id = vocab_size - 1 self.pad_token_id = vocab_size - 1 # for GIT, the sequence length is the sum of the text and patch tokens, + 1 due to the CLS token self.seq_length = self.text_seq_length + int((self.image_size / self.patch_size) ** 2) + 1 def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.text_seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.text_seq_length]) pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) config = self.get_config() return config, input_ids, input_mask, pixel_values def get_config(self): """ Returns a tiny configuration by default. """ return GitConfig( vision_config={ "num_channels": self.num_channels, "image_size": self.image_size, "patch_size": self.patch_size, "hidden_size": self.hidden_size, "projection_dim": 32, "num_hidden_layers": self.num_hidden_layers, "num_attention_heads": self.num_attention_heads, }, 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, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, ) def create_and_check_model(self, config, input_ids, input_mask, pixel_values): model = GitModel(config=config) model.to(torch_device) model.eval() # inference with pixel values result = model(input_ids, attention_mask=input_mask, pixel_values=pixel_values) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) # inference without pixel values result = model(input_ids, attention_mask=input_mask) result = model(input_ids) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.text_seq_length, self.hidden_size) ) def create_and_check_for_causal_lm(self, config, input_ids, input_mask, pixel_values): model = GitForCausalLM(config=config) model.to(torch_device) model.eval() # inference with pixel values result = model(input_ids, attention_mask=input_mask, pixel_values=pixel_values) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) # inference without pixel values result = model(input_ids, attention_mask=input_mask) result = model(input_ids) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.text_seq_length, self.vocab_size)) # training result = model(input_ids, attention_mask=input_mask, pixel_values=pixel_values, labels=input_ids) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertTrue(result.loss.item() > 0) def _test_batched_generate_captioning(self, config, input_ids, input_mask, pixel_values): model = GitForCausalLM(config=config) model.to(torch_device) model.eval() # generate generated_ids = model.generate( input_ids=None, # captioning -> no input_ids attention_mask=None, pixel_values=pixel_values, do_sample=False, min_length=20, max_length=20, num_beams=2, num_return_sequences=2, ) self.parent.assertEqual(generated_ids.shape, (self.batch_size * 2, 20)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, input_mask, pixel_values, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": input_mask, "pixel_values": pixel_values, } return config, inputs_dict @require_torch class GitModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (GitModel, GitForCausalLM) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": GitModel, "image-to-text": GitForCausalLM, "text-generation": GitForCausalLM, "image-text-to-text": GitForCausalLM, "any-to-any": GitForCausalLM, } if is_torch_available() else {} ) # special case for GitForCausalLM model def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) if return_labels: if model_class in get_values(MODEL_FOR_CAUSAL_LM_MAPPING): inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.text_seq_length), dtype=torch.long, device=torch_device, ) return inputs_dict def setUp(self): self.model_tester = GitModelTester(self) self.config_tester = ConfigTester(self, config_class=GitConfig, 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) def test_for_causal_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_causal_lm(*config_and_inputs) def test_batched_generate_captioning(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester._test_batched_generate_captioning(*config_and_inputs) def _check_attentions_for_generate( self, batch_size, attentions, prompt_length, output_length, config, decoder_past_key_values ): # GIT attention shape depends on image inputs, overwrite image_length = int((config.vision_config.image_size / config.vision_config.patch_size) ** 2 + 1) prompt_length += image_length output_length += image_length super()._check_attentions_for_generate( batch_size, attentions, prompt_length, output_length, config, decoder_past_key_values ) def _check_hidden_states_for_generate( self, batch_size, hidden_states, prompt_length, output_length, config, use_cache=False ): # GIT attention shape depends on image inputs, overwrite image_length = int((config.vision_config.image_size / config.vision_config.patch_size) ** 2 + 1) prompt_length += image_length output_length += image_length super()._check_hidden_states_for_generate( batch_size, hidden_states, prompt_length, output_length, config, use_cache=use_cache ) @slow def test_model_from_pretrained(self): model_name = "microsoft/git-base" model = GitModel.from_pretrained(model_name) self.assertIsNotNone(model) @unittest.skip(reason="GIT has pixel values as additional input") def test_beam_search_generate_dict_outputs_use_cache(self): pass @unittest.skip(reason="GIT has pixel values as additional input") def test_greedy_generate_dict_outputs_use_cache(self): pass @unittest.skip(reason="GIT input and output sequence lengths are not equal due to pixel values additional input") def test_forward_with_logits_to_keep(self): pass @require_torch @require_vision @slow class GitModelIntegrationTest(unittest.TestCase): def test_forward_pass(self): processor = GitProcessor.from_pretrained("microsoft/git-base") model = GitForCausalLM.from_pretrained("microsoft/git-base") model.to(torch_device) image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") inputs = processor(images=image, text="hello world", return_tensors="pt").to(torch_device) with torch.no_grad(): outputs = model(**inputs) expected_shape = torch.Size((1, 201, 30522)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = torch.tensor( [[-0.9514, -0.9512, -0.9507], [-0.5454, -0.5453, -0.5453], [-0.8862, -0.8857, -0.8848]], device=torch_device, ) torch.testing.assert_close(outputs.logits[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4) def test_inference_image_captioning(self): processor = GitProcessor.from_pretrained("microsoft/git-base") model = GitForCausalLM.from_pretrained("microsoft/git-base") model.to(torch_device) image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") inputs = processor(images=image, return_tensors="pt") pixel_values = inputs.pixel_values.to(torch_device) outputs = model.generate( pixel_values=pixel_values, max_length=20, output_scores=True, return_dict_in_generate=True ) generated_caption = processor.batch_decode(outputs.sequences, skip_special_tokens=True)[0] expected_shape = torch.Size((1, 9)) self.assertEqual(outputs.sequences.shape, expected_shape) self.assertEqual(generated_caption, "two cats laying on a pink blanket") self.assertTrue(outputs.scores[-1].shape, expected_shape) expected_slice = torch.tensor([-0.8805, -0.8803, -0.8799], device=torch_device) torch.testing.assert_close(outputs.scores[-1][0, :3], expected_slice, rtol=1e-4, atol=1e-4) def test_visual_question_answering(self): processor = GitProcessor.from_pretrained("microsoft/git-base-textvqa") model = GitForCausalLM.from_pretrained("microsoft/git-base-textvqa") model.to(torch_device) # prepare image file_path = hf_hub_download(repo_id="nielsr/textvqa-sample", filename="bus.png", repo_type="dataset") image = Image.open(file_path).convert("RGB") inputs = processor(images=image, return_tensors="pt") pixel_values = inputs.pixel_values.to(torch_device) # prepare question question = "what does the front of the bus say at the top?" input_ids = processor(text=question, add_special_tokens=False).input_ids input_ids = [processor.tokenizer.cls_token_id] + input_ids input_ids = torch.tensor(input_ids).unsqueeze(0).to(torch_device) generated_ids = model.generate(pixel_values=pixel_values, input_ids=input_ids, max_length=20) generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] expected_shape = torch.Size((1, 15)) self.assertEqual(generated_ids.shape, expected_shape) self.assertEqual(generated_caption, "what does the front of the bus say at the top? special") def test_batched_generation(self): processor = GitProcessor.from_pretrained("microsoft/git-base-coco") model = GitForCausalLM.from_pretrained("microsoft/git-base-coco") model.to(torch_device) # create batch of size 2 image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") inputs = processor(images=[image, image], return_tensors="pt") pixel_values = inputs.pixel_values.to(torch_device) # we have to prepare `input_ids` with the same batch size as `pixel_values` start_token_id = model.config.bos_token_id input_ids = torch.tensor([[start_token_id], [start_token_id]], device=torch_device) generated_ids = model.generate(pixel_values=pixel_values, input_ids=input_ids, max_length=50) generated_captions = processor.batch_decode(generated_ids, skip_special_tokens=True) self.assertEqual(generated_captions, ["two cats sleeping on a pink blanket next to remotes."] * 2) @slow def test_inference_interpolate_pos_encoding(self): # CLIP family models have an `interpolate_pos_encoding` argument in their forward method, # allowing to interpolate the pre-trained position embeddings in order to use # the model on higher resolutions. The DINO model by Facebook AI leverages this # to visualize self-attention on higher resolution images. model = GitModel.from_pretrained("microsoft/git-base").to(torch_device) processor = GitProcessor.from_pretrained( "microsoft/git-base", size={"height": 180, "width": 180}, crop_size={"height": 180, "width": 180} ) image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") inputs = processor(text="what's in the image", images=image, return_tensors="pt").to(torch_device) # interpolate_pos_encodiung false should return value error with self.assertRaises(ValueError, msg="doesn't match model"): with torch.no_grad(): model(**inputs, interpolate_pos_encoding=False) # forward pass with torch.no_grad(): outputs = model(**inputs, interpolate_pos_encoding=True) # verify the logits expected_shape = torch.Size((1, 130, 768)) self.assertEqual(outputs.last_hidden_state.shape, expected_shape) expected_slice = torch.tensor( [[-1.0296, 2.5960, 0.8703], [1.7027, 1.3302, -0.4543], [-1.4932, -0.1084, 0.0502]] ).to(torch_device) torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)