# 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 PyTorch Pix2Struct model.""" import copy import inspect import tempfile import unittest import numpy as np import requests from transformers import Pix2StructConfig, Pix2StructTextConfig, Pix2StructVisionConfig from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import is_torch_available, is_vision_available 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 ( Pix2StructForConditionalGeneration, Pix2StructProcessor, Pix2StructTextModel, Pix2StructVisionModel, ) if is_vision_available(): from PIL import Image class Pix2StructVisionModelTester: def __init__( self, parent, batch_size=12, image_size=30, patch_size=2, num_channels=3, is_training=True, hidden_size=12, patch_embed_hidden_size=12, projection_dim=32, max_patches=64, 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_embed_hidden_size = patch_embed_hidden_size self.patch_size = patch_size self.num_channels = num_channels self.is_training = is_training self.hidden_size = hidden_size self.max_patches = max_patches self.seq_length = self.max_patches self.patch_proj_dim = ((patch_size**2) * num_channels) + 2 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 def prepare_config_and_inputs(self): flattened_patches = floats_tensor([self.batch_size, self.max_patches, self.patch_proj_dim]) config = self.get_config() return config, flattened_patches def get_config(self): return Pix2StructVisionConfig( 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, patch_embed_hidden_size=self.patch_embed_hidden_size, ) def create_and_check_model(self, config, flattened_patches): model = Pix2StructVisionModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(flattened_patches) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, flattened_patches = config_and_inputs inputs_dict = { "flattened_patches": flattened_patches, "attention_mask": torch.randint(0, 2, (self.batch_size, self.max_patches)), } return config, inputs_dict @require_torch class Pix2StructVisionModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as Pix2Struct does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (Pix2StructVisionModel,) if is_torch_available() else () test_resize_embeddings = False def setUp(self): self.model_tester = Pix2StructVisionModelTester(self) self.config_tester = ConfigTester( self, config_class=Pix2StructVisionConfig, has_text_modality=False, hidden_size=37 ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="Pix2StructVision 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 = ["flattened_patches"] 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="Training is tested directly on `Pix2StructTextImageModelTest`") def test_training(self): pass @unittest.skip(reason="Training is tested directly on `Pix2StructTextImageModelTest`") 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="Training is tested directly on `Pix2StructTextImageModelTest`") def test_retain_grad_hidden_states_attentions(self): pass @slow def test_model_from_pretrained(self): model_name = "google/pix2struct-textcaps-base" model = Pix2StructVisionModel.from_pretrained(model_name) self.assertIsNotNone(model) class Pix2StructTextModelTester: 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=12, 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.d_kv = hidden_size // num_attention_heads 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: 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 config = self.get_config() return config, input_ids, input_mask def get_config(self): return Pix2StructTextConfig( 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, d_kv=self.d_kv, ) def create_and_check_model(self, config, input_ids, input_mask): model = Pix2StructTextModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(input_ids, attention_mask=input_mask) result = model(input_ids) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_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 Pix2StructTextModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (Pix2StructTextModel,) if is_torch_available() else () def setUp(self): self.model_tester = Pix2StructTextModelTester(self) self.config_tester = ConfigTester(self, config_class=Pix2StructTextConfig, 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="Training is tested directly on `Pix2StructTextImageModelTest`") def test_training(self): pass @unittest.skip(reason="Training is tested directly on `Pix2StructTextImageModelTest`") 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="Pix2Struct does not use inputs_embeds") def test_inputs_embeds(self): pass @slow def test_model_from_pretrained(self): model_name = "google/pix2struct-textcaps-base" model = Pix2StructTextModel.from_pretrained(model_name) self.assertIsNotNone(model) class Pix2StructModelTester: 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 = Pix2StructTextModelTester(parent, **text_kwargs) self.vision_model_tester = Pix2StructVisionModelTester(parent, **vision_kwargs) self.batch_size = self.text_model_tester.batch_size # need bs for batching_equivalence test self.seq_length = self.text_model_tester.seq_length # need seq_length for common tests self.is_training = is_training self.max_patches = self.vision_model_tester.max_patches def prepare_config_and_inputs(self): text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs() vision_config, flattened_patches = self.vision_model_tester.prepare_config_and_inputs() config = self.get_config(text_config, vision_config) return config, input_ids, attention_mask, flattened_patches def get_config(self, text_config, vision_config): return Pix2StructConfig( text_config=self.text_model_tester.get_config().to_dict(), vision_config=self.vision_model_tester.get_config().to_dict(), projection_dim=64, ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, decoder_attention_mask, flattened_patches = config_and_inputs attention_mask = (flattened_patches.sum(dim=-1) != 0).float() inputs_dict = { "decoder_input_ids": input_ids, "labels": input_ids, "decoder_attention_mask": decoder_attention_mask, "flattened_patches": flattened_patches, "attention_mask": attention_mask, } return config, inputs_dict @require_torch class Pix2StructModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (Pix2StructForConditionalGeneration,) if is_torch_available() else () pipeline_model_mapping = ( {"image-to-text": Pix2StructForConditionalGeneration, "image-text-to-text": Pix2StructForConditionalGeneration} if is_torch_available() else {} ) test_resize_embeddings = True test_attention_outputs = False def setUp(self): self.model_tester = Pix2StructModelTester(self) def test_model(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config).to(torch_device) output = model(**input_dict) self.assertEqual( output[1].shape, ( self.model_tester.vision_model_tester.batch_size, self.model_tester.text_model_tester.seq_length, self.model_tester.text_model_tester.vocab_size, ), ) def test_generative_model(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_generative_model_classes: model = model_class(config).eval().to(torch_device) output = model.generate(**input_dict, use_cache=False, min_new_tokens=10, max_new_tokens=10) output_use_cache = model.generate(**input_dict, use_cache=True, min_new_tokens=10, max_new_tokens=10) torch.testing.assert_close(output, output_use_cache) @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="Pix2StructModel does not have input/output embeddings") def test_model_get_set_embeddings(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.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = [ "flattened_patches", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "encoder_outputs", "past_key_values", "labels", "decoder_inputs_embeds", "use_cache", ] self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) def test_training(self): if not self.model_tester.is_training: self.skipTest(reason="model_tester.is_training is set to False") 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) model.to(torch_device) model.train() 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).loss loss.backward() def test_training_gradient_checkpointing(self): if not self.model_tester.is_training: self.skipTest(reason="model_tester.is_training is set to False") for model_class in self.all_model_classes[:-1]: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.use_cache = False config.return_dict = True model = model_class(config) model.to(torch_device) model.gradient_checkpointing_enable() model.train() 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).loss loss.backward() # overwrite because `vocab_size` is not an attribute of `Pix2StructConfig` but rather `Pix2StructTextConfig` def test_resize_tokens_embeddings(self): original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() if not self.test_resize_embeddings: self.skipTest(reason="test_resize_embeddings is set to False") for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config) model.to(torch_device) if self.model_tester.is_training is False: model.eval() model_vocab_size = config.text_config.vocab_size # Retrieve the embeddings and clone theme model_embed = model.resize_token_embeddings(model_vocab_size) cloned_embeddings = model_embed.weight.clone() # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_embed = model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.text_config.vocab_size, model_vocab_size + 10) # Check that it actually resizes the embeddings matrix self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model_embed = model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.text_config.vocab_size, model_vocab_size - 15) # Check that it actually resizes the embeddings matrix self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15) # Check that the model can still do a forward pass successfully (every parameter should be resized) # Decoder input ids should be clamped to the maximum size of the vocabulary if "decoder_input_ids" in inputs_dict: inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that adding and removing tokens has not modified the first part of the embedding matrix. models_equal = True for p1, p2 in zip(cloned_embeddings, model_embed.weight): if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) # overwrite because `vocab_size` is not an attribute of `Pix2StructConfig` but rather `Pix2StructTextConfig` def test_resize_embeddings_untied(self): original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() if not self.test_resize_embeddings: self.skipTest(reason="test_resize_embeddings is set to False") original_config.tie_word_embeddings = False # if model cannot untied embeddings -> leave test if original_config.tie_word_embeddings: self.skipTest(reason="Model cannot untie embeddings") for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config).to(torch_device) model.eval() # if no output embeddings -> leave test if model.get_output_embeddings() is None: continue # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_vocab_size = config.text_config.vocab_size model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.text_config.vocab_size, model_vocab_size + 10) output_embeds = model.get_output_embeddings() self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.text_config.vocab_size, model_vocab_size - 15) # Check that it actually resizes the embeddings matrix output_embeds = model.get_output_embeddings() self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15) # Check that the model can still do a forward pass successfully (every parameter should be resized) # Decoder input ids should be clamped to the maximum size of the vocabulary if "decoder_input_ids" in inputs_dict: inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) def test_load_vision_text_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Save Pix2StructConfig and check if we can load Pix2StructVisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = Pix2StructVisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save Pix2StructConfig and check if we can load Pix2StructTextConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) text_config = Pix2StructTextConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict()) def _check_encoder_attention_for_generate(self, attentions, batch_size, config, prompt_length): # overwrite because # pix2struct seq length depends on image inputs prompt_length = self.model_tester.max_patches encoder_expected_shape = (batch_size, config.num_attention_heads, prompt_length, prompt_length) self.assertIsInstance(attentions, tuple) self.assertListEqual( [layer_attentions.shape for layer_attentions in attentions], [encoder_expected_shape] * len(attentions), ) def _check_encoder_hidden_states_for_generate(self, hidden_states, batch_size, config, prompt_length): # overwrite because # pix2struct seq length depends on image inputs prompt_length = self.model_tester.max_patches encoder_expected_shape = (batch_size, prompt_length, config.hidden_size) self.assertIsInstance(hidden_states, tuple) self.assertListEqual( [layer_hidden_states.shape for layer_hidden_states in hidden_states], [encoder_expected_shape] * len(hidden_states), ) @unittest.skip("Pix2Struct has no base model, it was implemented before standardization") def test_model_base_model_prefix(self): pass # We will verify our results on an image of a stop sign def prepare_img(): url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg" im = Image.open(requests.get(url, stream=True).raw) return im @require_vision @require_torch @slow class Pix2StructIntegrationTest(unittest.TestCase): def test_inference_image_captioning(self): model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-textcaps-base").to(torch_device) processor = Pix2StructProcessor.from_pretrained("google/pix2struct-textcaps-base") image = prepare_img() # image only inputs = processor(images=image, return_tensors="pt").to(torch_device) predictions = model.generate(**inputs) self.assertEqual( processor.decode(predictions[0], skip_special_tokens=True), "A stop sign is on a street corner." ) def test_batched_inference_image_captioning(self): model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-textcaps-base").to(torch_device) processor = Pix2StructProcessor.from_pretrained("google/pix2struct-textcaps-base") image_1 = prepare_img() second_url = ( "https://www.connollycove.com/wp-content/uploads/2019/06/temple-bar-dublin-world-famous-irish-pub.jpg" ) image_2 = Image.open(requests.get(second_url, stream=True).raw) # image only inputs = processor(images=[image_1, image_2], return_tensors="pt").to(torch_device) predictions = model.generate(**inputs) self.assertEqual( processor.decode(predictions[0], skip_special_tokens=True), "A stop sign is on a street corner." ) self.assertEqual( processor.decode(predictions[1], skip_special_tokens=True), "A row of books including The Temple Bar and Guiness.", ) def test_batched_inference_image_captioning_conditioned(self): model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-textcaps-base").to(torch_device) processor = Pix2StructProcessor.from_pretrained("google/pix2struct-textcaps-base") image_1 = prepare_img() second_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/temple-bar-dublin-world-famous-irish-pub.jpg" image_2 = Image.open(requests.get(second_url, stream=True).raw) texts = ["A picture of", "An photography of"] # image only inputs = processor(images=[image_1, image_2], text=texts, return_tensors="pt", add_special_tokens=False).to( torch_device ) predictions = model.generate(**inputs) self.assertEqual( processor.decode(predictions[0], skip_special_tokens=True), "A picture of a stop sign with a red stop sign", ) self.assertEqual( processor.decode(predictions[1], skip_special_tokens=True), "An photography of the Temple Bar and other places in the city.", ) def test_vqa_model(self): model_id = "google/pix2struct-ai2d-base" image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg" image = Image.open(requests.get(image_url, stream=True).raw) model = Pix2StructForConditionalGeneration.from_pretrained(model_id, dtype=torch.bfloat16).to(torch_device) processor = Pix2StructProcessor.from_pretrained(model_id) # image only text = "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud" inputs = processor(images=image, return_tensors="pt", text=text).to(torch_device, torch.bfloat16) predictions = model.generate(**inputs) self.assertEqual(processor.decode(predictions[0], skip_special_tokens=True), "ash cloud") def test_vqa_model_batched(self): model_id = "google/pix2struct-ai2d-base" image_urls = [ "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg", "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo-2.png", ] images = [Image.open(requests.get(image_url, stream=True).raw) for image_url in image_urls] texts = [ "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud", "What is the producer in the diagram? (1) Phytoplankton (2) Zooplankton (3) Large fish (4) Small fish", ] model = Pix2StructForConditionalGeneration.from_pretrained(model_id, dtype=torch.bfloat16).to(torch_device) processor = Pix2StructProcessor.from_pretrained(model_id) inputs = processor(images=images, return_tensors="pt", text=texts).to(torch_device, torch.bfloat16) predictions = model.generate(**inputs) self.assertEqual(processor.decode(predictions[0], skip_special_tokens=True), "ash cloud") self.assertEqual(processor.decode(predictions[1], skip_special_tokens=True), "Phytoplankton")