# Copyright 2023 The Intel Team Authors, 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 TVP model.""" import unittest from functools import cached_property from transformers import ResNetConfig, TimmBackboneConfig, TvpConfig from transformers.testing_utils import require_timm, require_torch, require_vision, slow, torch_device from transformers.utils import is_torch_available, is_vision_available 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 transformers import TvpForVideoGrounding, TvpModel if is_vision_available(): from PIL import Image from transformers import TvpImageProcessor # Copied from test.models.videomae.test_modeling_videomae.VideoMAEModelTester with VideoMAE->TVP class TVPModelTester: def __init__( self, parent, batch_size=1, seq_length=2, alpha=1.0, beta=0.1, visual_prompter_type="framepad", visual_prompter_apply="replace", num_frames=2, max_img_size=448, visual_prompt_size=96, vocab_size=100, hidden_size=32, intermediate_size=32, num_hidden_layers=2, num_attention_heads=4, max_position_embeddings=30, max_grid_col_position_embeddings=30, max_grid_row_position_embeddings=30, hidden_dropout_prob=0.1, hidden_act="gelu", layer_norm_eps=1e-12, initializer_range=0.02, pad_token_id=0, type_vocab_size=2, attention_probs_dropout_prob=0.1, ): self.parent = parent self.batch_size = batch_size self.input_id_length = seq_length self.seq_length = seq_length + 10 + 784 # include text prompt length and visual input length self.alpha = alpha self.beta = beta self.visual_prompter_type = visual_prompter_type self.visual_prompter_apply = visual_prompter_apply self.num_frames = num_frames self.max_img_size = max_img_size self.visual_prompt_size = visual_prompt_size 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.max_grid_col_position_embeddings = max_grid_col_position_embeddings self.max_grid_row_position_embeddings = max_grid_row_position_embeddings self.layer_norm_eps = layer_norm_eps self.initializer_range = initializer_range self.pad_token_id = pad_token_id self.type_vocab_size = type_vocab_size self.is_training = False self.num_channels = 3 def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.input_id_length], self.vocab_size) attention_mask = random_attention_mask([self.batch_size, self.input_id_length]) pixel_values = floats_tensor( [self.batch_size, self.num_frames, self.num_channels, self.max_img_size, self.max_img_size] ) config = self.get_config() return (config, input_ids, pixel_values, attention_mask) def get_config(self): resnet_config = ResNetConfig( num_channels=3, embeddings_size=64, hidden_sizes=[64, 128], depths=[2, 2], hidden_act="relu", out_features=["stage2"], out_indices=[2], ) return TvpConfig( backbone_config=resnet_config, backbone=None, alpha=self.alpha, beta=self.beta, visual_prompter_type=self.visual_prompter_type, visual_prompter_apply=self.visual_prompter_apply, num_frames=self.num_frames, max_img_size=self.max_img_size, visual_prompt_size=self.visual_prompt_size, 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, max_grid_col_position_embeddings=self.max_grid_col_position_embeddings, max_grid_row_position_embeddings=self.max_grid_row_position_embeddings, layer_norm_eps=self.layer_norm_eps, initializer_range=self.initializer_range, pad_token_id=self.pad_token_id, type_vocab_size=self.type_vocab_size, ) def create_and_check_model(self, config, input_ids, pixel_values, attention_mask): model = TvpModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, pixel_values, attention_mask) 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, input_ids, pixel_values, attention_mask = config_and_inputs inputs_dict = {"input_ids": input_ids, "pixel_values": pixel_values, "attention_mask": attention_mask} return config, inputs_dict @require_torch class TVPModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as TVP does not use, inputs_embeds. The seq_length in TVP contain textual and visual inputs, and prompt. """ all_model_classes = (TvpModel, TvpForVideoGrounding) if is_torch_available() else () pipeline_model_mapping = ( {"feature-extraction": TvpModel, "temporal-video-grounding": TvpForVideoGrounding} if is_torch_available() else {} ) # TODO: Enable this once this model gets more usage def setUp(self): self.model_tester = TVPModelTester(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="TVP does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="TVPModel does not have input/output embeddings") def test_model_get_set_embeddings(self): pass @require_timm def test_backbone_selection(self): def _validate_backbone_init(): for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() # Confirm out_indices propagated to backbone if model.__class__.__name__ == "TvpModel": self.assertEqual(len(model.vision_model.backbone.out_indices), 2) elif model.__class__.__name__ == "TvpForVideoGrounding": self.assertEqual(len(model.model.vision_model.backbone.out_indices), 2) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Force load_backbone path config.is_hybrid = False # We load through configs, as the modeling file assumes config.backbone_config is always set config.use_pretrained_backbone = False config.backbone_kwargs = None # Load a timm backbone # We hack adding hidden_sizes to the config to test the backbone loading backbone_config = TimmBackboneConfig("resnet18", out_indices=[-2, -1], hidden_sizes=[64, 128]) config.backbone_config = backbone_config _validate_backbone_init() # Load a HF backbone backbone_config = ResNetConfig.from_pretrained("facebook/dinov2-small", out_indices=[-2, -1]) config.backbone_config = backbone_config _validate_backbone_init() # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_vision @require_torch @slow class TvpModelIntegrationTests(unittest.TestCase): @cached_property def default_image_processor(self): return TvpImageProcessor.from_pretrained("Jiqing/tiny-random-tvp", revision="refs/pr/1") def test_inference_no_head(self): model = TvpModel.from_pretrained("Jiqing/tiny-random-tvp", revision="refs/pr/1").to(torch_device) image_processor = self.default_image_processor image = prepare_img() encoding = image_processor(images=image, return_tensors="pt") input_ids = torch.tensor([[1, 2]]) attention_mask = torch.tensor([[1, 1]]) encoding.update({"input_ids": input_ids, "attention_mask": attention_mask}) encoding.to(torch_device) with torch.no_grad(): outputs = model(**encoding) expected_shape = torch.Size((1, 796, 128)) assert outputs.last_hidden_state.shape == expected_shape expected_slice = torch.tensor( [[-0.4902, -0.4121, -1.7872], [-0.2184, 2.1211, -0.9371], [0.1180, 0.5003, -0.1727]] ).to(torch_device) torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4) def test_inference_with_head(self): model = TvpForVideoGrounding.from_pretrained("Jiqing/tiny-random-tvp", revision="refs/pr/1").to(torch_device) image_processor = self.default_image_processor image = prepare_img() encoding = image_processor(images=image, return_tensors="pt") input_ids = torch.tensor([[1, 2]]) attention_mask = torch.tensor([[1, 1]]) encoding.update({"input_ids": input_ids, "attention_mask": attention_mask}) encoding.to(torch_device) with torch.no_grad(): outputs = model(**encoding) expected_shape = torch.Size((1, 2)) assert outputs.logits.shape == expected_shape expected_slice = torch.tensor([[0.5061, 0.4988]]).to(torch_device) torch.testing.assert_close(outputs.logits, expected_slice, rtol=1e-4, atol=1e-4) def test_interpolate_inference_no_head(self): model = TvpModel.from_pretrained("Jiqing/tiny-random-tvp", revision="refs/pr/1").to(torch_device) image_processor = self.default_image_processor image = prepare_img() # 480X640 encoding = image_processor( images=image, return_tensors="pt", do_resize=False, do_pad=False, do_center_crop=False ) input_ids = torch.tensor([[1, 2]]) attention_mask = torch.tensor([[1, 1]]) encoding.update({"input_ids": input_ids, "attention_mask": attention_mask}) encoding.to(torch_device) with torch.no_grad(): outputs = model(**encoding, interpolate_pos_encoding=True) expected_shape = torch.Size((1, 1212, 128)) assert outputs.last_hidden_state.shape == expected_shape def test_interpolate_inference_with_head(self): model = TvpForVideoGrounding.from_pretrained("Jiqing/tiny-random-tvp", revision="refs/pr/1").to(torch_device) image_processor = self.default_image_processor image = prepare_img() # 480X640 encoding = image_processor( images=image, return_tensors="pt", do_resize=False, do_pad=False, do_center_crop=False ) input_ids = torch.tensor([[1, 2]]) attention_mask = torch.tensor([[1, 1]]) encoding.update({"input_ids": input_ids, "attention_mask": attention_mask}) encoding.to(torch_device) with torch.no_grad(): outputs = model(**encoding, interpolate_pos_encoding=True, output_hidden_states=True) expected_shape = torch.Size((1, 1212, 128)) assert outputs.hidden_states[-1].shape == expected_shape