# 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 ViViT model.""" import copy import inspect import unittest from functools import cached_property import numpy as np from huggingface_hub import hf_hub_download from transformers import VivitConfig from transformers.models.auto import get_values from transformers.testing_utils import Expectations, require_torch, require_vision, slow, torch_device from transformers.utils import is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VivitForVideoClassification, VivitModel if is_vision_available(): from transformers import VivitImageProcessor class VivitModelTester: def __init__( self, parent, batch_size=2, is_training=True, use_labels=True, num_labels=10, image_size=10, num_frames=8, # decreased, because default 32 takes too much RAM at inference tubelet_size=[2, 4, 4], num_channels=3, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="gelu_fast", hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, initializer_range=0.02, layer_norm_eps=1e-06, qkv_bias=True, scope=None, attn_implementation="eager", mask_ratio=0.5, ): self.parent = parent self.batch_size = batch_size self.is_training = is_training self.use_labels = use_labels self.num_labels = num_labels self.image_size = image_size self.num_frames = num_frames self.tubelet_size = tubelet_size self.num_channels = num_channels 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.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.qkv_bias = qkv_bias self.scope = scope self.attn_implementation = attn_implementation self.seq_length = ( (self.image_size // self.tubelet_size[2]) * (self.image_size // self.tubelet_size[1]) * (self.num_frames // self.tubelet_size[0]) ) + 1 # CLS token self.mask_ratio = mask_ratio self.num_masks = int(mask_ratio * self.seq_length) def prepare_config_and_inputs(self): pixel_values = floats_tensor( [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] ) labels = None if self.use_labels: labels = ids_tensor([self.batch_size], self.num_labels) config = self.get_config() return config, pixel_values, labels def get_config(self): config = VivitConfig( num_frames=self.num_frames, image_size=self.image_size, tubelet_size=self.tubelet_size, num_channels=self.num_channels, 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, initializer_range=self.initializer_range, layer_norm_eps=self.layer_norm_eps, qkv_bias=self.qkv_bias, attn_implementation=self.attn_implementation, ) config.num_labels = self.num_labels return config def create_and_check_model(self, config, pixel_values, labels): model = VivitModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_for_video_classification(self, config, pixel_values, labels): model = VivitForVideoClassification(config) model.to(torch_device) model.eval() result = model(pixel_values) # verify the logits shape expected_shape = torch.Size((self.batch_size, self.num_labels)) self.parent.assertEqual(result.logits.shape, expected_shape) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, labels = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class VivitModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as Vivit does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (VivitModel, VivitForVideoClassification) if is_torch_available() else () pipeline_model_mapping = ( {"feature-extraction": VivitModel, "video-classification": VivitForVideoClassification} if is_torch_available() else {} ) test_resize_embeddings = False test_torch_exportable = True def setUp(self): self.model_tester = VivitModelTester(self) self.config_tester = ConfigTester(self, config_class=VivitConfig, has_text_modality=False, hidden_size=37) def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = copy.deepcopy(inputs_dict) if return_labels: if model_class in get_values(MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING): inputs_dict["labels"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) return inputs_dict def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="Vivit 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()] self.assertEqual(arg_names[0], "pixel_values") 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_video_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_video_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): model_name = "google/vivit-b-16x2-kinetics400" model = VivitModel.from_pretrained(model_name) self.assertIsNotNone(model) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True for model_class in self.all_model_classes: seq_len = self.model_tester.seq_length inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class._from_config(config, attn_implementation="eager") config = model.config model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, seq_len, seq_len], ) out_len = len(outputs) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) self.assertEqual(out_len + 1, len(outputs)) self_attentions = outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, seq_len, seq_len], ) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.hidden_states expected_num_layers = self.model_tester.num_hidden_layers + 1 self.assertEqual(len(hidden_states), expected_num_layers) seq_length = self.model_tester.seq_length self.assertListEqual( list(hidden_states[0].shape[-2:]), [seq_length, self.model_tester.hidden_size], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) # We will verify our results on a video of eating spaghetti # Frame indices used: [164 168 172 176 181 185 189 193 198 202 206 210 215 219 223 227] def prepare_video(): file = hf_hub_download( repo_id="hf-internal-testing/spaghetti-video", filename="eating_spaghetti_32_frames.npy", repo_type="dataset" ) video = np.load(file) return list(video) @require_torch @require_vision class VivitModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return VivitImageProcessor() if is_vision_available() else None @slow def test_inference_for_video_classification(self): model = VivitForVideoClassification.from_pretrained("google/vivit-b-16x2-kinetics400").to(torch_device) image_processor = self.default_image_processor video = prepare_video() inputs = image_processor(video, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the logits expected_shape = torch.Size((1, 400)) self.assertEqual(outputs.logits.shape, expected_shape) expectations = Expectations( { (None, None): [-0.9498, 2.7971, -1.4049, 0.1024, -1.8353], ("cuda", 8): [-0.9498, 2.7971, -1.4049, 0.1025, -1.8353], } ) expected_slice = torch.tensor(expectations.get_expectation()).to(torch_device) torch.testing.assert_close(outputs.logits[0, :5], expected_slice, rtol=2e-4, atol=2e-4) @slow def test_inference_interpolate_pos_encoding(self): # Vivit 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 = VivitModel.from_pretrained("google/vivit-b-16x2-kinetics400").to(torch_device) image_processor = VivitImageProcessor.from_pretrained("google/vivit-b-16x2-kinetics400") video = prepare_video() inputs = image_processor( video, size={"shortest_edge": 480}, crop_size={"height": 232, "width": 232}, return_tensors="pt" ) pixel_values = inputs.pixel_values.to(torch_device) # forward pass with torch.no_grad(): outputs = model(pixel_values, interpolate_pos_encoding=True) # verify the logits shape expected_shape = torch.Size((1, 3137, 768)) self.assertEqual(outputs.last_hidden_state.shape, expected_shape)