379 lines
14 KiB
Python
379 lines
14 KiB
Python
# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Testing suite for the PyTorch ViViT model. """
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import copy
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import inspect
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import unittest
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import numpy as np
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from huggingface_hub import hf_hub_download
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from transformers import VivitConfig
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from transformers.models.auto import get_values
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from transformers.testing_utils import require_torch, require_vision, slow, torch_device
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from transformers.utils import cached_property, is_torch_available, is_vision_available
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from torch import nn
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from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VivitForVideoClassification, VivitModel
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if is_vision_available():
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from transformers import VivitImageProcessor
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class VivitModelTester:
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def __init__(
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self,
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parent,
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batch_size=2,
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is_training=True,
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use_labels=True,
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num_labels=10,
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image_size=10,
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num_frames=8, # decreased, because default 32 takes too much RAM at inference
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tubelet_size=[2, 4, 4],
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num_channels=3,
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hidden_size=32,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu_fast",
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hidden_dropout_prob=0.0,
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attention_probs_dropout_prob=0.0,
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initializer_range=0.02,
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layer_norm_eps=1e-06,
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qkv_bias=True,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.is_training = is_training
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self.use_labels = use_labels
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self.num_labels = num_labels
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self.image_size = image_size
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self.num_frames = num_frames
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self.tubelet_size = tubelet_size
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self.num_channels = num_channels
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.qkv_bias = qkv_bias
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self.scope = scope
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self.seq_length = (
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(self.image_size // self.tubelet_size[2])
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* (self.image_size // self.tubelet_size[1])
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* (self.num_frames // self.tubelet_size[0])
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) + 1 # CLS token
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def prepare_config_and_inputs(self):
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pixel_values = floats_tensor(
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[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]
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)
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labels = None
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if self.use_labels:
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labels = ids_tensor([self.batch_size], self.num_labels)
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config = self.get_config()
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return config, pixel_values, labels
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def get_config(self):
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config = VivitConfig(
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num_frames=self.num_frames,
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image_size=self.image_size,
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tubelet_size=self.tubelet_size,
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num_channels=self.num_channels,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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initializer_range=self.initializer_range,
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layer_norm_eps=self.layer_norm_eps,
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qkv_bias=self.qkv_bias,
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)
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config.num_labels = self.num_labels
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return config
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def create_and_check_model(self, config, pixel_values, labels):
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model = VivitModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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def create_and_check_for_video_classification(self, config, pixel_values, labels):
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model = VivitForVideoClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values)
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# verify the logits shape
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expected_shape = torch.Size((self.batch_size, self.num_labels))
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self.parent.assertEqual(result.logits.shape, expected_shape)
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, pixel_values, labels = config_and_inputs
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inputs_dict = {"pixel_values": pixel_values}
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return config, inputs_dict
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@require_torch
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class VivitModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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"""
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Here we also overwrite some of the tests of test_modeling_common.py, as Vivit does not use input_ids, inputs_embeds,
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attention_mask and seq_length.
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"""
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all_model_classes = (VivitModel, VivitForVideoClassification) if is_torch_available() else ()
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pipeline_model_mapping = (
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{"feature-extraction": VivitModel, "video-classification": VivitForVideoClassification}
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if is_torch_available()
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else {}
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)
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test_pruning = False
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test_torchscript = False
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test_resize_embeddings = False
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test_head_masking = False
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def setUp(self):
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self.model_tester = VivitModelTester(self)
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self.config_tester = ConfigTester(self, config_class=VivitConfig, has_text_modality=False, hidden_size=37)
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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inputs_dict = copy.deepcopy(inputs_dict)
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if return_labels:
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if model_class in get_values(MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING):
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inputs_dict["labels"] = torch.zeros(
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self.model_tester.batch_size, dtype=torch.long, device=torch_device
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)
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return inputs_dict
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def test_config(self):
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self.config_tester.run_common_tests()
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@unittest.skip(reason="Vivit does not use inputs_embeds")
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def test_inputs_embeds(self):
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pass
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def test_model_common_attributes(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
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x = model.get_output_embeddings()
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self.assertTrue(x is None or isinstance(x, nn.Linear))
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def test_forward_signature(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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signature = inspect.signature(model.forward)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = ["pixel_values", "head_mask"]
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self.assertListEqual(arg_names[:2], expected_arg_names)
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_for_video_classification(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_video_classification(*config_and_inputs)
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@slow
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def test_model_from_pretrained(self):
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model_name = "google/vivit-b-16x2-kinetics400"
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model = VivitModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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def test_attention_outputs(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.return_dict = True
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for model_class in self.all_model_classes:
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seq_len = self.model_tester.seq_length
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = False
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config.return_dict = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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# check that output_attentions also work using config
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del inputs_dict["output_attentions"]
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config.output_attentions = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, seq_len, seq_len],
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)
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out_len = len(outputs)
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# Check attention is always last and order is fine
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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self.assertEqual(out_len + 1, len(outputs))
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self_attentions = outputs.attentions
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self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(self_attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, seq_len, seq_len],
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)
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def test_hidden_states_output(self):
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def check_hidden_states_output(inputs_dict, config, model_class):
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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hidden_states = outputs.hidden_states
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expected_num_layers = self.model_tester.num_hidden_layers + 1
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self.assertEqual(len(hidden_states), expected_num_layers)
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seq_length = self.model_tester.seq_length
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self.assertListEqual(
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list(hidden_states[0].shape[-2:]),
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[seq_length, self.model_tester.hidden_size],
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)
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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inputs_dict["output_hidden_states"] = True
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check_hidden_states_output(inputs_dict, config, model_class)
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# check that output_hidden_states also work using config
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del inputs_dict["output_hidden_states"]
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config.output_hidden_states = True
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check_hidden_states_output(inputs_dict, config, model_class)
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# We will verify our results on a video of eating spaghetti
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# Frame indices used: [164 168 172 176 181 185 189 193 198 202 206 210 215 219 223 227]
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def prepare_video():
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file = hf_hub_download(
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repo_id="hf-internal-testing/spaghetti-video", filename="eating_spaghetti_32_frames.npy", repo_type="dataset"
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)
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video = np.load(file)
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return list(video)
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@require_torch
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@require_vision
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class VivitModelIntegrationTest(unittest.TestCase):
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@cached_property
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def default_image_processor(self):
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return VivitImageProcessor() if is_vision_available() else None
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@slow
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def test_inference_for_video_classification(self):
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model = VivitForVideoClassification.from_pretrained("google/vivit-b-16x2-kinetics400").to(torch_device)
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image_processor = self.default_image_processor
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video = prepare_video()
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inputs = image_processor(video, return_tensors="pt").to(torch_device)
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# forward pass
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with torch.no_grad():
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outputs = model(**inputs)
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# verify the logits
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expected_shape = torch.Size((1, 400))
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self.assertEqual(outputs.logits.shape, expected_shape)
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# taken from original model
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expected_slice = torch.tensor([-0.9498, 2.7971, -1.4049, 0.1024, -1.8353]).to(torch_device)
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self.assertTrue(torch.allclose(outputs.logits[0, :5], expected_slice, atol=1e-4))
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@slow
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def test_inference_interpolate_pos_encoding(self):
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# Vivit models have an `interpolate_pos_encoding` argument in their forward method,
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# allowing to interpolate the pre-trained position embeddings in order to use
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# the model on higher resolutions. The DINO model by Facebook AI leverages this
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# to visualize self-attention on higher resolution images.
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model = VivitModel.from_pretrained("google/vivit-b-16x2").to(torch_device)
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image_processor = VivitImageProcessor.from_pretrained("google/vivit-b-16x2")
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video = prepare_video()
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inputs = image_processor(
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video, size={"shortest_edge": 480}, crop_size={"height": 480, "width": 480}, return_tensors="pt"
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)
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pixel_values = inputs.pixel_values.to(torch_device)
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# forward pass
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with torch.no_grad():
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outputs = model(pixel_values, interpolate_pos_encoding=True)
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# verify the logits shape
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expected_shape = torch.Size((1, 3137, 768))
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self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
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