267 lines
10 KiB
Python
267 lines
10 KiB
Python
# coding=utf-8
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# Copyright 2023 The Intel Team Authors, 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 TVP model."""
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import unittest
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from transformers import ResNetConfig, TvpConfig
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from transformers.testing_utils import require_torch, require_vision, torch_device
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from transformers.utils import cached_property, is_torch_available, is_vision_available
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from ...test_modeling_common import (
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ModelTesterMixin,
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_config_zero_init,
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floats_tensor,
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ids_tensor,
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random_attention_mask,
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)
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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 transformers import TvpForVideoGrounding, TvpModel
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if is_vision_available():
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from PIL import Image
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from transformers import TvpImageProcessor
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# Copied from test.models.videomae.test_modeling_videomae.VideoMAEModelTester with VideoMAE->TVP
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class TVPModelTester:
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def __init__(
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self,
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parent,
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batch_size=1,
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seq_length=2,
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alpha=1.0,
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beta=0.1,
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visual_prompter_type="framepad",
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visual_prompter_apply="replace",
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num_frames=2,
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max_img_size=448,
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visual_prompt_size=96,
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vocab_size=100,
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hidden_size=32,
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intermediate_size=32,
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num_hidden_layers=2,
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num_attention_heads=4,
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max_position_embeddings=30,
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max_grid_col_position_embeddings=30,
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max_grid_row_position_embeddings=30,
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hidden_dropout_prob=0.1,
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hidden_act="gelu",
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layer_norm_eps=1e-12,
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initializer_range=0.02,
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pad_token_id=0,
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type_vocab_size=2,
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attention_probs_dropout_prob=0.1,
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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.input_id_length = seq_length
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self.seq_length = seq_length + 10 + 784 # include text prompt length and visual input length
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self.alpha = alpha
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self.beta = beta
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self.visual_prompter_type = visual_prompter_type
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self.visual_prompter_apply = visual_prompter_apply
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self.num_frames = num_frames
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self.max_img_size = max_img_size
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self.visual_prompt_size = visual_prompt_size
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self.vocab_size = vocab_size
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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.max_position_embeddings = max_position_embeddings
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self.max_grid_col_position_embeddings = max_grid_col_position_embeddings
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self.max_grid_row_position_embeddings = max_grid_row_position_embeddings
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self.layer_norm_eps = layer_norm_eps
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self.initializer_range = initializer_range
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self.pad_token_id = pad_token_id
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self.type_vocab_size = type_vocab_size
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self.is_training = False
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self.num_channels = 3
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.input_id_length], self.vocab_size)
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attention_mask = random_attention_mask([self.batch_size, self.input_id_length])
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pixel_values = floats_tensor(
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[self.batch_size, self.num_frames, self.num_channels, self.max_img_size, self.max_img_size]
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)
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config = self.get_config()
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return (config, input_ids, pixel_values, attention_mask)
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def get_config(self):
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resnet_config = ResNetConfig(
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num_channels=3,
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embeddings_size=64,
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hidden_sizes=[64, 128],
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depths=[2, 2],
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hidden_act="relu",
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out_features=["stage2"],
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out_indices=[2],
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)
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return TvpConfig(
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backbone_config=resnet_config,
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backbone=None,
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alpha=self.alpha,
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beta=self.beta,
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visual_prompter_type=self.visual_prompter_type,
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visual_prompter_apply=self.visual_prompter_apply,
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num_frames=self.num_frames,
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max_img_size=self.max_img_size,
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visual_prompt_size=self.visual_prompt_size,
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vocab_size=self.vocab_size,
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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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max_position_embeddings=self.max_position_embeddings,
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max_grid_col_position_embeddings=self.max_grid_col_position_embeddings,
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max_grid_row_position_embeddings=self.max_grid_row_position_embeddings,
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layer_norm_eps=self.layer_norm_eps,
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initializer_range=self.initializer_range,
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pad_token_id=self.pad_token_id,
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type_vocab_size=self.type_vocab_size,
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)
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def create_and_check_model(self, config, input_ids, pixel_values, attention_mask):
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model = TvpModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, pixel_values, attention_mask)
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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 prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, input_ids, pixel_values, attention_mask = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "pixel_values": pixel_values, "attention_mask": attention_mask}
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return config, inputs_dict
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@require_torch
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class TVPModelTest(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 TVP does not use, inputs_embeds.
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The seq_length in TVP contain textual and visual inputs, and prompt.
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"""
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all_model_classes = (TvpModel, TvpForVideoGrounding) if is_torch_available() else ()
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pipeline_model_mapping = (
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{"feature-extraction": TvpModel, "temporal-video-grounding": TvpForVideoGrounding}
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if is_torch_available()
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else {}
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)
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# TODO: Enable this once this model gets more usage
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test_torchscript = False
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def setUp(self):
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self.model_tester = TVPModelTester(self)
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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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@unittest.skip(reason="TVP does not use inputs_embeds")
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def test_inputs_embeds(self):
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pass
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@unittest.skip(reason="TVPModel does not have input/output embeddings")
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def test_model_common_attributes(self):
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pass
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# override as the `logit_scale` parameter initilization is different for TVP
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def test_initialization(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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configs_no_init = _config_zero_init(config)
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for model_class in self.all_model_classes:
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model = model_class(config=configs_no_init)
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for name, param in model.named_parameters():
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if param.requires_grad:
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# params are randomly initialized.
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self.assertAlmostEqual(
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param.data.mean().item(),
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0.0,
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delta=1.0,
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msg=f"Parameter {name} of model {model_class} seems not properly initialized",
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)
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# We will verify our results on an image of cute cats
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def prepare_img():
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image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
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return image
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@require_vision
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@require_torch
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class TvpModelIntegrationTests(unittest.TestCase):
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@cached_property
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def default_image_processor(self):
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return TvpImageProcessor.from_pretrained("Jiqing/tiny-random-tvp") if is_vision_available() else None
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def test_inference_no_head(self):
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model = TvpModel.from_pretrained("Jiqing/tiny-random-tvp").to(torch_device)
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image_processor = self.default_image_processor
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image = prepare_img()
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encoding = image_processor(images=image, return_tensors="pt")
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input_ids = torch.tensor([[1, 2]])
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attention_mask = torch.tensor([[1, 1]])
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encoding.update({"input_ids": input_ids, "attention_mask": attention_mask})
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encoding.to(torch_device)
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with torch.no_grad():
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outputs = model(**encoding)
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expected_shape = torch.Size((1, 796, 128))
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assert outputs.last_hidden_state.shape == expected_shape
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expected_slice = torch.tensor(
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[[-0.4902, -0.4121, -1.7872], [-0.2184, 2.1211, -0.9371], [0.1180, 0.5003, -0.1727]]
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).to(torch_device)
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self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))
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def test_inference_with_head(self):
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model = TvpForVideoGrounding.from_pretrained("Jiqing/tiny-random-tvp").to(torch_device)
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image_processor = self.default_image_processor
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image = prepare_img()
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encoding = image_processor(images=image, return_tensors="pt")
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input_ids = torch.tensor([[1, 2]])
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attention_mask = torch.tensor([[1, 1]])
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encoding.update({"input_ids": input_ids, "attention_mask": attention_mask})
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encoding.to(torch_device)
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with torch.no_grad():
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outputs = model(**encoding)
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expected_shape = torch.Size((1, 2))
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assert outputs.logits.shape == expected_shape
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expected_slice = torch.tensor([[0.5061, 0.4988]]).to(torch_device)
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self.assertTrue(torch.allclose(outputs.logits, expected_slice, atol=1e-4))
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