274 lines
9.8 KiB
Python
274 lines
9.8 KiB
Python
# coding=utf-8
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# Copyright 2022 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 CvT model."""
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import unittest
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from math import floor
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from transformers import CvtConfig
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from transformers.file_utils import cached_property, is_torch_available, is_vision_available
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from transformers.testing_utils import require_torch, require_vision, slow, torch_device
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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 transformers import CvtForImageClassification, CvtModel
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if is_vision_available():
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from PIL import Image
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from transformers import AutoImageProcessor
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class CvtConfigTester(ConfigTester):
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def create_and_test_config_common_properties(self):
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config = self.config_class(**self.inputs_dict)
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self.parent.assertTrue(hasattr(config, "embed_dim"))
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self.parent.assertTrue(hasattr(config, "num_heads"))
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class CvtModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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image_size=64,
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num_channels=3,
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embed_dim=[16, 32, 48],
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num_heads=[1, 2, 3],
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depth=[1, 2, 10],
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patch_sizes=[7, 3, 3],
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patch_stride=[4, 2, 2],
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patch_padding=[2, 1, 1],
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stride_kv=[2, 2, 2],
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cls_token=[False, False, True],
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attention_drop_rate=[0.0, 0.0, 0.0],
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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is_training=True,
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use_labels=True,
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num_labels=2, # Check
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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.image_size = image_size
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self.patch_sizes = patch_sizes
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self.patch_stride = patch_stride
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self.patch_padding = patch_padding
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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.num_channels = num_channels
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self.embed_dim = embed_dim
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self.num_heads = num_heads
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self.stride_kv = stride_kv
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self.depth = depth
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self.cls_token = cls_token
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self.attention_drop_rate = attention_drop_rate
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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def prepare_config_and_inputs(self):
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pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
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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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return CvtConfig(
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image_size=self.image_size,
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num_labels=self.num_labels,
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num_channels=self.num_channels,
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embed_dim=self.embed_dim,
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num_heads=self.num_heads,
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patch_sizes=self.patch_sizes,
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patch_padding=self.patch_padding,
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patch_stride=self.patch_stride,
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stride_kv=self.stride_kv,
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depth=self.depth,
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cls_token=self.cls_token,
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attention_drop_rate=self.attention_drop_rate,
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initializer_range=self.initializer_range,
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)
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def create_and_check_model(self, config, pixel_values, labels):
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model = CvtModel(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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image_size = (self.image_size, self.image_size)
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height, width = image_size[0], image_size[1]
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for i in range(len(self.depth)):
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height = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1)
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width = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.embed_dim[-1], height, width))
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def create_and_check_for_image_classification(self, config, pixel_values, labels):
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config.num_labels = self.num_labels
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model = CvtForImageClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values, labels=labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
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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 CvtModelTest(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 Cvt 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 = (CvtModel, CvtForImageClassification) if is_torch_available() else ()
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pipeline_model_mapping = (
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{"image-feature-extraction": CvtModel, "image-classification": CvtForImageClassification}
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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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has_attentions = False
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def setUp(self):
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self.model_tester = CvtModelTester(self)
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self.config_tester = ConfigTester(self, config_class=CvtConfig, has_text_modality=False, hidden_size=37)
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def test_config(self):
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self.create_and_test_config_common_properties()
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self.config_tester.create_and_test_config_to_json_string()
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self.config_tester.create_and_test_config_to_json_file()
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self.config_tester.create_and_test_config_from_and_save_pretrained()
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self.config_tester.create_and_test_config_with_num_labels()
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self.config_tester.check_config_can_be_init_without_params()
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self.config_tester.check_config_arguments_init()
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def create_and_test_config_common_properties(self):
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return
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@unittest.skip(reason="Cvt does not output attentions")
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def test_attention_outputs(self):
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pass
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@unittest.skip(reason="Cvt 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="Cvt does not support input and output embeddings")
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def test_model_common_attributes(self):
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pass
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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_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 = len(self.model_tester.depth)
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self.assertEqual(len(hidden_states), expected_num_layers)
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# verify the first hidden states (first block)
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self.assertListEqual(
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list(hidden_states[0].shape[-3:]),
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[
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self.model_tester.embed_dim[0],
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self.model_tester.image_size // 4,
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self.model_tester.image_size // 4,
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],
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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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def test_for_image_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_image_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 = "microsoft/cvt-13"
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model = CvtModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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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_torch
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@require_vision
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class CvtModelIntegrationTest(unittest.TestCase):
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@cached_property
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def default_image_processor(self):
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return AutoImageProcessor.from_pretrained("microsoft/cvt-13")
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@slow
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def test_inference_image_classification_head(self):
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model = CvtForImageClassification.from_pretrained("microsoft/cvt-13").to(torch_device)
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image_processor = self.default_image_processor
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image = prepare_img()
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inputs = image_processor(images=image, 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, 1000))
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self.assertEqual(outputs.logits.shape, expected_shape)
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expected_slice = torch.tensor([0.9285, 0.9015, -0.3150]).to(torch_device)
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self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
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