386 lines
14 KiB
Python
386 lines
14 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 Dinat model."""
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import collections
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import unittest
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from transformers import DinatConfig
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from transformers.testing_utils import require_natten, 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_backbone_common import BackboneTesterMixin
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, _config_zero_init, 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 DinatBackbone, DinatForImageClassification, DinatModel
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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 DinatModelTester:
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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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patch_size=4,
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num_channels=3,
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embed_dim=16,
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depths=[1, 2, 1],
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num_heads=[2, 4, 8],
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kernel_size=3,
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dilations=[[3], [1, 2], [1]],
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mlp_ratio=2.0,
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qkv_bias=True,
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hidden_dropout_prob=0.0,
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attention_probs_dropout_prob=0.0,
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drop_path_rate=0.1,
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hidden_act="gelu",
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patch_norm=True,
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initializer_range=0.02,
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layer_norm_eps=1e-5,
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is_training=True,
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scope=None,
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use_labels=True,
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num_labels=10,
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out_features=["stage1", "stage2"],
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out_indices=[1, 2],
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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_size = patch_size
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self.num_channels = num_channels
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self.embed_dim = embed_dim
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self.depths = depths
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self.num_heads = num_heads
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self.kernel_size = kernel_size
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self.dilations = dilations
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self.mlp_ratio = mlp_ratio
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self.qkv_bias = qkv_bias
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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.drop_path_rate = drop_path_rate
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self.hidden_act = hidden_act
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self.patch_norm = patch_norm
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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.is_training = is_training
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self.scope = scope
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self.use_labels = use_labels
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self.num_labels = num_labels
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self.out_features = out_features
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self.out_indices = out_indices
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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 DinatConfig(
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num_labels=self.num_labels,
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image_size=self.image_size,
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patch_size=self.patch_size,
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num_channels=self.num_channels,
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embed_dim=self.embed_dim,
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depths=self.depths,
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num_heads=self.num_heads,
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kernel_size=self.kernel_size,
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dilations=self.dilations,
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mlp_ratio=self.mlp_ratio,
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qkv_bias=self.qkv_bias,
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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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drop_path_rate=self.drop_path_rate,
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hidden_act=self.hidden_act,
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patch_norm=self.patch_norm,
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layer_norm_eps=self.layer_norm_eps,
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initializer_range=self.initializer_range,
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out_features=self.out_features,
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out_indices=self.out_indices,
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)
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def create_and_check_model(self, config, pixel_values, labels):
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model = DinatModel(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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expected_height = expected_width = (config.image_size // config.patch_size) // (2 ** (len(config.depths) - 1))
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expected_dim = int(config.embed_dim * 2 ** (len(config.depths) - 1))
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self.parent.assertEqual(
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result.last_hidden_state.shape, (self.batch_size, expected_height, expected_width, expected_dim)
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)
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def create_and_check_for_image_classification(self, config, pixel_values, labels):
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model = DinatForImageClassification(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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# test greyscale images
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config.num_channels = 1
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model = DinatForImageClassification(config)
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model.to(torch_device)
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model.eval()
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pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
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result = model(pixel_values)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
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def create_and_check_backbone(self, config, pixel_values, labels):
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model = DinatBackbone(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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# verify hidden states
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self.parent.assertEqual(len(result.feature_maps), len(config.out_features))
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self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, model.channels[0], 16, 16])
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# verify channels
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self.parent.assertEqual(len(model.channels), len(config.out_features))
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# verify backbone works with out_features=None
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config.out_features = None
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model = DinatBackbone(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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# verify feature maps
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self.parent.assertEqual(len(result.feature_maps), 1)
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self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, model.channels[-1], 4, 4])
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# verify channels
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self.parent.assertEqual(len(model.channels), 1)
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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_natten
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@require_torch
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class DinatModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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DinatModel,
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DinatForImageClassification,
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DinatBackbone,
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)
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if is_torch_available()
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else ()
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)
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pipeline_model_mapping = (
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{"image-feature-extraction": DinatModel, "image-classification": DinatForImageClassification}
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if is_torch_available()
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else {}
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)
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fx_compatible = False
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test_torchscript = False
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test_pruning = 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 = DinatModelTester(self)
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self.config_tester = ConfigTester(self, config_class=DinatConfig, embed_dim=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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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_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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def test_backbone(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_backbone(*config_and_inputs)
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@unittest.skip(reason="Dinat 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="Dinat does not use feedforward chunking")
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def test_feed_forward_chunking(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_attention_outputs(self):
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self.skipTest("Dinat's attention operation is handled entirely by NATTEN.")
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def check_hidden_states_output(self, inputs_dict, config, model_class, image_size):
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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 = getattr(
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self.model_tester, "expected_num_hidden_layers", len(self.model_tester.depths) + 1
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)
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self.assertEqual(len(hidden_states), expected_num_layers)
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# Dinat has a different seq_length
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patch_size = (
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config.patch_size
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if isinstance(config.patch_size, collections.abc.Iterable)
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else (config.patch_size, config.patch_size)
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)
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height = image_size[0] // patch_size[0]
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width = image_size[1] // patch_size[1]
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self.assertListEqual(
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list(hidden_states[0].shape[-3:]),
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[height, width, self.model_tester.embed_dim],
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)
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if model_class.__name__ != "DinatBackbone":
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reshaped_hidden_states = outputs.reshaped_hidden_states
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self.assertEqual(len(reshaped_hidden_states), expected_num_layers)
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batch_size, num_channels, height, width = reshaped_hidden_states[0].shape
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reshaped_hidden_states = (
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reshaped_hidden_states[0].view(batch_size, num_channels, height, width).permute(0, 2, 3, 1)
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)
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self.assertListEqual(
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list(reshaped_hidden_states.shape[-3:]),
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[height, width, self.model_tester.embed_dim],
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)
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def test_hidden_states_output(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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image_size = (
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self.model_tester.image_size
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if isinstance(self.model_tester.image_size, collections.abc.Iterable)
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else (self.model_tester.image_size, self.model_tester.image_size)
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)
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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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self.check_hidden_states_output(inputs_dict, config, model_class, image_size)
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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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self.check_hidden_states_output(inputs_dict, config, model_class, image_size)
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@slow
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def test_model_from_pretrained(self):
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model_name = "shi-labs/dinat-mini-in1k-224"
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model = DinatModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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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 "embeddings" not in name and param.requires_grad:
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self.assertIn(
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((param.data.mean() * 1e9).round() / 1e9).item(),
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[0.0, 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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@require_natten
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@require_vision
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@require_torch
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class DinatModelIntegrationTest(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("shi-labs/dinat-mini-in1k-224") if is_vision_available() else None
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@slow
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def test_inference_image_classification_head(self):
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model = DinatForImageClassification.from_pretrained("shi-labs/dinat-mini-in1k-224").to(torch_device)
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image_processor = self.default_image_processor
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image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
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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([-0.1545, -0.7667, 0.4642]).to(torch_device)
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self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
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@require_torch
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@require_natten
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class DinatBackboneTest(unittest.TestCase, BackboneTesterMixin):
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all_model_classes = (DinatBackbone,) if is_torch_available() else ()
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config_class = DinatConfig
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def setUp(self):
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self.model_tester = DinatModelTester(self)
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