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355 lines
13 KiB
Python
355 lines
13 KiB
Python
# Copyright 2024 the Fast authors and 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 TextNet model."""
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import unittest
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import requests
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from PIL import Image
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from transformers import TextNetConfig
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from transformers.models.textnet.image_processing_textnet import TextNetImageProcessor
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from transformers.testing_utils import (
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require_torch,
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require_vision,
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slow,
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torch_device,
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)
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from transformers.utils import is_torch_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, 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 TextNetBackbone, TextNetForImageClassification, TextNetModel
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class TextNetConfigTester(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, "hidden_sizes"))
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self.parent.assertTrue(hasattr(config, "num_attention_heads"))
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self.parent.assertTrue(hasattr(config, "num_encoder_blocks"))
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class TextNetModelTester:
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def __init__(
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self,
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parent,
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stem_kernel_size=3,
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stem_stride=2,
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stem_in_channels=3,
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stem_out_channels=32,
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stem_act_func="relu",
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dropout_rate=0,
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ops_order="weight_bn_act",
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conv_layer_kernel_sizes=[
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[[3, 3]],
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[[3, 3]],
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[[3, 3]],
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[[3, 3]],
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],
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conv_layer_strides=[
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[2],
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[2],
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[2],
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[2],
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],
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out_features=["stage1", "stage2", "stage3", "stage4"],
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out_indices=[1, 2, 3, 4],
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batch_size=3,
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num_channels=3,
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image_size=[32, 32],
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is_training=True,
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use_labels=True,
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num_labels=3,
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hidden_sizes=[32, 32, 32, 32, 32],
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):
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self.parent = parent
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self.stem_kernel_size = stem_kernel_size
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self.stem_stride = stem_stride
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self.stem_in_channels = stem_in_channels
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self.stem_out_channels = stem_out_channels
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self.act_func = stem_act_func
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self.dropout_rate = dropout_rate
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self.ops_order = ops_order
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self.conv_layer_kernel_sizes = conv_layer_kernel_sizes
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self.conv_layer_strides = conv_layer_strides
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self.out_features = out_features
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self.out_indices = out_indices
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self.batch_size = batch_size
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self.num_channels = num_channels
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self.image_size = image_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.hidden_sizes = hidden_sizes
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self.num_stages = 5
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def get_config(self):
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return TextNetConfig(
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stem_kernel_size=self.stem_kernel_size,
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stem_stride=self.stem_stride,
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stem_num_channels=self.stem_in_channels,
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stem_out_channels=self.stem_out_channels,
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act_func=self.act_func,
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dropout_rate=self.dropout_rate,
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ops_order=self.ops_order,
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conv_layer_kernel_sizes=self.conv_layer_kernel_sizes,
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conv_layer_strides=self.conv_layer_strides,
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out_features=self.out_features,
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out_indices=self.out_indices,
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hidden_sizes=self.hidden_sizes,
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image_size=self.image_size,
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)
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def create_and_check_model(self, config, pixel_values, labels):
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model = TextNetModel(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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scale_h = self.image_size[0] // 32
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scale_w = self.image_size[1] // 32
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self.parent.assertEqual(
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result.last_hidden_state.shape,
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(self.batch_size, self.hidden_sizes[-1], scale_h, scale_w),
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)
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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 = TextNetForImageClassification(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(self):
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pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]])
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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 create_and_check_backbone(self, config, pixel_values, labels):
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model = TextNetBackbone(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), len(config.out_features))
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scale_h = self.image_size[0] // 32
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scale_w = self.image_size[1] // 32
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self.parent.assertListEqual(
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list(result.feature_maps[0].shape), [self.batch_size, self.hidden_sizes[1], 8 * scale_h, 8 * scale_w]
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)
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# verify channels
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self.parent.assertEqual(len(model.channels), len(config.out_features))
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self.parent.assertListEqual(model.channels, config.hidden_sizes[1:])
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# verify backbone works with out_features=None
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config.out_features = None
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model = TextNetBackbone(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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scale_h = self.image_size[0] // 32
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scale_w = self.image_size[1] // 32
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self.parent.assertListEqual(
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list(result.feature_maps[0].shape), [self.batch_size, self.hidden_sizes[0], scale_h, scale_w]
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)
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# verify channels
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self.parent.assertEqual(len(model.channels), 1)
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self.parent.assertListEqual(model.channels, [config.hidden_sizes[-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_torch
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class TextNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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"""
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Here we also overwrite some tests of test_modeling_common.py, as TextNet 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 = (TextNetModel, TextNetForImageClassification, TextNetBackbone) if is_torch_available() else ()
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pipeline_model_mapping = (
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{"feature-extraction": TextNetModel, "image-classification": TextNetForImageClassification}
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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_pruning = False
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test_resize_embeddings = False
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test_head_masking = False
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test_torch_exportable = True
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has_attentions = False
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def setUp(self):
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self.model_tester = TextNetModelTester(self)
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self.config_tester = TextNetConfigTester(self, config_class=TextNetConfig, has_text_modality=False)
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@unittest.skip(reason="TextNet 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="TextNet does not have input/output embeddings")
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def test_model_get_set_embeddings(self):
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pass
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@unittest.skip(reason="TextNet 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="TextNet 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_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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def test_initialization(self):
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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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model = model_class(config=config)
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for name, module in model.named_modules():
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if isinstance(module, (nn.BatchNorm2d, nn.GroupNorm)):
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self.assertTrue(
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torch.all(module.weight == 1),
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msg=f"Parameter {name} of model {model_class} seems not properly initialized",
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)
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self.assertTrue(
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torch.all(module.bias == 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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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.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
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self.assertEqual(len(hidden_states), self.model_tester.num_stages)
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self.assertListEqual(
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list(hidden_states[0].shape[-2:]),
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[self.model_tester.image_size[0] // 2, self.model_tester.image_size[1] // 2],
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)
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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layers_type = ["preactivation", "bottleneck"]
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for model_class in self.all_model_classes:
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for layer_type in layers_type:
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config.layer_type = layer_type
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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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@unittest.skip(reason="TextNet 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_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 = "czczup/textnet-base"
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model = TextNetModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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@require_torch
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@require_vision
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class TextNetModelIntegrationTest(unittest.TestCase):
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@slow
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def test_inference_no_head(self):
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processor = TextNetImageProcessor.from_pretrained("czczup/textnet-base")
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model = TextNetModel.from_pretrained("czczup/textnet-base").to(torch_device)
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# prepare image
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url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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image = Image.open(requests.get(url, stream=True).raw)
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inputs = 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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output = model(**inputs)
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# verify output
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self.assertEqual(output.last_hidden_state.shape, torch.Size([1, 512, 20, 27]))
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expected_slice_backbone = torch.tensor(
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[
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[0.0000, 1.7415, 1.2660],
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[0.0000, 1.0084, 1.9692],
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[0.0000, 1.7464, 1.7892],
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],
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device=torch_device,
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)
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torch.testing.assert_close(
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output.last_hidden_state[0, 12, :3, :3], expected_slice_backbone, rtol=1e-2, atol=1e-2
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)
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@require_torch
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# Copied from tests.models.bit.test_modeling_bit.BitBackboneTest with Bit->TextNet
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class TextNetBackboneTest(BackboneTesterMixin, unittest.TestCase):
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all_model_classes = (TextNetBackbone,) if is_torch_available() else ()
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config_class = TextNetConfig
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has_attentions = False
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def setUp(self):
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self.model_tester = TextNetModelTester(self)
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