303 lines
11 KiB
Python
303 lines
11 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 UperNet framework. """
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import unittest
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from huggingface_hub import hf_hub_download
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from transformers import ConvNextConfig, UperNetConfig
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from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
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from transformers.utils import is_torch_available, is_vision_available
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, _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 transformers import UperNetForSemanticSegmentation
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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 UperNetModelTester:
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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=32,
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num_channels=3,
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num_stages=4,
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hidden_sizes=[10, 20, 30, 40],
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depths=[1, 1, 1, 1],
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is_training=True,
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use_labels=True,
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intermediate_size=37,
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hidden_act="gelu",
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type_sequence_label_size=10,
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initializer_range=0.02,
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out_features=["stage2", "stage3", "stage4"],
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num_labels=3,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.image_size = image_size
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self.num_channels = num_channels
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self.num_stages = num_stages
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self.hidden_sizes = hidden_sizes
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self.depths = depths
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self.is_training = is_training
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self.use_labels = use_labels
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.out_features = out_features
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self.num_labels = num_labels
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self.scope = scope
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self.num_hidden_layers = num_stages
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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.type_sequence_label_size)
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config = self.get_config()
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return config, pixel_values, labels
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def get_backbone_config(self):
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return ConvNextConfig(
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num_channels=self.num_channels,
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num_stages=self.num_stages,
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hidden_sizes=self.hidden_sizes,
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depths=self.depths,
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is_training=self.is_training,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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out_features=self.out_features,
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)
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def get_config(self):
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return UperNetConfig(
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backbone_config=self.get_backbone_config(),
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backbone=None,
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hidden_size=64,
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pool_scales=[1, 2, 3, 6],
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use_auxiliary_head=True,
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auxiliary_loss_weight=0.4,
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auxiliary_in_channels=40,
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auxiliary_channels=32,
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auxiliary_num_convs=1,
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auxiliary_concat_input=False,
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loss_ignore_index=255,
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num_labels=self.num_labels,
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)
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def create_and_check_for_semantic_segmentation(self, config, pixel_values, labels):
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model = UperNetForSemanticSegmentation(config=config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values)
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self.parent.assertEqual(
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result.logits.shape, (self.batch_size, self.num_labels, self.image_size, self.image_size)
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)
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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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(
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config,
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pixel_values,
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labels,
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) = 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 UperNetModelTest(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 UperNet 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 = (UperNetForSemanticSegmentation,) if is_torch_available() else ()
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pipeline_model_mapping = {"image-segmentation": UperNetForSemanticSegmentation} if is_torch_available() else {}
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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_torchscript = False
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has_attentions = False
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def setUp(self):
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self.model_tester = UperNetModelTester(self)
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self.config_tester = ConfigTester(self, config_class=UperNetConfig, 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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def test_for_semantic_segmentation(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_semantic_segmentation(*config_and_inputs)
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@unittest.skip(reason="UperNet 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="UperNet 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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@unittest.skip(reason="UperNet does not have a base model")
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def test_save_load_fast_init_from_base(self):
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pass
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@unittest.skip(reason="UperNet does not have a base model")
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def test_save_load_fast_init_to_base(self):
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pass
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@require_torch_multi_gpu
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@unittest.skip(reason="UperNet has some layers using `add_module` which doesn't work well with `nn.DataParallel`")
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def test_multi_gpu_data_parallel_forward(self):
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pass
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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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expected_num_stages = self.model_tester.num_stages
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self.assertEqual(len(hidden_states), expected_num_stages + 1)
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# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
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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 // 4, self.model_tester.image_size // 4],
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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_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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configs_no_init.backbone_config = _config_zero_init(configs_no_init.backbone_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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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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@unittest.skip(reason="UperNet does not have tied weights")
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def test_tied_model_weights_key_ignore(self):
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pass
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@slow
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def test_model_from_pretrained(self):
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model_name = "openmmlab/upernet-convnext-tiny"
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model = UperNetForSemanticSegmentation.from_pretrained(model_name)
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self.assertIsNotNone(model)
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# We will verify our results on an image of ADE20k
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def prepare_img():
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filepath = hf_hub_download(
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repo_id="hf-internal-testing/fixtures_ade20k", repo_type="dataset", filename="ADE_val_00000001.jpg"
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)
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image = Image.open(filepath).convert("RGB")
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return image
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@require_torch
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@require_vision
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@slow
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class UperNetModelIntegrationTest(unittest.TestCase):
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def test_inference_swin_backbone(self):
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processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-swin-tiny")
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model = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-swin-tiny").to(torch_device)
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image = prepare_img()
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inputs = processor(images=image, return_tensors="pt").to(torch_device)
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with torch.no_grad():
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outputs = model(**inputs)
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expected_shape = torch.Size((1, model.config.num_labels, 512, 512))
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self.assertEqual(outputs.logits.shape, expected_shape)
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expected_slice = torch.tensor(
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[[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]
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).to(torch_device)
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self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3], expected_slice, atol=1e-4))
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def test_inference_convnext_backbone(self):
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processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-tiny")
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model = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-tiny").to(torch_device)
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image = prepare_img()
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inputs = processor(images=image, return_tensors="pt").to(torch_device)
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with torch.no_grad():
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outputs = model(**inputs)
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expected_shape = torch.Size((1, model.config.num_labels, 512, 512))
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self.assertEqual(outputs.logits.shape, expected_shape)
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expected_slice = torch.tensor(
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[[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]
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).to(torch_device)
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self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3], expected_slice, atol=1e-4))
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