wehub-resource-sync
335 lines
12 KiB
Python
335 lines
12 KiB
Python
# coding=utf-8
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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 Paddle DPT model. """
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import inspect
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import unittest
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import paddle
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import paddle.nn as nn
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from PIL import Image
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from paddlenlp.transformers import (
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DPTConfig,
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DPTForDepthEstimation,
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DPTForSemanticSegmentation,
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DPTImageProcessor,
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DPTModel,
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)
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from ...testing_utils import get_tests_dir, slow
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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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DPT_PRETRAINED_MODEL_ARCHIVE_LIST = [
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"Intel/dpt-large",
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"Intel/dpt-hybrid-midas",
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# See all DPT models at https://huggingface.co/models?filter=dpt
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]
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class DPTModelTester:
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def __init__(
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self,
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parent,
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batch_size=2,
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image_size=32,
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patch_size=16,
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num_channels=3,
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is_training=True,
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use_labels=True,
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hidden_size=32,
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num_hidden_layers=4,
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backbone_out_indices=[0, 1, 2, 3],
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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initializer_range=0.02,
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num_labels=3,
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is_hybrid=False,
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return_dict=True,
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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.patch_size = patch_size
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self.num_channels = num_channels
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self.is_training = is_training
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self.use_labels = use_labels
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.backbone_out_indices = backbone_out_indices
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.initializer_range = initializer_range
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self.num_labels = num_labels
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self.scope = scope
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self.is_hybrid = is_hybrid
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# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
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num_patches = (image_size // patch_size) ** 2
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self.seq_length = num_patches + 1
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self.return_dict = return_dict
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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.image_size, self.image_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 DPTConfig(
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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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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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backbone_out_indices=self.backbone_out_indices,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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is_decoder=False,
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initializer_range=self.initializer_range,
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is_hybrid=self.is_hybrid,
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return_dict=self.return_dict,
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)
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def create_and_check_model(self, config, pixel_values, labels):
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model = DPTModel(config=config)
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model.eval()
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result = model(pixel_values)
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self.parent.assertEqual(result.last_hidden_state.shape, [self.batch_size, self.seq_length, self.hidden_size])
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def create_and_check_for_depth_estimation(self, config, pixel_values, labels):
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config.num_labels = self.num_labels
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model = DPTForDepthEstimation(config)
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model.eval()
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result = model(pixel_values)
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self.parent.assertEqual(result.predicted_depth.shape, [self.batch_size, self.image_size, self.image_size])
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def create_and_check_for_semantic_segmentation(self, config, pixel_values, labels):
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config.num_labels = self.num_labels
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model = DPTForSemanticSegmentation(config)
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model.eval()
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result = model(pixel_values, labels=labels)
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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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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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class DPTModelTest(ModelTesterMixin, unittest.TestCase):
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"""
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Here we also overwrite some of the tests of test_modeling_common.py, as DPT 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 = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation)
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test_resize_embeddings = False
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def setUp(self):
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self.model_tester = DPTModelTester(self)
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self.config_tester = ConfigTester(self, config_class=DPTConfig, has_text_modality=False, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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@unittest.skip(reason="DPT does not use model_name_list")
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def test_model_name_list(self):
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pass
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@unittest.skip(reason="DPT does not use inputs_embeds")
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def test_inputs_embeds(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.Layer))
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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_forward_signature(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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signature = inspect.signature(model.forward)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = ["pixel_values"]
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self.assertListEqual(arg_names[:1], expected_arg_names)
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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_depth_estimation(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_depth_estimation(*config_and_inputs)
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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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def test_training(self):
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for model_class in self.all_model_classes:
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if model_class.__name__ in ["DPTModel", "DPTForDepthEstimation"]:
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continue
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.return_dict = True
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model = model_class(config)
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model.train()
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inputs = self._prepare_for_class(inputs_dict, model_class)
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inputs["labels"] = paddle.zeros(
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(self.model_tester.batch_size, self.model_tester.image_size, self.model_tester.image_size),
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dtype=paddle.int64,
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)
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loss = model(**inputs).loss
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loss.backward()
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@slow
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def test_training_gradient_checkpointing(self):
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for model_class in self.all_model_classes:
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if model_class.__name__ in ["DPTModel", "DPTForDepthEstimation"]:
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continue
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.use_cache = False
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config.return_dict = True
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if not model_class.supports_gradient_checkpointing:
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continue
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model = model_class(config)
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model.gradient_checkpointing_enable()
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model.train()
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inputs = self._prepare_for_class(inputs_dict, model_class)
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inputs["labels"] = paddle.zeros(
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(self.model_tester.batch_size, self.model_tester.image_size, self.model_tester.image_size),
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dtype=paddle.int64,
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)
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loss = model(**inputs).loss
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loss.backward()
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@slow
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def test_model_from_pretrained(self):
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for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = DPTModel.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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CUTE_CATS = get_tests_dir("fixtures/tests_samples/COCO/000000039769.png")
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image = Image.open(CUTE_CATS)
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return image
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@slow
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class DPTModelIntegrationTest(unittest.TestCase):
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def test_inference_depth_estimation(self):
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image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large")
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model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
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model.eval()
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image = prepare_img()
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inputs = image_processor(images=image, return_tensors="pd")
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# forward pass
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with paddle.no_grad():
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outputs = model(**inputs)
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predicted_depth = outputs.predicted_depth
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# verify the predicted depth
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expected_shape = [1, 384, 384]
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self.assertEqual(predicted_depth.shape, expected_shape)
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expected_slice = paddle.to_tensor(
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[[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]
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)
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self.assertTrue(paddle.allclose(outputs.predicted_depth[0, :3, :3], expected_slice, atol=1e-4))
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def test_inference_semantic_segmentation(self):
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image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large-ade")
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model = DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade")
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model.eval()
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image = prepare_img()
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inputs = image_processor(images=image, return_tensors="pd")
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# forward pass
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with paddle.no_grad():
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outputs = model(**inputs)
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# verify the logits
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expected_shape = [1, 150, 480, 480]
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self.assertEqual(outputs.logits.shape, expected_shape)
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expected_slice = paddle.to_tensor(
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[[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]
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)
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self.assertTrue(paddle.allclose(outputs.logits[0, 0, :3, :3], expected_slice, atol=1e-4))
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def test_post_processing_semantic_segmentation(self):
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image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large-ade")
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model = DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade")
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model.eval()
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image = prepare_img()
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inputs = image_processor(images=image, return_tensors="pd")
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# forward pass
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with paddle.no_grad():
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outputs = model(**inputs)
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outputs.logits = outputs.logits.detach().cpu()
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segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs, target_sizes=[[500, 300]])
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expected_shape = [500, 300]
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self.assertEqual(segmentation[0].shape, expected_shape)
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segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs)
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expected_shape = [480, 480]
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self.assertEqual(segmentation[0].shape, expected_shape)
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if __name__ == "__main__":
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unittest.main()
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