324 lines
12 KiB
Python
324 lines
12 KiB
Python
# coding=utf-8
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# Copyright 2023 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 Pvt model."""
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import unittest
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from transformers import is_torch_available, is_vision_available
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from transformers.testing_utils import (
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require_accelerate,
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require_torch,
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require_torch_accelerator,
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require_torch_fp16,
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slow,
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torch_device,
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)
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from transformers import PvtConfig, PvtForImageClassification, PvtImageProcessor, PvtModel
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from transformers.models.auto.modeling_auto import MODEL_MAPPING_NAMES
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if is_vision_available():
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from PIL import Image
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class PvtConfigTester(ConfigTester):
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def run_common_tests(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_encoder_blocks"))
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class PvtModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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image_size=64,
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num_channels=3,
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num_encoder_blocks=4,
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depths=[2, 2, 2, 2],
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sr_ratios=[8, 4, 2, 1],
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hidden_sizes=[16, 32, 64, 128],
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downsampling_rates=[1, 4, 8, 16],
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num_attention_heads=[1, 2, 4, 8],
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is_training=True,
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use_labels=True,
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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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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_encoder_blocks = num_encoder_blocks
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self.sr_ratios = sr_ratios
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self.depths = depths
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self.hidden_sizes = hidden_sizes
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self.downsampling_rates = downsampling_rates
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self.num_attention_heads = num_attention_heads
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self.is_training = is_training
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self.use_labels = use_labels
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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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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 PvtConfig(
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image_size=self.image_size,
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num_channels=self.num_channels,
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num_encoder_blocks=self.num_encoder_blocks,
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depths=self.depths,
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hidden_sizes=self.hidden_sizes,
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num_attention_heads=self.num_attention_heads,
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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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initializer_range=self.initializer_range,
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)
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def create_and_check_model(self, config, pixel_values, labels):
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model = PvtModel(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.assertIsNotNone(result.last_hidden_state)
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def create_and_check_for_image_classification(self, config, pixel_values, labels):
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config.num_labels = self.type_sequence_label_size
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model = PvtForImageClassification(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.type_sequence_label_size))
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# test greyscale images
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config.num_channels = 1
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model = PvtForImageClassification(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.type_sequence_label_size))
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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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# We will verify our results on an image of cute cats
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def prepare_img():
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image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
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return image
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@require_torch
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class PvtModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (PvtModel, PvtForImageClassification) if is_torch_available() else ()
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pipeline_model_mapping = (
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{"image-feature-extraction": PvtModel, "image-classification": PvtForImageClassification}
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if is_torch_available()
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else {}
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)
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test_head_masking = False
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test_pruning = False
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test_resize_embeddings = 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 = PvtModelTester(self)
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self.config_tester = PvtConfigTester(self, config_class=PvtConfig)
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def test_config(self):
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self.config_tester.run_common_tests()
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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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@unittest.skip("Pvt 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("Pvt does not have get_input_embeddings method and get_output_embeddings methods")
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def test_model_common_attributes(self):
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pass
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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, param in model.named_parameters():
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self.assertTrue(
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-1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 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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def test_hidden_states_output(self):
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def check_hidden_states_output(inputs_dict, config, model_class):
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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hidden_states = outputs.hidden_states
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expected_num_layers = sum(self.model_tester.depths) + 1
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self.assertEqual(len(hidden_states), expected_num_layers)
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# verify the first hidden states (first block)
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self.assertListEqual(
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list(hidden_states[0].shape[-3:]),
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[
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self.model_tester.batch_size,
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(self.model_tester.image_size // 4) ** 2,
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self.model_tester.image_size // 4,
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],
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)
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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inputs_dict["output_hidden_states"] = True
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check_hidden_states_output(inputs_dict, config, model_class)
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# check that output_hidden_states also work using config
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del inputs_dict["output_hidden_states"]
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config.output_hidden_states = True
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check_hidden_states_output(inputs_dict, config, model_class)
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def test_training(self):
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if not self.model_tester.is_training:
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return
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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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for model_class in self.all_model_classes:
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if model_class.__name__ in MODEL_MAPPING_NAMES.values():
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continue
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model = model_class(config)
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model.to(torch_device)
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model.train()
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inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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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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model_name = "Zetatech/pvt-tiny-224"
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model = PvtModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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@require_torch
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class PvtModelIntegrationTest(unittest.TestCase):
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@slow
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def test_inference_image_classification(self):
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# only resize + normalize
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image_processor = PvtImageProcessor.from_pretrained("Zetatech/pvt-tiny-224")
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model = PvtForImageClassification.from_pretrained("Zetatech/pvt-tiny-224").to(torch_device).eval()
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image = prepare_img()
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encoded_inputs = image_processor(images=image, return_tensors="pt")
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pixel_values = encoded_inputs.pixel_values.to(torch_device)
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with torch.no_grad():
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outputs = model(pixel_values)
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expected_shape = torch.Size((1, model.config.num_labels))
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self.assertEqual(outputs.logits.shape, expected_shape)
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expected_slice = torch.tensor([-1.4192, -1.9158, -0.9702]).to(torch_device)
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self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
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@slow
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def test_inference_model(self):
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model = PvtModel.from_pretrained("Zetatech/pvt-tiny-224").to(torch_device).eval()
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image_processor = PvtImageProcessor.from_pretrained("Zetatech/pvt-tiny-224")
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image = prepare_img()
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inputs = image_processor(images=image, return_tensors="pt")
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pixel_values = inputs.pixel_values.to(torch_device)
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# forward pass
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with torch.no_grad():
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outputs = model(pixel_values)
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# verify the logits
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expected_shape = torch.Size((1, 50, 512))
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self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
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expected_slice = torch.tensor(
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[[-0.3086, 1.0402, 1.1816], [-0.2880, 0.5781, 0.6124], [0.1480, 0.6129, -0.0590]]
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).to(torch_device)
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self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))
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@slow
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@require_accelerate
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@require_torch_accelerator
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@require_torch_fp16
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def test_inference_fp16(self):
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r"""
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A small test to make sure that inference work in half precision without any problem.
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"""
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model = PvtForImageClassification.from_pretrained("Zetatech/pvt-tiny-224", torch_dtype=torch.float16)
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model.to(torch_device)
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image_processor = PvtImageProcessor(size=224)
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image = prepare_img()
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inputs = image_processor(images=image, return_tensors="pt")
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pixel_values = inputs.pixel_values.to(torch_device, dtype=torch.float16)
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# forward pass to make sure inference works in fp16
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with torch.no_grad():
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_ = model(pixel_values)
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