420 lines
16 KiB
Python
420 lines
16 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 LeViT model."""
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import unittest
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import warnings
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from math import ceil, floor
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from transformers import LevitConfig
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from transformers.file_utils import cached_property, is_torch_available, is_vision_available
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from transformers.testing_utils import require_torch, require_vision, slow, torch_device
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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 (
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LevitForImageClassification,
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LevitForImageClassificationWithTeacher,
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LevitModel,
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)
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from transformers.models.auto.modeling_auto import (
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MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
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MODEL_MAPPING_NAMES,
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)
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if is_vision_available():
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from PIL import Image
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from transformers import LevitImageProcessor
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class LevitConfigTester(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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class LevitModelTester:
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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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kernel_size=3,
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stride=2,
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padding=1,
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patch_size=16,
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hidden_sizes=[16, 32, 48],
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num_attention_heads=[1, 2, 3],
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depths=[2, 3, 4],
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key_dim=[8, 8, 8],
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drop_path_rate=0,
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mlp_ratio=[2, 2, 2],
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attention_ratio=[2, 2, 2],
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initializer_range=0.02,
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is_training=True,
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use_labels=True,
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num_labels=2, # Check
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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.kernel_size = kernel_size
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self.stride = stride
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self.padding = padding
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self.hidden_sizes = hidden_sizes
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self.num_attention_heads = num_attention_heads
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self.depths = depths
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self.key_dim = key_dim
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self.drop_path_rate = drop_path_rate
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self.patch_size = patch_size
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self.attention_ratio = attention_ratio
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self.mlp_ratio = mlp_ratio
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self.initializer_range = initializer_range
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self.down_ops = [
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["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
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["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
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]
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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.initializer_range = initializer_range
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def prepare_config_and_inputs(self):
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pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
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labels = None
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if self.use_labels:
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labels = ids_tensor([self.batch_size], self.num_labels)
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config = self.get_config()
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return config, pixel_values, labels
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def get_config(self):
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return LevitConfig(
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image_size=self.image_size,
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num_channels=self.num_channels,
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kernel_size=self.kernel_size,
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stride=self.stride,
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padding=self.padding,
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patch_size=self.patch_size,
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hidden_sizes=self.hidden_sizes,
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num_attention_heads=self.num_attention_heads,
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depths=self.depths,
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key_dim=self.key_dim,
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drop_path_rate=self.drop_path_rate,
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mlp_ratio=self.mlp_ratio,
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attention_ratio=self.attention_ratio,
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initializer_range=self.initializer_range,
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down_ops=self.down_ops,
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)
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def create_and_check_model(self, config, pixel_values, labels):
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model = LevitModel(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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image_size = (self.image_size, self.image_size)
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height, width = image_size[0], image_size[1]
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for _ in range(4):
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height = floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1)
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width = floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1)
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self.parent.assertEqual(
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result.last_hidden_state.shape,
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(self.batch_size, ceil(height / 4) * ceil(width / 4), self.hidden_sizes[-1]),
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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 = LevitForImageClassification(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_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 LevitModelTest(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 Levit 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 = (
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(LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
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if is_torch_available()
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else ()
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)
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pipeline_model_mapping = (
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{
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"image-feature-extraction": LevitModel,
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"image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
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}
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if is_torch_available()
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else {}
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)
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test_pruning = False
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test_torchscript = False
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test_resize_embeddings = False
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test_head_masking = False
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has_attentions = False
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def setUp(self):
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self.model_tester = LevitModelTester(self)
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self.config_tester = ConfigTester(self, config_class=LevitConfig, 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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@unittest.skip(reason="Levit 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="Levit 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="Levit does not output attentions")
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def test_attention_outputs(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.hidden_states
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expected_num_layers = len(self.model_tester.depths) + 1
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self.assertEqual(len(hidden_states), expected_num_layers)
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image_size = (self.model_tester.image_size, self.model_tester.image_size)
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height, width = image_size[0], image_size[1]
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for _ in range(4):
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height = floor(
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(
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(height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
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/ self.model_tester.stride
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)
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+ 1
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)
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width = floor(
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(
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(width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
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/ self.model_tester.stride
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)
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+ 1
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)
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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[-2:]),
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[
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height * width,
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self.model_tester.hidden_sizes[0],
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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 _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
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if return_labels:
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if model_class.__name__ == "LevitForImageClassificationWithTeacher":
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del inputs_dict["labels"]
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return inputs_dict
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_for_image_classification(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
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# special case for LevitForImageClassificationWithTeacher model
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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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# LevitForImageClassificationWithTeacher supports inference-only
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if (
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model_class.__name__ in MODEL_MAPPING_NAMES.values()
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or model_class.__name__ == "LevitForImageClassificationWithTeacher"
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):
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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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def test_training_gradient_checkpointing(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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if not self.model_tester.is_training:
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return
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config.use_cache = False
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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() or not model_class.supports_gradient_checkpointing:
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continue
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# LevitForImageClassificationWithTeacher supports inference-only
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if model_class.__name__ == "LevitForImageClassificationWithTeacher":
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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.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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def test_problem_types(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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problem_types = [
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{"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
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{"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
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{"title": "regression", "num_labels": 1, "dtype": torch.float},
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]
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for model_class in self.all_model_classes:
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if (
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model_class.__name__
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not in [
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*MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES.values(),
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]
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or model_class.__name__ == "LevitForImageClassificationWithTeacher"
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):
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continue
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for problem_type in problem_types:
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with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"):
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config.problem_type = problem_type["title"]
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config.num_labels = problem_type["num_labels"]
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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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if problem_type["num_labels"] > 1:
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inputs["labels"] = inputs["labels"].unsqueeze(1).repeat(1, problem_type["num_labels"])
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inputs["labels"] = inputs["labels"].to(problem_type["dtype"])
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# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
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# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
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# they have the same size." which is a symptom something in wrong for the regression problem.
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# See https://github.com/huggingface/transformers/issues/11780
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with warnings.catch_warnings(record=True) as warning_list:
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loss = model(**inputs).loss
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for w in warning_list:
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if "Using a target size that is different to the input size" in str(w.message):
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raise ValueError(
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f"Something is going wrong in the regression problem: intercepted {w.message}"
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)
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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 = "facebook/levit-128S"
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model = LevitModel.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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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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@require_vision
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class LevitModelIntegrationTest(unittest.TestCase):
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@cached_property
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def default_image_processor(self):
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return LevitImageProcessor.from_pretrained("facebook/levit-128S")
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@slow
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def test_inference_image_classification_head(self):
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model = LevitForImageClassificationWithTeacher.from_pretrained("facebook/levit-128S").to(torch_device)
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image_processor = self.default_image_processor
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image = prepare_img()
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inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
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# forward pass
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with torch.no_grad():
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outputs = model(**inputs)
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# verify the logits
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expected_shape = torch.Size((1, 1000))
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self.assertEqual(outputs.logits.shape, expected_shape)
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expected_slice = torch.tensor([1.0448, -0.3745, -1.8317]).to(torch_device)
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self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
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