526 lines
21 KiB
Python
526 lines
21 KiB
Python
# coding=utf-8
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# Copyright 2021 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 BEiT model. """
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import inspect
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import unittest
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from datasets import load_dataset
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from transformers import BeitConfig
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from transformers.file_utils import cached_property, is_torch_available, is_vision_available
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from transformers.models.auto import get_values
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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, _config_zero_init, floats_tensor, ids_tensor
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if is_torch_available():
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import torch
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from torch import nn
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from transformers import (
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MODEL_MAPPING,
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BeitForImageClassification,
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BeitForMaskedImageModeling,
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BeitForSemanticSegmentation,
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BeitModel,
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)
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from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST, to_2tuple
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if is_vision_available():
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from PIL import Image
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from transformers import BeitFeatureExtractor
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class BeitModelTester:
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def __init__(
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self,
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parent,
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vocab_size=100,
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batch_size=13,
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image_size=30,
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patch_size=2,
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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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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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type_sequence_label_size=10,
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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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out_indices=[0, 1, 2, 3],
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):
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self.parent = parent
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self.vocab_size = 100
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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.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.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.scope = scope
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self.out_indices = out_indices
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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_config(self):
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return BeitConfig(
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vocab_size=self.vocab_size,
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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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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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out_indices=self.out_indices,
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)
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def create_and_check_model(self, config, pixel_values, labels):
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model = BeitModel(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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# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
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image_size = to_2tuple(self.image_size)
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patch_size = to_2tuple(self.patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
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def create_and_check_for_masked_lm(self, config, pixel_values, labels):
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model = BeitForMaskedImageModeling(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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# expected sequence length = num_patches
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image_size = to_2tuple(self.image_size)
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patch_size = to_2tuple(self.patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, self.vocab_size))
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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 = BeitForImageClassification(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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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 BeitModelTest(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 BEiT 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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(BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
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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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def setUp(self):
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self.model_tester = BeitModelTester(self)
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self.config_tester = ConfigTester(self, config_class=BeitConfig, 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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def test_inputs_embeds(self):
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# BEiT does not use inputs_embeds
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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.Module))
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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_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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# we don't test BeitForMaskedImageModeling
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if model_class in [*get_values(MODEL_MAPPING), BeitForMaskedImageModeling]:
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continue
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# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
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# this can then be incorporated into _prepare_for_class in test_modeling_common.py
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elif model_class.__name__ == "BeitForSemanticSegmentation":
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batch_size, num_channels, height, width = inputs_dict["pixel_values"].shape
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inputs_dict["labels"] = torch.zeros(
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[self.model_tester.batch_size, height, width], device=torch_device
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).long()
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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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# we don't test BeitForMaskedImageModeling
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if (
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model_class in [*get_values(MODEL_MAPPING), BeitForMaskedImageModeling]
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or not model_class.supports_gradient_checkpointing
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):
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continue
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# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
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# this can then be incorporated into _prepare_for_class in test_modeling_common.py
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elif model_class.__name__ == "BeitForSemanticSegmentation":
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batch_size, num_channels, height, width = inputs_dict["pixel_values"].shape
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inputs_dict["labels"] = torch.zeros(
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[self.model_tester.batch_size, height, width], device=torch_device
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).long()
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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_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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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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# we skip lambda parameters as these require special initial values
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# determined by config.layer_scale_init_value
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if "lambda" in name:
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continue
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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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def test_attention_outputs(self):
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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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# in BEiT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
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image_size = to_2tuple(self.model_tester.image_size)
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patch_size = to_2tuple(self.model_tester.patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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seq_len = num_patches + 1
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encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
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encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
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chunk_length = getattr(self.model_tester, "chunk_length", None)
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if chunk_length is not None and hasattr(self.model_tester, "num_hashes"):
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encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes
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for model_class in self.all_model_classes:
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = False
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config.return_dict = True
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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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attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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# check that output_attentions also work using config
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del inputs_dict["output_attentions"]
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config.output_attentions = True
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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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attentions = outputs.attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
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)
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out_len = len(outputs)
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# Check attention is always last and order is fine
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = True
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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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self.assertEqual(out_len + 1, len(outputs))
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self_attentions = outputs.attentions
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self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(self_attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
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)
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def test_hidden_states_output(self):
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def check_hidden_states_output(inputs_dict, config, model_class):
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
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expected_num_layers = getattr(
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self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
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)
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self.assertEqual(len(hidden_states), expected_num_layers)
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# BEiT has a different seq_length
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image_size = to_2tuple(self.model_tester.image_size)
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patch_size = to_2tuple(self.model_tester.patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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seq_length = num_patches + 1
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self.assertListEqual(
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list(hidden_states[0].shape[-2:]),
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[seq_length, self.model_tester.hidden_size],
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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_for_masked_lm(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_masked_lm(*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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@slow
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def test_model_from_pretrained(self):
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for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = BeitModel.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_vision
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class BeitModelIntegrationTest(unittest.TestCase):
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@cached_property
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def default_feature_extractor(self):
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return (
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BeitFeatureExtractor.from_pretrained("microsoft/beit-base-patch16-224") if is_vision_available() else None
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)
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@slow
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def test_inference_masked_image_modeling_head(self):
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model = BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k").to(torch_device)
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feature_extractor = self.default_feature_extractor
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image = prepare_img()
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pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values.to(torch_device)
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# prepare bool_masked_pos
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bool_masked_pos = torch.ones((1, 196), dtype=torch.bool).to(torch_device)
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# forward pass
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outputs = model(pixel_values=pixel_values, bool_masked_pos=bool_masked_pos)
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logits = outputs.logits
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# verify the logits
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expected_shape = torch.Size((1, 196, 8192))
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self.assertEqual(logits.shape, expected_shape)
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expected_slice = torch.tensor(
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[[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]
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).to(torch_device)
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self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3], expected_slice, atol=1e-2))
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@slow
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def test_inference_image_classification_head_imagenet_1k(self):
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model = BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224").to(torch_device)
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feature_extractor = self.default_feature_extractor
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image = prepare_img()
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inputs = feature_extractor(images=image, return_tensors="pt").to(torch_device)
|
|
|
|
# forward pass
|
|
outputs = model(**inputs)
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|
logits = outputs.logits
|
|
|
|
# verify the logits
|
|
expected_shape = torch.Size((1, 1000))
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|
self.assertEqual(logits.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor([-1.2385, -1.0987, -1.0108]).to(torch_device)
|
|
|
|
self.assertTrue(torch.allclose(logits[0, :3], expected_slice, atol=1e-4))
|
|
|
|
expected_class_idx = 281
|
|
self.assertEqual(logits.argmax(-1).item(), expected_class_idx)
|
|
|
|
@slow
|
|
def test_inference_image_classification_head_imagenet_22k(self):
|
|
model = BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k").to(
|
|
torch_device
|
|
)
|
|
|
|
feature_extractor = self.default_feature_extractor
|
|
image = prepare_img()
|
|
inputs = feature_extractor(images=image, return_tensors="pt").to(torch_device)
|
|
|
|
# forward pass
|
|
outputs = model(**inputs)
|
|
logits = outputs.logits
|
|
|
|
# verify the logits
|
|
expected_shape = torch.Size((1, 21841))
|
|
self.assertEqual(logits.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor([1.6881, -0.2787, 0.5901]).to(torch_device)
|
|
|
|
self.assertTrue(torch.allclose(logits[0, :3], expected_slice, atol=1e-4))
|
|
|
|
expected_class_idx = 2396
|
|
self.assertEqual(logits.argmax(-1).item(), expected_class_idx)
|
|
|
|
@slow
|
|
def test_inference_semantic_segmentation(self):
|
|
model = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640")
|
|
model = model.to(torch_device)
|
|
|
|
feature_extractor = BeitFeatureExtractor(do_resize=True, size=640, do_center_crop=False)
|
|
|
|
ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
|
|
image = Image.open(ds[0]["file"])
|
|
inputs = feature_extractor(images=image, return_tensors="pt").to(torch_device)
|
|
|
|
# forward pass
|
|
outputs = model(**inputs)
|
|
logits = outputs.logits
|
|
|
|
# verify the logits
|
|
expected_shape = torch.Size((1, 150, 160, 160))
|
|
self.assertEqual(logits.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor(
|
|
[
|
|
[[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
|
|
[[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
|
|
[[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
|
|
]
|
|
).to(torch_device)
|
|
|
|
self.assertTrue(torch.allclose(logits[0, :3, :3, :3], expected_slice, atol=1e-4))
|