434 lines
17 KiB
Python
434 lines
17 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 ViTMAE model. """
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import inspect
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import math
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import tempfile
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import unittest
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import numpy as np
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from transformers import ViTMAEConfig
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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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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 ViTMAEForPreTraining, ViTMAEModel
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from transformers.models.vit.modeling_vit import VIT_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 ViTFeatureExtractor
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class ViTMAEModelTester:
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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=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=5,
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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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):
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self.parent = parent
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self.batch_size = batch_size
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self.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.is_training = is_training
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self.use_labels = use_labels
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.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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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 ViTMAEConfig(
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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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)
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def create_and_check_model(self, config, pixel_values, labels):
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model = ViTMAEModel(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) * (1 - config.mask_ratio), rounded above
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# (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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expected_seq_len = int(math.ceil((1 - config.mask_ratio) * (num_patches + 1)))
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, expected_seq_len, self.hidden_size))
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def create_and_check_for_pretraining(self, config, pixel_values, labels):
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model = ViTMAEForPreTraining(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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expected_seq_len = num_patches
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expected_num_channels = self.patch_size**2 * self.num_channels
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self.parent.assertEqual(result.logits.shape, (self.batch_size, expected_seq_len, expected_num_channels))
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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 ViTMAEModelTest(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 ViTMAE 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 = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
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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 = ViTMAEModelTester(self)
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self.config_tester = ConfigTester(self, config_class=ViTMAEConfig, 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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# ViTMAE 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_for_pretraining(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_pretraining(*config_and_inputs)
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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 ViTMAE, the seq_len equals (number of patches + 1) * (1 - mask_ratio), rounded above
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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 = int(math.ceil((1 - config.mask_ratio) * (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.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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if chunk_length is not None:
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self.assertListEqual(
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list(attentions[0].shape[-4:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
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)
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else:
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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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if hasattr(self.model_tester, "num_hidden_states_types"):
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added_hidden_states = self.model_tester.num_hidden_states_types
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elif self.is_encoder_decoder:
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added_hidden_states = 2
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else:
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added_hidden_states = 1
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self.assertEqual(out_len + added_hidden_states, len(outputs))
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self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
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self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
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if chunk_length is not None:
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self.assertListEqual(
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list(self_attentions[0].shape[-4:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
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)
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else:
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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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# ViTMAE 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 = int(math.ceil((1 - config.mask_ratio) * (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_save_load(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)
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model.to(torch_device)
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model.eval()
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# make random mask reproducible
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torch.manual_seed(2)
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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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out_2 = outputs[0].cpu().numpy()
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out_2[np.isnan(out_2)] = 0
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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model = model_class.from_pretrained(tmpdirname)
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model.to(torch_device)
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# make random mask reproducible
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torch.manual_seed(2)
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with torch.no_grad():
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after_outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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# Make sure we don't have nans
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out_1 = after_outputs[0].cpu().numpy()
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out_1[np.isnan(out_1)] = 0
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max_diff = np.amax(np.abs(out_1 - out_2))
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self.assertLessEqual(max_diff, 1e-5)
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@unittest.skip(
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reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
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to get deterministic results."""
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)
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def test_determinism(self):
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pass
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@unittest.skip(
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reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
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to get deterministic results."""
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)
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def test_save_load_fast_init_from_base(self):
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pass
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@unittest.skip(
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reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
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to get deterministic results."""
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)
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def test_save_load_fast_init_to_base(self):
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pass
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@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load""")
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def test_model_outputs_equivalence(self):
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pass
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@slow
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def test_model_from_pretrained(self):
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for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = ViTMAEModel.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 ViTMAEModelIntegrationTest(unittest.TestCase):
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@cached_property
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def default_feature_extractor(self):
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return ViTFeatureExtractor.from_pretrained("facebook/vit-mae-base") if is_vision_available() else None
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@slow
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def test_inference_for_pretraining(self):
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# make random mask reproducible
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# note that the same seed on CPU and on GPU doesn’t mean they spew the same random number sequences,
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# as they both have fairly different PRNGs (for efficiency reasons).
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# source: https://discuss.pytorch.org/t/random-seed-that-spans-across-devices/19735
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torch.manual_seed(2)
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model = ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base").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)
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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, 196, 768))
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self.assertEqual(outputs.logits.shape, expected_shape)
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expected_slice_cpu = torch.tensor(
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[[0.7366, -1.3663, -0.2844], [0.7919, -1.3839, -0.3241], [0.4313, -0.7168, -0.2878]]
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)
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expected_slice_gpu = torch.tensor(
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[[0.8948, -1.0680, 0.0030], [0.9758, -1.1181, -0.0290], [1.0602, -1.1522, -0.0528]]
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)
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# set expected slice depending on device
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expected_slice = expected_slice_cpu if torch_device == "cpu" else expected_slice_gpu
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self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_slice.to(torch_device), atol=1e-4))
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