446 lines
17 KiB
Python
446 lines
17 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 DeiT model."""
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import unittest
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import warnings
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from transformers import DeiTConfig
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from transformers.testing_utils import (
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require_accelerate,
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require_torch,
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require_torch_accelerator,
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require_torch_fp16,
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require_vision,
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slow,
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torch_device,
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)
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from transformers.utils import cached_property, is_torch_available, is_vision_available
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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 torch import nn
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from transformers import (
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DeiTForImageClassification,
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DeiTForImageClassificationWithTeacher,
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DeiTForMaskedImageModeling,
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DeiTModel,
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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_FOR_SEQUENCE_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 DeiTImageProcessor
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class DeiTModelTester:
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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=2,
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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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encoder_stride=2,
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mask_ratio=0.5,
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attn_implementation="eager",
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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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self.encoder_stride = encoder_stride
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self.attn_implementation = attn_implementation
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# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
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num_patches = (image_size // patch_size) ** 2
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self.seq_length = num_patches + 2
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self.mask_ratio = mask_ratio
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self.num_masks = int(mask_ratio * self.seq_length)
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self.mask_length = num_patches
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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 DeiTConfig(
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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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encoder_stride=self.encoder_stride,
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attn_implementation=self.attn_implementation,
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)
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def create_and_check_model(self, config, pixel_values, labels):
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model = DeiTModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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def create_and_check_for_masked_image_modeling(self, config, pixel_values, labels):
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model = DeiTForMaskedImageModeling(config=config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values)
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self.parent.assertEqual(
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result.reconstruction.shape, (self.batch_size, self.num_channels, self.image_size, self.image_size)
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)
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# test greyscale images
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config.num_channels = 1
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model = DeiTForMaskedImageModeling(config)
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model.to(torch_device)
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model.eval()
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pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
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result = model(pixel_values)
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self.parent.assertEqual(result.reconstruction.shape, (self.batch_size, 1, self.image_size, self.image_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 = DeiTForImageClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values, labels=labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
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# test greyscale images
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config.num_channels = 1
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model = DeiTForImageClassification(config)
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model.to(torch_device)
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model.eval()
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pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
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result = model(pixel_values, 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 DeiTModelTest(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 DeiT 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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(
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DeiTModel,
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DeiTForImageClassification,
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DeiTForImageClassificationWithTeacher,
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DeiTForMaskedImageModeling,
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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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pipeline_model_mapping = (
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{
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"image-feature-extraction": DeiTModel,
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"image-classification": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
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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_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 = DeiTModelTester(self)
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self.config_tester = ConfigTester(self, config_class=DeiTConfig, has_text_modality=False, hidden_size=37)
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@unittest.skip(
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"Since `torch==2.3+cu121`, although this test passes, many subsequent tests have `CUDA error: misaligned address`."
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"If `nvidia-xxx-cu118` are also installed, no failure (even with `torch==2.3+cu121`)."
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)
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def test_multi_gpu_data_parallel_forward(self):
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super().test_multi_gpu_data_parallel_forward()
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def test_config(self):
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self.config_tester.run_common_tests()
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@unittest.skip(reason="DeiT does not use inputs_embeds")
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def test_inputs_embeds(self):
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pass
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def test_model_common_attributes(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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self.assertIsInstance(model.get_input_embeddings(), (nn.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_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_masked_image_modeling(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_image_modeling(*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 DeiTForImageClassificationWithTeacher model
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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__ == "DeiTForImageClassificationWithTeacher":
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del inputs_dict["labels"]
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return inputs_dict
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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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# DeiTForImageClassificationWithTeacher 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__ == "DeiTForImageClassificationWithTeacher"
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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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# DeiTForImageClassificationWithTeacher supports inference-only
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if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
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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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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant_false(self):
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pass
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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_SEQUENCE_CLASSIFICATION_MAPPING_NAMES.values(),
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*MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES.values(),
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]
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or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
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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/deit-base-distilled-patch16-224"
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model = DeiTModel.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 DeiTModelIntegrationTest(unittest.TestCase):
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@cached_property
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def default_image_processor(self):
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return (
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DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224")
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if is_vision_available()
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else None
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)
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@slow
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def test_inference_image_classification_head(self):
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model = DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224").to(
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torch_device
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)
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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([-1.0266, 0.1912, -1.2861]).to(torch_device)
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self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
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@slow
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@require_accelerate
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@require_torch_accelerator
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@require_torch_fp16
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def test_inference_fp16(self):
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r"""
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A small test to make sure that inference work in half precision without any problem.
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"""
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model = DeiTModel.from_pretrained(
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"facebook/deit-base-distilled-patch16-224", torch_dtype=torch.float16, device_map="auto"
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)
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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")
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pixel_values = inputs.pixel_values.to(torch_device)
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# forward pass to make sure inference works in fp16
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with torch.no_grad():
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_ = model(pixel_values)
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