377 lines
14 KiB
Python
377 lines
14 KiB
Python
# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Testing suite for the PyTorch MobileViTV2 model."""
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import unittest
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from transformers import MobileViTV2Config
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from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
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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 transformers import MobileViTV2ForImageClassification, MobileViTV2ForSemanticSegmentation, MobileViTV2Model
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from transformers.models.mobilevitv2.modeling_mobilevitv2 import (
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make_divisible,
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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 MobileViTImageProcessor
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class MobileViTV2ConfigTester(ConfigTester):
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def create_and_test_config_common_properties(self):
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config = self.config_class(**self.inputs_dict)
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self.parent.assertTrue(hasattr(config, "width_multiplier"))
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class MobileViTV2ModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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image_size=64,
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patch_size=2,
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num_channels=3,
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hidden_act="swish",
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conv_kernel_size=3,
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output_stride=32,
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classifier_dropout_prob=0.1,
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initializer_range=0.02,
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is_training=True,
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use_labels=True,
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num_labels=10,
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scope=None,
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width_multiplier=0.25,
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ffn_dropout=0.0,
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attn_dropout=0.0,
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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.last_hidden_size = make_divisible(512 * width_multiplier, divisor=8)
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self.hidden_act = hidden_act
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self.conv_kernel_size = conv_kernel_size
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self.output_stride = output_stride
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self.classifier_dropout_prob = classifier_dropout_prob
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self.use_labels = use_labels
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self.is_training = is_training
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self.num_labels = num_labels
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self.initializer_range = initializer_range
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self.scope = scope
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self.width_multiplier = width_multiplier
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self.ffn_dropout_prob = ffn_dropout
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self.attn_dropout_prob = attn_dropout
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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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pixel_labels = None
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if self.use_labels:
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labels = ids_tensor([self.batch_size], self.num_labels)
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pixel_labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels)
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config = self.get_config()
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return config, pixel_values, labels, pixel_labels
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def get_config(self):
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return MobileViTV2Config(
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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_act=self.hidden_act,
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conv_kernel_size=self.conv_kernel_size,
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output_stride=self.output_stride,
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classifier_dropout_prob=self.classifier_dropout_prob,
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initializer_range=self.initializer_range,
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width_multiplier=self.width_multiplier,
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ffn_dropout=self.ffn_dropout_prob,
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attn_dropout=self.attn_dropout_prob,
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base_attn_unit_dims=[16, 24, 32],
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n_attn_blocks=[1, 1, 2],
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aspp_out_channels=32,
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)
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def create_and_check_model(self, config, pixel_values, labels, pixel_labels):
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model = MobileViTV2Model(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.last_hidden_state.shape,
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(
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self.batch_size,
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self.last_hidden_size,
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self.image_size // self.output_stride,
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self.image_size // self.output_stride,
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),
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)
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def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels):
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config.num_labels = self.num_labels
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model = MobileViTV2ForImageClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values, labels=labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
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def create_and_check_for_semantic_segmentation(self, config, pixel_values, labels, pixel_labels):
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config.num_labels = self.num_labels
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model = MobileViTV2ForSemanticSegmentation(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.logits.shape,
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(
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self.batch_size,
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self.num_labels,
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self.image_size // self.output_stride,
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self.image_size // self.output_stride,
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),
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)
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result = model(pixel_values, labels=pixel_labels)
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self.parent.assertEqual(
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result.logits.shape,
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(
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self.batch_size,
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self.num_labels,
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self.image_size // self.output_stride,
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self.image_size // self.output_stride,
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),
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)
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, pixel_values, labels, pixel_labels = config_and_inputs
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inputs_dict = {"pixel_values": pixel_values}
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return config, inputs_dict
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@require_torch
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class MobileViTV2ModelTest(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 MobileViTV2 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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(MobileViTV2Model, MobileViTV2ForImageClassification, MobileViTV2ForSemanticSegmentation)
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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": MobileViTV2Model,
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"image-classification": MobileViTV2ForImageClassification,
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"image-segmentation": MobileViTV2ForSemanticSegmentation,
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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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has_attentions = False
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def setUp(self):
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self.model_tester = MobileViTV2ModelTester(self)
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self.config_tester = MobileViTV2ConfigTester(self, config_class=MobileViTV2Config, has_text_modality=False)
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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="MobileViTV2 does not use inputs_embeds")
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def test_inputs_embeds(self):
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pass
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@unittest.skip(reason="MobileViTV2 does not support input and output embeddings")
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def test_model_common_attributes(self):
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pass
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@unittest.skip(reason="MobileViTV2 does not output attentions")
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def test_attention_outputs(self):
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pass
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@require_torch_multi_gpu
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@unittest.skip(reason="Got `CUDA error: misaligned address` for tests after this one being run.")
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def test_multi_gpu_data_parallel_forward(self):
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pass
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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_hidden_states_output(self):
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def check_hidden_states_output(inputs_dict, config, model_class):
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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hidden_states = outputs.hidden_states
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expected_num_stages = 5
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self.assertEqual(len(hidden_states), expected_num_stages)
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# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
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# with the width and height being successively divided by 2.
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divisor = 2
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for i in range(len(hidden_states)):
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self.assertListEqual(
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list(hidden_states[i].shape[-2:]),
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[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor],
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)
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divisor *= 2
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self.assertEqual(self.model_tester.output_stride, divisor // 2)
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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_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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def test_for_semantic_segmentation(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_semantic_segmentation(*config_and_inputs)
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@slow
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def test_model_from_pretrained(self):
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model_name = "apple/mobilevitv2-1.0-imagenet1k-256"
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model = MobileViTV2Model.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 MobileViTV2ModelIntegrationTest(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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MobileViTImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256")
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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 = MobileViTV2ForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256").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([-1.6336e00, -7.3204e-02, -5.1883e-01]).to(torch_device)
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self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
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@slow
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def test_inference_semantic_segmentation(self):
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model = MobileViTV2ForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
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model = model.to(torch_device)
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image_processor = MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
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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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logits = outputs.logits
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# verify the logits
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expected_shape = torch.Size((1, 21, 32, 32))
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self.assertEqual(logits.shape, expected_shape)
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expected_slice = torch.tensor(
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[
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[[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
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[[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
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[[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
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],
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device=torch_device,
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)
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self.assertTrue(torch.allclose(logits[0, :3, :3, :3], expected_slice, atol=1e-4))
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@slow
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def test_post_processing_semantic_segmentation(self):
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model = MobileViTV2ForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
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model = model.to(torch_device)
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image_processor = MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
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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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outputs.logits = outputs.logits.detach().cpu()
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segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs, target_sizes=[(50, 60)])
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expected_shape = torch.Size((50, 60))
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self.assertEqual(segmentation[0].shape, expected_shape)
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segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs)
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expected_shape = torch.Size((32, 32))
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self.assertEqual(segmentation[0].shape, expected_shape)
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