427 lines
18 KiB
Python
427 lines
18 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 TensorFlow MobileViT model. """
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from __future__ import annotations
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import inspect
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import unittest
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from transformers import MobileViTConfig
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from transformers.file_utils import is_tf_available, is_vision_available
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from transformers.testing_utils import require_tf, slow
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_tf_available():
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import numpy as np
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import tensorflow as tf
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from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
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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 TFMobileViTConfigTester(ConfigTester):
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def create_and_test_config_common_properties(self):
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config = self.config_class(**self.inputs_dict)
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self.parent.assertTrue(hasattr(config, "hidden_sizes"))
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self.parent.assertTrue(hasattr(config, "neck_hidden_sizes"))
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self.parent.assertTrue(hasattr(config, "num_attention_heads"))
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class TFMobileViTModelTester:
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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=32,
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patch_size=2,
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num_channels=3,
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last_hidden_size=32,
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num_attention_heads=4,
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hidden_act="silu",
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conv_kernel_size=3,
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output_stride=32,
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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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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):
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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 = last_hidden_size
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self.num_attention_heads = num_attention_heads
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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.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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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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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 MobileViTConfig(
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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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num_attention_heads=self.num_attention_heads,
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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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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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classifier_dropout_prob=self.classifier_dropout_prob,
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initializer_range=self.initializer_range,
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hidden_sizes=[12, 16, 20],
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neck_hidden_sizes=[8, 8, 16, 16, 32, 32, 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 = TFMobileViTModel(config=config)
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result = model(pixel_values, training=False)
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expected_height = expected_width = self.image_size // self.output_stride
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self.parent.assertEqual(
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result.last_hidden_state.shape, (self.batch_size, self.last_hidden_size, expected_height, expected_width)
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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 = TFMobileViTForImageClassification(config)
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result = model(pixel_values, labels=labels, training=False)
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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 = TFMobileViTForSemanticSegmentation(config)
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expected_height = expected_width = self.image_size // self.output_stride
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result = model(pixel_values, training=False)
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self.parent.assertEqual(
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result.logits.shape, (self.batch_size, self.num_labels, expected_height, expected_width)
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)
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result = model(pixel_values, labels=pixel_labels, training=False)
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self.parent.assertEqual(
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result.logits.shape, (self.batch_size, self.num_labels, expected_height, expected_width)
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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_tf
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class TFMobileViTModelTest(TFModelTesterMixin, 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 MobileViT 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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(TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
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if is_tf_available()
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else ()
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)
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pipeline_model_mapping = (
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{"feature-extraction": TFMobileViTModel, "image-classification": TFMobileViTForImageClassification}
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if is_tf_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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test_onnx = False
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def setUp(self):
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self.model_tester = TFMobileViTModelTester(self)
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self.config_tester = TFMobileViTConfigTester(self, config_class=MobileViTConfig, 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="MobileViT 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="MobileViT 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="MobileViT does not output attentions")
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def test_attention_outputs(self):
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pass
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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.call)
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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_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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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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# MobileViT'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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@unittest.skipIf(
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not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
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reason="TF does not support backprop for grouped convolutions on CPU.",
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)
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def test_dataset_conversion(self):
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super().test_dataset_conversion()
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def check_keras_fit_results(self, val_loss1, val_loss2, atol=2e-1, rtol=2e-1):
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self.assertTrue(np.allclose(val_loss1, val_loss2, atol=atol, rtol=rtol))
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@unittest.skipIf(
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not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
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reason="TF does not support backprop for grouped convolutions on CPU.",
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)
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@slow
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def test_keras_fit(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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# Since `TFMobileViTModel` cannot operate with the default `fit()` method.
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if model_class.__name__ != "TFMobileViTModel":
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model = model_class(config)
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if getattr(model, "hf_compute_loss", None):
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super().test_keras_fit()
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# The default test_loss_computation() uses -100 as a proxy ignore_index
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# to test masked losses. Overridding to avoid -100 since semantic segmentation
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# models use `semantic_loss_ignore_index` from the config.
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def test_loss_computation(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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# set an ignore index to correctly test the masked loss used in
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# `TFMobileViTForSemanticSegmentation`.
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if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
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config.semantic_loss_ignore_index = 5
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model = model_class(config)
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if getattr(model, "hf_compute_loss", None):
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# The number of elements in the loss should be the same as the number of elements in the label
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prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
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added_label = prepared_for_class[
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sorted(prepared_for_class.keys() - inputs_dict.keys(), reverse=True)[0]
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]
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expected_loss_size = added_label.shape.as_list()[:1]
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# Test that model correctly compute the loss with kwargs
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prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
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possible_input_names = {"input_ids", "pixel_values", "input_features"}
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input_name = possible_input_names.intersection(set(prepared_for_class)).pop()
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model_input = prepared_for_class.pop(input_name)
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loss = model(model_input, **prepared_for_class)[0]
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self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
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# Test that model correctly compute the loss when we mask some positions
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prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
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possible_input_names = {"input_ids", "pixel_values", "input_features"}
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input_name = possible_input_names.intersection(set(prepared_for_class)).pop()
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model_input = prepared_for_class.pop(input_name)
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if "labels" in prepared_for_class:
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labels = prepared_for_class["labels"].numpy()
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if len(labels.shape) > 1 and labels.shape[1] != 1:
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# labels[0] = -100
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prepared_for_class["labels"] = tf.convert_to_tensor(labels)
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loss = model(model_input, **prepared_for_class)[0]
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self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
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self.assertTrue(not np.any(np.isnan(loss.numpy())))
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# Test that model correctly compute the loss with a dict
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prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
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loss = model(prepared_for_class)[0]
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self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
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# Test that model correctly compute the loss with a tuple
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prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
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# Get keys that were added with the _prepare_for_class function
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label_keys = prepared_for_class.keys() - inputs_dict.keys()
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signature = inspect.signature(model.call).parameters
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signature_names = list(signature.keys())
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# Create a dictionary holding the location of the tensors in the tuple
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tuple_index_mapping = {0: input_name}
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for label_key in label_keys:
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label_key_index = signature_names.index(label_key)
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tuple_index_mapping[label_key_index] = label_key
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sorted_tuple_index_mapping = sorted(tuple_index_mapping.items())
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# Initialize a list with their default values, update the values and convert to a tuple
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list_input = []
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for name in signature_names:
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if name != "kwargs":
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list_input.append(signature[name].default)
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for index, value in sorted_tuple_index_mapping:
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list_input[index] = prepared_for_class[value]
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tuple_input = tuple(list_input)
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# Send to model
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loss = model(tuple_input[:-1])[0]
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self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
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@slow
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def test_model_from_pretrained(self):
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model_name = "apple/mobilevit-small"
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model = TFMobileViTModel.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_tf
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class TFMobileViTModelIntegrationTest(unittest.TestCase):
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@slow
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def test_inference_image_classification_head(self):
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model = TFMobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small")
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image_processor = MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small")
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image = prepare_img()
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inputs = image_processor(images=image, return_tensors="tf")
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# forward pass
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outputs = model(**inputs, training=False)
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# verify the logits
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expected_shape = tf.TensorShape((1, 1000))
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self.assertEqual(outputs.logits.shape, expected_shape)
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expected_slice = tf.constant([-1.9364, -1.2327, -0.4653])
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tf.debugging.assert_near(outputs.logits[0, :3], expected_slice, atol=1e-4, rtol=1e-04)
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@slow
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def test_inference_semantic_segmentation(self):
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# `from_pt` will be removed
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model = TFMobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
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image_processor = MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
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image = prepare_img()
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inputs = image_processor(images=image, return_tensors="tf")
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# forward pass
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outputs = model(inputs.pixel_values, training=False)
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logits = outputs.logits
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# verify the logits
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expected_shape = tf.TensorShape((1, 21, 32, 32))
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self.assertEqual(logits.shape, expected_shape)
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expected_slice = tf.constant(
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[
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[[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
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[[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
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[[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
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]
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)
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tf.debugging.assert_near(logits[0, :3, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
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