364 lines
13 KiB
Python
364 lines
13 KiB
Python
# coding=utf-8
|
|
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
"""Testing suite for the PyTorch MobileViT model."""
|
|
|
|
import unittest
|
|
|
|
from transformers import MobileViTConfig
|
|
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
|
|
from transformers.utils import cached_property, is_torch_available, is_vision_available
|
|
|
|
from ...test_configuration_common import ConfigTester
|
|
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
|
|
from ...test_pipeline_mixin import PipelineTesterMixin
|
|
|
|
|
|
if is_torch_available():
|
|
import torch
|
|
|
|
from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
|
|
|
|
|
|
if is_vision_available():
|
|
from PIL import Image
|
|
|
|
from transformers import MobileViTImageProcessor
|
|
|
|
|
|
class MobileViTConfigTester(ConfigTester):
|
|
def create_and_test_config_common_properties(self):
|
|
config = self.config_class(**self.inputs_dict)
|
|
self.parent.assertTrue(hasattr(config, "hidden_sizes"))
|
|
self.parent.assertTrue(hasattr(config, "neck_hidden_sizes"))
|
|
self.parent.assertTrue(hasattr(config, "num_attention_heads"))
|
|
|
|
|
|
class MobileViTModelTester:
|
|
def __init__(
|
|
self,
|
|
parent,
|
|
batch_size=13,
|
|
image_size=32,
|
|
patch_size=2,
|
|
num_channels=3,
|
|
last_hidden_size=32,
|
|
num_attention_heads=4,
|
|
hidden_act="silu",
|
|
conv_kernel_size=3,
|
|
output_stride=32,
|
|
hidden_dropout_prob=0.1,
|
|
attention_probs_dropout_prob=0.1,
|
|
classifier_dropout_prob=0.1,
|
|
initializer_range=0.02,
|
|
is_training=True,
|
|
use_labels=True,
|
|
num_labels=10,
|
|
scope=None,
|
|
):
|
|
self.parent = parent
|
|
self.batch_size = batch_size
|
|
self.image_size = image_size
|
|
self.patch_size = patch_size
|
|
self.num_channels = num_channels
|
|
self.last_hidden_size = last_hidden_size
|
|
self.num_attention_heads = num_attention_heads
|
|
self.hidden_act = hidden_act
|
|
self.conv_kernel_size = conv_kernel_size
|
|
self.output_stride = output_stride
|
|
self.hidden_dropout_prob = hidden_dropout_prob
|
|
self.attention_probs_dropout_prob = attention_probs_dropout_prob
|
|
self.classifier_dropout_prob = classifier_dropout_prob
|
|
self.use_labels = use_labels
|
|
self.is_training = is_training
|
|
self.num_labels = num_labels
|
|
self.initializer_range = initializer_range
|
|
self.scope = scope
|
|
|
|
def prepare_config_and_inputs(self):
|
|
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
|
|
|
|
labels = None
|
|
pixel_labels = None
|
|
if self.use_labels:
|
|
labels = ids_tensor([self.batch_size], self.num_labels)
|
|
pixel_labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels)
|
|
|
|
config = self.get_config()
|
|
|
|
return config, pixel_values, labels, pixel_labels
|
|
|
|
def get_config(self):
|
|
return MobileViTConfig(
|
|
image_size=self.image_size,
|
|
patch_size=self.patch_size,
|
|
num_channels=self.num_channels,
|
|
num_attention_heads=self.num_attention_heads,
|
|
hidden_act=self.hidden_act,
|
|
conv_kernel_size=self.conv_kernel_size,
|
|
output_stride=self.output_stride,
|
|
hidden_dropout_prob=self.hidden_dropout_prob,
|
|
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
|
|
classifier_dropout_prob=self.classifier_dropout_prob,
|
|
initializer_range=self.initializer_range,
|
|
hidden_sizes=[12, 16, 20],
|
|
neck_hidden_sizes=[8, 8, 16, 16, 32, 32, 32],
|
|
)
|
|
|
|
def create_and_check_model(self, config, pixel_values, labels, pixel_labels):
|
|
model = MobileViTModel(config=config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
result = model(pixel_values)
|
|
self.parent.assertEqual(
|
|
result.last_hidden_state.shape,
|
|
(
|
|
self.batch_size,
|
|
self.last_hidden_size,
|
|
self.image_size // self.output_stride,
|
|
self.image_size // self.output_stride,
|
|
),
|
|
)
|
|
|
|
def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels):
|
|
config.num_labels = self.num_labels
|
|
model = MobileViTForImageClassification(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
result = model(pixel_values, labels=labels)
|
|
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
|
|
|
|
def create_and_check_for_semantic_segmentation(self, config, pixel_values, labels, pixel_labels):
|
|
config.num_labels = self.num_labels
|
|
model = MobileViTForSemanticSegmentation(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
result = model(pixel_values)
|
|
self.parent.assertEqual(
|
|
result.logits.shape,
|
|
(
|
|
self.batch_size,
|
|
self.num_labels,
|
|
self.image_size // self.output_stride,
|
|
self.image_size // self.output_stride,
|
|
),
|
|
)
|
|
result = model(pixel_values, labels=pixel_labels)
|
|
self.parent.assertEqual(
|
|
result.logits.shape,
|
|
(
|
|
self.batch_size,
|
|
self.num_labels,
|
|
self.image_size // self.output_stride,
|
|
self.image_size // self.output_stride,
|
|
),
|
|
)
|
|
|
|
def prepare_config_and_inputs_for_common(self):
|
|
config_and_inputs = self.prepare_config_and_inputs()
|
|
config, pixel_values, labels, pixel_labels = config_and_inputs
|
|
inputs_dict = {"pixel_values": pixel_values}
|
|
return config, inputs_dict
|
|
|
|
|
|
@require_torch
|
|
class MobileViTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
|
"""
|
|
Here we also overwrite some of the tests of test_modeling_common.py, as MobileViT does not use input_ids, inputs_embeds,
|
|
attention_mask and seq_length.
|
|
"""
|
|
|
|
all_model_classes = (
|
|
(MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
|
|
if is_torch_available()
|
|
else ()
|
|
)
|
|
pipeline_model_mapping = (
|
|
{
|
|
"image-feature-extraction": MobileViTModel,
|
|
"image-classification": MobileViTForImageClassification,
|
|
"image-segmentation": MobileViTForSemanticSegmentation,
|
|
}
|
|
if is_torch_available()
|
|
else {}
|
|
)
|
|
|
|
test_pruning = False
|
|
test_resize_embeddings = False
|
|
test_head_masking = False
|
|
has_attentions = False
|
|
|
|
def setUp(self):
|
|
self.model_tester = MobileViTModelTester(self)
|
|
self.config_tester = MobileViTConfigTester(self, config_class=MobileViTConfig, has_text_modality=False)
|
|
|
|
def test_config(self):
|
|
self.config_tester.run_common_tests()
|
|
|
|
@unittest.skip(reason="MobileViT does not use inputs_embeds")
|
|
def test_inputs_embeds(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="MobileViT does not support input and output embeddings")
|
|
def test_model_common_attributes(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="MobileViT does not output attentions")
|
|
def test_attention_outputs(self):
|
|
pass
|
|
|
|
def test_model(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_model(*config_and_inputs)
|
|
|
|
def test_hidden_states_output(self):
|
|
def check_hidden_states_output(inputs_dict, config, model_class):
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
hidden_states = outputs.hidden_states
|
|
|
|
expected_num_stages = 5
|
|
self.assertEqual(len(hidden_states), expected_num_stages)
|
|
|
|
# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
|
|
# with the width and height being successively divided by 2.
|
|
divisor = 2
|
|
for i in range(len(hidden_states)):
|
|
self.assertListEqual(
|
|
list(hidden_states[i].shape[-2:]),
|
|
[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor],
|
|
)
|
|
divisor *= 2
|
|
|
|
self.assertEqual(self.model_tester.output_stride, divisor // 2)
|
|
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
inputs_dict["output_hidden_states"] = True
|
|
check_hidden_states_output(inputs_dict, config, model_class)
|
|
|
|
# check that output_hidden_states also work using config
|
|
del inputs_dict["output_hidden_states"]
|
|
config.output_hidden_states = True
|
|
|
|
check_hidden_states_output(inputs_dict, config, model_class)
|
|
|
|
def test_for_image_classification(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
|
|
|
|
def test_for_semantic_segmentation(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_for_semantic_segmentation(*config_and_inputs)
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model_name = "apple/mobilevit-small"
|
|
model = MobileViTModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
# We will verify our results on an image of cute cats
|
|
def prepare_img():
|
|
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
|
|
return image
|
|
|
|
|
|
@require_torch
|
|
@require_vision
|
|
class MobileViTModelIntegrationTest(unittest.TestCase):
|
|
@cached_property
|
|
def default_image_processor(self):
|
|
return MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small") if is_vision_available() else None
|
|
|
|
@slow
|
|
def test_inference_image_classification_head(self):
|
|
model = MobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small").to(torch_device)
|
|
|
|
image_processor = self.default_image_processor
|
|
image = prepare_img()
|
|
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
|
|
|
|
# forward pass
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
|
|
# verify the logits
|
|
expected_shape = torch.Size((1, 1000))
|
|
self.assertEqual(outputs.logits.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor([-1.9364, -1.2327, -0.4653]).to(torch_device)
|
|
|
|
self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
|
|
|
|
@slow
|
|
def test_inference_semantic_segmentation(self):
|
|
model = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
|
|
model = model.to(torch_device)
|
|
|
|
image_processor = MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
|
|
|
|
image = prepare_img()
|
|
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
|
|
|
|
# forward pass
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
logits = outputs.logits
|
|
|
|
# verify the logits
|
|
expected_shape = torch.Size((1, 21, 32, 32))
|
|
self.assertEqual(logits.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor(
|
|
[
|
|
[[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
|
|
[[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
|
|
[[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
|
|
],
|
|
device=torch_device,
|
|
)
|
|
|
|
self.assertTrue(torch.allclose(logits[0, :3, :3, :3], expected_slice, atol=1e-4))
|
|
|
|
@slow
|
|
def test_post_processing_semantic_segmentation(self):
|
|
model = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
|
|
model = model.to(torch_device)
|
|
|
|
image_processor = MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
|
|
|
|
image = prepare_img()
|
|
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
|
|
|
|
# forward pass
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
|
|
outputs.logits = outputs.logits.detach().cpu()
|
|
|
|
segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs, target_sizes=[(50, 60)])
|
|
expected_shape = torch.Size((50, 60))
|
|
self.assertEqual(segmentation[0].shape, expected_shape)
|
|
|
|
segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs)
|
|
expected_shape = torch.Size((32, 32))
|
|
self.assertEqual(segmentation[0].shape, expected_shape)
|