codingutf8 2023 the huggingface inc team 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 orglicenseslicense2 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 mobilevitv2 model import unittest from transformers import mobilevitv2config from transformers testingutils import requiretorch requiretorchmultigpu requirevision slow torchdevice from transformers utils import cachedproperty istorchavailable isvisionavailable from testconfigurationcommon import configtester from testmodelingcommon import modeltestermixin floatstensor idstensor from testpipelinemixin import pipelinetestermixin if istorchavailable import torch from transformers import mobilevitv2forimageclassification mobilevitv2forsemanticsegmentation mobilevitv2model from transformers models mobilevitv2 modelingmobilevitv2 import mobilevitv2pretrainedmodelarchivelist makedivisible if isvisionavailable from pil import image from transformers import mobilevitimageprocessor class mobilevitv2configtesterconfigtester def createandtestconfigcommonpropertiesself config self configclassself inputsdict self parent asserttruehasattrconfig widthmultiplier class mobilevitv2modeltester def init self parent batchsize13 imagesize64 patchsize2 numchannels3 hiddenactswish convkernelsize3 outputstride32 classifierdropoutprob0 1 initializerrange0 02 istrainingtrue uselabelstrue numlabels10 scopenone widthmultiplier0 25 ffndropout0 0 attndropout0 0 self parent parent self batchsize batchsize self imagesize imagesize self patchsize patchsize self numchannels numchannels self lasthiddensize makedivisible512 widthmultiplier divisor8 self hiddenact hiddenact self convkernelsize convkernelsize self outputstride outputstride self classifierdropoutprob classifierdropoutprob self uselabels uselabels self istraining istraining self numlabels numlabels self initializerrange initializerrange self scope scope self widthmultiplier widthmultiplier self ffndropoutprob ffndropout self attndropoutprob attndropout def prepareconfigandinputsself pixelvalues floatstensorself batchsize self numchannels self imagesize self imagesize labels none pixellabels none if self uselabels labels idstensorself batchsize self numlabels pixellabels idstensorself batchsize self imagesize self imagesize self numlabels config self getconfig return config pixelvalues labels pixellabels def getconfigself return mobilevitv2config imagesizeself imagesize patchsizeself patchsize numchannelsself numchannels hiddenactself hiddenact convkernelsizeself convkernelsize outputstrideself outputstride classifierdropoutprobself classifierdropoutprob initializerrangeself initializerrange widthmultiplierself widthmultiplier ffndropoutself ffndropoutprob attndropoutself attndropoutprob baseattnunitdims16 24 32 nattnblocks1 1 2 asppoutchannels32 def createandcheckmodelself config pixelvalues labels pixellabels model mobilevitv2modelconfigconfig model totorchdevice model eval result modelpixelvalues self parent assertequal result lasthiddenstate shape self batchsize self lasthiddensize self imagesize self outputstride self imagesize self outputstride def createandcheckforimageclassificationself config pixelvalues labels pixellabels config numlabels self numlabels model mobilevitv2forimageclassificationconfig model totorchdevice model eval result modelpixelvalues labelslabels self parent assertequalresult logits shape self batchsize self numlabels def createandcheckforsemanticsegmentationself config pixelvalues labels pixellabels config numlabels self numlabels model mobilevitv2forsemanticsegmentationconfig model totorchdevice model eval result modelpixelvalues self parent assertequal result logits shape self batchsize self numlabels self imagesize self outputstride self imagesize self outputstride result modelpixelvalues labelspixellabels self parent assertequal result logits shape self batchsize self numlabels self imagesize self outputstride self imagesize self outputstride def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config pixelvalues labels pixellabels configandinputs inputsdict pixelvalues pixelvalues return config inputsdict requiretorch class mobilevitv2modeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses mobilevitv2model mobilevitv2forimageclassification mobilevitv2forsemanticsegmentation if istorchavailable else pipelinemodelmapping featureextraction mobilevitv2model imageclassification mobilevitv2forimageclassification imagesegmentation mobilevitv2forsemanticsegmentation if istorchavailable else testpruning false testresizeembeddings false testheadmasking false hasattentions false def setupself self modeltester mobilevitv2modeltesterself self configtester mobilevitv2configtesterself configclassmobilevitv2config hastextmodalityfalse def testconfigself self configtester runcommontests unittest skipreasonmobilevitv2 does not use inputsembeds def testinputsembedsself pass unittest skipreasonmobilevitv2 does not support input and output embeddings def testmodelcommonattributesself pass unittest skipreasonmobilevitv2 does not output attentions def testattentionoutputsself pass requiretorchmultigpu unittest skipreasongot cuda error misaligned address for tests after this one being run def testmultigpudataparallelforwardself pass def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs def testhiddenstatesoutputself def checkhiddenstatesoutputinputsdict config modelclass model modelclassconfig model totorchdevice model eval with torch nograd outputs modelself prepareforclassinputsdict modelclass hiddenstates outputs hiddenstates expectednumstages 5 self assertequallenhiddenstates expectednumstages mobilevitv2 s feature maps are of shape batchsize numchannels height width with the width and height being successively divided by 2 divisor 2 for i in rangelenhiddenstates self assertlistequal listhiddenstatesi shape2 self modeltester imagesize divisor self modeltester imagesize divisor divisor 2 self assertequalself modeltester outputstride divisor 2 config inputsdict self modeltester prepareconfigandinputsforcommon for modelclass in self allmodelclasses inputsdictoutputhiddenstates true checkhiddenstatesoutputinputsdict config modelclass check that outputhiddenstates also work using config del inputsdictoutputhiddenstates config outputhiddenstates true checkhiddenstatesoutputinputsdict config modelclass def testforimageclassificationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforimageclassificationconfigandinputs def testforsemanticsegmentationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforsemanticsegmentationconfigandinputs slow def testmodelfrompretrainedself for modelname in mobilevitv2pretrainedmodelarchivelist 1 model mobilevitv2model frompretrainedmodelname self assertisnotnonemodel we will verify our results on an image of cute cats def prepareimg image image open testsfixturestestssamplescoco000000039769 png return image requiretorch requirevision class mobilevitv2modelintegrationtestunittest testcase cachedproperty def defaultimageprocessorself return mobilevitimageprocessor frompretrainedapplemobilevitv21 0imagenet1k256 if isvisionavailable else none slow def testinferenceimageclassificationheadself model mobilevitv2forimageclassification frompretrainedapplemobilevitv21 0imagenet1k256 to torchdevice imageprocessor self defaultimageprocessor image prepareimg inputs imageprocessorimagesimage returntensorspt totorchdevice forward pass with torch nograd outputs modelinputs verify the logits expectedshape torch size1 1000 self assertequaloutputs logits shape expectedshape expectedslice torch tensor1 6336e00 7 3204e02 5 1883e01 totorchdevice self asserttruetorch allcloseoutputs logits0 3 expectedslice atol1e4 slow def testinferencesemanticsegmentationself model mobilevitv2forsemanticsegmentation frompretrainedshehan97mobilevitv21 0vocdeeplabv3 model model totorchdevice imageprocessor mobilevitimageprocessor frompretrainedshehan97mobilevitv21 0vocdeeplabv3 image prepareimg inputs imageprocessorimagesimage returntensorspt totorchdevice forward pass with torch nograd outputs modelinputs logits outputs logits verify the logits expectedshape torch size1 21 32 32 self assertequallogits shape expectedshape expectedslice torch tensor 7 0863 7 1525 6 8201 6 6931 6 8770 6 8933 6 2978 7 0366 6 9636 3 7134 3 6712 3 6675 3 5825 3 3549 3 4777 3 3435 3 3979 3 2857 2 9329 2 8003 2 7369 3 0564 2 4780 2 0207 2 6889 1 9298 1 7640 devicetorchdevice self asserttruetorch allcloselogits0 3 3 3 expectedslice atol1e4 slow def testpostprocessingsemanticsegmentationself model mobilevitv2forsemanticsegmentation frompretrainedshehan97mobilevitv21 0vocdeeplabv3 model model totorchdevice imageprocessor mobilevitimageprocessor frompretrainedshehan97mobilevitv21 0vocdeeplabv3 image prepareimg inputs imageprocessorimagesimage returntensorspt totorchdevice forward pass with torch nograd outputs modelinputs outputs logits outputs logits detach cpu segmentation imageprocessor postprocesssemanticsegmentationoutputsoutputs targetsizes50 60 expectedshape torch size50 60 self assertequalsegmentation0 shape expectedshape segmentation imageprocessor postprocesssemanticsegmentationoutputsoutputs expectedshape torch size32 32 self assertequalsegmentation0 shape expectedshape coding utf 8 2023 the huggingface inc team 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 mobilevitv2 model here we also overwrite some of the tests of test_modeling_common py as mobilevitv2 does not use input_ids inputs_embeds attention_mask and seq_length mobilevitv2 s feature maps are of shape batch_size num_channels height width with the width and height being successively divided by 2 check that output_hidden_states also work using config we will verify our results on an image of cute cats forward pass verify the logits forward pass verify the logits forward pass
import unittest

from transformers import MobileViTV2Config
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 MobileViTV2ForImageClassification, MobileViTV2ForSemanticSegmentation, MobileViTV2Model
    from transformers.models.mobilevitv2.modeling_mobilevitv2 import (
        MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        make_divisible,
    )


if is_vision_available():
    from PIL import Image

    from transformers import MobileViTImageProcessor


class MobileViTV2ConfigTester(ConfigTester):
    def create_and_test_config_common_properties(self):
        config = self.config_class(**self.inputs_dict)
        self.parent.assertTrue(hasattr(config, "width_multiplier"))


class MobileViTV2ModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        image_size=64,
        patch_size=2,
        num_channels=3,
        hidden_act="swish",
        conv_kernel_size=3,
        output_stride=32,
        classifier_dropout_prob=0.1,
        initializer_range=0.02,
        is_training=True,
        use_labels=True,
        num_labels=10,
        scope=None,
        width_multiplier=0.25,
        ffn_dropout=0.0,
        attn_dropout=0.0,
    ):
        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 = make_divisible(512 * width_multiplier, divisor=8)
        self.hidden_act = hidden_act
        self.conv_kernel_size = conv_kernel_size
        self.output_stride = output_stride
        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
        self.width_multiplier = width_multiplier
        self.ffn_dropout_prob = ffn_dropout
        self.attn_dropout_prob = attn_dropout

    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 MobileViTV2Config(
            image_size=self.image_size,
            patch_size=self.patch_size,
            num_channels=self.num_channels,
            hidden_act=self.hidden_act,
            conv_kernel_size=self.conv_kernel_size,
            output_stride=self.output_stride,
            classifier_dropout_prob=self.classifier_dropout_prob,
            initializer_range=self.initializer_range,
            width_multiplier=self.width_multiplier,
            ffn_dropout=self.ffn_dropout_prob,
            attn_dropout=self.attn_dropout_prob,
            base_attn_unit_dims=[16, 24, 32],
            n_attn_blocks=[1, 1, 2],
            aspp_out_channels=32,
        )

    def create_and_check_model(self, config, pixel_values, labels, pixel_labels):
        model = MobileViTV2Model(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 = MobileViTV2ForImageClassification(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 = MobileViTV2ForSemanticSegmentation(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 MobileViTV2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    

    all_model_classes = (
        (MobileViTV2Model, MobileViTV2ForImageClassification, MobileViTV2ForSemanticSegmentation)
        if is_torch_available()
        else ()
    )

    pipeline_model_mapping = (
        {
            "feature-extraction": MobileViTV2Model,
            "image-classification": MobileViTV2ForImageClassification,
            "image-segmentation": MobileViTV2ForSemanticSegmentation,
        }
        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 = MobileViTV2ModelTester(self)
        self.config_tester = MobileViTV2ConfigTester(self, config_class=MobileViTV2Config, has_text_modality=False)

    def test_config(self):
        self.config_tester.run_common_tests()

    @unittest.skip(reason="MobileViTV2 does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass

    @unittest.skip(reason="MobileViTV2 does not support input and output embeddings")
    def test_model_common_attributes(self):
        pass

    @unittest.skip(reason="MobileViTV2 does not output attentions")
    def test_attention_outputs(self):
        pass

    @require_torch_multi_gpu
    @unittest.skip(reason="Got `CUDA error: misaligned address` for tests after this one being run.")
    def test_multi_gpu_data_parallel_forward(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)

            
            
            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)

            
            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):
        for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = MobileViTV2Model.from_pretrained(model_name)
            self.assertIsNotNone(model)



def prepare_img():
    image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
    return image


@require_torch
@require_vision
class MobileViTV2ModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return (
            MobileViTImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256")
            if is_vision_available()
            else None
        )

    @slow
    def test_inference_image_classification_head(self):
        model = MobileViTV2ForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256").to(
            torch_device
        )

        image_processor = self.default_image_processor
        image = prepare_img()
        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)

        
        with torch.no_grad():
            outputs = model(**inputs)

        
        expected_shape = torch.Size((1, 1000))
        self.assertEqual(outputs.logits.shape, expected_shape)

        expected_slice = torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]).to(torch_device)

        self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))

    @slow
    def test_inference_semantic_segmentation(self):
        model = MobileViTV2ForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
        model = model.to(torch_device)

        image_processor = MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")

        image = prepare_img()
        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)

        
        with torch.no_grad():
            outputs = model(**inputs)
        logits = outputs.logits

        
        expected_shape = torch.Size((1, 21, 32, 32))
        self.assertEqual(logits.shape, expected_shape)

        expected_slice = torch.tensor(
            [
                [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
                [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
                [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
            ],
            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 = MobileViTV2ForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
        model = model.to(torch_device)

        image_processor = MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")

        image = prepare_img()
        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)

        
        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)