codingutf8 2022 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 mobilenetv2 model import unittest from transformers import mobilenetv2config from transformers testingutils import requiretorch 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 mobilenetv2forimageclassification mobilenetv2forsemanticsegmentation mobilenetv2model from transformers models mobilenetv2 modelingmobilenetv2 import mobilenetv2pretrainedmodelarchivelist if isvisionavailable from pil import image from transformers import mobilenetv2imageprocessor class mobilenetv2configtesterconfigtester def createandtestconfigcommonpropertiesself config self configclassself inputsdict self parent asserttruehasattrconfig tfpadding self parent asserttruehasattrconfig depthmultiplier class mobilenetv2modeltester def init self parent batchsize13 numchannels3 imagesize32 depthmultiplier0 25 depthdivisibleby8 mindepth8 expandratio6 outputstride32 firstlayerisexpansiontrue finegrainedoutputtrue tfpaddingtrue hiddenactrelu6 lasthiddensize1280 classifierdropoutprob0 1 initializerrange0 02 istrainingtrue uselabelstrue numlabels10 scopenone self parent parent self batchsize batchsize self numchannels numchannels self imagesize imagesize self depthmultiplier depthmultiplier self depthdivisibleby depthdivisibleby self mindepth mindepth self expandratio expandratio self tfpadding tfpadding self outputstride outputstride self firstlayerisexpansion firstlayerisexpansion self finegrainedoutput finegrainedoutput self hiddenact hiddenact self lasthiddensize lasthiddensize if finegrainedoutput else intlasthiddensize depthmultiplier self classifierdropoutprob classifierdropoutprob self uselabels uselabels self istraining istraining self numlabels numlabels self initializerrange initializerrange self scope scope 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 mobilenetv2config numchannelsself numchannels imagesizeself imagesize depthmultiplierself depthmultiplier depthdivisiblebyself depthdivisibleby mindepthself mindepth expandratioself expandratio outputstrideself outputstride firstlayerisexpansionself firstlayerisexpansion finegrainedoutputself finegrainedoutput hiddenactself hiddenact tfpaddingself tfpadding classifierdropoutprobself classifierdropoutprob initializerrangeself initializerrange def createandcheckmodelself config pixelvalues labels pixellabels model mobilenetv2modelconfigconfig model totorchdevice model eval result modelpixelvalues self parent assertequal result lasthiddenstate shape self batchsize self lasthiddensize self imagesize self outputstride self imagesize self outputstride self parent assertequal result pooleroutput shape self batchsize self lasthiddensize def createandcheckforimageclassificationself config pixelvalues labels pixellabels config numlabels self numlabels model mobilenetv2forimageclassificationconfig 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 mobilenetv2forsemanticsegmentationconfig 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 mobilenetv2modeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses mobilenetv2model mobilenetv2forimageclassification mobilenetv2forsemanticsegmentation if istorchavailable else pipelinemodelmapping featureextraction mobilenetv2model imageclassification mobilenetv2forimageclassification imagesegmentation mobilenetv2forsemanticsegmentation if istorchavailable else testpruning false testresizeembeddings false testheadmasking false hasattentions false def setupself self modeltester mobilenetv2modeltesterself self configtester mobilenetv2configtesterself configclassmobilenetv2config hastextmodalityfalse def testconfigself self configtester runcommontests unittest skipreasonmobilenetv2 does not use inputsembeds def testinputsembedsself pass unittest skipreasonmobilenetv2 does not support input and output embeddings def testmodelcommonattributesself pass unittest skipreasonmobilenetv2 does not output attentions def testattentionoutputsself 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 16 self assertequallenhiddenstates expectednumstages 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 mobilenetv2pretrainedmodelarchivelist 1 model mobilenetv2model 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 mobilenetv2modelintegrationtestunittest testcase cachedproperty def defaultimageprocessorself return mobilenetv2imageprocessor frompretrainedgooglemobilenetv21 0224 if isvisionavailable else none slow def testinferenceimageclassificationheadself model mobilenetv2forimageclassification frompretrainedgooglemobilenetv21 0224 totorchdevice imageprocessor self defaultimageprocessor image prepareimg inputs imageprocessorimagesimage returntensorspt totorchdevice forward pass with torch nograd outputs modelinputs verify the logits expectedshape torch size1 1001 self assertequaloutputs logits shape expectedshape expectedslice torch tensor0 2445 1 1993 0 1905 totorchdevice self asserttruetorch allcloseoutputs logits0 3 expectedslice atol1e4 slow def testinferencesemanticsegmentationself model mobilenetv2forsemanticsegmentation frompretrainedgoogledeeplabv3mobilenetv21 0513 model model totorchdevice imageprocessor mobilenetv2imageprocessor frompretrainedgoogledeeplabv3mobilenetv21 0513 image prepareimg inputs imageprocessorimagesimage returntensorspt totorchdevice forward pass with torch nograd outputs modelinputs logits outputs logits verify the logits expectedshape torch size1 21 65 65 self assertequallogits shape expectedshape expectedslice torch tensor 17 5790 17 7581 18 3355 18 3257 18 4230 18 8973 18 6169 18 8650 19 2187 2 1595 2 0977 2 3741 2 4226 2 3028 2 6835 2 7819 2 5991 2 7706 4 2058 4 8317 4 7638 4 4136 5 0361 4 9383 4 5028 4 9644 4 8734 devicetorchdevice self asserttruetorch allcloselogits0 3 3 3 expectedslice atol1e4 coding utf 8 2022 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 mobilenetv2 model here we also overwrite some of the tests of test_modeling_common py as mobilenetv2 does not use input_ids inputs_embeds attention_mask and seq_length 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
import unittest

from transformers import MobileNetV2Config
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 MobileNetV2ForImageClassification, MobileNetV2ForSemanticSegmentation, MobileNetV2Model
    from transformers.models.mobilenet_v2.modeling_mobilenet_v2 import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import MobileNetV2ImageProcessor


class MobileNetV2ConfigTester(ConfigTester):
    def create_and_test_config_common_properties(self):
        config = self.config_class(**self.inputs_dict)
        self.parent.assertTrue(hasattr(config, "tf_padding"))
        self.parent.assertTrue(hasattr(config, "depth_multiplier"))


class MobileNetV2ModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        num_channels=3,
        image_size=32,
        depth_multiplier=0.25,
        depth_divisible_by=8,
        min_depth=8,
        expand_ratio=6,
        output_stride=32,
        first_layer_is_expansion=True,
        finegrained_output=True,
        tf_padding=True,
        hidden_act="relu6",
        last_hidden_size=1280,
        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.num_channels = num_channels
        self.image_size = image_size
        self.depth_multiplier = depth_multiplier
        self.depth_divisible_by = depth_divisible_by
        self.min_depth = min_depth
        self.expand_ratio = expand_ratio
        self.tf_padding = tf_padding
        self.output_stride = output_stride
        self.first_layer_is_expansion = first_layer_is_expansion
        self.finegrained_output = finegrained_output
        self.hidden_act = hidden_act
        self.last_hidden_size = last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier)
        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 MobileNetV2Config(
            num_channels=self.num_channels,
            image_size=self.image_size,
            depth_multiplier=self.depth_multiplier,
            depth_divisible_by=self.depth_divisible_by,
            min_depth=self.min_depth,
            expand_ratio=self.expand_ratio,
            output_stride=self.output_stride,
            first_layer_is_expansion=self.first_layer_is_expansion,
            finegrained_output=self.finegrained_output,
            hidden_act=self.hidden_act,
            tf_padding=self.tf_padding,
            classifier_dropout_prob=self.classifier_dropout_prob,
            initializer_range=self.initializer_range,
        )

    def create_and_check_model(self, config, pixel_values, labels, pixel_labels):
        model = MobileNetV2Model(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,
            ),
        )
        self.parent.assertEqual(
            result.pooler_output.shape,
            (self.batch_size, self.last_hidden_size),
        )

    def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels):
        config.num_labels = self.num_labels
        model = MobileNetV2ForImageClassification(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 = MobileNetV2ForSemanticSegmentation(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 MobileNetV2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    

    all_model_classes = (
        (MobileNetV2Model, MobileNetV2ForImageClassification, MobileNetV2ForSemanticSegmentation)
        if is_torch_available()
        else ()
    )
    pipeline_model_mapping = (
        {
            "feature-extraction": MobileNetV2Model,
            "image-classification": MobileNetV2ForImageClassification,
            "image-segmentation": MobileNetV2ForSemanticSegmentation,
        }
        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 = MobileNetV2ModelTester(self)
        self.config_tester = MobileNetV2ConfigTester(self, config_class=MobileNetV2Config, has_text_modality=False)

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

    @unittest.skip(reason="MobileNetV2 does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass

    @unittest.skip(reason="MobileNetV2 does not support input and output embeddings")
    def test_model_common_attributes(self):
        pass

    @unittest.skip(reason="MobileNetV2 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 = 16
            self.assertEqual(len(hidden_states), expected_num_stages)

        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 MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = MobileNetV2Model.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 MobileNetV2ModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return (
            MobileNetV2ImageProcessor.from_pretrained("google/mobilenet_v2_1.0_224") if is_vision_available() else None
        )

    @slow
    def test_inference_image_classification_head(self):
        model = MobileNetV2ForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224").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, 1001))
        self.assertEqual(outputs.logits.shape, expected_shape)

        expected_slice = torch.tensor([0.2445, -1.1993, 0.1905]).to(torch_device)

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

    @slow
    def test_inference_semantic_segmentation(self):
        model = MobileNetV2ForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513")
        model = model.to(torch_device)

        image_processor = MobileNetV2ImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513")

        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, 65, 65))
        self.assertEqual(logits.shape, expected_shape)

        expected_slice = torch.tensor(
            [
                [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
                [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
                [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
            ],
            device=torch_device,
        )

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