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 mobilenetv1 model import unittest from transformers import mobilenetv1config 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 mobilenetv1forimageclassification mobilenetv1model from transformers models mobilenetv1 modelingmobilenetv1 import mobilenetv1pretrainedmodelarchivelist if isvisionavailable from pil import image from transformers import mobilenetv1imageprocessor class mobilenetv1configtesterconfigtester def createandtestconfigcommonpropertiesself config self configclassself inputsdict self parent asserttruehasattrconfig tfpadding self parent asserttruehasattrconfig depthmultiplier class mobilenetv1modeltester def init self parent batchsize13 numchannels3 imagesize32 depthmultiplier0 25 mindepth8 tfpaddingtrue lasthiddensize1024 outputstride32 hiddenactrelu6 classifierdropoutprob0 1 initializerrange0 02 istrainingtrue uselabelstrue numlabels10 scopenone self parent parent self batchsize batchsize self numchannels numchannels self imagesize imagesize self depthmultiplier depthmultiplier self mindepth mindepth self tfpadding tfpadding self lasthiddensize intlasthiddensize depthmultiplier self outputstride outputstride self hiddenact hiddenact 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 mobilenetv1config numchannelsself numchannels imagesizeself imagesize depthmultiplierself depthmultiplier mindepthself mindepth tfpaddingself tfpadding hiddenactself hiddenact classifierdropoutprobself classifierdropoutprob initializerrangeself initializerrange def createandcheckmodelself config pixelvalues labels pixellabels model mobilenetv1modelconfigconfig 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 mobilenetv1forimageclassificationconfig model totorchdevice model eval result modelpixelvalues labelslabels self parent assertequalresult logits shape self batchsize self numlabels def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config pixelvalues labels pixellabels configandinputs inputsdict pixelvalues pixelvalues return config inputsdict requiretorch class mobilenetv1modeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses mobilenetv1model mobilenetv1forimageclassification if istorchavailable else pipelinemodelmapping featureextraction mobilenetv1model imageclassification mobilenetv1forimageclassification if istorchavailable else testpruning false testresizeembeddings false testheadmasking false hasattentions false def setupself self modeltester mobilenetv1modeltesterself self configtester mobilenetv1configtesterself configclassmobilenetv1config hastextmodalityfalse def testconfigself self configtester runcommontests unittest skipreasonmobilenetv1 does not use inputsembeds def testinputsembedsself pass unittest skipreasonmobilenetv1 does not support input and output embeddings def testmodelcommonattributesself pass unittest skipreasonmobilenetv1 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 26 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 slow def testmodelfrompretrainedself for modelname in mobilenetv1pretrainedmodelarchivelist 1 model mobilenetv1model 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 mobilenetv1modelintegrationtestunittest testcase cachedproperty def defaultimageprocessorself return mobilenetv1imageprocessor frompretrainedgooglemobilenetv11 0224 if isvisionavailable else none slow def testinferenceimageclassificationheadself model mobilenetv1forimageclassification frompretrainedgooglemobilenetv11 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 tensor4 1739 1 1233 3 1205 totorchdevice self asserttruetorch allcloseoutputs logits0 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 mobilenetv1 model here we also overwrite some of the tests of test_modeling_common py as mobilenetv1 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
import unittest

from transformers import MobileNetV1Config
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 MobileNetV1ForImageClassification, MobileNetV1Model
    from transformers.models.mobilenet_v1.modeling_mobilenet_v1 import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import MobileNetV1ImageProcessor


class MobileNetV1ConfigTester(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 MobileNetV1ModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        num_channels=3,
        image_size=32,
        depth_multiplier=0.25,
        min_depth=8,
        tf_padding=True,
        last_hidden_size=1024,
        output_stride=32,
        hidden_act="relu6",
        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.min_depth = min_depth
        self.tf_padding = tf_padding
        self.last_hidden_size = int(last_hidden_size * depth_multiplier)
        self.output_stride = output_stride
        self.hidden_act = hidden_act
        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 MobileNetV1Config(
            num_channels=self.num_channels,
            image_size=self.image_size,
            depth_multiplier=self.depth_multiplier,
            min_depth=self.min_depth,
            tf_padding=self.tf_padding,
            hidden_act=self.hidden_act,
            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 = MobileNetV1Model(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 = MobileNetV1ForImageClassification(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 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 MobileNetV1ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    

    all_model_classes = (MobileNetV1Model, MobileNetV1ForImageClassification) if is_torch_available() else ()
    pipeline_model_mapping = (
        {"feature-extraction": MobileNetV1Model, "image-classification": MobileNetV1ForImageClassification}
        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 = MobileNetV1ModelTester(self)
        self.config_tester = MobileNetV1ConfigTester(self, config_class=MobileNetV1Config, has_text_modality=False)

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

    @unittest.skip(reason="MobileNetV1 does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass

    @unittest.skip(reason="MobileNetV1 does not support input and output embeddings")
    def test_model_common_attributes(self):
        pass

    @unittest.skip(reason="MobileNetV1 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 = 26
            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)

    @slow
    def test_model_from_pretrained(self):
        for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = MobileNetV1Model.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 MobileNetV1ModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return (
            MobileNetV1ImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224") if is_vision_available() else None
        )

    @slow
    def test_inference_image_classification_head(self):
        model = MobileNetV1ForImageClassification.from_pretrained("google/mobilenet_v1_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([-4.1739, -1.1233, 3.1205]).to(torch_device)

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