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 efficientnet model import unittest from transformers import efficientnetconfig from transformers testingutils import ispipelinetest 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 efficientnetforimageclassification efficientnetmodel from transformers models efficientnet modelingefficientnet import efficientnetpretrainedmodelarchivelist if isvisionavailable from pil import image from transformers import autoimageprocessor class efficientnetmodeltester def init self parent batchsize13 imagesize32 numchannels3 kernelsizes3 3 5 inchannels32 16 24 outchannels16 24 20 strides1 1 2 numblockrepeats1 1 2 expandratios1 6 6 istrainingtrue uselabelstrue intermediatesize37 hiddenactgelu numlabels10 self parent parent self batchsize batchsize self imagesize imagesize self numchannels numchannels self kernelsizes kernelsizes self inchannels inchannels self outchannels outchannels self strides strides self numblockrepeats numblockrepeats self expandratios expandratios self istraining istraining self hiddenact hiddenact self numlabels numlabels self uselabels uselabels def prepareconfigandinputsself pixelvalues floatstensorself batchsize self numchannels self imagesize self imagesize labels none if self uselabels labels idstensorself batchsize self numlabels config self getconfig return config pixelvalues labels def getconfigself return efficientnetconfig numchannelsself numchannels kernelsizesself kernelsizes inchannelsself inchannels outchannelsself outchannels stridesself strides numblockrepeatsself numblockrepeats expandratiosself expandratios hiddenactself hiddenact numlabelsself numlabels def createandcheckmodelself config pixelvalues labels model efficientnetmodelconfigconfig model totorchdevice model eval result modelpixelvalues expected last hidden states b c h 4 w 4 self parent assertequal result lasthiddenstate shape self batchsize config hiddendim self imagesize 4 self imagesize 4 def createandcheckforimageclassificationself config pixelvalues labels model efficientnetforimageclassificationconfig model totorchdevice model eval result modelpixelvalues labelslabels self parent assertequalresult logits shape self batchsize self numlabels def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config pixelvalues labels configandinputs inputsdict pixelvalues pixelvalues return config inputsdict requiretorch class efficientnetmodeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses efficientnetmodel efficientnetforimageclassification if istorchavailable else pipelinemodelmapping featureextraction efficientnetmodel imageclassification efficientnetforimageclassification if istorchavailable else fxcompatible false testpruning false testresizeembeddings false testheadmasking false hasattentions false def setupself self modeltester efficientnetmodeltesterself self configtester configtester self configclassefficientnetconfig hastextmodalityfalse hiddensize37 def testconfigself self createandtestconfigcommonproperties self configtester createandtestconfigtojsonstring self configtester createandtestconfigtojsonfile self configtester createandtestconfigfromandsavepretrained self configtester createandtestconfigwithnumlabels self configtester checkconfigcanbeinitwithoutparams self configtester checkconfigargumentsinit def createandtestconfigcommonpropertiesself return unittest skipreasonefficientnet does not use inputsembeds def testinputsembedsself pass unittest skipreasonefficientnet does not support input and output embeddings def testmodelcommonattributesself pass unittest skipreasonefficientnet does not use feedforward chunking def testfeedforwardchunkingself 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 encoderhiddenstates if config isencoderdecoder else outputs hiddenstates numblocks sumconfig numblockrepeats 4 self assertequallenhiddenstates numblocks efficientnet s feature maps are of shape batchsize numchannels height width self assertlistequal listhiddenstates0 shape2 self modeltester imagesize 2 self modeltester imagesize 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 slow def testmodelfrompretrainedself for modelname in efficientnetpretrainedmodelarchivelist 1 model efficientnetmodel frompretrainedmodelname self assertisnotnonemodel ispipelinetest requirevision slow def testpipelineimageclassificationself super testpipelineimageclassification we will verify our results on an image of cute cats def prepareimg image image open testsfixturestestssamplescoco000000039769 png return image requiretorch requirevision class efficientnetmodelintegrationtestunittest testcase cachedproperty def defaultimageprocessorself return autoimageprocessor frompretrainedgoogleefficientnetb7 if isvisionavailable else none slow def testinferenceimageclassificationheadself model efficientnetforimageclassification frompretrainedgoogleefficientnetb7 totorchdevice 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 tensor0 2962 0 4487 0 4499 totorchdevice self asserttruetorch allcloseoutputs logits0 3 expectedslice atol1e4 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 efficientnet model expected last hidden states b c h 4 w 4 here we also overwrite some of the tests of test_modeling_common py as efficientnet does not use input_ids inputs_embeds attention_mask and seq_length efficientnet s feature maps are of shape batch_size num_channels height width 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 EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, 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 EfficientNetForImageClassification, EfficientNetModel
    from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor


class EfficientNetModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        image_size=32,
        num_channels=3,
        kernel_sizes=[3, 3, 5],
        in_channels=[32, 16, 24],
        out_channels=[16, 24, 20],
        strides=[1, 1, 2],
        num_block_repeats=[1, 1, 2],
        expand_ratios=[1, 6, 6],
        is_training=True,
        use_labels=True,
        intermediate_size=37,
        hidden_act="gelu",
        num_labels=10,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.num_channels = num_channels
        self.kernel_sizes = kernel_sizes
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.strides = strides
        self.num_block_repeats = num_block_repeats
        self.expand_ratios = expand_ratios
        self.is_training = is_training
        self.hidden_act = hidden_act
        self.num_labels = num_labels
        self.use_labels = use_labels

    def prepare_config_and_inputs(self):
        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

        labels = None
        if self.use_labels:
            labels = ids_tensor([self.batch_size], self.num_labels)

        config = self.get_config()
        return config, pixel_values, labels

    def get_config(self):
        return EfficientNetConfig(
            num_channels=self.num_channels,
            kernel_sizes=self.kernel_sizes,
            in_channels=self.in_channels,
            out_channels=self.out_channels,
            strides=self.strides,
            num_block_repeats=self.num_block_repeats,
            expand_ratios=self.expand_ratios,
            hidden_act=self.hidden_act,
            num_labels=self.num_labels,
        )

    def create_and_check_model(self, config, pixel_values, labels):
        model = EfficientNetModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        
        self.parent.assertEqual(
            result.last_hidden_state.shape,
            (self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4),
        )

    def create_and_check_for_image_classification(self, config, pixel_values, labels):
        model = EfficientNetForImageClassification(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 = config_and_inputs
        inputs_dict = {"pixel_values": pixel_values}
        return config, inputs_dict


@require_torch
class EfficientNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    

    all_model_classes = (EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
    pipeline_model_mapping = (
        {"feature-extraction": EfficientNetModel, "image-classification": EfficientNetForImageClassification}
        if is_torch_available()
        else {}
    )

    fx_compatible = False
    test_pruning = False
    test_resize_embeddings = False
    test_head_masking = False
    has_attentions = False

    def setUp(self):
        self.model_tester = EfficientNetModelTester(self)
        self.config_tester = ConfigTester(
            self, config_class=EfficientNetConfig, has_text_modality=False, hidden_size=37
        )

    def test_config(self):
        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()

    def create_and_test_config_common_properties(self):
        return

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

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

    @unittest.skip(reason="EfficientNet does not use feedforward chunking")
    def test_feed_forward_chunking(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.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
            num_blocks = sum(config.num_block_repeats) * 4
            self.assertEqual(len(hidden_states), num_blocks)

            
            self.assertListEqual(
                list(hidden_states[0].shape[-2:]),
                [self.model_tester.image_size // 2, self.model_tester.image_size // 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)

    @slow
    def test_model_from_pretrained(self):
        for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = EfficientNetModel.from_pretrained(model_name)
            self.assertIsNotNone(model)

    @is_pipeline_test
    @require_vision
    @slow
    def test_pipeline_image_classification(self):
        super().test_pipeline_image_classification()



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


@require_torch
@require_vision
class EfficientNetModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return AutoImageProcessor.from_pretrained("google/efficientnet-b7") if is_vision_available() else None

    @slow
    def test_inference_image_classification_head(self):
        model = EfficientNetForImageClassification.from_pretrained("google/efficientnet-b7").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([-0.2962, 0.4487, 0.4499]).to(torch_device)
        self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))