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 tensorflow resnet model from future import annotations import inspect import unittest import numpy as np from transformers import resnetconfig from transformers testingutils import requiretf requirevision slow from transformers utils import cachedproperty istfavailable isvisionavailable from testconfigurationcommon import configtester from testmodelingtfcommon import tfmodeltestermixin floatstensor idstensor from testpipelinemixin import pipelinetestermixin if istfavailable import tensorflow as tf from transformers import tfresnetforimageclassification tfresnetmodel from transformers models resnet modelingtfresnet import tfresnetpretrainedmodelarchivelist if isvisionavailable from pil import image from transformers import autoimageprocessor class tfresnetmodeltester def init self parent batchsize3 imagesize32 numchannels3 embeddingssize10 hiddensizes10 20 30 40 depths1 1 2 1 istrainingtrue uselabelstrue hiddenactrelu numlabels3 scopenone self parent parent self batchsize batchsize self imagesize imagesize self numchannels numchannels self embeddingssize embeddingssize self hiddensizes hiddensizes self depths depths self istraining istraining self uselabels uselabels self hiddenact hiddenact self numlabels numlabels self scope scope self numstages lenhiddensizes 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 resnetconfig numchannelsself numchannels embeddingssizeself embeddingssize hiddensizesself hiddensizes depthsself depths hiddenactself hiddenact numlabelsself numlabels imagesizeself imagesize def createandcheckmodelself config pixelvalues labels model tfresnetmodelconfigconfig result modelpixelvalues expected last hidden states b c h 32 w 32 self parent assertequal result lasthiddenstate shape self batchsize self hiddensizes1 self imagesize 32 self imagesize 32 def createandcheckforimageclassificationself config pixelvalues labels config numlabels self numlabels model tfresnetforimageclassificationconfig 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 requiretf class tfresnetmodeltesttfmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses tfresnetmodel tfresnetforimageclassification if istfavailable else pipelinemodelmapping featureextraction tfresnetmodel imageclassification tfresnetforimageclassification if istfavailable else testpruning false testresizeembeddings false testheadmasking false testonnx false hasattentions false def setupself self modeltester tfresnetmodeltesterself self configtester configtesterself configclassresnetconfig hastextmodalityfalse 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 skipreasonresnet does not use inputsembeds def testinputsembedsself pass unittest skipreasonresnet does not support input and output embeddings def testmodelcommonattributesself pass def testforwardsignatureself config self modeltester prepareconfigandinputsforcommon for modelclass in self allmodelclasses model modelclassconfig signature inspect signaturemodel call signature parameters is an ordereddict so argnames order is deterministic argnames signature parameters keys expectedargnames pixelvalues self assertlistequalargnames 1 expectedargnames def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs def testhiddenstatesoutputself def checkhiddenstatesoutputinputsdict config modelclass model modelclassconfig outputs modelself prepareforclassinputsdict modelclass hiddenstates outputs encoderhiddenstates if config isencoderdecoder else outputs hiddenstates expectednumstages self modeltester numstages self assertequallenhiddenstates expectednumstages 1 resnet s feature maps are of shape batchsize numchannels height width self assertlistequal listhiddenstates0 shape2 self modeltester imagesize 4 self modeltester imagesize 4 config inputsdict self modeltester prepareconfigandinputsforcommon layerstype basic bottleneck for modelclass in self allmodelclasses for layertype in layerstype config layertype layertype 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 tfresnetpretrainedmodelarchivelist 1 model tfresnetmodel frompretrainedmodelname self assertisnotnonemodel we will verify our results on an image of cute cats def prepareimg image image open testsfixturestestssamplescoco000000039769 png return image requiretf requirevision class tfresnetmodelintegrationtestunittest testcase cachedproperty def defaultimageprocessorself return autoimageprocessor frompretrainedtfresnetpretrainedmodelarchivelist0 if isvisionavailable else none slow def testinferenceimageclassificationheadself model tfresnetforimageclassification frompretrainedtfresnetpretrainedmodelarchivelist0 imageprocessor self defaultimageprocessor image prepareimg inputs imageprocessorimagesimage returntensorstf forward pass outputs modelinputs verify the logits expectedshape tf tensorshape1 1000 self assertequaloutputs logits shape expectedshape expectedslice tf constant11 1069 9 7877 8 3777 self asserttruenp allcloseoutputs logits0 3 numpy 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 tensorflow resnet model expected last hidden states b c h 32 w 32 here we also overwrite some of the tests of test_modeling_common py as resnet does not use input_ids inputs_embeds attention_mask and seq_length signature parameters is an ordereddict so arg_names order is deterministic resnet 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
from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import TFResNetForImageClassification, TFResNetModel
    from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor


class TFResNetModelTester:
    def __init__(
        self,
        parent,
        batch_size=3,
        image_size=32,
        num_channels=3,
        embeddings_size=10,
        hidden_sizes=[10, 20, 30, 40],
        depths=[1, 1, 2, 1],
        is_training=True,
        use_labels=True,
        hidden_act="relu",
        num_labels=3,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.num_channels = num_channels
        self.embeddings_size = embeddings_size
        self.hidden_sizes = hidden_sizes
        self.depths = depths
        self.is_training = is_training
        self.use_labels = use_labels
        self.hidden_act = hidden_act
        self.num_labels = num_labels
        self.scope = scope
        self.num_stages = len(hidden_sizes)

    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 ResNetConfig(
            num_channels=self.num_channels,
            embeddings_size=self.embeddings_size,
            hidden_sizes=self.hidden_sizes,
            depths=self.depths,
            hidden_act=self.hidden_act,
            num_labels=self.num_labels,
            image_size=self.image_size,
        )

    def create_and_check_model(self, config, pixel_values, labels):
        model = TFResNetModel(config=config)
        result = model(pixel_values)
        
        self.parent.assertEqual(
            result.last_hidden_state.shape,
            (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),
        )

    def create_and_check_for_image_classification(self, config, pixel_values, labels):
        config.num_labels = self.num_labels
        model = TFResNetForImageClassification(config)
        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_tf
class TFResNetModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    

    all_model_classes = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
    pipeline_model_mapping = (
        {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
        if is_tf_available()
        else {}
    )

    test_pruning = False
    test_resize_embeddings = False
    test_head_masking = False
    test_onnx = False
    has_attentions = False

    def setUp(self):
        self.model_tester = TFResNetModelTester(self)
        self.config_tester = ConfigTester(self, config_class=ResNetConfig, has_text_modality=False)

    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="ResNet does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass

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

    def test_forward_signature(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            signature = inspect.signature(model.call)
            
            arg_names = [*signature.parameters.keys()]

            expected_arg_names = ["pixel_values"]
            self.assertListEqual(arg_names[:1], expected_arg_names)

    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)
            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

            expected_num_stages = self.model_tester.num_stages
            self.assertEqual(len(hidden_states), expected_num_stages + 1)

            
            self.assertListEqual(
                list(hidden_states[0].shape[-2:]),
                [self.model_tester.image_size // 4, self.model_tester.image_size // 4],
            )

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        layers_type = ["basic", "bottleneck"]
        for model_class in self.all_model_classes:
            for layer_type in layers_type:
                config.layer_type = layer_type
                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 TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = TFResNetModel.from_pretrained(model_name)
            self.assertIsNotNone(model)



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


@require_tf
@require_vision
class TFResNetModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return (
            AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
            if is_vision_available()
            else None
        )

    @slow
    def test_inference_image_classification_head(self):
        model = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])

        image_processor = self.default_image_processor
        image = prepare_img()
        inputs = image_processor(images=image, return_tensors="tf")

        
        outputs = model(**inputs)

        
        expected_shape = tf.TensorShape((1, 1000))
        self.assertEqual(outputs.logits.shape, expected_shape)

        expected_slice = tf.constant([-11.1069, -9.7877, -8.3777])

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