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 bit model import unittest from transformers import bitconfig from transformers testingutils import requiretorch requirevision slow torchdevice from transformers utils import cachedproperty istorchavailable isvisionavailable from testbackbonecommon import backbonetestermixin from testconfigurationcommon import configtester from testmodelingcommon import modeltestermixin floatstensor idstensor from testpipelinemixin import pipelinetestermixin if istorchavailable import torch from torch import nn from transformers import bitbackbone bitforimageclassification bitimageprocessor bitmodel from transformers models bit modelingbit import bitpretrainedmodelarchivelist if isvisionavailable from pil import image class bitmodeltester def init self parent batchsize3 imagesize32 numchannels3 embeddingssize10 hiddensizes8 16 32 64 depths1 1 2 1 istrainingtrue uselabelstrue hiddenactrelu numlabels3 scopenone outfeaturesstage2 stage3 stage4 outindices2 3 4 numgroups1 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 self outfeatures outfeatures self outindices outindices self numgroups numgroups 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 bitconfig numchannelsself numchannels embeddingssizeself embeddingssize hiddensizesself hiddensizes depthsself depths hiddenactself hiddenact numlabelsself numlabels outfeaturesself outfeatures outindicesself outindices numgroupsself numgroups def createandcheckmodelself config pixelvalues labels model bitmodelconfigconfig model totorchdevice model eval result modelpixelvalues 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 bitforimageclassificationconfig model totorchdevice model eval result modelpixelvalues labelslabels self parent assertequalresult logits shape self batchsize self numlabels def createandcheckbackboneself config pixelvalues labels model bitbackboneconfigconfig model totorchdevice model eval result modelpixelvalues verify feature maps self parent assertequallenresult featuremaps lenconfig outfeatures self parent assertlistequallistresult featuremaps0 shape self batchsize self hiddensizes1 4 4 verify channels self parent assertequallenmodel channels lenconfig outfeatures self parent assertlistequalmodel channels config hiddensizes1 verify backbone works with outfeaturesnone config outfeatures none model bitbackboneconfigconfig model totorchdevice model eval result modelpixelvalues verify feature maps self parent assertequallenresult featuremaps 1 self parent assertlistequallistresult featuremaps0 shape self batchsize self hiddensizes1 1 1 verify channels self parent assertequallenmodel channels 1 self parent assertlistequalmodel channels config hiddensizes1 def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config pixelvalues labels configandinputs inputsdict pixelvalues pixelvalues return config inputsdict requiretorch class bitmodeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses bitmodel bitforimageclassification bitbackbone if istorchavailable else pipelinemodelmapping featureextraction bitmodel imageclassification bitforimageclassification if istorchavailable else fxcompatible false testpruning false testresizeembeddings false testheadmasking false hasattentions false def setupself self modeltester bitmodeltesterself self configtester configtesterself configclassbitconfig 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 skipreasonbit does not output attentions def testattentionoutputsself pass unittest skipreasonbit does not use inputsembeds def testinputsembedsself pass unittest skipreasonbit does not support input and output embeddings def testmodelcommonattributesself pass def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs def testbackboneself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckbackboneconfigandinputs def testinitializationself config inputsdict self modeltester prepareconfigandinputsforcommon for modelclass in self allmodelclasses model modelclassconfigconfig for name module in model namedmodules if isinstancemodule nn batchnorm2d nn groupnorm self asserttrue torch allmodule weight 1 msgfparameter name of model modelclass seems not properly initialized self asserttrue torch allmodule bias 0 msgfparameter name of model modelclass seems not properly initialized 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 expectednumstages self modeltester numstages self assertequallenhiddenstates expectednumstages 1 bit 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 preactivation 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 unittest skipreasonbit does not use feedforward chunking def testfeedforwardchunkingself pass def testforimageclassificationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforimageclassificationconfigandinputs slow def testmodelfrompretrainedself for modelname in bitpretrainedmodelarchivelist 1 model bitmodel 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 bitmodelintegrationtestunittest testcase cachedproperty def defaultimageprocessorself return bitimageprocessor frompretrainedbitpretrainedmodelarchivelist0 if isvisionavailable else none slow def testinferenceimageclassificationheadself model bitforimageclassification frompretrainedbitpretrainedmodelarchivelist0 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 6526 0 5263 1 4398 totorchdevice self asserttruetorch allcloseoutputs logits0 3 expectedslice atol1e4 requiretorch class bitbackbonetestbackbonetestermixin unittest testcase allmodelclasses bitbackbone if istorchavailable else configclass bitconfig hasattentions false def setupself self modeltester bitmodeltesterself 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 bit model verify feature maps verify channels verify backbone works with out_features none verify feature maps verify channels here we also overwrite some of the tests of test_modeling_common py as bit does not use input_ids inputs_embeds attention_mask and seq_length bit 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 BitConfig
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_backbone_common import BackboneTesterMixin
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 torch import nn

    from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
    from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image


class BitModelTester:
    def __init__(
        self,
        parent,
        batch_size=3,
        image_size=32,
        num_channels=3,
        embeddings_size=10,
        hidden_sizes=[8, 16, 32, 64],
        depths=[1, 1, 2, 1],
        is_training=True,
        use_labels=True,
        hidden_act="relu",
        num_labels=3,
        scope=None,
        out_features=["stage2", "stage3", "stage4"],
        out_indices=[2, 3, 4],
        num_groups=1,
    ):
        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)
        self.out_features = out_features
        self.out_indices = out_indices
        self.num_groups = num_groups

    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 BitConfig(
            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,
            out_features=self.out_features,
            out_indices=self.out_indices,
            num_groups=self.num_groups,
        )

    def create_and_check_model(self, config, pixel_values, labels):
        model = BitModel(config=config)
        model.to(torch_device)
        model.eval()
        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 = BitForImageClassification(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_backbone(self, config, pixel_values, labels):
        model = BitBackbone(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)

        
        self.parent.assertEqual(len(result.feature_maps), len(config.out_features))
        self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, self.hidden_sizes[1], 4, 4])

        
        self.parent.assertEqual(len(model.channels), len(config.out_features))
        self.parent.assertListEqual(model.channels, config.hidden_sizes[1:])

        
        config.out_features = None
        model = BitBackbone(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)

        
        self.parent.assertEqual(len(result.feature_maps), 1)
        self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, self.hidden_sizes[-1], 1, 1])

        
        self.parent.assertEqual(len(model.channels), 1)
        self.parent.assertListEqual(model.channels, [config.hidden_sizes[-1]])

    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 BitModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    

    all_model_classes = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
    pipeline_model_mapping = (
        {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
        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 = BitModelTester(self)
        self.config_tester = ConfigTester(self, config_class=BitConfig, 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="Bit does not output attentions")
    def test_attention_outputs(self):
        pass

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

    @unittest.skip(reason="Bit does not support input and output embeddings")
    def test_model_common_attributes(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_backbone(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*config_and_inputs)

    def test_initialization(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config=config)
            for name, module in model.named_modules():
                if isinstance(module, (nn.BatchNorm2d, nn.GroupNorm)):
                    self.assertTrue(
                        torch.all(module.weight == 1),
                        msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                    )
                    self.assertTrue(
                        torch.all(module.bias == 0),
                        msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                    )

    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

            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 = ["preactivation", "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)

    @unittest.skip(reason="Bit does not use feedforward chunking")
    def test_feed_forward_chunking(self):
        pass

    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 BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = BitModel.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 BitModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return (
            BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]) if is_vision_available() else None
        )

    @slow
    def test_inference_image_classification_head(self):
        model = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).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([[-0.6526, -0.5263, -1.4398]]).to(torch_device)

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


@require_torch
class BitBackboneTest(BackboneTesterMixin, unittest.TestCase):
    all_model_classes = (BitBackbone,) if is_torch_available() else ()
    config_class = BitConfig

    has_attentions = False

    def setUp(self):
        self.model_tester = BitModelTester(self)