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 cvt model import unittest from math import floor from transformers import cvtconfig from transformers fileutils import cachedproperty istorchavailable isvisionavailable from transformers testingutils import requiretorch requirevision slow torchdevice from testconfigurationcommon import configtester from testmodelingcommon import modeltestermixin floatstensor idstensor from testpipelinemixin import pipelinetestermixin if istorchavailable import torch from transformers import cvtforimageclassification cvtmodel from transformers models cvt modelingcvt import cvtpretrainedmodelarchivelist if isvisionavailable from pil import image from transformers import autoimageprocessor class cvtconfigtesterconfigtester def createandtestconfigcommonpropertiesself config self configclassself inputsdict self parent asserttruehasattrconfig embeddim self parent asserttruehasattrconfig numheads class cvtmodeltester def init self parent batchsize13 imagesize64 numchannels3 embeddim16 32 48 numheads1 2 3 depth1 2 10 patchsizes7 3 3 patchstride4 2 2 patchpadding2 1 1 stridekv2 2 2 clstokenfalse false true attentiondroprate0 0 0 0 0 0 initializerrange0 02 layernormeps1e12 istrainingtrue uselabelstrue numlabels2 check self parent parent self batchsize batchsize self imagesize imagesize self patchsizes patchsizes self patchstride patchstride self patchpadding patchpadding self istraining istraining self uselabels uselabels self numlabels numlabels self numchannels numchannels self embeddim embeddim self numheads numheads self stridekv stridekv self depth depth self clstoken clstoken self attentiondroprate attentiondroprate self initializerrange initializerrange self layernormeps layernormeps 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 cvtconfig imagesizeself imagesize numlabelsself numlabels numchannelsself numchannels embeddimself embeddim numheadsself numheads patchsizesself patchsizes patchpaddingself patchpadding patchstrideself patchstride stridekvself stridekv depthself depth clstokenself clstoken attentiondroprateself attentiondroprate initializerrangeself initializerrange def createandcheckmodelself config pixelvalues labels model cvtmodelconfigconfig model totorchdevice model eval result modelpixelvalues imagesize self imagesize self imagesize height width imagesize0 imagesize1 for i in rangelenself depth height floorheight 2 self patchpaddingi self patchsizesi self patchstridei 1 width floorwidth 2 self patchpaddingi self patchsizesi self patchstridei 1 self parent assertequalresult lasthiddenstate shape self batchsize self embeddim1 height width def createandcheckforimageclassificationself config pixelvalues labels config numlabels self numlabels model cvtforimageclassificationconfig 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 cvtmodeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses cvtmodel cvtforimageclassification if istorchavailable else pipelinemodelmapping featureextraction cvtmodel imageclassification cvtforimageclassification if istorchavailable else testpruning false testtorchscript false testresizeembeddings false testheadmasking false hasattentions false def setupself self modeltester cvtmodeltesterself self configtester configtesterself configclasscvtconfig 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 skipreasoncvt does not output attentions def testattentionoutputsself pass unittest skipreasoncvt does not use inputsembeds def testinputsembedsself pass unittest skipreasoncvt does not support input and output embeddings def testmodelcommonattributesself 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 expectednumlayers lenself modeltester depth self assertequallenhiddenstates expectednumlayers verify the first hidden states first block self assertlistequal listhiddenstates0 shape3 self modeltester embeddim0 self modeltester imagesize 4 self modeltester imagesize 4 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 cvtpretrainedmodelarchivelist 1 model cvtmodel 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 cvtmodelintegrationtestunittest testcase cachedproperty def defaultimageprocessorself return autoimageprocessor frompretrainedcvtpretrainedmodelarchivelist0 slow def testinferenceimageclassificationheadself model cvtforimageclassification frompretrainedcvtpretrainedmodelarchivelist0 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 9285 0 9015 0 3150 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 cvt model check here we also overwrite some of the tests of test_modeling_common py as cvt does not use input_ids inputs_embeds attention_mask and seq_length verify the first hidden states first block 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 math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 CvtForImageClassification, CvtModel
    from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor


class CvtConfigTester(ConfigTester):
    def create_and_test_config_common_properties(self):
        config = self.config_class(**self.inputs_dict)
        self.parent.assertTrue(hasattr(config, "embed_dim"))
        self.parent.assertTrue(hasattr(config, "num_heads"))


class CvtModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        image_size=64,
        num_channels=3,
        embed_dim=[16, 32, 48],
        num_heads=[1, 2, 3],
        depth=[1, 2, 10],
        patch_sizes=[7, 3, 3],
        patch_stride=[4, 2, 2],
        patch_padding=[2, 1, 1],
        stride_kv=[2, 2, 2],
        cls_token=[False, False, True],
        attention_drop_rate=[0.0, 0.0, 0.0],
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        is_training=True,
        use_labels=True,
        num_labels=2,  
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.patch_sizes = patch_sizes
        self.patch_stride = patch_stride
        self.patch_padding = patch_padding
        self.is_training = is_training
        self.use_labels = use_labels
        self.num_labels = num_labels
        self.num_channels = num_channels
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.stride_kv = stride_kv
        self.depth = depth
        self.cls_token = cls_token
        self.attention_drop_rate = attention_drop_rate
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps

    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 CvtConfig(
            image_size=self.image_size,
            num_labels=self.num_labels,
            num_channels=self.num_channels,
            embed_dim=self.embed_dim,
            num_heads=self.num_heads,
            patch_sizes=self.patch_sizes,
            patch_padding=self.patch_padding,
            patch_stride=self.patch_stride,
            stride_kv=self.stride_kv,
            depth=self.depth,
            cls_token=self.cls_token,
            attention_drop_rate=self.attention_drop_rate,
            initializer_range=self.initializer_range,
        )

    def create_and_check_model(self, config, pixel_values, labels):
        model = CvtModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        image_size = (self.image_size, self.image_size)
        height, width = image_size[0], image_size[1]
        for i in range(len(self.depth)):
            height = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1)
            width = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.embed_dim[-1], height, width))

    def create_and_check_for_image_classification(self, config, pixel_values, labels):
        config.num_labels = self.num_labels
        model = CvtForImageClassification(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 CvtModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    

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

    test_pruning = False
    test_torchscript = False
    test_resize_embeddings = False
    test_head_masking = False
    has_attentions = False

    def setUp(self):
        self.model_tester = CvtModelTester(self)
        self.config_tester = ConfigTester(self, config_class=CvtConfig, 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="Cvt does not output attentions")
    def test_attention_outputs(self):
        pass

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

    @unittest.skip(reason="Cvt 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_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_layers = len(self.model_tester.depth)
            self.assertEqual(len(hidden_states), expected_num_layers)

            
            self.assertListEqual(
                list(hidden_states[0].shape[-3:]),
                [
                    self.model_tester.embed_dim[0],
                    self.model_tester.image_size // 4,
                    self.model_tester.image_size // 4,
                ],
            )

        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 CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = CvtModel.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 CvtModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0])

    @slow
    def test_inference_image_classification_head(self):
        model = CvtForImageClassification.from_pretrained(CVT_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([0.9285, 0.9015, -0.3150]).to(torch_device)

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