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 pvt model import unittest from transformers import istorchavailable isvisionavailable from transformers models auto import getvalues from transformers testingutils import requireaccelerate requiretorch requiretorchaccelerator requiretorchfp16 slow torchdevice from testconfigurationcommon import configtester from testmodelingcommon import modeltestermixin floatstensor idstensor from testpipelinemixin import pipelinetestermixin if istorchavailable import torch from transformers import modelmapping pvtconfig pvtforimageclassification pvtimageprocessor pvtmodel from transformers models pvt modelingpvt import pvtpretrainedmodelarchivelist if isvisionavailable from pil import image class pvtconfigtesterconfigtester def runcommontestsself config self configclassself inputsdict self parent asserttruehasattrconfig hiddensizes self parent asserttruehasattrconfig numencoderblocks class pvtmodeltester def init self parent batchsize13 imagesize64 numchannels3 numencoderblocks4 depths2 2 2 2 srratios8 4 2 1 hiddensizes16 32 64 128 downsamplingrates1 4 8 16 numattentionheads1 2 4 8 istrainingtrue uselabelstrue hiddenactgelu hiddendropoutprob0 1 attentionprobsdropoutprob0 1 initializerrange0 02 numlabels3 scopenone self parent parent self batchsize batchsize self imagesize imagesize self numchannels numchannels self numencoderblocks numencoderblocks self srratios srratios self depths depths self hiddensizes hiddensizes self downsamplingrates downsamplingrates self numattentionheads numattentionheads self istraining istraining self uselabels uselabels self hiddenact hiddenact self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self initializerrange initializerrange self numlabels numlabels self scope scope def prepareconfigandinputsself pixelvalues floatstensorself batchsize self numchannels self imagesize self imagesize labels none if self uselabels labels idstensorself batchsize self imagesize self imagesize self numlabels config self getconfig return config pixelvalues labels def getconfigself return pvtconfig imagesizeself imagesize numchannelsself numchannels numencoderblocksself numencoderblocks depthsself depths hiddensizesself hiddensizes numattentionheadsself numattentionheads hiddenactself hiddenact hiddendropoutprobself hiddendropoutprob attentionprobsdropoutprobself attentionprobsdropoutprob initializerrangeself initializerrange def createandcheckmodelself config pixelvalues labels model pvtmodelconfigconfig model totorchdevice model eval result modelpixelvalues self parent assertisnotnoneresult lasthiddenstate def createandcheckforimageclassificationself config pixelvalues labels config numlabels self typesequencelabelsize model pvtforimageclassificationconfig model totorchdevice model eval result modelpixelvalues labelslabels self parent assertequalresult logits shape self batchsize self typesequencelabelsize test greyscale images config numchannels 1 model pvtforimageclassificationconfig model totorchdevice model eval pixelvalues floatstensorself batchsize 1 self imagesize self imagesize result modelpixelvalues self parent assertequalresult logits shape self batchsize self typesequencelabelsize def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config pixelvalues labels configandinputs inputsdict pixelvalues pixelvalues return config inputsdict we will verify our results on an image of cute cats def prepareimg image image open testsfixturestestssamplescoco000000039769 png return image requiretorch class pvtmodeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses pvtmodel pvtforimageclassification if istorchavailable else pipelinemodelmapping featureextraction pvtmodel imageclassification pvtforimageclassification if istorchavailable else testheadmasking false testpruning false testresizeembeddings false testtorchscript false hasattentions false def setupself self modeltester pvtmodeltesterself self configtester pvtconfigtesterself configclasspvtconfig def testconfigself self configtester runcommontests def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs unittest skippvt does not use inputsembeds def testinputsembedsself pass unittest skippvt does not have getinputembeddings method and getoutputembeddings methods def testmodelcommonattributesself pass def testinitializationself config inputsdict self modeltester prepareconfigandinputsforcommon for modelclass in self allmodelclasses model modelclassconfigconfig for name param in model namedparameters self asserttrue 1 0 param data mean 1e9 round 1e9 item 1 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 hiddenstates expectednumlayers sumself modeltester depths 1 self assertequallenhiddenstates expectednumlayers verify the first hidden states first block self assertlistequal listhiddenstates0 shape3 self modeltester batchsize self modeltester imagesize 4 2 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 testtrainingself if not self modeltester istraining return config inputsdict self modeltester prepareconfigandinputsforcommon config returndict true for modelclass in self allmodelclasses if modelclass in getvaluesmodelmapping continue model modelclassconfig model totorchdevice model train inputs self prepareforclassinputsdict modelclass returnlabelstrue loss modelinputs loss loss backward slow def testmodelfrompretrainedself for modelname in pvtpretrainedmodelarchivelist 1 model pvtmodel frompretrainedmodelname self assertisnotnonemodel requiretorch class pvtmodelintegrationtestunittest testcase slow def testinferenceimageclassificationself only resize normalize imageprocessor pvtimageprocessor frompretrainedzetatechpvttiny224 model pvtforimageclassification frompretrainedzetatechpvttiny224 totorchdevice eval image prepareimg encodedinputs imageprocessorimagesimage returntensorspt pixelvalues encodedinputs pixelvalues totorchdevice with torch nograd outputs modelpixelvalues expectedshape torch size1 model config numlabels self assertequaloutputs logits shape expectedshape expectedslice torch tensor1 4192 1 9158 0 9702 totorchdevice self asserttruetorch allcloseoutputs logits0 3 expectedslice atol1e4 slow def testinferencemodelself model pvtmodel frompretrainedzetatechpvttiny224 totorchdevice eval imageprocessor pvtimageprocessor frompretrainedzetatechpvttiny224 image prepareimg inputs imageprocessorimagesimage returntensorspt pixelvalues inputs pixelvalues totorchdevice forward pass with torch nograd outputs modelpixelvalues verify the logits expectedshape torch size1 50 512 self assertequaloutputs lasthiddenstate shape expectedshape expectedslice torch tensor 0 3086 1 0402 1 1816 0 2880 0 5781 0 6124 0 1480 0 6129 0 0590 totorchdevice self asserttruetorch allcloseoutputs lasthiddenstate0 3 3 expectedslice atol1e4 slow requireaccelerate requiretorchaccelerator requiretorchfp16 def testinferencefp16self r a small test to make sure that inference work in half precision without any problem forward pass to make sure inference works in fp16 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 pvt model test greyscale images we will verify our results on an image of cute cats verify the first hidden states first block check that output_hidden_states also work using config only resize normalize forward pass verify the logits a small test to make sure that inference work in half precision without any problem forward pass to make sure inference works in fp16
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_accelerator,
    require_torch_fp16,
    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 MODEL_MAPPING, PvtConfig, PvtForImageClassification, PvtImageProcessor, PvtModel
    from transformers.models.pvt.modeling_pvt import PVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image


class PvtConfigTester(ConfigTester):
    def run_common_tests(self):
        config = self.config_class(**self.inputs_dict)
        self.parent.assertTrue(hasattr(config, "hidden_sizes"))
        self.parent.assertTrue(hasattr(config, "num_encoder_blocks"))


class PvtModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        image_size=64,
        num_channels=3,
        num_encoder_blocks=4,
        depths=[2, 2, 2, 2],
        sr_ratios=[8, 4, 2, 1],
        hidden_sizes=[16, 32, 64, 128],
        downsampling_rates=[1, 4, 8, 16],
        num_attention_heads=[1, 2, 4, 8],
        is_training=True,
        use_labels=True,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        initializer_range=0.02,
        num_labels=3,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.num_channels = num_channels
        self.num_encoder_blocks = num_encoder_blocks
        self.sr_ratios = sr_ratios
        self.depths = depths
        self.hidden_sizes = hidden_sizes
        self.downsampling_rates = downsampling_rates
        self.num_attention_heads = num_attention_heads
        self.is_training = is_training
        self.use_labels = use_labels
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.initializer_range = initializer_range
        self.num_labels = num_labels
        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
        if self.use_labels:
            labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels)

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

    def get_config(self):
        return PvtConfig(
            image_size=self.image_size,
            num_channels=self.num_channels,
            num_encoder_blocks=self.num_encoder_blocks,
            depths=self.depths,
            hidden_sizes=self.hidden_sizes,
            num_attention_heads=self.num_attention_heads,
            hidden_act=self.hidden_act,
            hidden_dropout_prob=self.hidden_dropout_prob,
            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
            initializer_range=self.initializer_range,
        )

    def create_and_check_model(self, config, pixel_values, labels):
        model = PvtModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        self.parent.assertIsNotNone(result.last_hidden_state)

    def create_and_check_for_image_classification(self, config, pixel_values, labels):
        config.num_labels = self.type_sequence_label_size
        model = PvtForImageClassification(config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values, labels=labels)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))

        
        config.num_channels = 1
        model = PvtForImageClassification(config)
        model.to(torch_device)
        model.eval()

        pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
        result = model(pixel_values)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))

    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



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


@require_torch
class PvtModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (PvtModel, PvtForImageClassification) if is_torch_available() else ()
    pipeline_model_mapping = (
        {"feature-extraction": PvtModel, "image-classification": PvtForImageClassification}
        if is_torch_available()
        else {}
    )

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

    def setUp(self):
        self.model_tester = PvtModelTester(self)
        self.config_tester = PvtConfigTester(self, config_class=PvtConfig)

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

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

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

    @unittest.skip("Pvt does not have get_input_embeddings method and get_output_embeddings methods")
    def test_model_common_attributes(self):
        pass

    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, param in model.named_parameters():
                self.assertTrue(
                    -1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
                    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.hidden_states

            expected_num_layers = sum(self.model_tester.depths) + 1
            self.assertEqual(len(hidden_states), expected_num_layers)

            
            self.assertListEqual(
                list(hidden_states[0].shape[-3:]),
                [
                    self.model_tester.batch_size,
                    (self.model_tester.image_size // 4) ** 2,
                    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_training(self):
        if not self.model_tester.is_training:
            return

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.return_dict = True

        for model_class in self.all_model_classes:
            if model_class in get_values(MODEL_MAPPING):
                continue
            model = model_class(config)
            model.to(torch_device)
            model.train()
            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            loss = model(**inputs).loss
            loss.backward()

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


@require_torch
class PvtModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference_image_classification(self):
        
        image_processor = PvtImageProcessor.from_pretrained("Zetatech/pvt-tiny-224")
        model = PvtForImageClassification.from_pretrained("Zetatech/pvt-tiny-224").to(torch_device).eval()

        image = prepare_img()
        encoded_inputs = image_processor(images=image, return_tensors="pt")
        pixel_values = encoded_inputs.pixel_values.to(torch_device)

        with torch.no_grad():
            outputs = model(pixel_values)

        expected_shape = torch.Size((1, model.config.num_labels))
        self.assertEqual(outputs.logits.shape, expected_shape)

        expected_slice = torch.tensor([-1.4192, -1.9158, -0.9702]).to(torch_device)

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

    @slow
    def test_inference_model(self):
        model = PvtModel.from_pretrained("Zetatech/pvt-tiny-224").to(torch_device).eval()

        image_processor = PvtImageProcessor.from_pretrained("Zetatech/pvt-tiny-224")
        image = prepare_img()
        inputs = image_processor(images=image, return_tensors="pt")
        pixel_values = inputs.pixel_values.to(torch_device)

        
        with torch.no_grad():
            outputs = model(pixel_values)

        
        expected_shape = torch.Size((1, 50, 512))
        self.assertEqual(outputs.last_hidden_state.shape, expected_shape)

        expected_slice = torch.tensor(
            [[-0.3086, 1.0402, 1.1816], [-0.2880, 0.5781, 0.6124], [0.1480, 0.6129, -0.0590]]
        ).to(torch_device)

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

    @slow
    @require_accelerate
    @require_torch_accelerator
    @require_torch_fp16
    def test_inference_fp16(self):
        r
        model = PvtForImageClassification.from_pretrained("Zetatech/pvt-tiny-224", torch_dtype=torch.float16)
        model.to(torch_device)
        image_processor = PvtImageProcessor(size=224)

        image = prepare_img()
        inputs = image_processor(images=image, return_tensors="pt")
        pixel_values = inputs.pixel_values.to(torch_device, dtype=torch.float16)

        
        with torch.no_grad():
            _ = model(pixel_values)