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 poolformer model import unittest from transformers import istorchavailable isvisionavailable from transformers models auto import getvalues from transformers testingutils import requiretorch slow torchdevice from testconfigurationcommon import configtester from testmodelingcommon import modeltestermixin floatstensor idstensor from testpipelinemixin import pipelinetestermixin if istorchavailable import torch from transformers import modelmapping poolformerconfig poolformerforimageclassification poolformermodel from transformers models poolformer modelingpoolformer import poolformerpretrainedmodelarchivelist if isvisionavailable from pil import image from transformers import poolformerimageprocessor class poolformerconfigtesterconfigtester def createandtestconfigcommonpropertiesself config self configclassself inputsdict self parent asserttruehasattrconfig hiddensizes self parent asserttruehasattrconfig numencoderblocks class poolformermodeltester def init self parent batchsize13 imagesize64 numchannels3 numencoderblocks4 depths2 2 2 2 srratios8 4 2 1 hiddensizes16 32 64 128 downsamplingrates1 4 8 16 istrainingfalse uselabelstrue hiddenactgelu hiddendropoutprob0 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 istraining istraining self uselabels uselabels self hiddenact hiddenact self hiddendropoutprob hiddendropoutprob 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 poolformerconfig imagesizeself imagesize numchannelsself numchannels numencoderblocksself numencoderblocks depthsself depths hiddensizesself hiddensizes hiddenactself hiddenact hiddendropoutprobself hiddendropoutprob initializerrangeself initializerrange return config pixelvalues labels def createandcheckmodelself config pixelvalues labels model poolformermodelconfigconfig model totorchdevice model eval result modelpixelvalues expectedheight expectedwidth self imagesize 32 0 self parent assertequal result lasthiddenstate shape self batchsize self hiddensizes1 expectedheight expectedwidth def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config pixelvalues labels configandinputs inputsdict pixelvalues pixelvalues return config inputsdict requiretorch class poolformermodeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses poolformermodel poolformerforimageclassification if istorchavailable else pipelinemodelmapping featureextraction poolformermodel imageclassification poolformerforimageclassification if istorchavailable else testheadmasking false testpruning false testresizeembeddings false testtorchscript false hasattentions false def setupself self modeltester poolformermodeltesterself self configtester poolformerconfigtesterself configclasspoolformerconfig def testconfigself self configtester runcommontests def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs unittest skippoolformer does not use inputsembeds def testinputsembedsself pass unittest skippoolformer does not have getinputembeddings method and getoutputembeddings methods def testmodelcommonattributesself pass def testhiddenstatesoutputself def checkhiddenstatesoutputinputsdict config modelclass model modelclassconfig model totorchdevice model eval with torch nograd outputs modelself prepareforclassinputsdict modelclass hiddenstates outputs hiddenstates expectednumlayers self modeltester numencoderblocks self assertequallenhiddenstates expectednumlayers verify the first hidden states first block self assertlistequal listhiddenstates0 shape3 self modeltester hiddensizes0 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 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 poolformerpretrainedmodelarchivelist 1 model poolformermodel frompretrainedmodelname self assertisnotnonemodel we will verify our results on an image of cute cats def prepareimg image image open testsfixturestestssamplescoco000000039769 png return image requiretorch class poolformermodelintegrationtestunittest testcase slow def testinferenceimageclassificationheadself imageprocessor poolformerimageprocessor model poolformerforimageclassification frompretrainedsailpoolformers12 totorchdevice inputs imageprocessorimagesprepareimg returntensorspt totorchdevice forward pass with torch nograd outputs modelinputs verify the logits expectedshape torch size1 1000 self assertequaloutputs logits shape expectedshape expectedslice torch tensor0 6113 0 1685 0 0492 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 poolformer model 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 transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
    from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import PoolFormerImageProcessor


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


class PoolFormerModelTester:
    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],
        is_training=False,
        use_labels=True,
        hidden_act="gelu",
        hidden_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.is_training = is_training
        self.use_labels = use_labels
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_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 = PoolFormerConfig(
            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,
            hidden_act=self.hidden_act,
            hidden_dropout_prob=self.hidden_dropout_prob,
            initializer_range=self.initializer_range,
        )

        return config, pixel_values, labels

    def create_and_check_model(self, config, pixel_values, labels):
        model = PoolFormerModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        expected_height = expected_width = self.image_size // 32.0
        self.parent.assertEqual(
            result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)
        )

    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 PoolFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
    pipeline_model_mapping = (
        {"feature-extraction": PoolFormerModel, "image-classification": PoolFormerForImageClassification}
        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 = PoolFormerModelTester(self)
        self.config_tester = PoolFormerConfigTester(self, config_class=PoolFormerConfig)

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

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

    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 = self.model_tester.num_encoder_blocks
            self.assertEqual(len(hidden_states), expected_num_layers)

            
            self.assertListEqual(
                list(hidden_states[0].shape[-3:]),
                [
                    self.model_tester.hidden_sizes[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_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 POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = PoolFormerModel.from_pretrained(model_name)
            self.assertIsNotNone(model)



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


@require_torch
class PoolFormerModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference_image_classification_head(self):
        image_processor = PoolFormerImageProcessor()
        model = PoolFormerForImageClassification.from_pretrained("sail/poolformer_s12").to(torch_device)

        inputs = image_processor(images=prepare_img(), 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([-0.6113, 0.1685, -0.0492]).to(torch_device)
        self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))