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 upernet framework import unittest from huggingfacehub import hfhubdownload from transformers import convnextconfig upernetconfig from transformers testingutils import requiretorch requiretorchmultigpu requirevision slow torchdevice from transformers utils import istorchavailable isvisionavailable from testconfigurationcommon import configtester from testmodelingcommon import modeltestermixin configzeroinit floatstensor idstensor from testpipelinemixin import pipelinetestermixin if istorchavailable import torch from transformers import upernetforsemanticsegmentation from transformers models upernet modelingupernet import upernetpretrainedmodelarchivelist if isvisionavailable from pil import image from transformers import autoimageprocessor class upernetmodeltester def init self parent batchsize13 imagesize32 numchannels3 numstages4 hiddensizes10 20 30 40 depths1 1 1 1 istrainingtrue uselabelstrue intermediatesize37 hiddenactgelu typesequencelabelsize10 initializerrange0 02 outfeaturesstage2 stage3 stage4 numlabels3 scopenone self parent parent self batchsize batchsize self imagesize imagesize self numchannels numchannels self numstages numstages self hiddensizes hiddensizes self depths depths self istraining istraining self uselabels uselabels self intermediatesize intermediatesize self hiddenact hiddenact self typesequencelabelsize typesequencelabelsize self initializerrange initializerrange self outfeatures outfeatures self numlabels numlabels self scope scope self numhiddenlayers numstages def prepareconfigandinputsself pixelvalues floatstensorself batchsize self numchannels self imagesize self imagesize labels none if self uselabels labels idstensorself batchsize self typesequencelabelsize config self getconfig return config pixelvalues labels def getbackboneconfigself return convnextconfig numchannelsself numchannels numstagesself numstages hiddensizesself hiddensizes depthsself depths istrainingself istraining intermediatesizeself intermediatesize hiddenactself hiddenact outfeaturesself outfeatures def getconfigself return upernetconfig backboneconfigself getbackboneconfig hiddensize64 poolscales1 2 3 6 useauxiliaryheadtrue auxiliarylossweight0 4 auxiliaryinchannels40 auxiliarychannels32 auxiliarynumconvs1 auxiliaryconcatinputfalse lossignoreindex255 numlabelsself numlabels def createandcheckforsemanticsegmentationself config pixelvalues labels model upernetforsemanticsegmentationconfigconfig model totorchdevice model eval result modelpixelvalues self parent assertequal result logits shape self batchsize self numlabels self imagesize self imagesize def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config pixelvalues labels configandinputs inputsdict pixelvalues pixelvalues return config inputsdict requiretorch class upernetmodeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses upernetforsemanticsegmentation if istorchavailable else pipelinemodelmapping imagesegmentation upernetforsemanticsegmentation if istorchavailable else fxcompatible false testpruning false testresizeembeddings false testheadmasking false testtorchscript false hasattentions false def setupself self modeltester upernetmodeltesterself self configtester configtesterself configclassupernetconfig 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 def testforsemanticsegmentationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforsemanticsegmentationconfigandinputs unittest skipreasonupernet does not use inputsembeds def testinputsembedsself pass unittest skipreasonupernet does not support input and output embeddings def testmodelcommonattributesself pass unittest skipreasonupernet does not have a base model def testsaveloadfastinitfrombaseself pass unittest skipreasonupernet does not have a base model def testsaveloadfastinittobaseself pass requiretorchmultigpu unittest skipreasonupernet has some layers using addmodule which doesn t work well with nn dataparallel def testmultigpudataparallelforwardself pass 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 convnext 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 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 testinitializationself config inputsdict self modeltester prepareconfigandinputsforcommon configsnoinit configzeroinitconfig configsnoinit backboneconfig configzeroinitconfigsnoinit backboneconfig for modelclass in self allmodelclasses model modelclassconfigconfigsnoinit for name param in model namedparameters if param requiresgrad self assertin param data mean 1e9 round 1e9 item 0 0 1 0 msgfparameter name of model modelclass seems not properly initialized unittest skipreasonupernet does not have tied weights def testtiedmodelweightskeyignoreself pass slow def testmodelfrompretrainedself for modelname in upernetpretrainedmodelarchivelist 1 model upernetforsemanticsegmentation frompretrainedmodelname self assertisnotnonemodel we will verify our results on an image of ade20k def prepareimg filepath hfhubdownload repoidhfinternaltestingfixturesade20k repotypedataset filenameadeval00000001 jpg image image openfilepath convertrgb return image requiretorch requirevision slow class upernetmodelintegrationtestunittest testcase def testinferenceswinbackboneself processor autoimageprocessor frompretrainedopenmmlabupernetswintiny model upernetforsemanticsegmentation frompretrainedopenmmlabupernetswintiny totorchdevice image prepareimg inputs processorimagesimage returntensorspt totorchdevice with torch nograd outputs modelinputs expectedshape torch size1 model config numlabels 512 512 self assertequaloutputs logits shape expectedshape expectedslice torch tensor 7 5958 7 5958 7 4302 7 5958 7 5958 7 4302 7 4797 7 4797 7 3068 totorchdevice self asserttruetorch allcloseoutputs logits0 0 3 3 expectedslice atol1e4 def testinferenceconvnextbackboneself processor autoimageprocessor frompretrainedopenmmlabupernetconvnexttiny model upernetforsemanticsegmentation frompretrainedopenmmlabupernetconvnexttiny totorchdevice image prepareimg inputs processorimagesimage returntensorspt totorchdevice with torch nograd outputs modelinputs expectedshape torch size1 model config numlabels 512 512 self assertequaloutputs logits shape expectedshape expectedslice torch tensor 8 8110 8 8110 8 6521 8 8110 8 8110 8 6521 8 7746 8 7746 8 6130 totorchdevice self asserttruetorch allcloseoutputs logits0 0 3 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 upernet framework here we also overwrite some of the tests of test_modeling_common py as upernet does not use input_ids inputs_embeds attention_mask and seq_length convnext 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 ade20k
import unittest

from huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import UperNetForSemanticSegmentation
    from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor


class UperNetModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        image_size=32,
        num_channels=3,
        num_stages=4,
        hidden_sizes=[10, 20, 30, 40],
        depths=[1, 1, 1, 1],
        is_training=True,
        use_labels=True,
        intermediate_size=37,
        hidden_act="gelu",
        type_sequence_label_size=10,
        initializer_range=0.02,
        out_features=["stage2", "stage3", "stage4"],
        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_stages = num_stages
        self.hidden_sizes = hidden_sizes
        self.depths = depths
        self.is_training = is_training
        self.use_labels = use_labels
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.type_sequence_label_size = type_sequence_label_size
        self.initializer_range = initializer_range
        self.out_features = out_features
        self.num_labels = num_labels
        self.scope = scope
        self.num_hidden_layers = num_stages

    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.type_sequence_label_size)

        config = self.get_config()

        return config, pixel_values, labels

    def get_backbone_config(self):
        return ConvNextConfig(
            num_channels=self.num_channels,
            num_stages=self.num_stages,
            hidden_sizes=self.hidden_sizes,
            depths=self.depths,
            is_training=self.is_training,
            intermediate_size=self.intermediate_size,
            hidden_act=self.hidden_act,
            out_features=self.out_features,
        )

    def get_config(self):
        return UperNetConfig(
            backbone_config=self.get_backbone_config(),
            hidden_size=64,
            pool_scales=[1, 2, 3, 6],
            use_auxiliary_head=True,
            auxiliary_loss_weight=0.4,
            auxiliary_in_channels=40,
            auxiliary_channels=32,
            auxiliary_num_convs=1,
            auxiliary_concat_input=False,
            loss_ignore_index=255,
            num_labels=self.num_labels,
        )

    def create_and_check_for_semantic_segmentation(self, config, pixel_values, labels):
        model = UperNetForSemanticSegmentation(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        self.parent.assertEqual(
            result.logits.shape, (self.batch_size, self.num_labels, self.image_size, self.image_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


@require_torch
class UperNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    

    all_model_classes = (UperNetForSemanticSegmentation,) if is_torch_available() else ()
    pipeline_model_mapping = {"image-segmentation": UperNetForSemanticSegmentation} if is_torch_available() else {}
    fx_compatible = False
    test_pruning = False
    test_resize_embeddings = False
    test_head_masking = False
    test_torchscript = False
    has_attentions = False

    def setUp(self):
        self.model_tester = UperNetModelTester(self)
        self.config_tester = ConfigTester(self, config_class=UperNetConfig, 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

    def test_for_semantic_segmentation(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*config_and_inputs)

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

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

    @unittest.skip(reason="UperNet does not have a base model")
    def test_save_load_fast_init_from_base(self):
        pass

    @unittest.skip(reason="UperNet does not have a base model")
    def test_save_load_fast_init_to_base(self):
        pass

    @require_torch_multi_gpu
    @unittest.skip(reason="UperNet has some layers using `add_module` which doesn't work well with `nn.DataParallel`")
    def test_multi_gpu_data_parallel_forward(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.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()

        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_initialization(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        configs_no_init = _config_zero_init(config)
        configs_no_init.backbone_config = _config_zero_init(configs_no_init.backbone_config)
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            for name, param in model.named_parameters():
                if param.requires_grad:
                    self.assertIn(
                        ((param.data.mean() * 1e9).round() / 1e9).item(),
                        [0.0, 1.0],
                        msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                    )

    @unittest.skip(reason="UperNet does not have tied weights")
    def test_tied_model_weights_key_ignore(self):
        pass

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



def prepare_img():
    filepath = hf_hub_download(
        repo_id="hf-internal-testing/fixtures_ade20k", repo_type="dataset", filename="ADE_val_00000001.jpg"
    )
    image = Image.open(filepath).convert("RGB")
    return image


@require_torch
@require_vision
@slow
class UperNetModelIntegrationTest(unittest.TestCase):
    def test_inference_swin_backbone(self):
        processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-swin-tiny")
        model = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-swin-tiny").to(torch_device)

        image = prepare_img()
        inputs = processor(images=image, return_tensors="pt").to(torch_device)

        with torch.no_grad():
            outputs = model(**inputs)

        expected_shape = torch.Size((1, model.config.num_labels, 512, 512))
        self.assertEqual(outputs.logits.shape, expected_shape)

        expected_slice = torch.tensor(
            [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]
        ).to(torch_device)
        self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3], expected_slice, atol=1e-4))

    def test_inference_convnext_backbone(self):
        processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-tiny")
        model = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-tiny").to(torch_device)

        image = prepare_img()
        inputs = processor(images=image, return_tensors="pt").to(torch_device)

        with torch.no_grad():
            outputs = model(**inputs)

        expected_shape = torch.Size((1, model.config.num_labels, 512, 512))
        self.assertEqual(outputs.logits.shape, expected_shape)

        expected_slice = torch.tensor(
            [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]
        ).to(torch_device)
        self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3], expected_slice, atol=1e-4))