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 vitmatte model import unittest from huggingfacehub import hfhubdownload from transformers import vitmatteconfig from transformers testingutils import requiretorch slow torchdevice from transformers utils import istorchavailable isvisionavailable from testconfigurationcommon import configtester from testmodelingcommon import modeltestermixin floatstensor from testpipelinemixin import pipelinetestermixin if istorchavailable import torch from transformers import vitdetconfig vitmatteforimagematting from transformers models vitmatte modelingvitmatte import vitmattepretrainedmodelarchivelist if isvisionavailable from pil import image from transformers import vitmatteimageprocessor class vitmattemodeltester def init self parent batchsize13 imagesize32 patchsize16 numchannels4 istrainingtrue uselabelsfalse hiddensize2 numhiddenlayers2 numattentionheads2 hiddenactgelu typesequencelabelsize10 initializerrange0 02 scopenone outfeaturesstage1 fusionhiddensizes128 64 32 16 self parent parent self batchsize batchsize self imagesize imagesize self patchsize patchsize self numchannels numchannels self istraining istraining self uselabels uselabels self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self hiddenact hiddenact self typesequencelabelsize typesequencelabelsize self initializerrange initializerrange self scope scope self outfeatures outfeatures self fusionhiddensizes fusionhiddensizes self seqlength self imagesize self patchsize 2 def prepareconfigandinputsself pixelvalues floatstensorself batchsize self numchannels self imagesize self imagesize labels none if self uselabels raise notimplementederrortraining is not yet supported config self getconfig return config pixelvalues labels def getbackboneconfigself return vitdetconfig imagesizeself imagesize patchsizeself patchsize numchannelsself numchannels numhiddenlayersself numhiddenlayers numattentionheadsself numattentionheads hiddensizeself hiddensize istrainingself istraining hiddenactself hiddenact outfeaturesself outfeatures def getconfigself return vitmatteconfig backboneconfigself getbackboneconfig hiddensizeself hiddensize fusionhiddensizesself fusionhiddensizes def createandcheckmodelself config pixelvalues labels model vitmatteforimagemattingconfigconfig model totorchdevice model eval result modelpixelvalues self parent assertequalresult alphas shape self batchsize 1 self imagesize self imagesize def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config pixelvalues labels configandinputs inputsdict pixelvalues pixelvalues return config inputsdict requiretorch class vitmattemodeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses vitmatteforimagematting if istorchavailable else pipelinemodelmapping fxcompatible false testpruning false testresizeembeddings false testheadmasking false def setupself self modeltester vitmattemodeltesterself self configtester configtesterself configclassvitmatteconfig 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 skipreasonvitmatte does not use inputsembeds def testinputsembedsself pass unittest skipreasontraining is not yet supported def testtrainingself pass unittest skipreasontraining is not yet supported def testtraininggradientcheckpointingself pass unittest skip reasonthis architecure seem to not compute gradients properly when using gc check https github comhuggingfacetransformerspull27124 def testtraininggradientcheckpointingusereentrantself pass unittest skip reasonthis architecure seem to not compute gradients properly when using gc check https github comhuggingfacetransformerspull27124 def testtraininggradientcheckpointingusereentrantfalseself pass unittest skipreasonvitmatte does not support input and output embeddings def testmodelcommonattributesself pass def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs slow def testmodelfrompretrainedself for modelname in vitmattepretrainedmodelarchivelist 1 model vitmatteforimagematting frompretrainedmodelname self assertisnotnonemodel unittest skipreasonvitmatte does not support retaining gradient on attention logits def testretaingradhiddenstatesattentionsself 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 expectednumlayers getattr self modeltester expectednumhiddenlayers self modeltester numhiddenlayers 1 self assertequallenhiddenstates expectednumlayers self assertlistequal listhiddenstates0 shape2 2 2 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 printhello we re here checkhiddenstatesoutputinputsdict config modelclass requiretorch class vitmattemodelintegrationtestunittest testcase slow def testinferenceself processor vitmatteimageprocessor frompretrainedhustvlvitmattesmallcomposition1k model vitmatteforimagematting frompretrainedhustvlvitmattesmallcomposition1k totorchdevice filepath hfhubdownload repoidhfinternaltestingimagemattingfixtures filenameimage png repotypedataset image image openfilepath convertrgb filepath hfhubdownload repoidhfinternaltestingimagemattingfixtures filenametrimap png repotypedataset trimap image openfilepath convertl prepare image trimap for the model inputs processorimagesimage trimapstrimap returntensorspt totorchdevice with torch nograd alphas modelinputs alphas expectedshape torch size1 1 640 960 self assertequalalphas shape expectedshape expectedslice torch tensor 0 9977 0 9987 0 9990 0 9980 0 9998 0 9998 0 9983 0 9998 0 9998 devicetorchdevice self asserttruetorch allclosealphas0 0 3 3 expectedslice atol1e4 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 vitmatte model here we also overwrite some of the tests of test_modeling_common py as vitmatte does not use input_ids inputs_embeds attention_mask and seq_length check that output_hidden_states also work using config prepare image trimap for the model
import unittest

from huggingface_hub import hf_hub_download

from transformers import VitMatteConfig
from transformers.testing_utils import (
    require_torch,
    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, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import VitDetConfig, VitMatteForImageMatting
    from transformers.models.vitmatte.modeling_vitmatte import VITMATTE_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import VitMatteImageProcessor


class VitMatteModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        image_size=32,
        patch_size=16,
        num_channels=4,
        is_training=True,
        use_labels=False,
        hidden_size=2,
        num_hidden_layers=2,
        num_attention_heads=2,
        hidden_act="gelu",
        type_sequence_label_size=10,
        initializer_range=0.02,
        scope=None,
        out_features=["stage1"],
        fusion_hidden_sizes=[128, 64, 32, 16],
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.is_training = is_training
        self.use_labels = use_labels
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.hidden_act = hidden_act
        self.type_sequence_label_size = type_sequence_label_size
        self.initializer_range = initializer_range
        self.scope = scope
        self.out_features = out_features
        self.fusion_hidden_sizes = fusion_hidden_sizes

        self.seq_length = (self.image_size // self.patch_size) ** 2

    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:
            raise NotImplementedError("Training is not yet supported")

        config = self.get_config()

        return config, pixel_values, labels

    def get_backbone_config(self):
        return VitDetConfig(
            image_size=self.image_size,
            patch_size=self.patch_size,
            num_channels=self.num_channels,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            hidden_size=self.hidden_size,
            is_training=self.is_training,
            hidden_act=self.hidden_act,
            out_features=self.out_features,
        )

    def get_config(self):
        return VitMatteConfig(
            backbone_config=self.get_backbone_config(),
            hidden_size=self.hidden_size,
            fusion_hidden_sizes=self.fusion_hidden_sizes,
        )

    def create_and_check_model(self, config, pixel_values, labels):
        model = VitMatteForImageMatting(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        self.parent.assertEqual(result.alphas.shape, (self.batch_size, 1, 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 VitMatteModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    

    all_model_classes = (VitMatteForImageMatting,) if is_torch_available() else ()
    pipeline_model_mapping = {}

    fx_compatible = False
    test_pruning = False
    test_resize_embeddings = False
    test_head_masking = False

    def setUp(self):
        self.model_tester = VitMatteModelTester(self)
        self.config_tester = ConfigTester(self, config_class=VitMatteConfig, 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="VitMatte does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass

    @unittest.skip(reason="Training is not yet supported")
    def test_training(self):
        pass

    @unittest.skip(reason="Training is not yet supported")
    def test_training_gradient_checkpointing(self):
        pass

    @unittest.skip(
        reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
    )
    def test_training_gradient_checkpointing_use_reentrant(self):
        pass

    @unittest.skip(
        reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
    )
    def test_training_gradient_checkpointing_use_reentrant_false(self):
        pass

    @unittest.skip(reason="ViTMatte 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)

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

    @unittest.skip(reason="ViTMatte does not support retaining gradient on attention logits")
    def test_retain_grad_hidden_states_attentions(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_layers = getattr(
                self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
            )
            self.assertEqual(len(hidden_states), expected_num_layers)

            self.assertListEqual(
                list(hidden_states[0].shape[-2:]),
                [2, 2],
            )

        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

            print("Hello we're here")

            check_hidden_states_output(inputs_dict, config, model_class)


@require_torch
class VitMatteModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference(self):
        processor = VitMatteImageProcessor.from_pretrained("hustvl/vitmatte-small-composition-1k")
        model = VitMatteForImageMatting.from_pretrained("hustvl/vitmatte-small-composition-1k").to(torch_device)

        filepath = hf_hub_download(
            repo_id="hf-internal-testing/image-matting-fixtures", filename="image.png", repo_type="dataset"
        )
        image = Image.open(filepath).convert("RGB")
        filepath = hf_hub_download(
            repo_id="hf-internal-testing/image-matting-fixtures", filename="trimap.png", repo_type="dataset"
        )
        trimap = Image.open(filepath).convert("L")

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

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

        expected_shape = torch.Size((1, 1, 640, 960))
        self.assertEqual(alphas.shape, expected_shape)

        expected_slice = torch.tensor(
            [[0.9977, 0.9987, 0.9990], [0.9980, 0.9998, 0.9998], [0.9983, 0.9998, 0.9998]], device=torch_device
        )
        self.assertTrue(torch.allclose(alphas[0, 0, :3, :3], expected_slice, atol=1e-4))