Python gluoncv.model_zoo方法代碼示例

# 需要導入模塊: import gluoncv [as 別名]
# 或者: from gluoncv import model_zoo [as 別名]
def __init__(self, backbone, channels=1, ctx=mx.cpu(), pretrained=False, **kwargs):
        super().__init__()
        self.k = kwargs.get('k', 1)
        self.channels = channels
        model_dict = {'resnet18_v1b': ['layers1_relu3_fwd', 'layers2_relu3_fwd', 'layers3_relu3_fwd', 'layers4_relu3_fwd'],
                      'resnet34_v1b': ['layers1_relu5_fwd', 'layers2_relu7_fwd', 'layers3_relu11_fwd', 'layers4_relu3_fwd']}
        backbone_model = getattr(gcv_model_zoo, backbone)

        backbone_outputs = model_dict[backbone]
        base_network = backbone_model(pretrained=pretrained, norm_layer=nn.BatchNorm, ctx=ctx, **kwargs)
        self.features = FPNFeatureExpander(
            network=base_network,
            outputs=backbone_outputs, num_filters=[256, 256, 256, 256], use_1x1=True,
            use_upsample=True, use_elewadd=True, use_p6=False, no_bias=True, pretrained=pretrained,
            ctx=ctx)

        self.extrac_convs = []

        for i in range(4):
            weight_init = mx.init.Normal(0.001)
            extra_conv = nn.HybridSequential(prefix='extra_conv_{}'.format(i))
            with extra_conv.name_scope():
                extra_conv.add(nn.Conv2D(256, 3, 1, 1))
                extra_conv.add(nn.BatchNorm())
                extra_conv.add(nn.Activation('relu'))
            extra_conv.initialize(weight_init, ctx=ctx)
            self.register_child(extra_conv)
            self.extrac_convs.append(extra_conv)

        self.decoder_out = nn.HybridSequential(prefix='decoder_out')
        with self.decoder_out.name_scope():
            weight_init = mx.init.Normal(0.001)
            self.decoder_out.add(nn.Conv2D(256, 3, 1, 1))
            self.decoder_out.add(nn.BatchNorm())
            self.decoder_out.add(nn.Activation('relu'))
            self.decoder_out.add(nn.Conv2D(self.channels, 1, 1))
            self.decoder_out.initialize(weight_init, ctx=ctx) 

# 需要導入模塊: import gluoncv [as 別名]
# 或者: from gluoncv import model_zoo [as 別名]
def test(args):
    # output folder
    outdir = 'outdir'
    if not os.path.exists(outdir):
        os.makedirs(outdir)
    # image transform
    input_transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize([.485, .456, .406], [.229, .224, .225]),
    ])
    # dataset and dataloader
    if args.eval:
        testset = get_segmentation_dataset(
            args.dataset, split='val', mode='testval', transform=input_transform)
        total_inter, total_union, total_correct, total_label = \
            np.int64(0), np.int64(0), np.int64(0), np.int64(0)
    else:
        testset = get_segmentation_dataset(
            args.dataset, split='test', mode='test', transform=input_transform)
    test_data = gluon.data.DataLoader(
        testset, args.test_batch_size, shuffle=False, last_batch='keep',
        batchify_fn=ms_batchify_fn, num_workers=args.workers)
    # create network
    if args.model_zoo is not None:
        model = get_model(args.model_zoo, pretrained=True)
    else:
        model = get_segmentation_model(model=args.model, dataset=args.dataset, ctx=args.ctx,
                                       backbone=args.backbone, norm_layer=args.norm_layer,
                                       norm_kwargs=args.norm_kwargs, aux=args.aux,
                                       base_size=args.base_size, crop_size=args.crop_size)
        # load pretrained weight
        assert args.resume is not None, '=> Please provide the checkpoint using --resume'
        if os.path.isfile(args.resume):
            model.load_parameters(args.resume, ctx=args.ctx)
        else:
            raise RuntimeError("=> no checkpoint found at '{}'" \
                .format(args.resume))
    print(model)
    evaluator = MultiEvalModel(model, testset.num_class, ctx_list=args.ctx)
    metric = gluoncv.utils.metrics.SegmentationMetric(testset.num_class)

    tbar = tqdm(test_data)
    for i, (data, dsts) in enumerate(tbar):
        if args.eval:
            predicts = [pred[0] for pred in evaluator.parallel_forward(data)]
            targets = [target.as_in_context(predicts[0].context) \
                       for target in dsts]
            metric.update(targets, predicts)
            pixAcc, mIoU = metric.get()
            tbar.set_description( 'pixAcc: %.4f, mIoU: %.4f' % (pixAcc, mIoU))
        else:
            im_paths = dsts
            predicts = evaluator.parallel_forward(data)
            for predict, impath in zip(predicts, im_paths):
                predict = mx.nd.squeeze(mx.nd.argmax(predict[0], 1)).asnumpy() + \
                    testset.pred_offset
                mask = get_color_pallete(predict, args.dataset)
                outname = os.path.splitext(impath)[0] + '.png'
                mask.save(os.path.join(outdir, outname)) 

# 需要導入模塊: import gluoncv [as 別名]
# 或者: from gluoncv import model_zoo [as 別名]
def __init__(self, args):
        self.args = args
        # image transform
        input_transform = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize([.485, .456, .406], [.229, .224, .225]),
        ])
        # dataset and dataloader
        data_kwargs = {'transform': input_transform, 'base_size': args.base_size,
                       'crop_size': args.crop_size}
        trainset = get_segmentation_dataset(
            args.dataset, split=args.train_split, mode='train', **data_kwargs)
        valset = get_segmentation_dataset(
            args.dataset, split='val', mode='val', **data_kwargs)
        self.train_data = gluon.data.DataLoader(
            trainset, args.batch_size, shuffle=True, last_batch='rollover',
            num_workers=args.workers)
        self.eval_data = gluon.data.DataLoader(valset, args.test_batch_size,
            last_batch='rollover', num_workers=args.workers)
        # create network
        if args.model_zoo is not None:
            model = get_model(args.model_zoo, pretrained=True)
        else:
            model = get_segmentation_model(model=args.model, dataset=args.dataset,
                                           backbone=args.backbone, norm_layer=args.norm_layer,
                                           norm_kwargs=args.norm_kwargs, aux=args.aux,
                                           crop_size=args.crop_size)
        model.cast(args.dtype)
        print(model)
        self.net = DataParallelModel(model, args.ctx, args.syncbn)
        self.evaluator = DataParallelModel(SegEvalModel(model), args.ctx)
        # resume checkpoint if needed
        if args.resume is not None:
            if os.path.isfile(args.resume):
                model.load_parameters(args.resume, ctx=args.ctx)
            else:
                raise RuntimeError("=> no checkpoint found at '{}'" \
                    .format(args.resume))
        # create criterion
        criterion = MixSoftmaxCrossEntropyLoss(args.aux, aux_weight=args.aux_weight)
        self.criterion = DataParallelCriterion(criterion, args.ctx, args.syncbn)
        # optimizer and lr scheduling
        self.lr_scheduler = LRScheduler(mode='poly', base_lr=args.lr,
                                        nepochs=args.epochs,
                                        iters_per_epoch=len(self.train_data),
                                        power=0.9)
        kv = mx.kv.create(args.kvstore)
        optimizer_params = {'lr_scheduler': self.lr_scheduler,
                            'wd':args.weight_decay,
                            'momentum': args.momentum}
        if args.dtype == 'float16':
            optimizer_params['multi_precision'] = True

        if args.no_wd:
            for k, v in self.net.module.collect_params('.*beta|.*gamma|.*bias').items():
                v.wd_mult = 0.0

        self.optimizer = gluon.Trainer(self.net.module.collect_params(), 'sgd',
                                       optimizer_params, kvstore = kv)
        # evaluation metrics
        self.metric = gluoncv.utils.metrics.SegmentationMetric(trainset.num_class)