Python XavierGluon Exemples

Exemple #1

def init_model(cfg):
    model_cfg = edict()
    model_cfg.crop_size = (512, 512)
    model_cfg.input_normalization = {
        'mean': [.485, .456, .406],
        'std': [.229, .224, .225]
    }

    model_cfg.input_transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize(model_cfg.input_normalization['mean'],
                             model_cfg.input_normalization['std']),
    ])

    model = HRNetIHModel(BMCONFIGS['improved_dih512'],
                         mask_fusion='',
                         downsize_hrnet_input=True,
                         small=False)

    model.to(cfg.device)
    model.apply(initializer.XavierGluon(rnd_type='gaussian', magnitude=2.0))
    model.backbone.load_pretrained_weights(
        cfg.IMAGENET_PRETRAINED_MODELS.HRNETV2_W18)

    return model, model_cfg

Exemple #2

def init_model(cfg):
    model_cfg = edict()
    model_cfg.crop_size = (512, 512)
    model_cfg.input_normalization = {
        'mean': [.485, .456, .406],
        'std': [.229, .224, .225]
    }

    model_cfg.input_transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize(model_cfg.input_normalization['mean'], model_cfg.input_normalization['std']),
    ])

    model = DeepImageHarmonization(depth=8, batchnorm_from=2, image_fusion=True)

    model.to(cfg.device)
    model.apply(initializer.XavierGluon(rnd_type='gaussian', magnitude=2.0))

    return model, model_cfg

Exemple #3

def init_model(cfg):
    model_cfg = edict()
    model_cfg.crop_size = (256, 256)
    model_cfg.input_normalization = {
        'mean': [.485, .456, .406],
        'std': [.229, .224, .225]
    }

    model_cfg.input_transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize(model_cfg.input_normalization['mean'],
                             model_cfg.input_normalization['std']),
    ])

    model = DeepLabIHModel(BMCONFIGS['improved_dih256'])

    model.to(cfg.device)
    model.apply(initializer.XavierGluon(rnd_type='gaussian', magnitude=2.0))
    model.backbone.load_pretrained_weights()

    return model, model_cfg

Exemple #4

Fichier : iseunet_v1.py Projet : peternara/image_harmonization-gan

def init_model(cfg):
    model_cfg = edict()
    model_cfg.crop_size = (256, 256)
    model_cfg.input_normalization = {
        'mean': [.485, .456, .406],
        'std': [.229, .224, .225]
    }
    model_cfg.depth = 4

    model_cfg.input_transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize(model_cfg.input_normalization['mean'],
                             model_cfg.input_normalization['std']),
    ])

    model = ISEUNetV1(depth=4, batchnorm_from=2, attend_from=1, ch=64)

    model.to(cfg.device)
    model.apply(initializer.XavierGluon(rnd_type='gaussian', magnitude=1.0))

    return model, model_cfg

Exemple #5

Fichier : hrnet18s_v2p_idih.py Projet : peternara/image_harmonization-gan

def init_model(cfg):
    model_cfg = edict()
    model_cfg.crop_size = (256, 256)
    model_cfg.input_normalization = {
        'mean': [.485, .456, .406],
        'std': [.229, .224, .225]
    }

    model_cfg.input_transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize(model_cfg.input_normalization['mean'],
                             model_cfg.input_normalization['std']),
    ])

    model = HRNetIHModel(BMCONFIGS['improved_dih256'], pyramid_channels=256)

    model.to(cfg.device)
    model.apply(initializer.XavierGluon(rnd_type='gaussian', magnitude=2.0))
    model.backbone.load_pretrained_weights(
        cfg.IMAGENET_PRETRAINED_MODELS.HRNETV2_W18_SMALL)

    return model, model_cfg