Python model.D_NET128屬性代碼示例

本文整理匯總了Python中model.D_NET128屬性的典型用法代碼示例。如果您正苦於以下問題：Python model.D_NET128屬性的具體用法？Python model.D_NET128怎麽用？Python model.D_NET128使用的例子？那麽, 這裏精選的屬性代碼示例或許可以為您提供幫助。您也可以進一步了解該屬性所在類model的用法示例。

在下文中一共展示了model.D_NET128屬性的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: load_network

# 需要導入模塊: import model [as 別名]
# 或者: from model import D_NET128 [as 別名]
def load_network(gpus):
    netG = G_NET()
    netG.apply(weights_init)
    netG = torch.nn.DataParallel(netG, device_ids=gpus)
    print(netG)

    netsD = []
    if cfg.TREE.BRANCH_NUM > 0:
        netsD.append(D_NET64())
    if cfg.TREE.BRANCH_NUM > 1:
        netsD.append(D_NET128())
    if cfg.TREE.BRANCH_NUM > 2:
        netsD.append(D_NET256())
    if cfg.TREE.BRANCH_NUM > 3:
        netsD.append(D_NET512())
    if cfg.TREE.BRANCH_NUM > 4:
        netsD.append(D_NET1024())
    # TODO: if cfg.TREE.BRANCH_NUM > 5:

    for i in range(len(netsD)):
        netsD[i].apply(weights_init)
        netsD[i] = torch.nn.DataParallel(netsD[i], device_ids=gpus)
        # print(netsD[i])
    print('# of netsD', len(netsD))

    count = 0
    if cfg.TRAIN.NET_G != '':
        state_dict = torch.load(cfg.TRAIN.NET_G)
        netG.load_state_dict(state_dict)
        print('Load ', cfg.TRAIN.NET_G)

        istart = cfg.TRAIN.NET_G.rfind('_') + 1
        iend = cfg.TRAIN.NET_G.rfind('.')
        count = cfg.TRAIN.NET_G[istart:iend]
        count = int(count) + 1

    if cfg.TRAIN.NET_D != '':
        for i in range(len(netsD)):
            print('Load %s_%d.pth' % (cfg.TRAIN.NET_D, i))
            state_dict = torch.load('%s%d.pth' % (cfg.TRAIN.NET_D, i))
            netsD[i].load_state_dict(state_dict)

    inception_model = INCEPTION_V3()

    if cfg.CUDA:
        netG.cuda()
        for i in range(len(netsD)):
            netsD[i].cuda()
        inception_model = inception_model.cuda()
    inception_model.eval()

    return netG, netsD, len(netsD), inception_model, count

開發者ID:netanelyo，項目名稱:Recipe2ImageGAN，代碼行數:54，代碼來源:blah.py

示例2: load_network

# 需要導入模塊: import model [as 別名]
# 或者: from model import D_NET128 [as 別名]
def load_network(gpus):
    netG = G_NET()
    netG.apply(weights_init)
    netG = torch.nn.DataParallel(netG, device_ids=gpus)
    print(netG)

    netsD = []
    if cfg.TREE.BRANCH_NUM > 0:
        netsD.append(D_NET64())
    if cfg.TREE.BRANCH_NUM > 1:
        netsD.append(D_NET128())
    if cfg.TREE.BRANCH_NUM > 2:
        netsD.append(D_NET256())
    if cfg.TREE.BRANCH_NUM > 3:
        netsD.append(D_NET512())
    if cfg.TREE.BRANCH_NUM > 4:
        netsD.append(D_NET1024())
    # TODO: if cfg.TREE.BRANCH_NUM > 5:

    for i in range(len(netsD)):
        netsD[i].apply(weights_init)
        netsD[i] = torch.nn.DataParallel(netsD[i], device_ids=gpus)
        # print(netsD[i])
    print('# of netsD', len(netsD))

    count = 0
    if cfg.TRAIN.NET_G != '':
        state_dict = torch.load(cfg.TRAIN.NET_G)
        netG.load_state_dict(state_dict)
        print('Load ', cfg.TRAIN.NET_G)

        try:
            istart = cfg.TRAIN.NET_G.rfind('_') + 1
            iend = cfg.TRAIN.NET_G.rfind('.')
            count = cfg.TRAIN.NET_G[istart:iend]
            count = int(count)
        except:
            last_run_dir = cfg.DATA_DIR + '/' + cfg.LAST_RUN_DIR + '/Model'
            with open(last_run_dir + '/count.txt', 'r') as f:
                count = int(f.read())

        count = int(count) + 1

    if cfg.TRAIN.NET_D != '':
        for i in range(len(netsD)):
            print('Load %s_%d.pth' % (cfg.TRAIN.NET_D, i))
            state_dict = torch.load('%s%d.pth' % (cfg.TRAIN.NET_D, i))
            netsD[i].load_state_dict(state_dict)

    inception_model = INCEPTION_V3()

    if cfg.CUDA:
        netG.cuda()
        for i in range(len(netsD)):
            netsD[i].cuda()
        inception_model = inception_model.cuda()
    inception_model.eval()

    return netG, netsD, len(netsD), inception_model, count

開發者ID:netanelyo，項目名稱:Recipe2ImageGAN，代碼行數:61，代碼來源:trainer.py

注：本文中的model.D_NET128屬性示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。