Python common.MeanShift方法代碼示例

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

在下文中一共展示了common.MeanShift方法的9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: init

# 需要導入模塊: from model import common [as 別名]
# 或者: from model.common import MeanShift [as 別名]
def __init__(self, conv_index, rgb_range=1):
        super(VGG, self).__init__()
        vgg_features = models.vgg19(pretrained=True).features
        modules = [m for m in vgg_features]
        if conv_index == '22':
            self.vgg = nn.Sequential(*modules[:8])
        elif conv_index == '54':
            self.vgg = nn.Sequential(*modules[:35])

        vgg_mean = (0.485, 0.456, 0.406)
        vgg_std = (0.229 * rgb_range, 0.224 * rgb_range, 0.225 * rgb_range)
        self.sub_mean = common.MeanShift(rgb_range, vgg_mean, vgg_std)
        self.vgg.requires_grad = False

開發者ID:HolmesShuan，項目名稱:OISR-PyTorch，代碼行數:15，代碼來源:vgg.py

示例2: init

# 需要導入模塊: from model import common [as 別名]
# 或者: from model.common import MeanShift [as 別名]
def __init__(self, args, conv=common.default_conv):
        super(MDSR, self).__init__()
        n_resblocks = args.n_resblocks
        n_feats = args.n_feats
        kernel_size = 3
        act = nn.ReLU(True)
        self.scale_idx = 0
        self.url = url['r{}f{}'.format(n_resblocks, n_feats)]
        self.sub_mean = common.MeanShift(args.rgb_range)
        self.add_mean = common.MeanShift(args.rgb_range, sign=1)

        m_head = [conv(args.n_colors, n_feats, kernel_size)]

        self.pre_process = nn.ModuleList([
            nn.Sequential(
                common.ResBlock(conv, n_feats, 5, act=act),
                common.ResBlock(conv, n_feats, 5, act=act)
            ) for _ in args.scale
        ])

        m_body = [
            common.ResBlock(
                conv, n_feats, kernel_size, act=act
            ) for _ in range(n_resblocks)
        ]
        m_body.append(conv(n_feats, n_feats, kernel_size))

        self.upsample = nn.ModuleList([
            common.Upsampler(conv, s, n_feats, act=False) for s in args.scale
        ])

        m_tail = [conv(n_feats, args.n_colors, kernel_size)]

        self.head = nn.Sequential(*m_head)
        self.body = nn.Sequential(*m_body)
        self.tail = nn.Sequential(*m_tail)

開發者ID:HolmesShuan，項目名稱:OISR-PyTorch，代碼行數:38，代碼來源:mdsr.py

示例3: init

# 需要導入模塊: from model import common [as 別名]
# 或者: from model.common import MeanShift [as 別名]
def __init__(self, args, conv=common.default_conv):
        super(EDSR, self).__init__()

        n_resblocks = args.n_resblocks
        n_feats = args.n_feats
        kernel_size = 3 
        scale = args.scale[0]
        act = nn.ReLU(True)
        self.sub_mean = common.MeanShift(args.rgb_range)
        self.add_mean = common.MeanShift(args.rgb_range, sign=1)

        # define head module
        m_head = [conv(args.n_colors, n_feats, kernel_size)]

        # define body module
        m_body = [
            common.ResBlock(
                conv, n_feats, kernel_size, act=act, res_scale=args.res_scale
            ) for _ in range(n_resblocks)
        ]
        m_body.append(conv(n_feats, n_feats, kernel_size))

        # define tail module
        m_tail = [
            common.Upsampler(conv, scale, n_feats, act=False),
            conv(n_feats, args.n_colors, kernel_size)
        ]

        self.head = nn.Sequential(*m_head)
        self.body = nn.Sequential(*m_body)
        self.tail = nn.Sequential(*m_tail)

開發者ID:HolmesShuan，項目名稱:OISR-PyTorch，代碼行數:33，代碼來源:edsr.py

示例4: init

# 需要導入模塊: from model import common [as 別名]
# 或者: from model.common import MeanShift [as 別名]
def __init__(self, args, conv=common.default_conv):
        super(RCAN, self).__init__()
        
        n_resgroups = args.n_resgroups
        n_resblocks = args.n_resblocks
        n_feats = args.n_feats
        kernel_size = 3
        reduction = args.reduction 
        scale = args.scale[0]
        act = nn.ReLU(True)
        
        # RGB mean for DIV2K
        self.sub_mean = common.MeanShift(args.rgb_range)
        
        # define head module
        modules_head = [conv(args.n_colors, n_feats, kernel_size)]

        # define body module
        modules_body = [
            ResidualGroup(
                conv, n_feats, kernel_size, reduction, act=act, res_scale=args.res_scale, n_resblocks=n_resblocks) \
            for _ in range(n_resgroups)]

        modules_body.append(conv(n_feats, n_feats, kernel_size))

        # define tail module
        modules_tail = [
            common.Upsampler(conv, scale, n_feats, act=False),
            conv(n_feats, args.n_colors, kernel_size)]

        self.add_mean = common.MeanShift(args.rgb_range, sign=1)

        self.head = nn.Sequential(*modules_head)
        self.body = nn.Sequential(*modules_body)
        self.tail = nn.Sequential(*modules_tail)

開發者ID:HolmesShuan，項目名稱:OISR-PyTorch，代碼行數:37，代碼來源:rcan.py

示例5: init

# 需要導入模塊: from model import common [as 別名]
# 或者: from model.common import MeanShift [as 別名]
def __init__(self, args, conv=common.default_conv):
        super(EDSR, self).__init__()

        n_resblocks = args.n_resblocks
        n_feats = args.n_feats
        kernel_size = 3 
        scale = args.scale[0]
        act = nn.ReLU(True)
        # self.url = url['r{}f{}x{}'.format(n_resblocks, n_feats, scale)]
        self.sub_mean = common.MeanShift(args.rgb_range)
        self.add_mean = common.MeanShift(args.rgb_range, sign=1)

        # define head module
        m_head = [conv(args.n_colors, n_feats, kernel_size)]

        # define body module
        m_body = [
            common.ResBlock(
                conv, n_feats, kernel_size, act=act, res_scale=args.res_scale
            ) for _ in range(n_resblocks)
        ]
        m_body.append(conv(n_feats, n_feats, kernel_size))

        # define tail module
        m_tail = [
            common.Upsampler(conv, scale, n_feats, act=False),
            conv(n_feats, args.n_colors, kernel_size)
        ]

        self.head = nn.Sequential(*m_head)
        self.body = nn.Sequential(*m_body)
        self.tail = nn.Sequential(*m_tail)

開發者ID:HolmesShuan，項目名稱:OISR-PyTorch，代碼行數:34，代碼來源:edsr.py

示例6: init

# 需要導入模塊: from model import common [as 別名]
# 或者: from model.common import MeanShift [as 別名]
def __init__(self, conv_index, rgb_range=1):
        super(VGG, self).__init__()
        vgg_features = models.vgg19(pretrained=True).features
        modules = [m for m in vgg_features]
        if conv_index.find('22') >= 0:
            self.vgg = nn.Sequential(*modules[:8])
        elif conv_index.find('54') >= 0:
            self.vgg = nn.Sequential(*modules[:35])

        vgg_mean = (0.485, 0.456, 0.406)
        vgg_std = (0.229 * rgb_range, 0.224 * rgb_range, 0.225 * rgb_range)
        self.sub_mean = common.MeanShift(rgb_range, vgg_mean, vgg_std)
        for p in self.parameters():
            p.requires_grad = False

開發者ID:thstkdgus35，項目名稱:EDSR-PyTorch，代碼行數:16，代碼來源:vgg.py

示例7: init

# 需要導入模塊: from model import common [as 別名]
# 或者: from model.common import MeanShift [as 別名]
def __init__(self, args, conv=common.default_conv):
        super(EDSR, self).__init__()

        n_resblocks = args.n_resblocks
        n_feats = args.n_feats
        kernel_size = 3 
        scale = args.scale[0]
        act = nn.ReLU(True)
        url_name = 'r{}f{}x{}'.format(n_resblocks, n_feats, scale)
        if url_name in url:
            self.url = url[url_name]
        else:
            self.url = None
        self.sub_mean = common.MeanShift(args.rgb_range)
        self.add_mean = common.MeanShift(args.rgb_range, sign=1)

        # define head module
        m_head = [conv(args.n_colors, n_feats, kernel_size)]

        # define body module
        m_body = [
            common.ResBlock(
                conv, n_feats, kernel_size, act=act, res_scale=args.res_scale
            ) for _ in range(n_resblocks)
        ]
        m_body.append(conv(n_feats, n_feats, kernel_size))

        # define tail module
        m_tail = [
            common.Upsampler(conv, scale, n_feats, act=False),
            conv(n_feats, args.n_colors, kernel_size)
        ]

        self.head = nn.Sequential(*m_head)
        self.body = nn.Sequential(*m_body)
        self.tail = nn.Sequential(*m_tail)

開發者ID:thstkdgus35，項目名稱:EDSR-PyTorch，代碼行數:38，代碼來源:edsr.py

示例8: init

# 需要導入模塊: from model import common [as 別名]
# 或者: from model.common import MeanShift [as 別名]
def __init__(self, args, conv=common.default_conv):
        super(EDSR, self).__init__()

        n_resblock = args.n_resblocks
        n_feats = args.n_feats
        kernel_size = 3 
        scale = args.scale[0]
        act = nn.ReLU(True)

        rgb_mean = (0.4488, 0.4371, 0.4040)
        rgb_std = (1.0, 1.0, 1.0)
        self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std)
        
        # define head module
        m_head = [conv(args.n_colors, n_feats, kernel_size)]

        # define body module
        m_body = [
            common.ResBlock(
                conv, n_feats, kernel_size, act=act, res_scale=args.res_scale
            ) for _ in range(n_resblock)
        ]
        m_body.append(conv(n_feats, n_feats, kernel_size))

        # define tail module
        m_tail = [
            common.Upsampler(conv, scale, n_feats, act=False),
            conv(n_feats, args.n_colors, kernel_size)
        ]

        self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 1)

        self.head = nn.Sequential(*m_head)
        self.body = nn.Sequential(*m_body)
        self.tail = nn.Sequential(*m_tail)
        # from IPython import embed; embed(); exit()

開發者ID:ofsoundof，項目名稱:3D_Appearance_SR，代碼行數:38，代碼來源:edsr.py

示例9: init

# 需要導入模塊: from model import common [as 別名]
# 或者: from model.common import MeanShift [as 別名]
def __init__(self, args):
        super(CARN, self).__init__()

        #scale = kwargs.get("scale")
        #multi_scale = kwargs.get("multi_scale")
        #group = kwargs.get("group", 1)
        multi_scale = len(args.scale) > 1
        self.scale_idx = 0
        scale = args.scale[self.scale_idx]
        group = 1
        self.scale = args.scale
        rgb_mean = (0.4488, 0.4371, 0.4040)
        rgb_std = (1.0, 1.0, 1.0)
        self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std)
        self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 1)
        #self.sub_mean = ops.MeanShift((0.4488, 0.4371, 0.4040), sub=True)
        #self.add_mean = ops.MeanShift((0.4488, 0.4371, 0.4040), sub=False)

        self.entry = nn.Conv2d(3, 64, 3, 1, 1)

        self.b1 = Block(64, 64)
        self.b2 = Block(64, 64)
        self.b3 = Block(64, 64)
        self.c1 = ops.BasicBlock(64*2, 64, 1, 1, 0)
        self.c2 = ops.BasicBlock(64*3, 64, 1, 1, 0)
        self.c3 = ops.BasicBlock(64*4, 64, 1, 1, 0)

        self.upsample = ops.UpsampleBlock(64, scale=scale,
                                          multi_scale=multi_scale,
                                          group=group)
        self.exit = nn.Conv2d(64, 3, 3, 1, 1)

開發者ID:ofsoundof，項目名稱:3D_Appearance_SR，代碼行數:33，代碼來源:carn.py

注：本文中的model.common.MeanShift方法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。

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