python类Deconvolution2D()的实例源码

net.py 文件源码 项目:chainer-fast-neuralstyle 作者: yusuketomoto 项目源码 文件源码 阅读 35 收藏 0 点赞 0 评论 0
def __init__(self):
        super(FastStyleNet, self).__init__(
            c1=L.Convolution2D(3, 32, 9, stride=1, pad=4),
            c2=L.Convolution2D(32, 64, 4, stride=2, pad=1),
            c3=L.Convolution2D(64, 128, 4,stride=2, pad=1),
            r1=ResidualBlock(128, 128),
            r2=ResidualBlock(128, 128),
            r3=ResidualBlock(128, 128),
            r4=ResidualBlock(128, 128),
            r5=ResidualBlock(128, 128),
            d1=L.Deconvolution2D(128, 64, 4, stride=2, pad=1),
            d2=L.Deconvolution2D(64, 32, 4, stride=2, pad=1),
            d3=L.Deconvolution2D(32, 3, 9, stride=1, pad=4),
            b1=L.BatchNormalization(32),
            b2=L.BatchNormalization(64),
            b3=L.BatchNormalization(128),
            b4=L.BatchNormalization(64),
            b5=L.BatchNormalization(32),
        )
net.py 文件源码 项目:tensorboard-pytorch 作者: lanpa 项目源码 文件源码 阅读 20 收藏 0 点赞 0 评论 0
def __init__(self, n_hidden, bottom_width=4, ch=512, wscale=0.02):
        super(Generator, self).__init__()
        self.n_hidden = n_hidden
        self.ch = ch
        self.bottom_width = bottom_width

        with self.init_scope():
            w = chainer.initializers.Normal(wscale)
            self.l0 = L.Linear(self.n_hidden, bottom_width * bottom_width * ch,
                               initialW=w)
            self.dc1 = L.Deconvolution2D(ch, ch // 2, 4, 2, 1, initialW=w)
            self.dc2 = L.Deconvolution2D(ch // 2, ch // 4, 4, 2, 1, initialW=w)
            self.dc3 = L.Deconvolution2D(ch // 4, ch // 8, 4, 2, 1, initialW=w)
            self.dc4 = L.Deconvolution2D(ch // 8, 3, 3, 1, 1, initialW=w)
            self.bn0 = L.BatchNormalization(bottom_width * bottom_width * ch)
            self.bn1 = L.BatchNormalization(ch // 2)
            self.bn2 = L.BatchNormalization(ch // 4)
            self.bn3 = L.BatchNormalization(ch // 8)
test_deconvolution_2d.py 文件源码 项目:chainer-deconv 作者: germanRos 项目源码 文件源码 阅读 19 收藏 0 点赞 0 评论 0
def setUp(self):
        self.link = L.Deconvolution2D(
            self.in_channels, self.out_channels, self.ksize,
            stride=self.stride, pad=self.pad, nobias=self.nobias)
        self.link.W.data[...] = numpy.random.uniform(
            -1, 1, self.link.W.data.shape).astype(numpy.float32)
        if not self.nobias:
            self.link.b.data[...] = numpy.random.uniform(
                -1, 1, self.link.b.data.shape).astype(numpy.float32)

        self.link.zerograds()

        N = 2
        h, w = 3, 2
        kh, kw = _pair(self.ksize)
        out_h = conv.get_deconv_outsize(h, kh, self.stride, self.pad)
        out_w = conv.get_deconv_outsize(w, kw, self.stride, self.pad)
        self.gy = numpy.random.uniform(
            -1, 1, (N, self.out_channels, out_h, out_w)).astype(numpy.float32)
        self.x = numpy.random.uniform(
            -1, 1, (N, self.in_channels, h, w)).astype(numpy.float32)
net.py 文件源码 项目:chainermn 作者: chainer 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
def __init__(self, n_hidden, bottom_width=4, ch=512, wscale=0.02):
        super(Generator, self).__init__()
        self.n_hidden = n_hidden
        self.ch = ch
        self.bottom_width = bottom_width

        with self.init_scope():
            w = chainer.initializers.Normal(wscale)
            self.l0 = L.Linear(self.n_hidden, bottom_width * bottom_width * ch,
                               initialW=w)
            self.dc1 = L.Deconvolution2D(ch, ch // 2, 4, 2, 1, initialW=w)
            self.dc2 = L.Deconvolution2D(ch // 2, ch // 4, 4, 2, 1, initialW=w)
            self.dc3 = L.Deconvolution2D(ch // 4, ch // 8, 4, 2, 1, initialW=w)
            self.dc4 = L.Deconvolution2D(ch // 8, 3, 3, 1, 1, initialW=w)
            self.bn0 = L.BatchNormalization(bottom_width * bottom_width * ch)
            self.bn1 = L.BatchNormalization(ch // 2)
            self.bn2 = L.BatchNormalization(ch // 4)
            self.bn3 = L.BatchNormalization(ch // 8)
net.py 文件源码 项目:chainer-examples 作者: nocotan 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def __init__(self, n_hidden=128, bottom_width=4, ch=512, wscale=0.02):
        super(Generator, self).__init__()
        self.n_hidden = n_hidden
        self.ch = ch
        self.bottom_width = bottom_width

        with self.init_scope():
            w = chainer.initializers.Normal(wscale)
            self.l0 = L.Linear(self.n_hidden, bottom_width*bottom_width*ch,
                               initialW=w)
            self.dc1 = L.Deconvolution2D(ch, ch//2, 4, 2, 1, initialW=w)
            self.dc2 = L.Deconvolution2D(ch//2, ch//4, 4, 2, 1, initialW=w)
            self.dc3 = L.Deconvolution2D(ch//4, ch//8, 4, 2, 1, initialW=w)
            self.dc4 = L.Deconvolution2D(ch//8, 3, 3, 1, 1, initialW=w)
            self.bn0 = L.BatchNormalization(bottom_width*bottom_width*ch)
            self.bn1 = L.BatchNormalization(ch//2)
            self.bn2 = L.BatchNormalization(ch//4)
            self.bn3 = L.BatchNormalization(ch//8)
net.py 文件源码 项目:chainer-examples 作者: nocotan 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def __init__(self, n_hidden=128, bottom_width=4, ch=512, wscale=0.02):
        super(Generator, self).__init__()
        self.n_hidden = n_hidden
        self.ch = ch
        self.bottom_width = bottom_width

        with self.init_scope():
            w = chainer.initializers.Normal(wscale)
            self.l0 = L.Linear(self.n_hidden, bottom_width*bottom_width*ch,
                               initialW=w)
            self.dc1 = L.Deconvolution2D(ch, ch//2, 4, 2, 1, initialW=w)
            self.dc2 = L.Deconvolution2D(ch//2, ch//4, 4, 2, 1, initialW=w)
            self.dc3 = L.Deconvolution2D(ch//4, ch//8, 4, 2, 1, initialW=w)
            self.dc4 = L.Deconvolution2D(ch//8, 3, 3, 1, 1, initialW=w)
            self.bn0 = L.BatchNormalization(bottom_width*bottom_width*ch)
            self.bn1 = L.BatchNormalization(ch//2)
            self.bn2 = L.BatchNormalization(ch//4)
            self.bn3 = L.BatchNormalization(ch//8)
wgan.py 文件源码 项目:chainer-image-generation 作者: fukuta0614 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
def __init__(self, n_hidden, activate='sigmoid', size=64, ch=512, wscale=0.02):
        assert (size % 16 == 0)
        initial_size = size // 16
        self.n_hidden = n_hidden
        if activate == 'sigmoid':
            self.activate = F.sigmoid
        elif activate == 'tanh':
            self.activate = F.tanh
        else:
            raise ValueError('invalid activate function')
        self.ch = ch
        self.initial_size = initial_size
        w = chainer.initializers.Normal(wscale)
        super(Generator, self).__init__(
            l0=L.Linear(self.n_hidden, initial_size * initial_size * ch, initialW=w),
            dc1=L.Deconvolution2D(ch // 1, ch // 2, 4, 2, 1, initialW=w),
            dc2=L.Deconvolution2D(ch // 2, ch // 4, 4, 2, 1, initialW=w),
            dc3=L.Deconvolution2D(ch // 4, ch // 8, 4, 2, 1, initialW=w),
            dc4=L.Deconvolution2D(ch // 8, 3, 4, 2, 1, initialW=w),
            bn0=L.BatchNormalization(initial_size * initial_size * ch),
            bn1=L.BatchNormalization(ch // 2),
            bn2=L.BatchNormalization(ch // 4),
            bn3=L.BatchNormalization(ch // 8),
        )
wgan.py 文件源码 项目:chainer-image-generation 作者: fukuta0614 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
def __init__(self, n_hidden, activate='sigmoid', size=64, ch=512, wscale=0.02):
        assert (size % 8 == 0)
        initial_size = size // 8
        self.n_hidden = n_hidden
        self.ch = ch
        self.initial_size = initial_size
        if activate == 'sigmoid':
            self.activate = F.sigmoid
        elif activate == 'tanh':
            self.activate = F.tanh
        else:
            raise ValueError('invalid activate function')
        w = chainer.initializers.Normal(wscale)
        super(Generator2, self).__init__(
            l0=L.Linear(self.n_hidden, initial_size * initial_size * ch, initialW=w),
            dc1=L.Deconvolution2D(ch // 1, ch // 2, 4, 2, 1, initialW=w),
            dc2=L.Deconvolution2D(ch // 2, ch // 4, 4, 2, 1, initialW=w),
            dc3=L.Deconvolution2D(ch // 4, ch // 8, 4, 2, 1, initialW=w),
            dc4=L.Deconvolution2D(ch // 8, 3, 3, 1, 1, initialW=w),
            bn0=L.BatchNormalization(initial_size * initial_size * ch),
            bn1=L.BatchNormalization(ch // 2),
            bn2=L.BatchNormalization(ch // 4),
            bn3=L.BatchNormalization(ch // 8),
        )
iwgan.py 文件源码 项目:chainer-image-generation 作者: fukuta0614 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def __init__(self, n_hidden, activate='sigmoid', size=64, ch=512, wscale=0.02):
        assert (size % 8 == 0)
        initial_size = size // 8
        self.n_hidden = n_hidden
        if activate == 'sigmoid':
            self.activate = F.sigmoid
        elif activate == 'tanh':
            self.activate = F.tanh
        else:
            raise ValueError('invalid activate function')
        self.ch = ch
        self.initial_size = initial_size
        w = chainer.initializers.Normal(wscale)
        super(Generator, self).__init__(
            l0=L.Linear(self.n_hidden, initial_size * initial_size * ch, initialW=w),
            dc1=L.Deconvolution2D(ch // 1, ch // 2, 4, 2, 1, initialW=w),
            dc2=L.Deconvolution2D(ch // 2, ch // 4, 4, 2, 1, initialW=w),
            dc3=L.Deconvolution2D(ch // 4, ch // 8, 4, 2, 1, initialW=w),
            dc4=L.Deconvolution2D(ch // 8, 3, 3, 1, 1, initialW=w),
        )
vaegan.py 文件源码 项目:chainer-image-generation 作者: fukuta0614 项目源码 文件源码 阅读 32 收藏 0 点赞 0 评论 0
def __init__(self, density=1, size=64, latent_size=128, channel=3):
        assert (size % 16 == 0)
        initial_size = size / 16
        super(Generator, self).__init__(
            g1=L.Linear(latent_size, initial_size * initial_size * 256 * density, wscale=0.02 * math.sqrt(latent_size)),
            norm1=L.BatchNormalization(initial_size * initial_size * 256 * density),
            g2=L.Deconvolution2D(256 * density, 128 * density, 4, stride=2, pad=1,
                                 wscale=0.02 * math.sqrt(4 * 4 * 256 * density)),
            norm2=L.BatchNormalization(128 * density),
            g3=L.Deconvolution2D(128 * density, 64 * density, 4, stride=2, pad=1,
                                 wscale=0.02 * math.sqrt(4 * 4 * 128 * density)),
            norm3=L.BatchNormalization(64 * density),
            g4=L.Deconvolution2D(64 * density, 32 * density, 4, stride=2, pad=1,
                                 wscale=0.02 * math.sqrt(4 * 4 * 64 * density)),
            norm4=L.BatchNormalization(32 * density),
            g5=L.Deconvolution2D(32 * density, channel, 4, stride=2, pad=1,
                                 wscale=0.02 * math.sqrt(4 * 4 * 32 * density)),
        )
        self.density = density
        self.latent_size = latent_size
        self.initial_size = initial_size
vaegan.py 文件源码 项目:chainer-image-generation 作者: fukuta0614 项目源码 文件源码 阅读 41 收藏 0 点赞 0 评论 0
def __init__(self, density=1, size=64, latent_size=100, channel=3):
        assert (size % 16 == 0)
        initial_size = size / 16
        super(Generator_origin, self).__init__(
            g1=L.Linear(latent_size, initial_size * initial_size * 256 * density, wscale=0.02 * math.sqrt(latent_size)),
            norm1=L.BatchNormalization(initial_size * initial_size * 256 * density),
            g2=L.Deconvolution2D(256 * density, 128 * density, 4, stride=2, pad=1,
                                 wscale=0.02 * math.sqrt(4 * 4 * 256 * density)),
            norm2=L.BatchNormalization(128 * density),
            g3=L.Deconvolution2D(128 * density, 64 * density, 4, stride=2, pad=1,
                                 wscale=0.02 * math.sqrt(4 * 4 * 128 * density)),
            norm3=L.BatchNormalization(64 * density),
            g4=L.Deconvolution2D(64 * density, 32 * density, 4, stride=2, pad=1,
                                 wscale=0.02 * math.sqrt(4 * 4 * 64 * density)),
            norm4=L.BatchNormalization(32 * density),
            g5=L.Deconvolution2D(32 * density, channel, 4, stride=2, pad=1,
                                 wscale=0.02 * math.sqrt(4 * 4 * 32 * density)),
        )
        self.density = density
        self.latent_size = latent_size
        self.initial_size = initial_size
model.py 文件源码 项目:chainer-image-generation 作者: fukuta0614 项目源码 文件源码 阅读 20 收藏 0 点赞 0 评论 0
def __init__(self, ch0, ch1, bn=True, sample='down', activation=F.relu, dropout=False, noise=False):
        self.bn = bn
        self.activation = activation
        self.dropout = dropout
        self.noise = noise
        layers = {}
        w = chainer.initializers.Normal(0.02)
        if sample == 'down':
            layers['c'] = L.Convolution2D(ch0, ch1, 4, 2, 1, initialW=w)
        elif sample == 'up':
            layers['c'] = L.Deconvolution2D(ch0, ch1, 4, 2, 1, initialW=w)
        elif sample == 'c7s1':
            layers['c'] = L.Convolution2D(ch0, ch1, 7, 1, 3, initialW=w)
        if bn:
            if self.noise:
                layers['batchnorm'] = L.BatchNormalization(ch1, use_gamma=False)
            else:
                layers['batchnorm'] = L.BatchNormalization(ch1)
        super(CBR, self).__init__(**layers)
model.py 文件源码 项目:chainer-image-generation 作者: fukuta0614 项目源码 文件源码 阅读 18 收藏 0 点赞 0 评论 0
def __init__(self, ch=128, wscale=0.02):
        w = chainer.initializers.Normal(wscale)
        super(GeneratorOld, self).__init__(
            conv1=L.Convolution2D(3, ch // 4, 5, 1, 2, initialW=w),
            conv2=L.Convolution2D(ch // 4, ch // 2, 3, 2, 1, initialW=w),
            conv3=L.Convolution2D(ch // 2, ch, 3, 2, 1, initialW=w),
            res1=ResBlock(ch, ch, bn=False),
            res2=ResBlock(ch, ch, bn=False),
            res3=ResBlock(ch, ch, bn=False),
            res4=ResBlock(ch, ch, bn=False),
            res5=ResBlock(ch, ch, bn=False),
            res6=ResBlock(ch, ch, bn=False),
            res7=ResBlock(ch, ch, bn=False),
            res8=ResBlock(ch, ch, bn=False),
            res9=ResBlock(ch, ch, bn=False),
            dc1=L.Deconvolution2D(ch, ch // 2, 4, 2, 1, initialW=w),
            dc2=L.Deconvolution2D(ch // 2, ch // 4, 4, 2, 1, initialW=w),
            dc3=L.Convolution2D(ch // 4, 3, 5, 1, 2, initialW=w),
        )

    # noinspection PyCallingNonCallable,PyUnresolvedReferences
fcn32s.py 文件源码 项目:Semantic-Segmentation-using-Adversarial-Networks 作者: oyam 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def __init__(self, n_class=21):
        self.train=True
        super(FCN32s, self).__init__(
            conv1_1=L.Convolution2D(3, 64, 3, stride=1, pad=100),
            conv1_2=L.Convolution2D(64, 64, 3, stride=1, pad=1),
            conv2_1=L.Convolution2D(64, 128, 3, stride=1, pad=1),
            conv2_2=L.Convolution2D(128, 128, 3, stride=1, pad=1),
            conv3_1=L.Convolution2D(128, 256, 3, stride=1, pad=1),
            conv3_2=L.Convolution2D(256, 256, 3, stride=1, pad=1),
            conv3_3=L.Convolution2D(256, 256, 3, stride=1, pad=1),
            conv4_1=L.Convolution2D(256, 512, 3, stride=1, pad=1),
            conv4_2=L.Convolution2D(512, 512, 3, stride=1, pad=1),
            conv4_3=L.Convolution2D(512, 512, 3, stride=1, pad=1),
            conv5_1=L.Convolution2D(512, 512, 3, stride=1, pad=1),
            conv5_2=L.Convolution2D(512, 512, 3, stride=1, pad=1),
            conv5_3=L.Convolution2D(512, 512, 3, stride=1, pad=1),
            fc6=L.Convolution2D(512, 4096, 7, stride=1, pad=0),
            fc7=L.Convolution2D(4096, 4096, 1, stride=1, pad=0),
            score_fr=L.Convolution2D(4096, n_class, 1, stride=1, pad=0,
                nobias=True, initialW=np.zeros((n_class, 4096, 1, 1))),
            upscore=L.Deconvolution2D(n_class, n_class, 64, stride=32, pad=0,
                nobias=True, initialW=f.bilinear_interpolation_kernel(n_class, n_class, ksize=64)),
        )
net.py 文件源码 项目:cv-api 作者: yasunorikudo 项目源码 文件源码 阅读 19 收藏 0 点赞 0 评论 0
def __init__(self):
        super(FastStyleNet, self).__init__(
            c1=L.Convolution2D(3, 32, 9, stride=1, pad=4),
            c2=L.Convolution2D(32, 64, 4, stride=2, pad=1),
            c3=L.Convolution2D(64, 128, 4,stride=2, pad=1),
            r1=ResidualBlock(128, 128),
            r2=ResidualBlock(128, 128),
            r3=ResidualBlock(128, 128),
            r4=ResidualBlock(128, 128),
            r5=ResidualBlock(128, 128),
            d1=L.Deconvolution2D(128, 64, 4, stride=2, pad=1),
            d2=L.Deconvolution2D(64, 32, 4, stride=2, pad=1),
            d3=L.Deconvolution2D(32, 3, 9, stride=1, pad=4),
            b1=L.BatchNormalization(32),
            b2=L.BatchNormalization(64),
            b3=L.BatchNormalization(128),
            b4=L.BatchNormalization(64),
            b5=L.BatchNormalization(32),
        )
net.py 文件源码 项目:GAN 作者: lyakaap 项目源码 文件源码 阅读 17 收藏 0 点赞 0 评论 0
def __init__(self, n_hidden, bottom_width=4, ch=512, wscale=0.02):
        super(Generator, self).__init__()
        self.n_hidden = n_hidden
        self.ch = ch
        self.bottom_width = bottom_width

        with self.init_scope():
            w = chainer.initializers.Normal(wscale)
            self.l0 = L.Linear(self.n_hidden, bottom_width * bottom_width * ch,
                               initialW=w)
            self.dc1 = L.Deconvolution2D(ch, ch // 2, 4, 2, 1, initialW=w)
            self.dc2 = L.Deconvolution2D(ch // 2, ch // 4, 4, 2, 1, initialW=w)
            self.dc3 = L.Deconvolution2D(ch // 4, ch // 8, 4, 2, 1, initialW=w)
            self.dc4 = L.Deconvolution2D(ch // 8, 3, 3, 1, 1, initialW=w)
            self.bn0 = L.BatchNormalization(bottom_width * bottom_width * ch)
            self.bn1 = L.BatchNormalization(ch // 2)
            self.bn2 = L.BatchNormalization(ch // 4)
            self.bn3 = L.BatchNormalization(ch // 8)
vgg.py 文件源码 项目:chainer-visualization 作者: hvy 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
def check_add_deconv_layers(self, nobias=True):

        """Add a deconvolutional layer for each convolutional layer already
        defined in the network."""

        if len(self.deconv_blocks) == len(self.conv_blocks):
            return

        for conv_block in self.conv_blocks:
            deconv_block = []
            for conv in conv_block:
                out_channels, in_channels, kh, kw = conv.W.data.shape

                if isinstance(conv.W.data, cuda.ndarray):
                    initialW = cuda.cupy.asnumpy(conv.W.data)
                else:
                    initialW = conv.W.data

                deconv = L.Deconvolution2D(out_channels, in_channels,
                                           (kh, kw), stride=conv.stride,
                                           pad=conv.pad,
                                           initialW=initialW,
                                           nobias=nobias)

                if isinstance(conv.W.data, cuda.ndarray):
                    deconv.to_gpu()

                self.add_link('de{}'.format(conv.name), deconv)
                deconv_block.append(deconv)

            self.deconv_blocks.append(deconv_block)
fcn16s.py 文件源码 项目:fcn 作者: wkentaro 项目源码 文件源码 阅读 20 收藏 0 点赞 0 评论 0
def __init__(self, n_class=21):
        self.n_class = n_class
        kwargs = {
            'initialW': chainer.initializers.Zero(),
            'initial_bias': chainer.initializers.Zero(),
        }
        super(FCN16s, self).__init__()
        with self.init_scope():
            self.conv1_1 = L.Convolution2D(3, 64, 3, 1, 100, **kwargs)
            self.conv1_2 = L.Convolution2D(64, 64, 3, 1, 1, **kwargs)

            self.conv2_1 = L.Convolution2D(64, 128, 3, 1, 1, **kwargs)
            self.conv2_2 = L.Convolution2D(128, 128, 3, 1, 1, **kwargs)

            self.conv3_1 = L.Convolution2D(128, 256, 3, 1, 1, **kwargs)
            self.conv3_2 = L.Convolution2D(256, 256, 3, 1, 1, **kwargs)
            self.conv3_3 = L.Convolution2D(256, 256, 3, 1, 1, **kwargs)

            self.conv4_1 = L.Convolution2D(256, 512, 3, 1, 1, **kwargs)
            self.conv4_2 = L.Convolution2D(512, 512, 3, 1, 1, **kwargs)
            self.conv4_3 = L.Convolution2D(512, 512, 3, 1, 1, **kwargs)

            self.conv5_1 = L.Convolution2D(512, 512, 3, 1, 1, **kwargs)
            self.conv5_2 = L.Convolution2D(512, 512, 3, 1, 1, **kwargs)
            self.conv5_3 = L.Convolution2D(512, 512, 3, 1, 1, **kwargs)

            self.fc6 = L.Convolution2D(512, 4096, 7, 1, 0, **kwargs)
            self.fc7 = L.Convolution2D(4096, 4096, 1, 1, 0, **kwargs)

            self.score_fr = L.Convolution2D(4096, n_class, 1, 1, 0, **kwargs)
            self.score_pool4 = L.Convolution2D(512, n_class, 1, 1, 0, **kwargs)

            self.upscore2 = L.Deconvolution2D(
                n_class, n_class, 4, 2, nobias=True,
                initialW=initializers.UpsamplingDeconvWeight())
            self.upscore16 = L.Deconvolution2D(
                n_class, n_class, 32, 16, nobias=True,
                initialW=initializers.UpsamplingDeconvWeight())
fcn32s.py 文件源码 项目:fcn 作者: wkentaro 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def __init__(self, n_class=21):
        self.n_class = n_class
        kwargs = {
            'initialW': chainer.initializers.Zero(),
            'initial_bias': chainer.initializers.Zero(),
        }
        super(FCN32s, self).__init__()
        with self.init_scope():
            self.conv1_1 = L.Convolution2D(3, 64, 3, 1, 100, **kwargs)
            self.conv1_2 = L.Convolution2D(64, 64, 3, 1, 1, **kwargs)

            self.conv2_1 = L.Convolution2D(64, 128, 3, 1, 1, **kwargs)
            self.conv2_2 = L.Convolution2D(128, 128, 3, 1, 1, **kwargs)

            self.conv3_1 = L.Convolution2D(128, 256, 3, 1, 1, **kwargs)
            self.conv3_2 = L.Convolution2D(256, 256, 3, 1, 1, **kwargs)
            self.conv3_3 = L.Convolution2D(256, 256, 3, 1, 1, **kwargs)

            self.conv4_1 = L.Convolution2D(256, 512, 3, 1, 1, **kwargs)
            self.conv4_2 = L.Convolution2D(512, 512, 3, 1, 1, **kwargs)
            self.conv4_3 = L.Convolution2D(512, 512, 3, 1, 1, **kwargs)

            self.conv5_1 = L.Convolution2D(512, 512, 3, 1, 1, **kwargs)
            self.conv5_2 = L.Convolution2D(512, 512, 3, 1, 1, **kwargs)
            self.conv5_3 = L.Convolution2D(512, 512, 3, 1, 1, **kwargs)

            self.fc6 = L.Convolution2D(512, 4096, 7, 1, 0, **kwargs)
            self.fc7 = L.Convolution2D(4096, 4096, 1, 1, 0, **kwargs)

            self.score_fr = L.Convolution2D(4096, n_class, 1, 1, 0, **kwargs)

            self.upscore = L.Deconvolution2D(
                n_class, n_class, 64, 32, 0, nobias=True,
                initialW=initializers.UpsamplingDeconvWeight())
net.py 文件源码 项目:chainer-stack-gan 作者: dsanno 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def __init__(self):
        initialW = chainer.initializers.Normal(0.02)
        super(Generator1, self).__init__(
            conv1=L.Deconvolution2D(100, 1024, 4, initialW=initialW),
            bn1=L.BatchNormalization(1024),
            up=UpSampling(4, 1024, 64),
        )
myfcn.py 文件源码 项目:Human-Pose-Estimation-Using-FCN 作者: jessiechouuu 项目源码 文件源码 阅读 20 收藏 0 点赞 0 评论 0
def __init__(self):
        super(MyFcn, self).__init__(
            conv1_1=L.Convolution2D(  3,  64, 3, stride=1, pad=1),
            conv1_2=L.Convolution2D( 64,  64, 3, stride=1, pad=1),

            conv2_1=L.Convolution2D( 64, 128, 3, stride=1, pad=1),
            conv2_2=L.Convolution2D(128, 128, 3, stride=1, pad=1),

            conv3_1=L.Convolution2D(128, 256, 3, stride=1, pad=1),
            conv3_2=L.Convolution2D(256, 256, 3, stride=1, pad=1),
            conv3_3=L.Convolution2D(256, 256, 3, stride=1, pad=1),

            conv4_1=L.Convolution2D(256, 512, 3, stride=1, pad=1),
            conv4_2=L.Convolution2D(512, 512, 3, stride=1, pad=1),
            conv4_3=L.Convolution2D(512, 512, 3, stride=1, pad=1),

            conv5_1=L.Convolution2D(512, 512, 3, stride=1, pad=1),
            conv5_2=L.Convolution2D(512, 512, 3, stride=1, pad=1),
            conv5_3=L.Convolution2D(512, 512, 3, stride=1, pad=1),

            score_pool3=L.Convolution2D(256, MyFcn.CLASSES, 1, stride=1, pad=0),
            score_pool4=L.Convolution2D(512, MyFcn.CLASSES, 1, stride=1, pad=0),
            score_pool5=L.Convolution2D(512, MyFcn.CLASSES, 1, stride=1, pad=0),

            upsample_pool4=L.Deconvolution2D(MyFcn.CLASSES, MyFcn.CLASSES, ksize= 4, stride=2, pad=1),
            upsample_pool5=L.Deconvolution2D(MyFcn.CLASSES, MyFcn.CLASSES, ksize= 8, stride=4, pad=2),
            upsample_final=L.Deconvolution2D(MyFcn.CLASSES, MyFcn.CLASSES, ksize=16, stride=8, pad=4),
        )
        self.train = True
models.py 文件源码 项目:chainer-wasserstein-gan 作者: hvy 项目源码 文件源码 阅读 19 收藏 0 点赞 0 评论 0
def __init__(self):
        super().__init__(
            dc1=L.Deconvolution2D(None, 256, 4, stride=1, pad=0, nobias=True),
            dc2=L.Deconvolution2D(256, 128, 4, stride=2, pad=1, nobias=True),
            dc3=L.Deconvolution2D(128, 64, 4, stride=2, pad=1, nobias=True),
            dc4=L.Deconvolution2D(64, 3, 4, stride=2, pad=1, nobias=True),
            bn_dc1=L.BatchNormalization(256),
            bn_dc2=L.BatchNormalization(128),
            bn_dc3=L.BatchNormalization(64)
        )
models.py 文件源码 项目:chainer-LSGAN 作者: pfnet-research 项目源码 文件源码 阅读 19 收藏 0 点赞 0 评论 0
def __init__(self, size=None):

        super().__init__(
            dc1=L.Deconvolution2D(None, 256, 4, stride=1, pad=0, nobias=True),
            dc2=L.Deconvolution2D(256, 128, 4, stride=2, pad=1, nobias=True),
            dc3=L.Deconvolution2D(128, 64, 4, stride=2, pad=1, nobias=True),
            dc4=L.Deconvolution2D(64, 3, 4, stride=2, pad=1, nobias=True),
            bn_dc1=L.BatchNormalization(256),
            bn_dc2=L.BatchNormalization(128),
            bn_dc3=L.BatchNormalization(64)
        )
models.py 文件源码 项目:chainer-LSGAN 作者: pfnet-research 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def __init__(self, size=None):

        super().__init__(
            dc1=L.Deconvolution2D(None, 256, 4, stride=1, pad=0, nobias=True),
            dc2=L.Deconvolution2D(256, 128, 4, stride=2, pad=1, nobias=True),
            dc3=L.Deconvolution2D(128, 64, 4, stride=2, pad=2, nobias=True),
            dc4=L.Deconvolution2D(64, 1, 4, stride=2, pad=1, nobias=True),
            bn_dc1=L.BatchNormalization(256),
            bn_dc2=L.BatchNormalization(128),
            bn_dc3=L.BatchNormalization(64)
        )
model.py 文件源码 项目:chainer-pix2pix 作者: wuhuikai 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def __init__(self, feature_map_nc, output_nc, w_init=None):
        super(Generator, self).__init__(
            c1=L.Convolution2D(None, feature_map_nc, ksize=4, stride=2, pad=1, initialW=w_init),
            c2=L.Convolution2D(None, 2*feature_map_nc, ksize=4, stride=2, pad=1, initialW=w_init),
            c3=L.Convolution2D(None, 4*feature_map_nc, ksize=4, stride=2, pad=1, initialW=w_init),
            c4=L.Convolution2D(None, 8*feature_map_nc, ksize=4, stride=2, pad=1, initialW=w_init),
            c5=L.Convolution2D(None, 8*feature_map_nc, ksize=4, stride=2, pad=1, initialW=w_init),
            c6=L.Convolution2D(None, 8*feature_map_nc, ksize=4, stride=2, pad=1, initialW=w_init),
            c7=L.Convolution2D(None, 8*feature_map_nc, ksize=4, stride=2, pad=1, initialW=w_init),
            c8=L.Convolution2D(None, 8*feature_map_nc, ksize=4, stride=2, pad=1, initialW=w_init),
            dc1=L.Deconvolution2D(None, 8*feature_map_nc, ksize=4, stride=2, pad=1, initialW=w_init),
            dc2=L.Deconvolution2D(None, 8*feature_map_nc, ksize=4, stride=2, pad=1, initialW=w_init),
            dc3=L.Deconvolution2D(None, 8*feature_map_nc, ksize=4, stride=2, pad=1, initialW=w_init),
            dc4=L.Deconvolution2D(None, 8*feature_map_nc, ksize=4, stride=2, pad=1, initialW=w_init),
            dc5=L.Deconvolution2D(None, 4*feature_map_nc, ksize=4, stride=2, pad=1, initialW=w_init),
            dc6=L.Deconvolution2D(None, 2*feature_map_nc, ksize=4, stride=2, pad=1, initialW=w_init),
            dc7=L.Deconvolution2D(None, feature_map_nc, ksize=4, stride=2, pad=1, initialW=w_init),
            dc8=L.Deconvolution2D(None, output_nc, ksize=4, stride=2, pad=1, initialW=w_init),
            b2=L.BatchNormalization(2*feature_map_nc),
            b3=L.BatchNormalization(4*feature_map_nc),
            b4=L.BatchNormalization(8*feature_map_nc),
            b5=L.BatchNormalization(8*feature_map_nc),
            b6=L.BatchNormalization(8*feature_map_nc),
            b7=L.BatchNormalization(8*feature_map_nc),
            b8=L.BatchNormalization(8*feature_map_nc),
            b1_d=L.BatchNormalization(8*feature_map_nc),
            b2_d=L.BatchNormalization(8*feature_map_nc),
            b3_d=L.BatchNormalization(8*feature_map_nc),
            b4_d=L.BatchNormalization(8*feature_map_nc),
            b5_d=L.BatchNormalization(4*feature_map_nc),
            b6_d=L.BatchNormalization(2*feature_map_nc),
            b7_d=L.BatchNormalization(feature_map_nc)
        )
net.py 文件源码 项目:chainer-pix2pix 作者: pfnet-research 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def __init__(self, ch0, ch1, bn=True, sample='down', activation=F.relu, dropout=False):
        self.bn = bn
        self.activation = activation
        self.dropout = dropout
        layers = {}
        w = chainer.initializers.Normal(0.02)
        if sample=='down':
            layers['c'] = L.Convolution2D(ch0, ch1, 4, 2, 1, initialW=w)
        else:
            layers['c'] = L.Deconvolution2D(ch0, ch1, 4, 2, 1, initialW=w)
        if bn:
            layers['batchnorm'] = L.BatchNormalization(ch1)
        super(CBR, self).__init__(**layers)
lib.py 文件源码 项目:alphabetic 作者: hitokun-s 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def __init__(self, nz=30):
        super(Generator, self).__init__(
                l0z=L.Linear(nz, 6 * 6 * 128, wscale=0.02 * math.sqrt(nz)),
                dc1=L.Deconvolution2D(128, 64, 4, stride=2, pad=1, wscale=0.02 * math.sqrt(4 * 4 * 128)),
                dc2=L.Deconvolution2D(64, 32, 4, stride=2, pad=1, wscale=0.02 * math.sqrt(4 * 4 * 64)),
                dc3=L.Deconvolution2D(32, 1, 4, stride=2, pad=1, wscale=0.02 * math.sqrt(4 * 4 * 32)),
                bn0l=L.BatchNormalization(6 * 6 * 128),
                bn0=L.BatchNormalization(128),
                bn1=L.BatchNormalization(64),
                bn2=L.BatchNormalization(32)
        )
net.py 文件源码 项目:chainer-examples 作者: nocotan 项目源码 文件源码 阅读 16 收藏 0 点赞 0 评论 0
def __init__(self, ch=512, wscale=0.02):
        w = chainer.initializers.Normal(wscale)
        self.ch = ch
        super(Discriminator, self).__init__()
        with self.init_scope():
            self.c0 = L.Convolution2D(3, ch // 8, 3, 1, 1, initialW=w)
            self.c1 = L.Convolution2D(ch // 8, ch // 4, 4, 2, 1, initialW=w)
            self.c2 = L.Convolution2D(ch // 4, ch // 2, 4, 2, 1, initialW=w)
            self.c3 = L.Convolution2D(ch // 2, ch // 1, 4, 2, 1, initialW=w)
            self.l4 = L.Linear(4*4*ch, 128, initialW=w)
            self.l5 = L.Linear(128, 4*4*ch, initialW=w)
            self.dc3 = L.Deconvolution2D(ch // 1, ch // 2, 4, 2, 1, initialW=w)
            self.dc2 = L.Deconvolution2D(ch // 2, ch // 4, 4, 2, 1, initialW=w)
            self.dc1 = L.Deconvolution2D(ch // 4, ch // 8, 4, 2, 1, initialW=w)
            self.dc0 = L.Deconvolution2D(ch // 8, 3, 3, 1, 1, initialW=w)
mnist_dcgan.py 文件源码 项目:chainer-examples 作者: nocotan 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
def __init__(self, z_dim):
        super(Generator, self).__init__(
            l1=L.Deconvolution2D(z_dim, 128, 3, 2, 0),
            bn1=L.BatchNormalization(128),
            l2=L.Deconvolution2D(128, 128, 3, 2, 1),
            bn2=L.BatchNormalization(128),
            l3=L.Deconvolution2D(128, 128, 3, 2, 1),
            bn3=L.BatchNormalization(128),
            l4=L.Deconvolution2D(128, 128, 3, 2, 2),
            bn4=L.BatchNormalization(128),
            l5=L.Deconvolution2D(128, 1, 3, 2, 2, outsize=(28, 28)),
        )
        self.train = True
models.py 文件源码 项目:pose2img 作者: Hi-king 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def __init__(self, ch0, ch1, bn=True, sample='down', activation=F.relu, dropout=False):
        self.bn = bn
        self.activation = activation
        self.dropout = dropout
        layers = {}
        w = chainer.initializers.Normal(0.02)
        if sample == 'down':
            layers['c'] = L.Convolution2D(ch0, ch1, 4, 2, 1, initialW=w)
        else:
            layers['c'] = L.Deconvolution2D(ch0, ch1, 4, 2, 1, initialW=w)
        if bn:
            layers['batchnorm'] = L.BatchNormalization(ch1)
        super(CBR, self).__init__(**layers)


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