Python chainer.functions 模块,unpooling_2d() 实例源码

我们从Python开源项目中,提取了以下24个代码示例,用于说明如何使用chainer.functions.unpooling_2d()

项目:chainer-cyclegan    作者:Aixile    | 项目源码 | 文件源码
def __call__(self, x, test):
        if self.sample=="down" or self.sample=="none" or self.sample=='none-9' or self.sample=='none-7' or self.sample=='none-5':
            h = self.c(x)
        elif self.sample=="up":
            h = F.unpooling_2d(x, 2, 2, 0, cover_all=False)
            h = self.c(h)
        else:
            print("unknown sample method %s"%self.sample)
        if self.bn:
            h = self.batchnorm(h, test=test)
        if self.noise:
            h = add_noise(h, test=test)
        if self.dropout:
            h = F.dropout(h, train=not test)
        if not self.activation is None:
            h = self.activation(h)
        return h
项目:chainer-gan-experiments    作者:Aixile    | 项目源码 | 文件源码
def _do_before_cal(self, x):
        if self.nn == 'up_unpooling':
            x = F.unpooling_2d(x, 2, 2, 0, cover_all=False)
        return x
项目:chainer-stack-gan    作者:dsanno    | 项目源码 | 文件源码
def __call__(self, x, train=True):
        b, c, height, width = x.shape
        h = self.conv(F.unpooling_2d(x, 2, outsize=(height * 2, width * 2)))
        if self.activate:
            h = F.relu(self.bn(h, test=not train))
        return h
项目:chainer-began    作者:hvy    | 项目源码 | 文件源码
def __call__(self, x):
        h = F.reshape(self.l0(x), ((x.shape[0],) + self.embed_shape))

        for i in range(self.n_blocks):
            for j in range(self.block_size):
                h = F.elu(getattr(self, 'c{}'.format(i*j+j))(h))
            if i < self.n_blocks - 1:
                h = F.unpooling_2d(h, ksize=2, stride=2, cover_all=False)

        return self.ln(h)
项目:chainer-speech-recognition    作者:musyoku    | 项目源码 | 文件源码
def __call__(self, x):
        return functions.unpooling_2d(x, self.ksize, self.stride, self.pad, self.outsize, self.cover_all)
项目:chainer-deconv    作者:germanRos    | 项目源码 | 文件源码
def check_forward(self, x_data):
        x = chainer.Variable(x_data)
        y = functions.unpooling_2d(x, self.ksize, outsize=self.outsize,
                                   cover_all=self.cover_all)
        self.assertEqual(y.data.dtype, self.dtype)
        y_data = cuda.to_cpu(y.data)

        self.assertEqual(self.gy.shape, y_data.shape)
        for i in six.moves.range(self.N):
            for c in six.moves.range(self.n_channels):
                outsize = self.outsize or self.expected_outsize
                assert y_data.shape[2:] == outsize
                if outsize == (5, 2):
                    expect = numpy.zeros(outsize, dtype=self.dtype)
                    expect[:2, :] = self.x[i, c, 0, 0]
                    expect[2:4, :] = self.x[i, c, 1, 0]
                elif outsize == (4, 2):
                    expect = numpy.array([
                        [self.x[i, c, 0, 0], self.x[i, c, 0, 0]],
                        [self.x[i, c, 0, 0], self.x[i, c, 0, 0]],
                        [self.x[i, c, 1, 0], self.x[i, c, 1, 0]],
                        [self.x[i, c, 1, 0], self.x[i, c, 1, 0]],
                    ])
                elif outsize == (3, 1):
                    expect = numpy.array([
                        [self.x[i, c, 0, 0]],
                        [self.x[i, c, 0, 0]],
                        [self.x[i, c, 1, 0]],
                    ])
                else:
                    raise ValueError('Unsupported outsize: {}'.format(outsize))
                gradient_check.assert_allclose(expect, y_data[i, c])
项目:chainer-deconv    作者:germanRos    | 项目源码 | 文件源码
def __call__(self, input_blob, test_mode=False):
        # explicit and very flexible DAG!
        #################################
        data = input_blob[0]
        labels = input_blob[1]

        if(len(input_blob) >= 3):
            weights_classes = input_blob[2]
        else:
            weights_classes = chainer.Variable(cuda.cupy.ones((self.classes, 1), dtype='float32'))

        # ---- CONTRACTION BLOCKS ---- #
        blob_b0  = self.bnorm0(data)
        (blob_b1, indices_b1, size_b1)  = F.max_pooling_2dIndices(self.bnorm1(F.relu(self.conv1(blob_b0)), test=test_mode), (2, 2), stride=(2,2), pad=(0, 0))
        (blob_b2, indices_b2, size_b2)  = F.max_pooling_2dIndices(self.bnorm2(F.relu(self.conv2(blob_b1)), test=test_mode), (2, 2), stride=(2,2), pad=(0, 0))
        (blob_b3, indices_b3, size_b3)  = F.max_pooling_2dIndices(self.bnorm3(F.relu(self.conv3(blob_b2)), test=test_mode), (2, 2), stride=(2,2), pad=(0, 0))
        (blob_b4, indices_b4, size_b4)  = F.max_pooling_2dIndices(self.bnorm4(F.relu(self.conv4(blob_b3)), test=test_mode), (2, 2), stride=(2,2), pad=(0, 0))

        # ---- EXPANSION BLOCKS ---- #
        blob_b5  = self.bnorm5(F.relu(self.conv5(F.unpooling_2d(blob_b4, indices_b4, size_b4))), test=test_mode)
        blob_b6  = self.bnorm6(F.relu(self.conv6(F.unpooling_2d(blob_b5, indices_b3, size_b3))), test=test_mode)
        blob_b7  = self.bnorm7(F.relu(self.conv7(F.unpooling_2d(blob_b6, indices_b2, size_b2))), test=test_mode)
        blob_b8  = self.bnorm8(F.relu(self.conv8(F.unpooling_2d(blob_b7, indices_b1, size_b1))), test=test_mode)

        #ipdb.set_trace()

        # ---- SOFTMAX CLASSIFIER ---- #
        self.blob_class = self.classi(blob_b8)
        self.probs = F.softmax(self.blob_class)

        # ---- CROSS-ENTROPY LOSS ---- #
        #ipdb.set_trace()
            self.loss = F.weighted_cross_entropy(self.probs, labels, weights_classes, normalize=True)
        self.output_point = self.probs

        return self.loss
项目:ddnn    作者:kunglab    | 项目源码 | 文件源码
def __init__(self, ksize, stride=None, pad=0, outsize=None, cover_all=True):
        self._function = "unpooling_2d"
        self.ksize = ksize
        self.stride = stride
        self.pad = pad
        self.outsize = outsize
        self.cover_all = cover_all
项目:ddnn    作者:kunglab    | 项目源码 | 文件源码
def __call__(self, x):
        return F.unpooling_2d(x, self.ksize, self.stride, self.pad, self.outsize, self.cover_all)
项目:ddnn    作者:kunglab    | 项目源码 | 文件源码
def __init__(self, ksize, stride=None, pad=0, outsize=None, cover_all=True):
        self._function = "unpooling_2d"
        self.ksize = ksize
        self.stride = stride
        self.pad = pad
        self.outsize = outsize
        self.cover_all = cover_all
项目:ddnn    作者:kunglab    | 项目源码 | 文件源码
def __call__(self, x):
        return F.unpooling_2d(x, self.ksize, self.stride, self.pad, self.outsize, self.cover_all)
项目:adversarial-autoencoder    作者:musyoku    | 项目源码 | 文件源码
def __call__(self, x):
        return functions.unpooling_2d(x, self.ksize, self.stride, self.pad, self.outsize, self.cover_all)
项目:unrolled-gan    作者:musyoku    | 项目源码 | 文件源码
def __init__(self, ksize, stride=None, pad=0, outsize=None, cover_all=True):
        self._function = "unpooling_2d"
        self.ksize = ksize
        self.stride = stride
        self.pad = pad
        self.outsize = outsize
        self.cover_all = cover_all
项目:unrolled-gan    作者:musyoku    | 项目源码 | 文件源码
def __call__(self, x):
        return F.unpooling_2d(x, self.ksize, self.stride, self.pad, self.outsize, self.cover_all)
项目:unrolled-gan    作者:musyoku    | 项目源码 | 文件源码
def __init__(self, ksize, stride=None, pad=0, outsize=None, cover_all=True):
        self._function = "unpooling_2d"
        self.ksize = ksize
        self.stride = stride
        self.pad = pad
        self.outsize = outsize
        self.cover_all = cover_all
项目:unrolled-gan    作者:musyoku    | 项目源码 | 文件源码
def __call__(self, x):
        return F.unpooling_2d(x, self.ksize, self.stride, self.pad, self.outsize, self.cover_all)
项目:LSGAN    作者:musyoku    | 项目源码 | 文件源码
def __init__(self, ksize, stride=None, pad=0, outsize=None, cover_all=True):
        self._function = "unpooling_2d"
        self.ksize = ksize
        self.stride = stride
        self.pad = pad
        self.outsize = outsize
        self.cover_all = cover_all
项目:LSGAN    作者:musyoku    | 项目源码 | 文件源码
def __call__(self, x):
        return F.unpooling_2d(x, self.ksize, self.stride, self.pad, self.outsize, self.cover_all)
项目:LSGAN    作者:musyoku    | 项目源码 | 文件源码
def __init__(self, ksize, stride=None, pad=0, outsize=None, cover_all=True):
        self._function = "unpooling_2d"
        self.ksize = ksize
        self.stride = stride
        self.pad = pad
        self.outsize = outsize
        self.cover_all = cover_all
项目:LSGAN    作者:musyoku    | 项目源码 | 文件源码
def __call__(self, x):
        return F.unpooling_2d(x, self.ksize, self.stride, self.pad, self.outsize, self.cover_all)
项目:adgm    作者:musyoku    | 项目源码 | 文件源码
def __init__(self, ksize, stride=None, pad=0, outsize=None, cover_all=True):
        self._function = "unpooling_2d"
        self.ksize = ksize
        self.stride = stride
        self.pad = pad
        self.outsize = outsize
        self.cover_all = cover_all
项目:adgm    作者:musyoku    | 项目源码 | 文件源码
def __call__(self, x):
        return F.unpooling_2d(x, self.ksize, self.stride, self.pad, self.outsize, self.cover_all)
项目:adgm    作者:musyoku    | 项目源码 | 文件源码
def __init__(self, ksize, stride=None, pad=0, outsize=None, cover_all=True):
        self._function = "unpooling_2d"
        self.ksize = ksize
        self.stride = stride
        self.pad = pad
        self.outsize = outsize
        self.cover_all = cover_all
项目:adgm    作者:musyoku    | 项目源码 | 文件源码
def __call__(self, x):
        return F.unpooling_2d(x, self.ksize, self.stride, self.pad, self.outsize, self.cover_all)