Python functions.unpooling_2d方法代码示例

# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import unpooling_2d [as 别名]
def forward(self, x):
        hs = self.base(x)

        with flags.for_unroll():
            for i in range(self.n_base_output_minus1, -1, -1):
                hs[i] = self.inner[i](hs[i])
                if i < self.n_base_output_minus1:
                    hs[i] += F.unpooling_2d(hs[i + 1], 2, cover_all=False)

            for i in range(self.n_base_output):
                hs[i] = self.outer[i](hs[i])

            for _ in range(self.scales_minus_n_base_output):
                hs.append(F.max_pooling_2d(hs[-1], 1, stride=2, cover_all=False))

        return hs


# ====================================== 

# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import unpooling_2d [as 别名]
def __call__(self, x, alpha=1.0):
        if self.depth > 0 and alpha < 1.0:
            h = x
            for i in range(self.depth-1):
                h = self['b%d'%i](h)

            h1 = self['b%d'%(self.depth-1)](h)
            h2 = F.unpooling_2d(h1, 2, 2, outsize=self['b%d'%self.depth].outsize)
            h3 = self['b%d'%(self.depth-1)].toRGB(h2)
            h4 = self['b%d'%self.depth](h1, True)
            
            h = h3 * (1 - alpha) + h4 * alpha
        else:
            h = x
            for i in range(self.depth):
                h = self['b%d'%i](h)

            h = self['b%d'%self.depth](h, True)
        
        return h 

# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import unpooling_2d [as 别名]
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 

# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import unpooling_2d [as 别名]
def _upsample(x):
    h, w = x.shape[2:]
    return F.unpooling_2d(x, 2, outsize=(h * 2, w * 2)) 

# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import unpooling_2d [as 别名]
def _upsample_frq(x):
    h, w = x.shape[2:]
    return F.unpooling_2d(x, (1,2), outsize=(h, w * 2)) 

# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import unpooling_2d [as 别名]
def upscale2x(h):
    return F.unpooling_2d(h, 2, 2, 0, outsize=(h.shape[2] * 2, h.shape[3] * 2)) 

# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import unpooling_2d [as 别名]
def forward(self, x):
        y = F.unpooling_2d(x, 2, cover_all=False)
        return y 

# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import unpooling_2d [as 别名]
def __call__(self, x, mask):
        #h = self.c(x) - self.b
        self.m.W.data = self.xp.array(self.maskW) #mask windows are set by 1
        h = self.c(x*mask) #(B,C,H,W)
        B,C,H,W = h.shape
        #b = F.transpose(F.broadcast_to(self.c.b,(B,H,W,C)),(0,3,1,2))
        #h = h - b
        mask_sums = self.m(mask)
        mask_new = (self.xp.sign(mask_sums.data-0.5)+1.0)*0.5
        mask_new_b = mask_new.astype("bool")
        
        mask_sums = F.where(mask_new_b,mask_sums,0.01*Variable(self.xp.ones(mask_sums.shape).astype("f")))
        h = h/mask_sums 
        #h = h/mask_sums + b
         
        mask_new = Variable(mask_new)
        h = F.where(mask_new_b, h, Variable(self.xp.zeros(h.shape).astype("f"))) 

        #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)
        if self.noise:
            h = add_noise(h)
        if self.dropout:
            h = F.dropout(h)
        if not self.activation is None:
            h = self.activation(h)
        return h, mask_new 

# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import unpooling_2d [as 别名]
def __call__(self, x):
        return F.unpooling_2d(
            x=x,
            ksize=self.scale_factor,
            cover_all=False) 

# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import unpooling_2d [as 别名]
def __call__(self, x):
        x = self.conv(x)
        return F.unpooling_2d(
            x=x,
            ksize=self.scale_factor,
            cover_all=False) 

# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import unpooling_2d [as 别名]
def check_forward_consistency_regression(self, backend_config):
        # Regression test to two-dimensional unpooling layer.
        inputs, = self.generate_inputs()
        x = chainer.Variable(backend_config.get_array(inputs))

        ksize = self.ksize
        stride = self.stride
        pad = self.pad

        y_nd = functions.unpooling_nd(x, ksize, stride=stride, pad=pad,
                                      cover_all=self.cover_all)
        y_2d = functions.unpooling_2d(x, ksize, stride=stride, pad=pad,
                                      cover_all=self.cover_all)
        testing.assert_allclose(
            y_nd.array, y_2d.array, **self.check_forward_options) 

# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import unpooling_2d [as 别名]
def check_backward_consistency_regression(self, backend_config):
        # Regression test to two-dimensional unpooling layer.

        x_data, = self.generate_inputs()
        gy_data = numpy.random.uniform(-1, 1, self.gy_shape).astype(self.dtype)

        ksize = self.ksize
        stride = self.stride
        pad = self.pad
        xp = backend.get_array_module(x_data)

        # Backward computation for N-dimensional unpooling layer.
        x_nd = chainer.Variable(xp.array(x_data))
        y_nd = functions.unpooling_nd(
            x_nd, ksize, stride=stride, pad=pad, cover_all=self.cover_all)
        y_nd.grad = gy_data
        y_nd.backward()

        # Backward computation for two-dimensional unpooling layer.
        x_2d = chainer.Variable(xp.array(x_data))
        y_2d = functions.unpooling_2d(
            x_2d, ksize, stride=stride, pad=pad, cover_all=self.cover_all)
        y_2d.grad = gy_data
        y_2d.backward()

        # Test that the two result gradients are close enough.
        opt = self.check_backward_options
        testing.assert_allclose(
            x_nd.grad, x_2d.grad, atol=opt['atol'], rtol=opt['rtol']) 

# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import unpooling_2d [as 别名]
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))
                testing.assert_allclose(expect, y_data[i, c]) 

# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import unpooling_2d [as 别名]
def check_backward(self, x_data, y_grad):
        def f(x):
            return functions.unpooling_2d(x, self.ksize, outsize=self.outsize,
                                          cover_all=self.cover_all)
        gradient_check.check_backward(
            f, x_data, y_grad, dtype=numpy.float64,
            **self.check_backward_options) 

# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import unpooling_2d [as 别名]
def check_double_backward(self, x_data, y_grad, x_grad_grad,
                              use_cudnn='always'):
        def f(x):
            return functions.unpooling_2d(x, self.ksize, outsize=self.outsize,
                                          cover_all=self.cover_all)
        with chainer.using_config('use_cudnn', use_cudnn):
            gradient_check.check_double_backward(
                f, x_data, y_grad, x_grad_grad, dtype=numpy.float64,
                **self.check_double_backward_options)