Python nn.HybridBlock方法代碼示例

# 需要導入模塊: from mxnet.gluon import nn [as 別名]
# 或者: from mxnet.gluon.nn import HybridBlock [as 別名]
def test_constant():
    class Test(gluon.HybridBlock):
        def __init__(self, **kwargs):
            super(Test, self).__init__(**kwargs)
            self.value = np.asarray([[1,2], [3,4]])
            self.const = self.params.get_constant('const', self.value)

        def hybrid_forward(self, F, x, const):
            return x + const

    test = Test()
    test.initialize()
    trainer = gluon.Trainer(test.collect_params(), 'sgd',
                            {'learning_rate': 1.0, 'momentum': 0.5})

    with mx.autograd.record():
        x = mx.nd.ones((2,2))
        x.attach_grad()
        y = test(x)
        y.backward()

    trainer.step(1)

    assert (test.const.data().asnumpy() == test.value).all()
    assert (x.grad.asnumpy() == 1).all() 

# 需要導入模塊: from mxnet.gluon import nn [as 別名]
# 或者: from mxnet.gluon.nn import HybridBlock [as 別名]
def test_conv2d_16c():
    chn_list = [16, 256]
    kernel_list = [1, 3]
    kernel_list.append(224)
    batch_size = 4
    class Net(gluon.HybridBlock):
        def __init__(self,
                     chn_num,
                     kernel,
                     **kwargs):
            super(Net, self).__init__(**kwargs)
            with self.name_scope():
                self.conv0 = gluon.nn.Conv2D(chn_num, (kernel, kernel))

        def hybrid_forward(self, F, x):
            out = self.conv0(x)
            return out

    x = mx.nd.random.uniform(-1.0, 1.0, shape=(batch_size, 3, 224, 224))
    for i in range(len(chn_list)):
        for j in range(len(kernel_list)):
            net = Net(chn_list[i], kernel_list[j])
            check_layer_forward_withinput(net, x) 

# 需要導入模塊: from mxnet.gluon import nn [as 別名]
# 或者: from mxnet.gluon.nn import HybridBlock [as 別名]
def test_deconv2d_16c():
    in_chn_list = [1024, 512, 256, 128, 64, 32, 16]
    out_chn_list = [512, 256, 128, 64, 32, 16, 3]
    kernel_list = [1, 3, 5, 7]
    in_shape = [4, 8, 16, 32, 64, 224]
    batch_size = 4
    class Net(gluon.HybridBlock):
        def __init__(self, chn_num, kernel, **kwargs):
            super(Net, self).__init__(**kwargs)
            with self.name_scope():
                self.deconv0 = gluon.nn.Conv2DTranspose(chn_num, (kernel, kernel))

        def hybrid_forward(self, F, x):
            out = self.deconv0(x)
            return out
    for i in range(len(in_shape)):
        x = mx.nd.random.uniform(-1.0, 1.0, shape=(batch_size, in_chn_list[i], in_shape[i], in_shape[i]))
        for j in range(len(kernel_list)):
            net = Net(out_chn_list[i], kernel_list[j])
            check_layer_forward_withinput(net, x) 

# 需要導入模塊: from mxnet.gluon import nn [as 別名]
# 或者: from mxnet.gluon.nn import HybridBlock [as 別名]
def test_reshape_conv_reshape_conv():
    class Net(gluon.HybridBlock):
        def __init__(self, **kwargs):
            super(Net, self).__init__(**kwargs)
            with self.name_scope():
                self.conv0 = nn.Conv2D(64, (3, 3))
                self.conv1 = nn.Conv2D(128, (3, 3))

        def hybrid_forward(self, F, x):
            x_reshape = x.reshape((0, 0, 128, 32))
            y = self.conv0(x_reshape)
            "spatial shape of y is (62, 62)"
            y_reshape = y.reshape((0, 0, 124, 31))
            out = self.conv1(y_reshape)
            return out
    x = mx.nd.random.uniform(shape=(4, 3, 64, 64))
    net = Net()
    check_layer_forward_withinput(net, x) 

# 需要導入模塊: from mxnet.gluon import nn [as 別名]
# 或者: from mxnet.gluon.nn import HybridBlock [as 別名]
def test_slice_conv_slice_conv():
    class Net(gluon.HybridBlock):
        def __init__(self, **kwargs):
            super(Net, self).__init__(**kwargs)
            with self.name_scope():
                self.conv0 = nn.Conv2D(32, (3, 3))
                self.conv1 = nn.Conv2D(16, (1, 1))

        def hybrid_forward(self, F, x):
            x_slice = x.slice(begin=(0, 0, 0, 0), end=(4, 16, 16, 16))
            y = self.conv0(x_slice)
            "shape of y is (4, 32, 14, 14)"
            y_slice = y.slice(begin=(0, 0, 0, 0), end=(4, 16, 3, 3))
            out = self.conv1(y_slice)
            return out
    x = mx.nd.random.uniform(shape=(4, 32, 32, 32))
    net = Net()
    check_layer_forward_withinput(net, x) 

# 需要導入模塊: from mxnet.gluon import nn [as 別名]
# 或者: from mxnet.gluon.nn import HybridBlock [as 別名]
def test_slice_conv_reshape_conv():
    class Net(gluon.HybridBlock):
        def __init__(self, **kwargs):
            super(Net, self).__init__(**kwargs)
            with self.name_scope():
                self.conv0 = nn.Conv2D(64, (3, 3))
                self.conv1 = nn.Conv2D(128, (3, 3))

        def hybrid_forward(self, F, x):
            x_slice = x.slice(begin=(0, 0, 1, 1), end=(4, 16, 33, 33))
            y = self.conv0(x_slice)
            "shape of y is (4, 64, 30, 30)"
            y_reshape = y.reshape((0, 0, 60, 15))
            out = self.conv1(y_reshape)
            return out

    x = mx.nd.random.uniform(shape=(4, 32, 64, 64))
    net = Net()
    check_layer_forward_withinput(net, x) 

# 需要導入模塊: from mxnet.gluon import nn [as 別名]
# 或者: from mxnet.gluon.nn import HybridBlock [as 別名]
def test_reshape_dense():
    class Net(gluon.HybridBlock):
        def __init__(self, **kwargs):
            super(Net, self).__init__(**kwargs)
            with self.name_scope():
                channel0 = np.random.randint(1, 17)
                self.dense0 = nn.Dense(channel0)

        def hybrid_forward(self, F, x):
            x_reshape = x.reshape((8, 64, 128, -1))
            out = self.dense0(x_reshape)
            return out

    x = mx.nd.random.uniform(shape=(4, 32, 64, 64))
    net = Net()
    check_layer_forward_withinput(net, x) 

# 需要導入模塊: from mxnet.gluon import nn [as 別名]
# 或者: from mxnet.gluon.nn import HybridBlock [as 別名]
def test_slice_dense():
    class Net(gluon.HybridBlock):
        def __init__(self, slice, **kwargs):
            super(Net, self).__init__(**kwargs)
            with self.name_scope():
                channel0 = np.random.randint(1, 17)
                self.dense0 = nn.Dense(channel0)
                self.slice = slice

        def hybrid_forward(self, F, x):
            x_slice = x.slice(begin=tuple(self.slice[0]),
                              end=tuple(self.slice[1]))
            out = self.dense0(x_slice)
            return out

    x = mx.nd.random.uniform(shape=(16, 32, 64, 64))
    slice = [[0, 16, 0, 0], [4, 32, 32, 32]]
    net = Net(slice)
    check_layer_forward_withinput(net, x) 

# 需要導入模塊: from mxnet.gluon import nn [as 別名]
# 或者: from mxnet.gluon.nn import HybridBlock [as 別名]
def test_slice_dense_slice_dense():
    class Net(gluon.HybridBlock):
        def __init__(self, slice, **kwargs):
            super(Net, self).__init__(**kwargs)
            with self.name_scope():
                channel0 = 32
                channel1 = np.random.randint(1, 17)
                self.dense0 = nn.Dense(channel0)
                self.dense1 = nn.Dense(channel1)
                self.slice = slice

        def hybrid_forward(self, F, x):
            x_slice = x.slice(begin=tuple(self.slice[0]), end=tuple(self.slice[1]))
            y = self.dense0(x_slice)
            y_slice = y.slice(begin=(1, 0), end=(3, 10))
            out = self.dense1(y_slice)
            return out

    x = mx.nd.random.uniform(shape=(16, 32, 64, 64))
    slice = [[0, 16, 0, 0], [4, 32, 32, 32]]
    net = Net(slice)
    check_layer_forward_withinput(net, x) 

# 需要導入模塊: from mxnet.gluon import nn [as 別名]
# 或者: from mxnet.gluon.nn import HybridBlock [as 別名]
def test_reshape_dense_reshape_dense():
    class Net(gluon.HybridBlock):
        def __init__(self, **kwargs):
            super(Net, self).__init__(**kwargs)
            with self.name_scope():
                channel0 = np.random.randint(1, 17)
                channel1 = np.random.randint(1, 33)
                self.dense0 = nn.Dense(channel0)
                self.dense1 = nn.Dense(channel1)

        def hybrid_forward(self, F, x):
            x_reshape = x.reshape((4, 16, 128, 32))
            y = self.dense0(x_reshape)
            y_reshape = y.reshape((1, -1))
            out = self.dense1(y_reshape)
            return out

    x = mx.nd.random.uniform(shape=(4, 16, 64, 64))
    net = Net()
    check_layer_forward_withinput(net, x) 

# 需要導入模塊: from mxnet.gluon import nn [as 別名]
# 或者: from mxnet.gluon.nn import HybridBlock [as 別名]
def test_slice_dense_reshape_dense():
    class Net(gluon.HybridBlock):
        def __init__(self, slice, **kwargs):
            super(Net, self).__init__(**kwargs)
            with self.name_scope():
                channel0 = np.random.randint(1, 17)
                channel1 = np.random.randint(1, 17)
                self.dense0 = nn.Dense(channel0)
                self.dense1 = nn.Dense(channel1)
                self.slice = slice

        def hybrid_forward(self, F, x):
            x_slice = x.slice(begin=tuple(self.slice[0]), end=tuple(self.slice[1]))
            y = self.dense0(x_slice)
            y_reshape = y.reshape((1, -1))
            out = self.dense1(y_reshape)
            return out

    x = mx.nd.random.uniform(shape=(16, 32, 64, 64))
    slice = [[0, 16, 0, 0], [4, 32, 32, 32]]
    net = Net(slice)
    check_layer_forward_withinput(net, x) 

# 需要導入模塊: from mxnet.gluon import nn [as 別名]
# 或者: from mxnet.gluon.nn import HybridBlock [as 別名]
def test_reshape_batchnorm():
    class Net(gluon.HybridBlock):
        def __init__(self, shape, **kwargs):
            super(Net, self).__init__(**kwargs)
            with self.name_scope():
                self.conv0 = nn.Conv2D(96, (1, 1))
                self.bn0 = nn.BatchNorm()
                self.reshape = shape

        def hybrid_forward(self, F, x):
            x_in = self.conv0(x)
            x_reshape = x_in.reshape(self.reshape)
            out = self.bn0(x_reshape)
            return out

    x = mx.nd.random.uniform(shape=(4, 32, 64, 64))
    shape = (4, 64, 64, -1)
    net = Net(shape)
    check_layer_forward_withinput(net, x) 

# 需要導入模塊: from mxnet.gluon import nn [as 別名]
# 或者: from mxnet.gluon.nn import HybridBlock [as 別名]
def test_slice_batchnorm():
    class Net(gluon.HybridBlock):
        def __init__(self, slice, **kwargs):
            super(Net, self).__init__(**kwargs)
            with self.name_scope():
                self.conv0 = nn.Conv2D(128, (1, 1))
                self.bn0 = nn.BatchNorm()
                self.slice = slice

        def hybrid_forward(self, F, x):
            x_in = self.conv0(x)
            x_slice = x_in.slice(begin=tuple(self.slice[0]),
                              end=tuple(self.slice[1]))
            out = self.bn0(x_slice)
            return out

    x = mx.nd.random.uniform(shape=(16, 128, 256, 256))
    slice = [[0, 0, 0, 0], [4, 32, 32, 32]]
    net = Net(slice)
    check_layer_forward_withinput(net, x) 

# 需要導入模塊: from mxnet.gluon import nn [as 別名]
# 或者: from mxnet.gluon.nn import HybridBlock [as 別名]
def test_slice_batchnorm_slice_batchnorm():
    class Net(gluon.HybridBlock):
        def __init__(self, slice, **kwargs):
            super(Net, self).__init__(**kwargs)
            with self.name_scope():
                self.conv0 = nn.Conv2D(128, (1, 1))
                self.bn0 = nn.BatchNorm()
                self.bn1 = nn.BatchNorm()
                self.slice = slice

        def hybrid_forward(self, F, x):
            x_in = self.conv0(x)
            x_slice = x_in.slice(begin=tuple(self.slice[0][0]), end=tuple(self.slice[0][1]))
            y = self.bn0(x_slice)
            y_slice = y.slice(begin=tuple(self.slice[1][0]), end=tuple(self.slice[1][1]))
            out = self.bn1(y_slice)
            return out

    x = mx.nd.random.uniform(shape=(16, 128, 256, 256))
    slice = [[[0, 0, 0, 0], [4, 32, 32, 32]], [[0, 0, 0, 0], [2, 64, 16, 16]]]
    net = Net(slice)
    check_layer_forward_withinput(net, x) 

# 需要導入模塊: from mxnet.gluon import nn [as 別名]
# 或者: from mxnet.gluon.nn import HybridBlock [as 別名]
def test_reshape_batchnorm_reshape_batchnorm():
    class Net(gluon.HybridBlock):
        def __init__(self, shape, **kwargs):
            super(Net, self).__init__(**kwargs)
            with self.name_scope():
                self.conv0 = nn.Conv2D(128, (1, 1))
                self.bn0 = nn.BatchNorm()
                self.bn1 = nn.BatchNorm()
                self.reshape = shape

        def hybrid_forward(self, F, x):
            x_in = self.conv0(x)
            x_reshape = x_in.reshape(self.reshape[0])
            y = self.bn0(x_reshape)
            y_reshape = y.reshape(self.reshape[1])
            out = self.bn1(y_reshape)
            return out

    x = mx.nd.random.uniform(shape=(4, 32, 64, 64))
    shape = [(4, 64, 64, -1), (4, 128, -1, 32)]
    net = Net(shape)
    check_layer_forward_withinput(net, x)