Python parametric_functions.batch_normalization函数代码示例

def resnet_model(ctx, x, inmaps=64, act=F.relu, test=False):
    # Conv -> BN -> Relu
    with nn.context_scope(ctx):
        with nn.parameter_scope("conv1"):
            h = PF.convolution(x, inmaps, kernel=(3, 3), pad=(1, 1), with_bias=False)
            h = PF.batch_normalization(h, decay_rate=0.9, batch_stat=not test)
            h = act(h)
        
        h = res_unit(h, "conv2", act, False) # -> 32x32
        h = res_unit(h, "conv3", act, True)  # -> 16x16
        with nn.parameter_scope("bn0"):
            h = PF.batch_normalization(h, batch_stat=not test)
        if not test:
            h = F.dropout(h)
        h = res_unit(h, "conv4", act, False) # -> 16x16
        h = res_unit(h, "conv5", act, True)  # -> 8x8
        with nn.parameter_scope("bn1"):
            h = PF.batch_normalization(h, batch_stat=not test)
        if not test:
            h = F.dropout(h)
        h = res_unit(h, "conv6", act, False) # -> 8x8
        h = res_unit(h, "conv7", act, True)  # -> 4x4
        with nn.parameter_scope("bn2"):
            h = PF.batch_normalization(h, batch_stat=not test)
        if not test:
            h = F.dropout(h)
        h = res_unit(h, "conv8", act, False) # -> 4x4
        h = F.average_pooling(h, kernel=(4, 4))  # -> 1x1
        
        pred = PF.affine(h, 10)
    return pred

    def res_unit(x, scope_name, dn=False, test=False):
        C = x.shape[1]
        with nn.parameter_scope(scope_name):

            # Conv -> BN -> Relu
            with nn.parameter_scope("conv1"):
                h = PF.convolution(x, C / 2, kernel=(1, 1), pad=(0, 0),
                                   with_bias=False)
                h = PF.batch_normalization(h, batch_stat=not test)
                h = F.relu(h)
            # Conv -> BN -> Relu
            with nn.parameter_scope("conv2"):
                h = PF.convolution(h, C / 2, kernel=(3, 3), pad=(1, 1),
                                   with_bias=False)
                h = PF.batch_normalization(h, batch_stat=not test)
                h = F.relu(h)
            # Conv -> BN
            with nn.parameter_scope("conv3"):
                h = PF.convolution(h, C, kernel=(1, 1), pad=(0, 0),
                                   with_bias=False)
                h = PF.batch_normalization(h, batch_stat=not test)
            # Residual -> Relu
            h = F.relu(h + x)

            # Maxpooling
            if dn:
                h = F.max_pooling(h, kernel=(2, 2), stride=(2, 2))
            return h

def cnn_model_003(ctx, x, act=F.relu, test=False):
    with nn.context_scope(ctx):
        # Convblock0
        h = conv_unit(x, "conv00", 128, k=3, s=1, p=1, act=act, test=test)
        h = conv_unit(h, "conv01", 128, k=3, s=1, p=1, act=act, test=test)
        h = conv_unit(h, "conv02", 128, k=3, s=1, p=1, act=act, test=test)
        h = F.max_pooling(h, (2, 2))  # 32 -> 16
        with nn.parameter_scope("bn0"):
            h = PF.batch_normalization(h, batch_stat=not test)
        if not test:
            h = F.dropout(h)

        # Convblock 1
        h = conv_unit(h, "conv10", 256, k=3, s=1, p=1, act=act, test=test)
        h = conv_unit(h, "conv11", 256, k=3, s=1, p=1, act=act, test=test)
        h = conv_unit(h, "conv12", 256, k=3, s=1, p=1, act=act, test=test)
        h = F.max_pooling(h, (2, 2))  # 16 -> 8
        with nn.parameter_scope("bn1"):
            h = PF.batch_normalization(h, batch_stat=not test)
        if not test:
            h = F.dropout(h)

        # Convblock 2
        h = conv_unit(h, "conv20", 512, k=3, s=1, p=0, act=act, test=test)  # 8 -> 6
        h = conv_unit(h, "conv21", 256, k=1, s=1, p=0, act=act, test=test)
        h = conv_unit(h, "conv22", 128, k=1, s=1, p=0, act=act, test=test)
        h = conv_unit(h, "conv23", 10, k=1, s=1, p=0, act=act, test=test)

        # Convblock 3
        h = F.average_pooling(h, (6, 6))
        with nn.parameter_scope("bn2"):
            h = PF.batch_normalization(h, batch_stat=not test)
        h = F.reshape(h, (h.shape[0], np.prod(h.shape[1:])))
        return h

def test_save_load_parameters():
    v = nn.Variable([64, 1, 28, 28], need_grad=False)
    with nn.parameter_scope("param1"):
        with nn.parameter_scope("conv1"):
            h = PF.convolution(v, 32, (3, 3))
            b = PF.batch_normalization(h, batch_stat=True)
        with nn.parameter_scope("conv2"):
            h1 = PF.convolution(v, 32, (3, 3))
            b2 = PF.batch_normalization(h1, batch_stat=True)

    for k, v in iteritems(nn.get_parameters(grad_only=False)):
        v.data.cast(np.float32)[...] = np.random.randn(*v.shape)

    with nn.parameter_scope("param1"):
        param1 = nn.get_parameters(grad_only=False)
        nn.save_parameters("tmp.h5")
        nn.save_parameters("tmp.protobuf")

    with nn.parameter_scope("param2"):
        nn.load_parameters('tmp.h5')
        param2 = nn.get_parameters(grad_only=False)

    with nn.parameter_scope("param3"):
        nn.load_parameters('tmp.protobuf')
        param3 = nn.get_parameters(grad_only=False)

    for par2 in [param2, param3]:
        assert param1.keys() == par2.keys()  # Check order
        for (n1, p1), (n2, p2) in zip(sorted(param1.items()), sorted(par2.items())):
            assert n1 == n2
            assert np.all(p1.d == p2.d)
            assert p1.data.dtype == p2.data.dtype
            assert p1.need_grad == p2.need_grad

def cnn_model_003(ctx, x, act=F.elu, do=True, test=False):
    with nn.context_scope(ctx):
        # Convblock0
        h = conv_unit(x, "conv00", 128, k=3, s=1, p=1, act=act, test=test)
        h = conv_unit(h, "conv01", 128, k=3, s=1, p=1, act=act, test=test)
        h = conv_unit(h, "conv02", 128, k=3, s=1, p=1, act=act, test=test)
        h = F.max_pooling(h, (2, 2))  # 32 -> 16
        with nn.parameter_scope("bn0"):
            h = PF.batch_normalization(h, batch_stat=not test)
        if not test and do:
            h = F.dropout(h)

        # Convblock 1
        h = conv_unit(h, "conv10", 256, k=3, s=1, p=1, act=act, test=test)
        h = conv_unit(h, "conv11", 256, k=3, s=1, p=1, act=act, test=test)
        h = conv_unit(h, "conv12", 256, k=3, s=1, p=1, act=act, test=test)
        h = F.max_pooling(h, (2, 2))  # 16 -> 8
        with nn.parameter_scope("bn1"):
            h = PF.batch_normalization(h, batch_stat=not test)
        if not test and do:
            h = F.dropout(h)

        # Convblock 2
        h = conv_unit(h, "conv20", 512, k=3, s=1, p=0, act=act, test=test)  # 8 -> 6
        h = conv_unit(h, "conv21", 256, k=1, s=1, p=0, act=act, test=test)
        h = conv_unit(h, "conv22", 128, k=1, s=1, p=0, act=act, test=test)
        h_branch = h

        # Convblock 3
        h = conv_unit(h_branch, "conv23", 10, k=1, s=1, p=0, act=act, test=test)
        h = F.average_pooling(h, (6, 6))
        with nn.parameter_scope("bn2"):
            h = PF.batch_normalization(h, batch_stat=not test)
        pred = F.reshape(h, (h.shape[0], np.prod(h.shape[1:])))

        # Uncertainty
        u0 = conv_unit(h_branch, "u0", 10, k=1, s=1, p=0, act=act, test=test)
        u0 = F.average_pooling(u0, (6, 6))
        with nn.parameter_scope("u0bn"):
            u0 = PF.batch_normalization(u0, batch_stat=not test)
            log_var = F.reshape(u0, (u0.shape[0], np.prod(u0.shape[1:])))

        # Uncertainty for uncertainty
        u1 = conv_unit(h_branch, "u1", 10, k=1, s=1, p=0, act=act, test=test)
        u1 = F.average_pooling(u1, (6, 6))
        with nn.parameter_scope("u1bn"):
            u1 = PF.batch_normalization(u1, batch_stat=not test)
            log_s = F.reshape(u1, (u1.shape[0], np.prod(u1.shape[1:])))

        return pred, log_var, log_s

def cnn_model_003(ctx, h, act=F.elu, do=True, test=False):
    with nn.context_scope(ctx):
        if not test:
            b, c, s, s = h.shape
            h = F.image_augmentation(h, (c, s, s),
                                     min_scale=1.0, max_scale=1.5,
                                     angle=0.5, aspect_ratio=1.3, distortion=0.2,
                                     flip_lr=True)
        # Convblock0
        h = conv_unit(h, "conv00", 128, k=3, s=1, p=1, act=act, test=test)
        h = conv_unit(h, "conv01", 128, k=3, s=1, p=1, act=act, test=test)
        h = conv_unit(h, "conv02", 128, k=3, s=1, p=1, act=act, test=test)
        h = F.max_pooling(h, (2, 2))  # 32 -> 16
        with nn.parameter_scope("bn0"):
            h = PF.batch_normalization(h, batch_stat=not test)
        if not test and do:
            h = F.dropout(h)

        # Convblock 1
        h = conv_unit(h, "conv10", 256, k=3, s=1, p=1, act=act, test=test)
        h = conv_unit(h, "conv11", 256, k=3, s=1, p=1, act=act, test=test)
        h = conv_unit(h, "conv12", 256, k=3, s=1, p=1, act=act, test=test)
        h = F.max_pooling(h, (2, 2))  # 16 -> 8
        with nn.parameter_scope("bn1"):
            h = PF.batch_normalization(h, batch_stat=not test)
        if not test and do:
            h = F.dropout(h)

        # Convblock 2
        h = conv_unit(h, "conv20", 512, k=3, s=1, p=0, act=act, test=test)  # 8 -> 6
        h = conv_unit(h, "conv21", 256, k=1, s=1, p=0, act=act, test=test)
        h = conv_unit(h, "conv22", 128, k=1, s=1, p=0, act=act, test=test)
        u = h

        # Convblock 3
        h = conv_unit(h, "conv23", 10, k=1, s=1, p=0, act=act, test=test)
        h = F.average_pooling(h, (6, 6))
        with nn.parameter_scope("bn2"):
            h = PF.batch_normalization(h, batch_stat=not test)
        pred = F.reshape(h, (h.shape[0], np.prod(h.shape[1:])))

        # Uncertainty
        u = conv_unit(u, "u0", 10, k=1, s=1, p=0, act=act, test=test)
        u = F.average_pooling(u, (6, 6))
        with nn.parameter_scope("u0bn"):
            u = PF.batch_normalization(u, batch_stat=not test)
            log_var = F.reshape(u, (u.shape[0], np.prod(u.shape[1:])))

        return pred, log_var

def cifar10_resnet23_prediction(ctx, image, test=False):
    """
    Construct ResNet 23
    """
    # Residual Unit
    def res_unit(x, scope_name, dn=False, test=False):
        C = x.shape[1]
        with nn.parameter_scope(scope_name):

            # Conv -> BN -> Relu
            with nn.parameter_scope("conv1"):
                h = PF.convolution(x, C / 2, kernel=(1, 1), pad=(0, 0),
                                   with_bias=False)
                h = PF.batch_normalization(h, batch_stat=not test)
                h = F.relu(h)
            # Conv -> BN -> Relu
            with nn.parameter_scope("conv2"):
                h = PF.convolution(h, C / 2, kernel=(3, 3), pad=(1, 1),
                                   with_bias=False)
                h = PF.batch_normalization(h, batch_stat=not test)
                h = F.relu(h)
            # Conv -> BN
            with nn.parameter_scope("conv3"):
                h = PF.convolution(h, C, kernel=(1, 1), pad=(0, 0),
                                   with_bias=False)
                h = PF.batch_normalization(h, batch_stat=not test)
            # Residual -> Relu
            h = F.relu(h + x)

            # Maxpooling
            if dn:
                h = F.max_pooling(h, kernel=(2, 2), stride=(2, 2))
            return h

    # Random generator for using the same init parameters in all devices
    nmaps = 64
    ncls = 10

    # Conv -> BN -> Relu
    with nn.context_scope(ctx):
        with nn.parameter_scope("conv1"):
            h = PF.convolution(image, nmaps, kernel=(3, 3), pad=(1, 1),
                               with_bias=False)
            h = PF.batch_normalization(h, batch_stat=not test)
            h = F.relu(h)

        h = res_unit(h, "conv2", False)    # -> 32x32
        h = res_unit(h, "conv3", True)     # -> 16x16
        h = bn_dropout(h, "bn_dropout1", test)
        h = res_unit(h, "conv4", False)    # -> 16x16
        h = res_unit(h, "conv5", True)     # -> 8x8
        h = bn_dropout(h, "bn_dropout2", test)
        h = res_unit(h, "conv6", False)    # -> 8x8
        h = res_unit(h, "conv7", True)     # -> 4x4
        h = bn_dropout(h, "bn_dropout3",  test)
        h = res_unit(h, "conv8", False)    # -> 4x4
        h = F.average_pooling(h, kernel=(4, 4))  # -> 1x1
        pred = PF.affine(h, ncls)

    return pred

def conv_unit(x, scope, maps, k=4, s=2, p=1, act=F.relu, test=False):
    with nn.parameter_scope(scope):
        h = PF.convolution(x, maps, kernel=(k, k), stride=(s, s), pad=(p, p))
        if act is None:
            return h
        h = PF.batch_normalization(h, batch_stat=not test)
        h = act(h)
        return h

def conv_unit(x, scope, maps, k=4, s=2, p=1, act=F.prelu, test=False):
    with nn.parameter_scope(scope):
        h = PF.convolution(x, maps, kernel=(k, k), stride=(s, s), pad=(p, p))
        h = PF.batch_normalization(h, batch_stat=not test)
        shape = h.shape
        w = nn.Variable()
        w.d = 0.3
        h = act(h, w)
        return h

def res_block(x, scope_name, act=F.relu, dn=False, test=False):
    C = x.shape[1]
    with nn.parameter_scope(scope_name):
        # Conv -> BN -> Relu
        with nn.parameter_scope("conv1"):
            h = PF.convolution(x, C/2, kernel=(1, 1), pad=(0, 0), with_bias=False)
            h = PF.batch_normalization(h, decay_rate=0.9, batch_stat=not test)
            h = act(h)
        # Conv -> BN -> Relu
        with nn.parameter_scope("conv2"):
            h = PF.convolution(h, C/2, kernel=(3, 3), pad=(1, 1), with_bias=False)
            h = PF.batch_normalization(h, decay_rate=0.9, batch_stat=not test)
            h = act(h)
        # Conv -> BN
        with nn.parameter_scope("conv3"): 
            h = PF.convolution(h, C, kernel=(1, 1), pad=(0, 0), with_bias=False)
            h = PF.batch_normalization(h, decay_rate=0.9, batch_stat=not test)
    return h

def mnist_binary_connect_lenet_prediction(image, test=False):
    """
    Construct LeNet for MNIST (BinaryNet version).
    """
    with nn.parameter_scope("conv1"):
        c1 = PF.binary_connect_convolution(image, 16, (5, 5))
        c1 = PF.batch_normalization(c1, batch_stat=not test)
        c1 = F.elu(F.average_pooling(c1, (2, 2)))
    with nn.parameter_scope("conv2"):
        c2 = PF.binary_connect_convolution(c1, 16, (5, 5))
        c2 = PF.batch_normalization(c2, batch_stat=not test)
        c2 = F.elu(F.average_pooling(c2, (2, 2)))
    with nn.parameter_scope("fc3"):
        c3 = PF.binary_connect_affine(c2, 50)
        c3 = PF.batch_normalization(c3, batch_stat=not test)
        c3 = F.elu(c3)
    with nn.parameter_scope("fc4"):
        c4 = PF.binary_connect_affine(c3, 10)
        c4 = PF.batch_normalization(c4, batch_stat=not test)
    return c4

def mlp_net(x, n_h, n_y, test=False):
    """
    Function for building multi-layer-perceptron with batch_normalization

    Args:
        x(`~nnabla.Variable`): N-D array 
        n_h(int): number of units in an intermediate layer
        n_y(int): number of classes
        test: operation type train=True, test=False

    Returns:
        ~nnabla.Variable: log(p(y|x))
    """

    h = x
    with nn.parameter_scope("fc1"):
        h = F.relu(PF.batch_normalization(
            PF.affine(h, n_h), batch_stat=not test), inplace=True)
    with nn.parameter_scope("fc2"):
        h = F.relu(PF.batch_normalization(
            PF.affine(h, n_h), batch_stat=not test), inplace=True)
    with nn.parameter_scope("fc3"):
        h = PF.affine(h, n_y)
    return h

def cifar100_resnet23_prediction(image,
                                 ctx, test=False):
    """
    Construct ResNet 23
    """
    # Residual Unit
    def res_unit(x, scope_name, rng, dn=False, test=False):
        C = x.shape[1]
        with nn.parameter_scope(scope_name):

            # Conv -> BN -> Relu
            with nn.parameter_scope("conv1"):
                w_init = UniformInitializer(
                    calc_uniform_lim_glorot(C, C / 2, kernel=(1, 1)),
                    rng=rng)
                h = PF.convolution(x, C / 2, kernel=(1, 1), pad=(0, 0),
                                   w_init=w_init, with_bias=False)
                h = PF.batch_normalization(h, batch_stat=not test)
                h = F.relu(h)
            # Conv -> BN -> Relu
            with nn.parameter_scope("conv2"):
                w_init = UniformInitializer(
                    calc_uniform_lim_glorot(C / 2, C / 2, kernel=(3, 3)),
                    rng=rng)
                h = PF.convolution(h, C / 2, kernel=(3, 3), pad=(1, 1),
                                   w_init=w_init, with_bias=False)
                h = PF.batch_normalization(h, batch_stat=not test)
                h = F.relu(h)
            # Conv -> BN
            with nn.parameter_scope("conv3"):
                w_init = UniformInitializer(
                    calc_uniform_lim_glorot(C / 2, C, kernel=(1, 1)),
                    rng=rng)
                h = PF.convolution(h, C, kernel=(1, 1), pad=(0, 0),
                                   w_init=w_init, with_bias=False)
                h = PF.batch_normalization(h, batch_stat=not test)
            # Residual -> Relu
            h = F.relu(h + x)

            # Maxpooling
            if dn:
                h = F.max_pooling(h, kernel=(2, 2), stride=(2, 2))

            return h

    # Random generator for using the same init parameters in all devices
    rng = np.random.RandomState(0)
    nmaps = 384
    ncls = 100

    # Conv -> BN -> Relu
    with nn.context_scope(ctx):
        with nn.parameter_scope("conv1"):
            # Preprocess
            if not test:

                image = F.image_augmentation(image, contrast=1.0,
                                             angle=0.25,
                                             flip_lr=True)
                image.need_grad = False

            w_init = UniformInitializer(
                calc_uniform_lim_glorot(3, nmaps, kernel=(3, 3)),
                rng=rng)
            h = PF.convolution(image, nmaps, kernel=(3, 3), pad=(1, 1),
                               w_init=w_init, with_bias=False)
            h = PF.batch_normalization(h, batch_stat=not test)
            h = F.relu(h)

        h = res_unit(h, "conv2", rng, False)    # -> 32x32
        h = res_unit(h, "conv3", rng, True)     # -> 16x16
        h = res_unit(h, "conv4", rng, False)    # -> 16x16
        h = res_unit(h, "conv5", rng, True)     # -> 8x8
        h = res_unit(h, "conv6", rng, False)    # -> 8x8
        h = res_unit(h, "conv7", rng, True)     # -> 4x4
        h = res_unit(h, "conv8", rng, False)    # -> 4x4
        h = F.average_pooling(h, kernel=(4, 4))  # -> 1x1

        w_init = UniformInitializer(
            calc_uniform_lim_glorot(int(np.prod(h.shape[1:])), ncls, kernel=(1, 1)), rng=rng)
        pred = PF.affine(h, ncls, w_init=w_init)

    return pred

 def bn(xx):
     # Batch normalization
     return PF.batch_normalization(xx, batch_stat=not test)

def cnn_model_003_with_cross_attention(ctx, x_list, act=F.relu, test=False):
    """With attention before pooling
    """
    with nn.context_scope(ctx):
        # Convblock0
        h0_list = []
        for x in x_list:
            h = conv_unit(x, "conv00", 128, k=3, s=1, p=1, act=act, test=test)
            h = conv_unit(h, "conv01", 128, k=3, s=1, p=1, act=act, test=test)
            h = conv_unit(h, "conv02", 128, k=3, s=1, p=1, act=act, test=test)
            h0_list.append(h)

        # Corss attention
        ca0 = attention(h0_list[0], h0_list[1], h0_list[1], 
                        div_dim=True, softmax=True)
        ca1 = attention(h0_list[1], h0_list[0], h0_list[0], 
                        div_dim=True, softmax=True)

        # Maxpooing, Batchnorm, Dropout
        h0_list = []
        for h in [ca0, ca1]:
            h = F.max_pooling(h, (2, 2))  # 32 -> 16
            with nn.parameter_scope("bn0"):
                h = PF.batch_normalization(h, batch_stat=not test)
            if not test:
                h = F.dropout(h)
            h0_list.append(h)

        # Convblock 1
        h1_list = []
        for h in h0_list:
            h = conv_unit(h, "conv10", 256, k=3, s=1, p=1, act=act, test=test)
            h = conv_unit(h, "conv11", 256, k=3, s=1, p=1, act=act, test=test)
            h = conv_unit(h, "conv12", 256, k=3, s=1, p=1, act=act, test=test)
            h1_list.append(h)

        # Corss attention
        ca0 = attention(h1_list[0], h1_list[1], h1_list[1], 
                        div_dim=True, softmax=True)
        ca1 = attention(h1_list[1], h1_list[0], h1_list[0], 
                        div_dim=True, softmax=True)
            
        # Maxpooing, Batchnorm, Dropout
        h1_list = []
        for h in [ca0, ca1]:
            h = F.max_pooling(h, (2, 2))  # 16 -> 8
            with nn.parameter_scope("bn1"):
                h = PF.batch_normalization(h, batch_stat=not test)
            if not test:
                h = F.dropout(h)
                h1_list.append(h)

        # Convblock 2
        h2_list = []
        for h in h1_list:
            h = conv_unit(h, "conv20", 512, k=3, s=1, p=0, act=act, test=test)  # 8 -> 6
            h = conv_unit(h, "conv21", 256, k=1, s=1, p=0, act=act, test=test)
            h = conv_unit(h, "conv22", 128, k=1, s=1, p=0, act=act, test=test)
            h = conv_unit(h, "conv23", 10, k=1, s=1, p=0, act=act, test=test)
            h2_list.append(h)

        # Corss attention
        ca0 = attention(h2_list[0], h2_list[1], h2_list[1], 
                        div_dim=True, softmax=True)
        ca1 = attention(h2_list[1], h2_list[0], h2_list[0], 
                        div_dim=True, softmax=True)

        # Convblock 3
        h3_list = []
        for h in [ca0, ca1]:
            h = F.average_pooling(h, (6, 6))
            with nn.parameter_scope("bn2"):
                h = PF.batch_normalization(h, batch_stat=not test)
            h = F.reshape(h, (h.shape[0], np.prod(h.shape[1:])))
            h3_list.append(h)
        return h3_list