Python functions.max_pooling函数代码示例

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 mnist_lenet_prediction(image, test=False):
    """
    Construct LeNet for MNIST.
    """
    image /= 255.0
    c1 = PF.convolution(image, 16, (5, 5), name='conv1')
    c1 = F.relu(F.max_pooling(c1, (2, 2)), inplace=True)
    c2 = PF.convolution(c1, 16, (5, 5), name='conv2')
    c2 = F.relu(F.max_pooling(c2, (2, 2)), inplace=True)
    c3 = F.relu(PF.affine(c2, 50, name='fc3'), inplace=True)
    c4 = PF.affine(c3, 10, name='fc4')
    return c4

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 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 mnist_resnet_prediction(image, test=False):
    """
    Construct ResNet for MNIST.
    """
    image /= 255.0

    def bn(x):
        return PF.batch_normalization(x, batch_stat=not test)

    def res_unit(x, scope):
        C = x.shape[1]
        with nn.parameter_scope(scope):
            with nn.parameter_scope('conv1'):
                h = F.elu(bn(PF.convolution(x, C / 2, (1, 1), with_bias=False)))
            with nn.parameter_scope('conv2'):
                h = F.elu(
                    bn(PF.convolution(h, C / 2, (3, 3), pad=(1, 1), with_bias=False)))
            with nn.parameter_scope('conv3'):
                h = bn(PF.convolution(h, C, (1, 1), with_bias=False))
        return F.elu(F.add2(h, x, inplace=True))
    # Conv1 --> 64 x 32 x 32
    with nn.parameter_scope("conv1"):
        c1 = F.elu(
            bn(PF.convolution(image, 64, (3, 3), pad=(3, 3), with_bias=False)))
    # Conv2 --> 64 x 16 x 16
    c2 = F.max_pooling(res_unit(c1, "conv2"), (2, 2))
    # Conv3 --> 64 x 8 x 8
    c3 = F.max_pooling(res_unit(c2, "conv3"), (2, 2))
    # Conv4 --> 64 x 8 x 8
    c4 = res_unit(c3, "conv4")
    # Conv5 --> 64 x 4 x 4
    c5 = F.max_pooling(res_unit(c4, "conv5"), (2, 2))
    # Conv5 --> 64 x 4 x 4
    c6 = res_unit(c5, "conv6")
    pl = F.average_pooling(c6, (4, 4))
    with nn.parameter_scope("classifier"):
        y = PF.affine(pl, 10)
    return y

def stochastic_res_unit(x, scope_name, act=F.relu, dn=False, test=False):
    if not test:
        flag = np.random.randint(2)
        if flag:
            h = res_block(x, scope_name, act=act, dn=dn, test=test)
            h = F.add2(h, x)
        else:
            h = x
    else:
        h = res_block(x, scope_name, act=act, dn=dn, test=test)
        h = F.add2(h, x)
    h = act(h)
    # Maxpooling
    if dn:
        h = F.max_pooling(h, kernel=(2, 2), stride=(2, 2))
    return h

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