Python keras.layers.core.SpatialDropout2D() Examples

def double_conv_layer(x, size, dropout=0.0, batch_norm=True):
    if K.image_dim_ordering() == 'th':
        axis = 1
    else:
        axis = 3
    conv = Conv2D(size, (3, 3), padding='same')(x)
    if batch_norm is True:
        conv = BatchNormalization(axis=axis)(conv)
    conv = Activation('relu')(conv)
    conv = Conv2D(size, (3, 3), padding='same')(conv)
    if batch_norm is True:
        conv = BatchNormalization(axis=axis)(conv)
    conv = Activation('relu')(conv)
    if dropout > 0:
        conv = SpatialDropout2D(dropout)(conv)
    return conv 

def get_unet_resnet(input_shape):
    resnet_base = ResNet50(input_shape=input_shape, include_top=False)

    if args.show_summary:
        resnet_base.summary()

    for l in resnet_base.layers:
        l.trainable = True
    conv1 = resnet_base.get_layer("activation_1").output
    conv2 = resnet_base.get_layer("activation_10").output
    conv3 = resnet_base.get_layer("activation_22").output
    conv4 = resnet_base.get_layer("activation_40").output
    conv5 = resnet_base.get_layer("activation_49").output

    up6 = concatenate([UpSampling2D()(conv5), conv4], axis=-1)
    conv6 = conv_block_simple(up6, 256, "conv6_1")
    conv6 = conv_block_simple(conv6, 256, "conv6_2")

    up7 = concatenate([UpSampling2D()(conv6), conv3], axis=-1)
    conv7 = conv_block_simple(up7, 192, "conv7_1")
    conv7 = conv_block_simple(conv7, 192, "conv7_2")

    up8 = concatenate([UpSampling2D()(conv7), conv2], axis=-1)
    conv8 = conv_block_simple(up8, 128, "conv8_1")
    conv8 = conv_block_simple(conv8, 128, "conv8_2")

    up9 = concatenate([UpSampling2D()(conv8), conv1], axis=-1)
    conv9 = conv_block_simple(up9, 64, "conv9_1")
    conv9 = conv_block_simple(conv9, 64, "conv9_2")

    vgg = VGG16(input_shape=input_shape, input_tensor=resnet_base.input, include_top=False)
    for l in vgg.layers:
        l.trainable = False
    vgg_first_conv = vgg.get_layer("block1_conv2").output
    up10 = concatenate([UpSampling2D()(conv9), resnet_base.input, vgg_first_conv], axis=-1)
    conv10 = conv_block_simple(up10, 32, "conv10_1")
    conv10 = conv_block_simple(conv10, 32, "conv10_2")
    conv10 = SpatialDropout2D(0.2)(conv10)
    x = Conv2D(1, (1, 1), activation="sigmoid", name="prediction")(conv10)
    model = Model(resnet_base.input, x)
    return model 

def get_simple_unet(input_shape):
    img_input = Input(input_shape)
    conv1 = conv_block_simple(img_input, 32, "conv1_1")
    conv1 = conv_block_simple(conv1, 32, "conv1_2")
    pool1 = MaxPooling2D((2, 2), strides=(2, 2), padding="same", name="pool1")(conv1)

    conv2 = conv_block_simple(pool1, 64, "conv2_1")
    conv2 = conv_block_simple(conv2, 64, "conv2_2")
    pool2 = MaxPooling2D((2, 2), strides=(2, 2), padding="same", name="pool2")(conv2)

    conv3 = conv_block_simple(pool2, 128, "conv3_1")
    conv3 = conv_block_simple(conv3, 128, "conv3_2")
    pool3 = MaxPooling2D((2, 2), strides=(2, 2), padding="same", name="pool3")(conv3)

    conv4 = conv_block_simple(pool3, 256, "conv4_1")
    conv4 = conv_block_simple(conv4, 256, "conv4_2")
    conv4 = conv_block_simple(conv4, 256, "conv4_3")

    up5 = concatenate([UpSampling2D()(conv4), conv3], axis=-1)
    conv5 = conv_block_simple(up5, 128, "conv5_1")
    conv5 = conv_block_simple(conv5, 128, "conv5_2")

    up6 = concatenate([UpSampling2D()(conv5), conv2], axis=-1)
    conv6 = conv_block_simple(up6, 64, "conv6_1")
    conv6 = conv_block_simple(conv6, 64, "conv6_2")

    up7 = concatenate([UpSampling2D()(conv6), conv1], axis=-1)
    conv7 = conv_block_simple(up7, 32, "conv7_1")
    conv7 = conv_block_simple(conv7, 32, "conv7_2")

    conv7 = SpatialDropout2D(0.2)(conv7)

    prediction = Conv2D(1, (1, 1), activation="sigmoid", name="prediction")(conv7)
    model = Model(img_input, prediction)
    return model 

def get_unet_mobilenet(input_shape):
    base_model = MobileNet(include_top=False, input_shape=input_shape)

    conv1 = base_model.get_layer('conv_pw_1_relu').output
    conv2 = base_model.get_layer('conv_pw_3_relu').output
    conv3 = base_model.get_layer('conv_pw_5_relu').output
    conv4 = base_model.get_layer('conv_pw_11_relu').output
    conv5 = base_model.get_layer('conv_pw_13_relu').output
    up6 = concatenate([UpSampling2D()(conv5), conv4], axis=-1)
    conv6 = conv_block_simple(up6, 256, "conv6_1")
    conv6 = conv_block_simple(conv6, 256, "conv6_2")

    up7 = concatenate([UpSampling2D()(conv6), conv3], axis=-1)
    conv7 = conv_block_simple(up7, 256, "conv7_1")
    conv7 = conv_block_simple(conv7, 256, "conv7_2")

    up8 = concatenate([UpSampling2D()(conv7), conv2], axis=-1)
    conv8 = conv_block_simple(up8, 192, "conv8_1")
    conv8 = conv_block_simple(conv8, 128, "conv8_2")

    up9 = concatenate([UpSampling2D()(conv8), conv1], axis=-1)
    conv9 = conv_block_simple(up9, 96, "conv9_1")
    conv9 = conv_block_simple(conv9, 64, "conv9_2")

    up10 = concatenate([UpSampling2D()(conv9), base_model.input], axis=-1)
    conv10 = conv_block_simple(up10, 48, "conv10_1")
    conv10 = conv_block_simple(conv10, 32, "conv10_2")
    conv10 = SpatialDropout2D(0.2)(conv10)
    x = Conv2D(1, (1, 1), activation="sigmoid", name="prediction")(conv10)
    model = Model(base_model.input, x)
    return model 

def get_unet_inception_resnet_v2(input_shape):
    base_model = InceptionResNetV2(include_top=False, input_shape=input_shape)
    conv1 = base_model.get_layer('activation_3').output
    conv2 = base_model.get_layer('activation_5').output
    conv3 = base_model.get_layer('block35_10_ac').output
    conv4 = base_model.get_layer('block17_20_ac').output
    conv5 = base_model.get_layer('conv_7b_ac').output
    up6 = concatenate([UpSampling2D()(conv5), conv4], axis=-1)
    conv6 = conv_block_simple(up6, 256, "conv6_1")
    conv6 = conv_block_simple(conv6, 256, "conv6_2")

    up7 = concatenate([UpSampling2D()(conv6), conv3], axis=-1)
    conv7 = conv_block_simple(up7, 256, "conv7_1")
    conv7 = conv_block_simple(conv7, 256, "conv7_2")

    up8 = concatenate([UpSampling2D()(conv7), conv2], axis=-1)
    conv8 = conv_block_simple(up8, 128, "conv8_1")
    conv8 = conv_block_simple(conv8, 128, "conv8_2")

    up9 = concatenate([UpSampling2D()(conv8), conv1], axis=-1)
    conv9 = conv_block_simple(up9, 64, "conv9_1")
    conv9 = conv_block_simple(conv9, 64, "conv9_2")

    up10 = concatenate([UpSampling2D()(conv9), base_model.input], axis=-1)
    conv10 = conv_block_simple(up10, 48, "conv10_1")
    conv10 = conv_block_simple(conv10, 32, "conv10_2")
    conv10 = SpatialDropout2D(0.4)(conv10)
    x = Conv2D(1, (1, 1), activation="sigmoid", name="prediction")(conv10)
    model = Model(base_model.input, x)
    return model 

def test_tiny_conv_dropout_random(self):
        np.random.seed(1988)
        num_samples = 1
        input_dim = 8
        input_shape = (input_dim, input_dim, 3)
        num_kernels = 2
        kernel_height = 5
        kernel_width = 5
        hidden_dim = 4

        # Define a model
        model = Sequential()
        model.add(
            Conv2D(
                input_shape=input_shape,
                filters=num_kernels,
                kernel_size=(kernel_height, kernel_width),
            )
        )
        model.add(SpatialDropout2D(0.5))
        model.add(Flatten())
        model.add(Dense(hidden_dim))

        # Set some random weights
        model.set_weights([np.random.rand(*w.shape) for w in model.get_weights()])

        # Get the coreml model
        self._test_model(model) 

def multi_conv_layer(x, layers, size, dropout, batch_norm):
    from keras.layers import Conv2D
    from keras.layers.normalization import BatchNormalization
    from keras.layers.core import SpatialDropout2D, Activation

    for i in range(layers):
        x = Conv2D(size, (3, 3), padding='same')(x)
        if batch_norm is True:
            x = BatchNormalization(axis=1)(x)
        x = Activation('relu')(x)
    if dropout > 0:
        x = SpatialDropout2D(dropout)(x)
    return x 

def double_conv_layer(inputs, filter):
    conv = Conv2D(filter, (3, 3), padding='same', kernel_initializer='he_normal')(inputs)
    conv = BatchNormalization(axis=3)(conv)
    conv = Activation('relu')(conv)
    conv = Conv2D(filter, (3, 3), padding='same', kernel_initializer='he_normal')(conv)
    conv = BatchNormalization(axis=3)(conv)
    conv = Activation('relu')(conv)
    conv = SpatialDropout2D(0.1)(conv)
    return conv 

def bottleneck(inp, output, internal_scale=4, asymmetric=0, dilated=0, downsample=False, dropout_rate=0.1):
    # main branch
    internal = output // internal_scale
    encoder = inp

    # 1x1
    input_stride = 2 if downsample else 1  # the 1st 1x1 projection is replaced with a 2x2 convolution when downsampling
    encoder = Conv2D(internal, (input_stride, input_stride),
                            # padding='same',
                            strides=(input_stride, input_stride), use_bias=False)(encoder)
    # Batch normalization + PReLU
    encoder = BatchNormalization(momentum=0.1)(encoder)  # enet uses momentum of 0.1, keras default is 0.99
    encoder = PReLU(shared_axes=[1, 2])(encoder)

    # conv
    if not asymmetric and not dilated:
        encoder = Conv2D(internal, (3, 3), padding='same')(encoder)
    elif asymmetric:
        encoder = Conv2D(internal, (1, asymmetric), padding='same', use_bias=False)(encoder)
        encoder = Conv2D(internal, (asymmetric, 1), padding='same')(encoder)
    elif dilated:
        encoder = Conv2D(internal, (3, 3), dilation_rate=(dilated, dilated), padding='same')(encoder)
    else:
        raise(Exception('You shouldn\'t be here'))

    encoder = BatchNormalization(momentum=0.1)(encoder)  # enet uses momentum of 0.1, keras default is 0.99
    encoder = PReLU(shared_axes=[1, 2])(encoder)
    
    # 1x1
    encoder = Conv2D(output, (1, 1), use_bias=False)(encoder)

    encoder = BatchNormalization(momentum=0.1)(encoder)  # enet uses momentum of 0.1, keras default is 0.99
    encoder = SpatialDropout2D(dropout_rate)(encoder)

    other = inp
    # other branch
    if downsample:
        other = MaxPooling2D()(other)

        other = Permute((1, 3, 2))(other)
        pad_feature_maps = output - inp.get_shape().as_list()[3]
        tb_pad = (0, 0)
        lr_pad = (0, pad_feature_maps)
        other = ZeroPadding2D(padding=(tb_pad, lr_pad))(other)
        other = Permute((1, 3, 2))(other)

    encoder = add([encoder, other])
    encoder = PReLU(shared_axes=[1, 2])(encoder)
    return encoder 

def bottleneck(inp, output, internal_scale=4, asymmetric=0, dilated=0, downsample=False, dropout_rate=0.1):
    # main branch
    internal = output // internal_scale
    encoder = inp

    # 1x1
    input_stride = 2 if downsample else 1  # the 1st 1x1 projection is replaced with a 2x2 convolution when downsampling
    encoder = Conv2D(internal, (input_stride, input_stride),
                     # padding='same',
                     strides=(input_stride, input_stride), use_bias=False)(encoder)
    # Batch normalization + PReLU
    encoder = BatchNormalization(momentum=0.1)(encoder)  # enet_unpooling uses momentum of 0.1, keras default is 0.99
    encoder = PReLU(shared_axes=[1, 2])(encoder)

    # conv
    if not asymmetric and not dilated:
        encoder = Conv2D(internal, (3, 3), padding='same')(encoder)
    elif asymmetric:
        encoder = Conv2D(internal, (1, asymmetric), padding='same', use_bias=False)(encoder)
        encoder = Conv2D(internal, (asymmetric, 1), padding='same')(encoder)
    elif dilated:
        encoder = Conv2D(internal, (3, 3), dilation_rate=(dilated, dilated), padding='same')(encoder)
    else:
        raise(Exception('You shouldn\'t be here'))

    encoder = BatchNormalization(momentum=0.1)(encoder)  # enet_unpooling uses momentum of 0.1, keras default is 0.99
    encoder = PReLU(shared_axes=[1, 2])(encoder)
    
    # 1x1
    encoder = Conv2D(output, (1, 1), use_bias=False)(encoder)

    encoder = BatchNormalization(momentum=0.1)(encoder)  # enet_unpooling uses momentum of 0.1, keras default is 0.99
    encoder = SpatialDropout2D(dropout_rate)(encoder)

    other = inp
    # other branch
    if downsample:
        other, indices = MaxPoolingWithArgmax2D()(other)

        other = Permute((1, 3, 2))(other)
        pad_feature_maps = output - inp.get_shape().as_list()[3]
        tb_pad = (0, 0)
        lr_pad = (0, pad_feature_maps)
        other = ZeroPadding2D(padding=(tb_pad, lr_pad))(other)
        other = Permute((1, 3, 2))(other)

    encoder = add([encoder, other])
    encoder = PReLU(shared_axes=[1, 2])(encoder)
    if downsample:
        return encoder, indices
    else:
        return encoder