Python keras.layers.convolutional 模块，Cropping2D() 实例源码
我们从Python开源项目中，提取了以下6个代码示例，用于说明如何使用keras.layers.convolutional.Cropping2D()。
def get_unet0(num_start_filters=32):
inputs = Input((img_rows, img_cols, num_channels))
conv1 = ConvBN2(inputs, num_start_filters)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = ConvBN2(pool1, 2 * num_start_filters)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = ConvBN2(pool2, 4 * num_start_filters)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = ConvBN2(pool3, 8 * num_start_filters)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = ConvBN2(pool4, 16 * num_start_filters)
up6 = concatenate([UpSampling2D(size=(2, 2))(conv5), conv4])
conv6 = ConvBN2(up6, 8 * num_start_filters)
up7 = concatenate([UpSampling2D(size=(2, 2))(conv6), conv3])
conv7 = ConvBN2(up7, 4 * num_start_filters)
up8 = concatenate([UpSampling2D(size=(2, 2))(conv7), conv2])
conv8 = ConvBN2(up8, 2 * num_start_filters)
up9 = concatenate([UpSampling2D(size=(2, 2))(conv8), conv1])
conv9 = Conv2D(num_start_filters, (3, 3), padding="same", kernel_initializer="he_uniform")(up9)
conv9 = BatchNormalization()(conv9)
conv9 = Activation('selu')(conv9)
conv9 = Conv2D(num_start_filters, (3, 3), padding="same", kernel_initializer="he_uniform")(conv9)
crop9 = Cropping2D(cropping=((16, 16), (16, 16)))(conv9)
conv9 = BatchNormalization()(crop9)
conv9 = Activation('selu')(conv9)
conv10 = Conv2D(num_mask_channels, (1, 1))(conv9)
model = Model(inputs=inputs, outputs=conv10)
return model
def get_unet0(num_start_filters=32):
inputs = Input((img_rows, img_cols, num_channels))
conv1 = ConvBN2(inputs, num_start_filters)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = ConvBN2(pool1, 2 * num_start_filters)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = ConvBN2(pool2, 4 * num_start_filters)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = ConvBN2(pool3, 8 * num_start_filters)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = ConvBN2(pool4, 16 * num_start_filters)
up6 = concatenate([UpSampling2D(size=(2, 2))(conv5), conv4])
conv6 = ConvBN2(up6, 8 * num_start_filters)
up7 = concatenate([UpSampling2D(size=(2, 2))(conv6), conv3])
conv7 = ConvBN2(up7, 4 * num_start_filters)
up8 = concatenate([UpSampling2D(size=(2, 2))(conv7), conv2])
conv8 = ConvBN2(up8, 2 * num_start_filters)
up9 = concatenate([UpSampling2D(size=(2, 2))(conv8), conv1])
conv9 = Conv2D(num_start_filters, (3, 3), padding="same", kernel_initializer="he_uniform")(up9)
conv9 = BatchNormalization()(conv9)
conv9 = Activation('selu')(conv9)
conv9 = Conv2D(num_start_filters, (3, 3), padding="same", kernel_initializer="he_uniform")(conv9)
crop9 = Cropping2D(cropping=((16, 16), (16, 16)))(conv9)
conv9 = BatchNormalization()(crop9)
conv9 = Activation('selu')(conv9)
conv10 = Conv2D(num_mask_channels, (1, 1))(conv9)
model = Model(inputs=inputs, outputs=conv10)
return model
def test_cropping_2d():
nb_samples = 2
stack_size = 2
input_len_dim1 = 8
input_len_dim2 = 8
cropping = ((2, 2), (3, 3))
dim_ordering = K.image_dim_ordering()
if dim_ordering == 'th':
input = np.random.rand(nb_samples, stack_size,
input_len_dim1, input_len_dim2)
else:
input = np.random.rand(nb_samples,
input_len_dim1, input_len_dim2,
stack_size)
# basic test
layer_test(convolutional.Cropping2D,
kwargs={'cropping': cropping,
'dim_ordering': dim_ordering},
input_shape=input.shape)
# correctness test
layer = convolutional.Cropping2D(cropping=cropping,
dim_ordering=dim_ordering)
layer.build(input.shape)
output = layer(K.variable(input))
np_output = K.eval(output)
# compare with numpy
if dim_ordering == 'th':
expected_out = input[:,
:,
cropping[0][0]: -cropping[0][1],
cropping[1][0]: -cropping[1][1]]
else:
expected_out = input[:,
cropping[0][0]: -cropping[0][1],
cropping[1][0]: -cropping[1][1],
:]
assert_allclose(np_output, expected_out)
def test_cropping_2d():
nb_samples = 2
stack_size = 2
input_len_dim1 = 8
input_len_dim2 = 8
cropping = ((2, 2), (3, 3))
dim_ordering = K.image_dim_ordering()
if dim_ordering == 'th':
input = np.random.rand(nb_samples, stack_size,
input_len_dim1, input_len_dim2)
else:
input = np.random.rand(nb_samples,
input_len_dim1, input_len_dim2,
stack_size)
# basic test
layer_test(convolutional.Cropping2D,
kwargs={'cropping': cropping,
'dim_ordering': dim_ordering},
input_shape=input.shape)
# correctness test
layer = convolutional.Cropping2D(cropping=cropping,
dim_ordering=dim_ordering)
layer.build(input.shape)
output = layer(K.variable(input))
np_output = K.eval(output)
# compare with numpy
if dim_ordering == 'th':
expected_out = input[:,
:,
cropping[0][0]: -cropping[0][1],
cropping[1][0]: -cropping[1][1]]
else:
expected_out = input[:,
cropping[0][0]: -cropping[0][1],
cropping[1][0]: -cropping[1][1],
:]
assert_allclose(np_output, expected_out)
def test_cropping_2d():
nb_samples = 2
stack_size = 2
input_len_dim1 = 8
input_len_dim2 = 8
cropping = ((2, 2), (3, 3))
dim_ordering = K.image_dim_ordering()
if dim_ordering == 'th':
input = np.random.rand(nb_samples, stack_size,
input_len_dim1, input_len_dim2)
else:
input = np.random.rand(nb_samples,
input_len_dim1, input_len_dim2,
stack_size)
# basic test
layer_test(convolutional.Cropping2D,
kwargs={'cropping': cropping,
'dim_ordering': dim_ordering},
input_shape=input.shape)
# correctness test
layer = convolutional.Cropping2D(cropping=cropping,
dim_ordering=dim_ordering)
layer.build(input.shape)
output = layer(K.variable(input))
np_output = K.eval(output)
# compare with numpy
if dim_ordering == 'th':
expected_out = input[:,
:,
cropping[0][0]: -cropping[0][1],
cropping[1][0]: -cropping[1][1]]
else:
expected_out = input[:,
cropping[0][0]: -cropping[0][1],
cropping[1][0]: -cropping[1][1],
:]
assert_allclose(np_output, expected_out)
# another correctness test (no cropping)
cropping = ((0, 0), (0, 0))
layer = convolutional.Cropping2D(cropping=cropping,
dim_ordering=dim_ordering)
layer.build(input.shape)
output = layer(K.variable(input))
np_output = K.eval(output)
# compare with input
assert_allclose(np_output, input)
def CarND(input_shape, crop_shape):
model = Sequential()
# Crop
# model.add(Cropping2D(((80,20),(1,1)), input_shape=input_shape, name="Crop"))
model.add(Cropping2D(crop_shape, input_shape=input_shape, name="Crop"))
# Resize
model.add(AveragePooling2D(pool_size=(1,4), name="Resize", trainable=False))
# Normalize input.
model.add(BatchNormalization(axis=1, name="Normalize"))
# Reduce dimensions through trainable convolution, activation, and
# pooling layers.
model.add(Convolution2D(24, 3, 3, subsample=(2,2), name="Convolution2D1", activation="relu"))
model.add(MaxPooling2D(name="MaxPool1"))
model.add(Convolution2D(36, 3, 3, subsample=(1,1), name="Convolution2D2", activation="relu"))
model.add(MaxPooling2D(name="MaxPool2"))
model.add(Convolution2D(48, 3, 3, subsample=(1,1), name="Convolution2D3", activation="relu"))
model.add(MaxPooling2D(name="MaxPool3"))
# Dropout for regularization
model.add(Dropout(0.1, name="Dropout"))
# Flatten input in a non-trainable layer before feeding into
# fully-connected layers.
model.add(Flatten(name="Flatten"))
# Model steering through trainable layers comprising dense units
# as ell as dropout units for regularization.
model.add(Dense(100, activation="relu", name="FC2"))
model.add(Dense(50, activation="relu", name="FC3"))
model.add(Dense(10, activation="relu", name="FC4"))
# Generate output (steering angles) with a single non-trainable
# node.
model.add(Dense(1, name="Readout", trainable=False))
return model
# #+RESULTS:
# Here is a summary of the actual model, as generated directly by
# =model.summary= in Keras.