def _build(self):
input_img = Input(self.config.net_input_shape, name='input')
input_mask = Input(self.config.net_mask_shape, name='dist_mask')
unet_kwargs = {
k[5:]: v
for (k, v) in vars(self.config).items() if k.startswith('unet_')
}
unet = unet_block(**unet_kwargs)(input_img)
if self.config.net_conv_after_unet > 0:
unet = Conv2D(self.config.net_conv_after_unet,
self.config.unet_kernel_size,
name='features',
padding='same',
activation='relu')(unet)
output_prob = Conv2D(1, (1, 1),
name='prob',
padding='same',
activation='sigmoid')(unet)
output_dist = Conv2D(self.config.n_rays, (1, 1),
name='dist',
padding='same',
activation='linear')(unet)
return Model([input_img, input_mask], [output_prob, output_dist])
def _build_unet(self):
assert self.config.backbone == 'unet'
input_img = Input(self.config.net_input_shape, name='input')
if backend_channels_last():
grid_shape = tuple(n//g if n is not None else None for g,n in zip(self.config.grid, self.config.net_mask_shape[:-1])) + (1,)
else:
grid_shape = (1,) + tuple(n//g if n is not None else None for g,n in zip(self.config.grid, self.config.net_mask_shape[1:]))
input_mask = Input(grid_shape, name='dist_mask')
unet_kwargs = {k[len('unet_'):]:v for (k,v) in vars(self.config).items() if k.startswith('unet_')}
# maxpool input image to grid size
pooled = np.array([1,1,1])
pooled_img = input_img
while tuple(pooled) != tuple(self.config.grid):
pool = 1 + (np.asarray(self.config.grid) > pooled)
pooled *= pool
for _ in range(self.config.unet_n_conv_per_depth):
pooled_img = Conv3D(self.config.unet_n_filter_base, self.config.unet_kernel_size,
padding="same", activation=self.config.unet_activation)(pooled_img)
pooled_img = MaxPooling3D(pool)(pooled_img)
unet = unet_block(**unet_kwargs)(pooled_img)
if self.config.net_conv_after_unet > 0:
unet = Conv3D(self.config.net_conv_after_unet, self.config.unet_kernel_size,
name='features', padding='same', activation=self.config.unet_activation)(unet)
output_prob = Conv3D(1, (1,1,1), name='prob', padding='same', activation='sigmoid')(unet)
output_dist = Conv3D(self.config.n_rays, (1,1,1), name='dist', padding='same', activation='linear')(unet)
return Model([input_img,input_mask], [output_prob,output_dist])
def _build(self):
self.config.backbone == 'unet' or _raise(NotImplementedError())
unet_kwargs = {k[len('unet_'):]:v for (k,v) in vars(self.config).items() if k.startswith('unet_')}
input_img = Input(self.config.net_input_shape, name='input')
# maxpool input image to grid size
pooled = np.array([1,1])
pooled_img = input_img
while tuple(pooled) != tuple(self.config.grid):
pool = 1 + (np.asarray(self.config.grid) > pooled)
pooled *= pool
for _ in range(self.config.unet_n_conv_per_depth):
pooled_img = Conv2D(self.config.unet_n_filter_base, self.config.unet_kernel_size,
padding='same', activation=self.config.unet_activation)(pooled_img)
pooled_img = MaxPooling2D(pool)(pooled_img)
unet = unet_block(**unet_kwargs)(pooled_img)
if self.config.net_conv_after_unet > 0:
unet = Conv2D(self.config.net_conv_after_unet, self.config.unet_kernel_size,
name='features', padding='same', activation=self.config.unet_activation)(unet)
output_prob = Conv2D(1, (1,1), name='prob', padding='same', activation='sigmoid')(unet)
output_dist = Conv2D(self.config.n_rays, (1,1), name='dist', padding='same', activation='linear')(unet)
return Model([input_img], [output_prob,output_dist])
def _build(self):
#self.config.backbone == 'unet'|'unet2' or _raise(NotImplementedError())
self.config.backbone in ('unet','unet2') or _raise(NotImplementedError())
input_img = Input(self.config.net_input_shape, name='input')
if backend_channels_last():
grid_shape = tuple(n//g if n is not None else None for g,n in zip(self.config.grid, self.config.net_mask_shape[:-1])) + (1,)
else:
grid_shape = (1,) + tuple(n//g if n is not None else None for g,n in zip(self.config.grid, self.config.net_mask_shape[1:]))
input_mask = Input(grid_shape, name='dist_mask')
unet_kwargs = {k[len('unet_'):]:v for (k,v) in vars(self.config).items() if k.startswith('unet_')}
# maxpool input image to grid size
pooled = np.array([1,1])
pooled_img = input_img
while tuple(pooled) != tuple(self.config.grid):
pool = 1 + (np.asarray(self.config.grid) > pooled)
pooled *= pool
for _ in range(self.config.unet_n_conv_per_depth):
pooled_img = Conv2D(self.config.unet_n_filter_base, self.config.unet_kernel_size,
padding='same', activation=self.config.unet_activation)(pooled_img)
pooled_img = MaxPooling2D(pool)(pooled_img)
if(self.config.backbone == 'unet2'):
unet = unet_block2(**unet_kwargs)(pooled_img)
if self.config.net_conv_after_unet > 0:
unet = Conv2D(self.config.net_conv_after_unet, self.config.unet_kernel_size,
name='features', padding='same', activation=self.config.unet_activation)(unet)
## extra dropout layer after the feature layer
unet = Dropout(rate = self.config.feature_dropout)(unet)
output_prob = Conv2D(1, (1,1), name='prob', padding='same', activation='sigmoid',kernel_initializer='glorot_uniform' )(unet)
if self.config.y_range is None:
output_dist = Conv2D(self.config.n_rays, (1,1), name='dist', padding='same', activation='linear')(unet)
else:
output_dist = Conv2D(self.config.n_rays, (1,1), name='linear', padding='same', activation='linear')(unet)
output_dist = RangedSig(y_min=self.config.y_range[0], y_max=self.config.y_range[1], name='dist')(output_dist)
# if self.config.y_range is not None:
# y_min = self.config.y_range[0]
# y_max = self.config.y_range[1]
# output_dist = Conv2D(self.config.n_rays, (1,1), name='dist', padding='same',
# == activation=Activation(lambda x: output_to_y_range(x, y_min, y_max)))(unet)
# else:
# output_dist = Conv2D(self.config.n_rays, (1,1), name='dist', padding='same', activation='linear')(unet)
else:
unet = unet_block(**unet_kwargs)(pooled_img)
if self.config.net_conv_after_unet > 0:
unet = Conv2D(self.config.net_conv_after_unet, self.config.unet_kernel_size,
name='features', padding='same', activation=self.config.unet_activation)(unet)
output_prob = Conv2D(1, (1,1), name='prob', padding='same', activation='sigmoid')(unet)
if self.config.y_range is not None:
y_min = self.config.y_range[0]
y_max = self.config.y_range[1]
output_dist = Conv2D(self.config.n_rays, (1,1), name='dist', padding='same',
activation=Activation(lambda x: output_to_y_range(x, y_min, y_max)))(unet)
else:
output_dist = Conv2D(self.config.n_rays, (1,1), name='dist', padding='same', activation='linear')(unet)
# output_dist = Conv2D(self.config.n_rays, (1,1), name='dist', padding='same', activation='linear')(unet)
return Model([input_img,input_mask], [output_prob,output_dist])
def _build(self):
#~~
#with tf.device('gpu:0'):
#~~Indentation
self.config.backbone == 'unet' or _raise(NotImplementedError())
input_img = Input(self.config.net_input_shape, name='input')
if backend_channels_last():
grid_shape = tuple(n // g if n is not None else None
for g, n in zip(self.config.grid, self.config.
net_mask_shape[:-1])) + (1, )
else:
grid_shape = (1, ) + tuple(
n // g if n is not None else None for g, n in zip(
self.config.grid, self.config.net_mask_shape[1:]))
input_mask = Input(grid_shape, name='dist_mask')
unet_kwargs = {
k[len('unet_'):]: v
for (k, v) in vars(self.config).items() if k.startswith('unet_')
}
# maxpool input image to grid size
pooled = np.array([1, 1])
pooled_img = input_img
while tuple(pooled) != tuple(self.config.grid):
pool = 1 + (np.asarray(self.config.grid) > pooled)
pooled *= pool
for _ in range(self.config.unet_n_conv_per_depth):
pooled_img = Conv2D(
self.config.unet_n_filter_base,
self.config.unet_kernel_size,
padding='same',
activation=self.config.unet_activation)(pooled_img)
pooled_img = MaxPooling2D(pool)(pooled_img)
unet = unet_block(**unet_kwargs)(pooled_img)
if self.config.net_conv_after_unet > 0:
unet = Conv2D(self.config.net_conv_after_unet,
self.config.unet_kernel_size,
name='features',
padding='same',
activation=self.config.unet_activation)(unet)
output_prob = Conv2D(1, (1, 1),
name='prob',
padding='same',
activation='sigmoid')(unet)
output_dist = Conv2D(self.config.n_rays, (1, 1),
name='dist',
padding='same',
activation='linear')(unet)
#~~
#with tf. device("gpu:0"):
compModel = Model([input_img, input_mask], [output_prob, output_dist])
compModel.compile(optimizer=keras.optimizers.Adagrad(
lr=self.config.lr))
return compModel
def build_single_unet_per_channel(input_shape,
last_activation,
n_depth=2,
n_filter_base=16,
kernel_size=(3, 3, 3),
n_conv_per_depth=2,
activation="relu",
batch_norm=False,
dropout=0.0,
pool_size=(2, 2, 2),
residual=False,
prob_out=False,
eps_scale=1e-3):
""" TODO """
if last_activation is None:
raise ValueError(
"last activation has to be given (e.g. 'sigmoid', 'relu')!")
all((s % 2 == 1 for s in kernel_size)) or _raise(
ValueError('kernel size should be odd in all dimensions.'))
channel_axis = -1 if backend_channels_last() else 1
n_dim = len(kernel_size)
conv = Conv2D if n_dim == 2 else Conv3D
num_channels = input_shape[channel_axis]
input = Input(input_shape, name="input")
out_channels = []
num_channel_out = 1
for i in range(num_channels):
c = Lambda(lambda x: x[:, ..., i:i + 1])(input)
unet = unet_block(n_depth,
n_filter_base,
kernel_size,
activation=activation,
dropout=dropout,
batch_norm=batch_norm,
n_conv_per_depth=n_conv_per_depth,
pool=pool_size,
prefix='channel_{}'.format(i))(c)
final = conv(num_channel_out, (1, ) * n_dim, activation='linear')(unet)
if residual:
if not (num_channel_out == 1
if backend_channels_last() else num_channel_out == 1):
raise ValueError(
"number of input and output channels must be the same for a residual net."
)
final = Add()([final, input])
final = Activation(activation=last_activation)(final)
if prob_out:
scale = conv(num_channel_out, (1, ) * n_dim,
activation='softplus')(unet)
scale = Lambda(lambda x: x + np.float32(eps_scale))(scale)
final = Concatenate(axis=channel_axis)([final, scale])
out_channels.append(final)
if len(out_channels) > 1:
output = Concatenate(axis=channel_axis)(out_channels)
return Model(inputs=input, outputs=output)
else:
return Model(inputs=input, outputs=out_channels[0])
