def custom_unet(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),
n_channel_out=1,
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
input = Input(input_shape, name="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)(input)
final = conv(n_channel_out, (1, ) * n_dim, activation='linear')(unet)
if residual:
if not (n_channel_out == input_shape[-1] if backend_channels_last()
else n_channel_out == input_shape[0]):
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(n_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])
return Model(inputs=input, outputs=final)
def predict(self, img, resizer=PadAndCropResizer(), **predict_kwargs):
"""Predict.
Parameters
----------
img : :class:`numpy.ndarray`
Input image
resizer : :class:`csbdeep.data.Resizer` or None
If necessary, input image is resized to enable neural network prediction and result is (possibly)
resized to yield original image size.
Returns
-------
(:class:`numpy.ndarray`,:class:`numpy.ndarray`)
Returns the tuple (`prob`, `dist`) of per-pixel object probabilities and star-convex polygon distances.
"""
if resizer is None:
resizer = NoResizer()
isinstance(resizer, Resizer) or _raise(ValueError())
img.ndim in (2, 3) or _raise(ValueError())
x = img
if x.ndim == 2:
x = np.expand_dims(x, (-1 if backend_channels_last() else 0))
channel = x.ndim - 1 if backend_channels_last() else 0
axes = 'YXC' if backend_channels_last() else 'CYX'
self.config.n_channel_in == x.shape[channel] or _raise(ValueError())
# resize: make divisible by power of 2 to allow downsampling steps in unet
axes_div_by = tuple(2**self.config.unet_n_depth if a != 'C' else 1
for a in axes)
x = resizer.before(x, axes, axes_div_by)
if backend_channels_last():
sh = x.shape[:-1] + (1, )
else:
sh = (1, ) + x.shape[1:]
dummy = np.empty((1, ) + sh, np.float32)
prob, dist = self.keras_model.predict([np.expand_dims(x, 0), dummy],
**predict_kwargs)
prob, dist = prob[0], dist[0]
prob = resizer.after(prob, axes)
dist = resizer.after(dist, axes)
prob = np.take(prob, 0, axis=channel)
dist = np.moveaxis(dist, channel, -1)
return prob, dist
def __init__(self, n_rays=32, n_channel_in=1, **kwargs):
"""See class docstring."""
# directly set by parameters
self.n_rays = n_rays
self.n_channel_in = int(n_channel_in)
# default config (can be overwritten by kwargs below)
self.unet_n_depth = 3
self.unet_kernel_size = (3, 3)
self.unet_n_filter_base = 32
self.net_conv_after_unet = 128
if backend_channels_last():
self.net_input_shape = (None, None, self.n_channel_in)
self.net_mask_shape = (None, None, 1)
else:
self.net_input_shape = (self.n_channel_in, None, None)
self.net_mask_shape = (1, None, None)
self.train_shape_completion = False
self.train_completion_crop = 32
self.train_patch_size = (256, 256)
self.train_dist_loss = 'mae'
self.train_epochs = 100
self.train_steps_per_epoch = 400
self.train_learning_rate = 0.0003
self.train_batch_size = 4
self.train_tensorboard = True
self.train_checkpoint = 'weights_best.h5'
self.train_reduce_lr = {'factor': 0.5, 'patience': 10}
for k in kwargs:
setattr(self, k, kwargs[k])
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 __init__(self, axes='YX', n_rays=32, n_channel_in=1, grid=(1,1), backbone='unet', **kwargs):
"""See class docstring."""
super().__init__(axes=axes, n_channel_in=n_channel_in, n_channel_out=1+n_rays)
# directly set by parameters
self.n_rays = int(n_rays)
self.grid = _normalize_grid(grid,2)
self.backbone = str(backbone).lower()
# default config (can be overwritten by kwargs below)
if self.backbone == 'unet':
self.unet_n_depth = 3
self.unet_kernel_size = 3,3
self.unet_n_filter_base = 32
self.unet_n_conv_per_depth = 2
self.unet_pool = 2,2
self.unet_activation = 'relu'
self.unet_last_activation = 'relu'
self.unet_batch_norm = False
self.unet_dropout = 0.0
self.unet_prefix = ''
self.net_conv_after_unet = 128
else:
# TODO: resnet backbone for 2D model?
raise ValueError("backbone '%s' not supported." % self.backbone)
if backend_channels_last():
self.net_input_shape = None,None,self.n_channel_in
self.net_mask_shape = None,None,1
else:
self.net_input_shape = self.n_channel_in,None,None
self.net_mask_shape = 1,None,None
self.train_shape_completion = False
self.train_completion_crop = 32
self.train_patch_size = 256,256
self.train_background_reg = 1e-4
self.train_dist_loss = 'mae'
self.train_loss_weights = 1,0.2
self.train_epochs = 400
self.train_steps_per_epoch = 100
self.train_learning_rate = 0.0003
self.train_batch_size = 4
self.train_n_val_patches = None
self.train_tensorboard = True
# the parameter 'min_delta' was called 'epsilon' for keras<=2.1.5
min_delta_key = 'epsilon' if LooseVersion(keras.__version__)<=LooseVersion('2.1.5') else 'min_delta'
self.train_reduce_lr = {'factor': 0.5, 'patience': 40, min_delta_key: 0}
self.use_gpu = False
# remove derived attributes that shouldn't be overwritten
for k in ('n_dim', 'n_channel_out'):
try: del kwargs[k]
except KeyError: pass
self.update_parameters(False, **kwargs)
def _build_resnet(self):
assert self.config.backbone == 'resnet'
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')
n_filter = self.config.resnet_n_filter_base
resnet_kwargs = dict(
kernel_size=self.config.resnet_kernel_size,
n_conv_per_block=self.config.resnet_n_conv_per_block,
batch_norm=self.config.resnet_batch_norm,
kernel_initializer=self.config.resnet_kernel_init,
activation=self.config.resnet_activation,
)
layer = input_img
layer = Conv3D(
n_filter, (7, 7, 7),
padding="same",
kernel_initializer=self.config.resnet_kernel_init)(layer)
layer = Conv3D(
n_filter, (3, 3, 3),
padding="same",
kernel_initializer=self.config.resnet_kernel_init)(layer)
pooled = np.array([1, 1, 1])
for n in range(self.config.resnet_n_blocks):
pool = 1 + (np.asarray(self.config.grid) > pooled)
pooled *= pool
if any(p > 1 for p in pool):
n_filter *= 2
layer = resnet_block(n_filter, pool=tuple(pool),
**resnet_kwargs)(layer)
if self.config.net_conv_after_resnet > 0:
layer = Conv3D(self.config.net_conv_after_resnet,
self.config.resnet_kernel_size,
name='features',
padding='same',
activation=self.config.resnet_activation)(layer)
output_prob = Conv3D(1, (1, 1, 1),
name='prob',
padding='same',
activation='sigmoid')(layer)
output_dist = Conv3D(self.config.n_rays, (1, 1, 1),
name='dist',
padding='same',
activation='linear')(layer)
return Model([input_img, input_mask], [output_prob, output_dist])
def resnet_block(n_filter,
kernel_size=(3, 3),
pool=(1, 1),
n_conv_per_block=2,
batch_norm=False,
kernel_initializer='he_normal',
activation='relu'):
n_conv_per_block >= 2 or _raise(
ValueError('required: n_conv_per_block >= 2'))
len(pool) == len(kernel_size) or _raise(
ValueError('kernel and pool sizes must match.'))
n_dim = len(kernel_size)
n_dim in (2, 3) or _raise(ValueError('resnet_block only 2d or 3d.'))
conv_layer = Conv2D if n_dim == 2 else Conv3D
conv_kwargs = dict(
padding='same',
use_bias=not batch_norm,
kernel_initializer=kernel_initializer,
)
channel_axis = -1 if backend_channels_last() else 1
def f(inp):
x = conv_layer(n_filter, kernel_size, strides=pool, **conv_kwargs)(inp)
if batch_norm:
x = BatchNormalization(axis=channel_axis)(x)
x = Activation(activation)(x)
for _ in range(n_conv_per_block - 2):
x = conv_layer(n_filter, kernel_size, **conv_kwargs)(x)
if batch_norm:
x = BatchNormalization(axis=channel_axis)(x)
x = Activation(activation)(x)
x = conv_layer(n_filter, kernel_size, **conv_kwargs)(x)
if batch_norm:
x = BatchNormalization(axis=channel_axis)(x)
if any(p != 1 for p in pool) or n_filter != K.int_shape(inp)[-1]:
inp = conv_layer(n_filter, (1, ) * n_dim,
strides=pool,
**conv_kwargs)(inp)
x = Add()([inp, x])
x = Activation(activation)(x)
return x
return f
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 __init__(self, axes='YX', n_rays=32, n_channel_in=1, grid=(1,1), backbone='unet', **kwargs):
"""See class docstring."""
super().__init__(axes=axes, n_channel_in=n_channel_in, n_channel_out=1+n_rays)
# directly set by parameters
self.n_rays = int(n_rays)
self.grid = _normalize_grid(grid,2)
self.backbone = str(backbone).lower()
# default config (can be overwritten by kwargs below)
if self.backbone in ('unet', 'unet2'):
self.unet_n_depth = 3
self.unet_kernel_size = 3,3
self.unet_n_filter_base = 32
self.unet_n_conv_per_depth = 2
self.unet_pool = 2,2
self.unet_activation = 'relu'
self.unet_last_activation = 'relu'
self.unet_batch_norm = False
self.unet_dropout = 0.0
self.unet_prefix = ''
self.net_conv_after_unet = 128
## add unet kernel initialization params
if self.backbone == 'unet2':
self.unet_kernel_init = 'he_uniform'
else:
# TODO: resnet backbone for 2D model?
raise ValueError("backbone '%s' not supported." % self.backbone)
if backend_channels_last():
self.net_input_shape = None,None,self.n_channel_in
self.net_mask_shape = None,None,1
else:
self.net_input_shape = self.n_channel_in,None,None
self.net_mask_shape = 1,None,None
self.train_shape_completion = False
self.train_completion_crop = 32
self.train_patch_size = 256,256
## whether the dist loss will be normalized by mean(mask)
self.norm_by_mask = True
self.train_background_reg = 1e-4
self.train_foreground_only = 0.9
self.train_dist_loss = 'mae'
self.train_loss_weights = 1,0.2
self.train_epochs = 400
self.train_steps_per_epoch = 100
self.train_learning_rate = 0.0003
self.train_batch_size = 4
self.train_n_val_patches = None
self.train_tensorboard = True
# the parameter 'min_delta' was called 'epsilon' for keras<=2.1.5
min_delta_key = 'epsilon' if LooseVersion(keras.__version__)<=LooseVersion('2.1.5') else 'min_delta'
self.train_reduce_lr = {'factor': 0.5, 'patience': 40, min_delta_key: 0}
## implement one cycle learning rate training policy
self.train_one_cycle_lr_max = None
## implement constrained distance output range. If we have a know range of object(nucleus) radius to predict
#self.y_range = [0.0,self.train_patch_size[0]/(2*self.grid[0])]
self.y_range = None
## implement EDT probability threshold, prob value below threshold will be set to zero
self.EDT_prob_threshold = 0
## implement dropout layer and droprate after the feature layer
self.feature_dropout = 0
## implement EDT border constraint
self.EDT_border_R = 9
self.EDT_black_border = False
self.use_gpu = False
# remove derived attributes that shouldn't be overwritten
for k in ('n_dim', 'n_channel_out'):
try: del kwargs[k]
except KeyError: pass
self.update_parameters(False, **kwargs)
def __init__(self, X, **kwargs):
"""See class docstring"""
# X is empty if config is None
if X.size != 0:
assert len(X.shape) == 4 or len(X.shape) == 5, "Only 'SZYXC' or 'SYXC' as dimensions is supported."
n_dim = len(X.shape) - 2
if n_dim == 2:
axes = 'SYXC'
elif n_dim == 3:
axes = 'SZYXC'
# parse and check axes
axes = axes_check_and_normalize(axes)
ax = axes_dict(axes)
ax = {a: (ax[a] is not None) for a in ax}
(ax['X'] and ax['Y']) or _raise(ValueError('lateral axes X and Y must be present.'))
not (ax['Z'] and ax['T']) or _raise(ValueError('using Z and T axes together not supported.'))
axes.startswith('S') or (not ax['S']) or _raise(ValueError('sample axis S must be first.'))
axes = axes.replace('S', '') # remove sample axis if it exists
if backend_channels_last():
if ax['C']:
axes[-1] == 'C' or _raise(ValueError('channel axis must be last for backend (%s).' % K.backend()))
else:
axes += 'C'
else:
if ax['C']:
axes[0] == 'C' or _raise(ValueError('channel axis must be first for backend (%s).' % K.backend()))
else:
axes = 'C' + axes
means, stds = [], []
for i in range(X.shape[-1]):
means.append(np.mean(X[..., i]))
stds.append(np.std(X[..., i]))
# normalization parameters
self.means = [str(el) for el in means]
self.stds = [str(el) for el in stds]
# directly set by parameters
self.n_dim = n_dim
self.axes = axes
# fixed parameters
self.n_channel_in = 1
self.n_channel_out = 4
self.train_loss = 'denoiseg'
# default config (can be overwritten by kwargs below)
self.unet_n_depth = 4
self.relative_weights = [1.0, 1.0, 5.0]
self.unet_kern_size = 3
self.unet_n_first = 32
self.unet_last_activation = 'linear'
self.probabilistic = False
self.unet_residual = False
if backend_channels_last():
self.unet_input_shape = self.n_dim * (None,) + (self.n_channel_in,)
else:
self.unet_input_shape = (self.n_channel_in,) + self.n_dim * (None,)
self.train_epochs = 200
self.train_steps_per_epoch = 400
self.train_learning_rate = 0.0004
self.train_batch_size = 128
self.train_tensorboard = False
self.train_checkpoint = 'weights_best.h5'
self.train_checkpoint_last = 'weights_last.h5'
self.train_checkpoint_epoch = 'weights_now.h5'
self.train_reduce_lr = {'monitor': 'val_loss', 'factor': 0.5, 'patience': 10}
self.batch_norm = True
self.n2v_perc_pix = 1.5
self.n2v_patch_shape = (64, 64) if self.n_dim == 2 else (64, 64, 64)
self.n2v_manipulator = 'uniform_withCP'
self.n2v_neighborhood_radius = 5
self.denoiseg_alpha = 0.5
# disallow setting 'probabilistic' manually
try:
kwargs['probabilistic'] = False
except:
pass
# disallow setting 'unet_residual' manually
try:
kwargs['unet_residual'] = False
except:
pass
for k in kwargs:
setattr(self, k, kwargs[k])
def __init__(self, axes='ZYX', rays=None, n_channel_in=1, grid=(1,1,1), anisotropy=None, backbone='resnet', **kwargs):
if rays is None:
if 'rays_json' in kwargs:
rays = rays_from_json(kwargs['rays_json'])
elif 'n_rays' in kwargs:
rays = Rays_GoldenSpiral(kwargs['n_rays'])
else:
rays = Rays_GoldenSpiral(96)
elif np.isscalar(rays):
rays = Rays_GoldenSpiral(rays)
super().__init__(axes=axes, n_channel_in=n_channel_in, n_channel_out=1+len(rays))
# directly set by parameters
self.n_rays = len(rays)
self.grid = _normalize_grid(grid,3)
self.anisotropy = anisotropy if anisotropy is None else tuple(anisotropy)
self.backbone = str(backbone).lower()
self.rays_json = rays.to_json()
if 'anisotropy' in self.rays_json['kwargs']:
if self.rays_json['kwargs']['anisotropy'] is None and self.anisotropy is not None:
self.rays_json['kwargs']['anisotropy'] = self.anisotropy
print("Changing 'anisotropy' of rays to %s" % str(anisotropy))
elif self.rays_json['kwargs']['anisotropy'] != self.anisotropy:
warnings.warn("Mismatch of 'anisotropy' of rays and 'anisotropy'.")
# default config (can be overwritten by kwargs below)
if self.backbone == 'unet':
self.unet_n_depth = 2
self.unet_kernel_size = 3,3,3
self.unet_n_filter_base = 32
self.unet_n_conv_per_depth = 2
self.unet_pool = 2,2,2
self.unet_activation = 'relu'
self.unet_last_activation = 'relu'
self.unet_batch_norm = False
self.unet_dropout = 0.0
self.unet_prefix = ''
self.net_conv_after_unet = 128
elif self.backbone == 'resnet':
self.resnet_n_blocks = 4
self.resnet_kernel_size = 3,3,3
self.resnet_kernel_init = 'he_normal'
self.resnet_n_filter_base = 32
self.resnet_n_conv_per_block = 3
self.resnet_activation = 'relu'
self.resnet_batch_norm = False
self.net_conv_after_resnet = 128
else:
raise ValueError("backbone '%s' not supported." % self.backbone)
if backend_channels_last():
self.net_input_shape = None,None,None,self.n_channel_in
self.net_mask_shape = None,None,None,1
else:
self.net_input_shape = self.n_channel_in,None,None,None
self.net_mask_shape = 1,None,None,None
# self.train_shape_completion = False
# self.train_completion_crop = 32
self.train_patch_size = 128,128,128
self.train_background_reg = 1e-4
self.train_dist_loss = 'mae'
self.train_loss_weights = 1,0.2
self.train_epochs = 400
self.train_steps_per_epoch = 100
self.train_learning_rate = 0.0003
self.train_batch_size = 1
self.train_n_val_patches = None
self.train_tensorboard = True
# the parameter 'min_delta' was called 'epsilon' for keras<=2.1.5
min_delta_key = 'epsilon' if LooseVersion(keras.__version__)<=LooseVersion('2.1.5') else 'min_delta'
self.train_reduce_lr = {'factor': 0.5, 'patience': 40, min_delta_key: 0}
self.use_gpu = False
# remove derived attributes that shouldn't be overwritten
for k in ('n_dim', 'n_channel_out', 'n_rays', 'rays_json'):
try: del kwargs[k]
except KeyError: pass
self.update_parameters(False, **kwargs)
def __init__(self, X, **kwargs):
"""See class docstring."""
assert len(X.shape) == 4 or len(X.shape) == 5, "Only 'SZYXC' or 'SYXC' as dimensions is supported."
n_dim = len(X.shape) - 2
n_channel_in = X.shape[-1]
n_channel_out = n_channel_in
mean = np.mean(X)
std = np.std(X)
if n_dim == 2:
axes = 'SYXC'
elif n_dim == 3:
axes = 'SZYXC'
# parse and check axes
axes = axes_check_and_normalize(axes)
ax = axes_dict(axes)
ax = {a: (ax[a] is not None) for a in ax}
(ax['X'] and ax['Y']) or _raise(ValueError('lateral axes X and Y must be present.'))
not (ax['Z'] and ax['T']) or _raise(ValueError('using Z and T axes together not supported.'))
axes.startswith('S') or (not ax['S']) or _raise(ValueError('sample axis S must be first.'))
axes = axes.replace('S','') # remove sample axis if it exists
if backend_channels_last():
if ax['C']:
axes[-1] == 'C' or _raise(ValueError('channel axis must be last for backend (%s).' % K.backend()))
else:
axes += 'C'
else:
if ax['C']:
axes[0] == 'C' or _raise(ValueError('channel axis must be first for backend (%s).' % K.backend()))
else:
axes = 'C'+axes
# normalization parameters
self.mean = str(mean)
self.std = str(std)
# directly set by parameters
self.n_dim = n_dim
self.axes = axes
self.n_channel_in = int(n_channel_in)
self.n_channel_out = int(n_channel_out)
# default config (can be overwritten by kwargs below)
self.unet_residual = False
self.unet_n_depth = 2
self.unet_kern_size = 5 if self.n_dim==2 else 3
self.unet_n_first = 32
self.unet_last_activation = 'linear'
if backend_channels_last():
self.unet_input_shape = self.n_dim*(None,) + (self.n_channel_in,)
else:
self.unet_input_shape = (self.n_channel_in,) + self.n_dim*(None,)
self.train_loss = 'mae'
self.train_epochs = 100
self.train_steps_per_epoch = 400
self.train_learning_rate = 0.0004
self.train_batch_size = 16
self.train_tensorboard = True
self.train_checkpoint = 'weights_best.h5'
self.train_reduce_lr = {'factor': 0.5, 'patience': 10}
self.batch_norm = True
self.n2v_perc_pix = 1.5
self.n2v_patch_shape = (64, 64) if self.n_dim==2 else (64, 64, 64)
self.n2v_manipulator = 'uniform_withCP'
self.n2v_neighborhood_radius = 5
# disallow setting 'n_dim' manually
try:
del kwargs['n_dim']
# warnings.warn("ignoring parameter 'n_dim'")
except:
pass
for k in kwargs:
setattr(self, k, kwargs[k])
def from_tensor(x, channel=0, single_sample=True):
return np.moveaxis((x[0] if single_sample else x),
(-1 if backend_channels_last() else 1), channel)
def load_training_data_direct(X, Y, validation_split=0, axes=None, n_images=None, verbose=False):
"""Load training data from file in ``.npz`` format.
The data file is expected to have the keys:
- ``X`` : Array of training input images.
- ``Y`` : Array of corresponding target images.
- ``axes`` : Axes of the training images.
Parameters
----------
file : str
File name
validation_split : float
Fraction of images to use as validation set during training.
axes: str, optional
Must be provided in case the loaded data does not contain ``axes`` information.
n_images : int, optional
Can be used to limit the number of images loaded from data.
verbose : bool, optional
Can be used to display information about the loaded images.
Returns
-------
tuple( tuple(:class:`numpy.ndarray`, :class:`numpy.ndarray`), tuple(:class:`numpy.ndarray`, :class:`numpy.ndarray`), str )
Returns two tuples (`X_train`, `Y_train`), (`X_val`, `Y_val`) of training and validation sets
and the axes of the input images.
The tuple of validation data will be ``None`` if ``validation_split = 0``.
"""
# f = np.load(file)
# X, Y = f['X'], f['Y']
# if axes is None:
# axes = f['axes']
axes = axes_check_and_normalize(axes)
assert X.shape == Y.shape
assert len(axes) == X.ndim
assert 'C' in axes
if n_images is None:
n_images = X.shape[0]
assert X.shape[0] == Y.shape[0]
assert 0 < n_images <= X.shape[0]
assert 0 <= validation_split < 1
X, Y = X[:n_images], Y[:n_images]
channel = axes_dict(axes)['C']
if validation_split > 0:
n_val = int(round(n_images * validation_split))
n_train = n_images - n_val
assert 0 < n_val and 0 < n_train
X_t, Y_t = X[-n_val:], Y[-n_val:]
X, Y = X[:n_train], Y[:n_train]
assert X.shape[0] == n_train and X_t.shape[0] == n_val
X_t = move_channel_for_backend(X_t, channel=channel)
Y_t = move_channel_for_backend(Y_t, channel=channel)
X = move_channel_for_backend(X, channel=channel)
Y = move_channel_for_backend(Y, channel=channel)
axes = axes.replace('C', '') # remove channel
if backend_channels_last():
axes = axes + 'C'
else:
axes = axes[:1] + 'C' + axes[1:]
data_val = (X_t, Y_t) if validation_split > 0 else None
if verbose:
ax = axes_dict(axes)
n_train, n_val = len(X), len(X_t) if validation_split > 0 else 0
image_size = tuple(X.shape[ax[a]] for a in 'TZYX' if a in axes)
n_dim = len(image_size)
n_channel_in, n_channel_out = X.shape[ax['C']], Y.shape[ax['C']]
print('number of training images:\t', n_train)
print('number of validation images:\t', n_val)
print('image size (%dD):\t\t' % n_dim, image_size)
print('axes:\t\t\t\t', axes)
print('channels in / out:\t\t', n_channel_in, '/', n_channel_out)
return (X, Y), data_val, axes
def __init__(self, X,**kwargs):
if X.size != 0:
assert len(X.shape) == 4 or len(X.shape) == 5, "Only 'SZYXC' or 'SYXC' as dimensions is supported."
n_dim = len(X.shape) - 2
if n_dim == 2:
axes = 'SYXC'
elif n_dim == 3:
axes = 'SZYXC'
# parse and check axes
axes = axes_check_and_normalize(axes)
ax = axes_dict(axes)
ax = {a: (ax[a] is not None) for a in ax}
(ax['X'] and ax['Y']) or _raise(ValueError('lateral axes X and Y must be present.'))
not (ax['Z'] and ax['T']) or _raise(ValueError('using Z and T axes together not supported.'))
axes.startswith('S') or (not ax['S']) or _raise(ValueError('sample axis S must be first.'))
axes = axes.replace('S', '') # remove sample axis if it exists
if backend_channels_last():
if ax['C']:
axes[-1] == 'C' or _raise(ValueError('channel axis must be last for backend (%s).' % K.backend()))
else:
axes += 'C'
else:
if ax['C']:
axes[0] == 'C' or _raise(ValueError('channel axis must be first for backend (%s).' % K.backend()))
else:
axes = 'C' + axes
means, stds = [], []
for i in range(X.shape[-1]):
means.append(np.mean(X[..., i]))
stds.append(np.std(X[..., i]))
# normalization parameters
self.means = [str(el) for el in means]
self.stds = [str(el) for el in stds]
# directly set by parameters
self.n_dim = n_dim
self.axes = axes
# fixed parameters
if 'C' in axes:
self.n_channel_in = X.shape[-1]
else:
self.n_channel_in = 1
self.train_loss = 'demix'
# default config (can be overwritten by kwargs below)
self.unet_n_depth = 2
self.unet_kern_size = 3
self.unet_n_first = 64
self.unet_last_activation = 'linear'
self.probabilistic = False
self.unet_residual = False
if backend_channels_last():
self.unet_input_shape = self.n_dim * (None,) + (self.n_channel_in,)
else:
self.unet_input_shape = (self.n_channel_in,) + self.n_dim * (None,)
# fixed parameters
self.train_epochs = 200
self.train_steps_per_epoch = 50
self.train_learning_rate = 0.0004
self.train_batch_size = 64
self.train_tensorboard = False
self.train_checkpoint = 'weights_best.h5'
self.train_checkpoint_last = 'weights_last.h5'
self.train_checkpoint_epoch = 'weights_now.h5'
self.train_reduce_lr = {'monitor': 'val_loss', 'factor': 0.5, 'patience': 10}
self.batch_norm = False
self.n2v_perc_pix = 1.5
self.n2v_patch_shape = (128, 128) if self.n_dim == 2 else (64, 64, 64)
self.n2v_manipulator = 'uniform_withCP'
self.n2v_neighborhood_radius = 5
self.single_net_per_channel = False
self.channel_denoised = False
self.multi_objective = True
self.normalizer = 'none'
self.weights_objectives = [0.1, 0, 0.45, 0.45]
self.distributions = 'gauss'
self.n2v_leave_center = False
self.scale_aug = False
self.structN2Vmask = None
self.n_back_modes = 2
self.n_fore_modes = 2
self.n_instance_seg = 0
self.n_back_i_modes = 1
self.n_fore_i_modes = 1
self.fit_std = False
self.fit_mean = True
# self.n_channel_out = (self.n_back_modes + self.n_fore_modes) * self.n_channel_in * self.n_components +\
# self.n_instance_seg * (self.n_back_i_modes+self.n_fore_i_modes)
try:
kwargs['probabilistic'] = False
except:
pass
# disallow setting 'unet_residual' manually
try:
kwargs['unet_residual'] = False
except:
pass
# print('KWARGS')
for k in kwargs:
# print(k, kwargs[k])
setattr(self, k, kwargs[k])
self.n_components = 3 if (self.fit_std & self.fit_mean) else 2
self.n_channel_out = (self.n_back_modes + self.n_fore_modes) * self.n_channel_in * self.n_components + \
self.n_instance_seg * (self.n_back_i_modes + self.n_fore_i_modes)
def __init__(self, X, **kwargs):
"""See class docstring"""
assert X.size != 0
assert len(X.shape) == 4 or len(
X.shape) == 5, "Only 'SZYXC' or 'SYXC' as dimensions is supported."
n_dim = len(X.shape) - 2
if n_dim == 2:
axes = 'SYXC'
elif n_dim == 3:
axes = 'SZYXC'
# parse and check axes
axes = axes_check_and_normalize(axes)
ax = axes_dict(axes)
ax = {a: (ax[a] is not None) for a in ax}
(ax['X'] and ax['Y']) or _raise(
ValueError('lateral axes X and Y must be present.'))
not (ax['Z'] and ax['T']) or _raise(
ValueError('using Z and T axes together not supported.'))
axes.startswith('S') or (not ax['S']) or _raise(
ValueError('sample axis S must be first.'))
axes = axes.replace('S', '') # remove sample axis if it exists
if backend_channels_last():
if ax['C']:
axes[-1] == 'C' or _raise(
ValueError('channel axis must be last for backend (%s).' %
K.backend()))
else:
axes += 'C'
else:
if ax['C']:
axes[0] == 'C' or _raise(
ValueError('channel axis must be first for backend (%s).' %
K.backend()))
else:
axes = 'C' + axes
assert X.shape[-1] == 1
n_channel_in = 1
n_channel_out = 3
# directly set by parameters
self.n_dim = n_dim
self.axes = axes
# fixed parameters
self.n_channel_in = n_channel_in
self.n_channel_out = n_channel_out
self.train_loss = 'seg'
# default config (can be overwritten by kwargs below)
self.unet_n_depth = 4
self.relative_weights = [1.0, 1.0, 5.0]
self.unet_kern_size = 3
self.unet_n_first = 32
self.unet_last_activation = 'linear'
self.probabilistic = False
self.unet_residual = False
if backend_channels_last():
self.unet_input_shape = self.n_dim * (None, ) + (
self.n_channel_in, )
else:
self.unet_input_shape = (
self.n_channel_in, ) + self.n_dim * (None, )
self.train_epochs = 200
self.train_steps_per_epoch = 400
self.train_learning_rate = 0.0004
self.train_batch_size = 128
self.train_tensorboard = False
self.train_checkpoint = 'weights_best.h5'
self.train_checkpoint_last = 'weights_last.h5'
self.train_checkpoint_epoch = 'weights_now.h5'
self.train_reduce_lr = {'factor': 0.5, 'patience': 10}
self.batch_norm = True
# disallow setting 'n_dim' manually
try:
del kwargs['n_dim']
# warnings.warn("ignoring parameter 'n_dim'")
except:
pass
# disallow setting 'n_channel_in' manually
try:
del kwargs['n_channel_in']
# warnings.warn("ignoring parameter 'n_dim'")
except:
pass
# disallow setting 'n_channel_out' manually
try:
del kwargs['n_channel_out']
# warnings.warn("ignoring parameter 'n_dim'")
except:
pass
# disallow setting 'train_loss' manually
try:
del kwargs['train_loss']
# warnings.warn("ignoring parameter 'n_dim'")
except:
pass
# disallow setting 'probabilistic' manually
try:
del kwargs['probabilistic']
except:
pass
# disallow setting 'unet_residual' manually
try:
del kwargs['unet_residual']
except:
pass
for k in kwargs:
setattr(self, k, kwargs[k])
def tensor_num_channels(x):
return x.shape[-1 if backend_channels_last() else 1]
import keras.backend as K
from keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, TensorBoard
from keras.layers import Input, Conv2D
from keras.models import Model
from keras.utils import Sequence
from keras.optimizers import Adam
from csbdeep.internals.blocks import unet_block
from csbdeep.utils import _raise, Path, load_json, save_json, backend_channels_last
from csbdeep.data import Resizer, NoResizer, PadAndCropResizer
from .utils import star_dist, edt_prob
from skimage.segmentation import clear_border
if not backend_channels_last():
raise NotImplementedError(
"Keras is configured to use the '%s' image data format, which is currently not supported. "
"Please change it to use 'channels_last' instead: "
"https://keras.io/getting-started/faq/#where-is-the-keras-configuration-file-stored"
% K.image_data_format())
def masked_loss(mask, penalty):
def _loss(d_true, d_pred):
return K.mean(mask * penalty(d_pred - d_true), axis=-1)
return _loss
def masked_loss_mae(mask):
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 unet_block(n_depth=2,
n_filter_base=16,
kernel_size=(3, 3),
n_conv_per_depth=2,
activation="relu",
batch_norm=False,
dropout=0.0,
last_activation=None,
pool=(2, 2),
prefix=''):
if len(pool) != len(kernel_size):
raise ValueError('kernel and pool sizes must match.')
n_dim = len(kernel_size)
if n_dim not in (2, 3):
raise ValueError('unet_block only 2d or 3d.')
conv_block = conv_block2 if n_dim == 2 else conv_block3
pooling = MaxPooling2D if n_dim == 2 else MaxPooling3D
upsampling = UpSampling2D if n_dim == 2 else UpSampling3D
if last_activation is None:
last_activation = activation
channel_axis = -1 if backend_channels_last() else 1
def _name(s):
return prefix + s
def _func(input):
skip_layers = []
layer = input
# down ...
for n in range(n_depth):
for i in range(n_conv_per_depth):
layer = conv_block(n_filter_base * 2**n,
*kernel_size,
dropout=dropout,
activation=activation,
batch_norm=batch_norm,
name=_name("down_level_%s_no_%s" %
(n, i)))(layer)
skip_layers.append(layer)
layer = pooling(pool, name=_name("max_%s" % n))(layer)
# middle
for i in range(n_conv_per_depth - 1):
layer = conv_block(n_filter_base * 2**n_depth,
*kernel_size,
dropout=dropout,
activation=activation,
batch_norm=batch_norm,
name=_name("middle_%s" % i))(layer)
layer = conv_block(n_filter_base * 2**max(0, n_depth - 1),
*kernel_size,
dropout=dropout,
activation=activation,
batch_norm=batch_norm,
name=_name("middle_%s" % n_conv_per_depth))(layer)
# ...and up with skip layers
for n in reversed(range(n_depth)):
layer = Concatenate(axis=channel_axis)(
[upsampling(pool)(layer), skip_layers[n]])
for i in range(n_conv_per_depth - 1):
layer = conv_block(n_filter_base * 2**n,
*kernel_size,
dropout=dropout,
activation=activation,
batch_norm=batch_norm,
name=_name("up_level_%s_no_%s" %
(n, i)))(layer)
layer = conv_block(
n_filter_base * 2**max(0, n - 1),
*kernel_size,
dropout=dropout,
activation=activation if n > 0 else last_activation,
batch_norm=batch_norm,
name=_name("up_level_%s_no_%s" % (n, n_conv_per_depth)))(layer)
return layer
return _func
