from __future__ import print_function, unicode_literals, absolute_import, division
import numpy as np
import sys
import warnings
import math
from tqdm import tqdm
from collections import namedtuple
from pathlib import Path
import threading
import functools
import scipy.ndimage as ndi
import numbers
from csbdeep.models.base_model import BaseModel
from csbdeep.utils.tf import export_SavedModel, keras_import, IS_TF_1, CARETensorBoard
import tensorflow as tf
K = keras_import('backend')
Sequence = keras_import('utils', 'Sequence')
Adam = keras_import('optimizers', 'Adam')
ReduceLROnPlateau, TensorBoard = keras_import('callbacks', 'ReduceLROnPlateau', 'TensorBoard')
from csbdeep.utils import _raise, backend_channels_last, axes_check_and_normalize, axes_dict, load_json, save_json
from csbdeep.internals.predict import tile_iterator, total_n_tiles
from csbdeep.internals.train import RollingSequence
from csbdeep.data import Resizer
from ..sample_patches import get_valid_inds
from ..nms import _ind_prob_thresh
from ..utils import _is_power_of_2, _is_floatarray, optimize_threshold
# TODO: helper function to check if receptive field of cnn is sufficient for object sizes in GT
def generic_masked_loss(mask, loss, weights=1, norm_by_mask=True, reg_weight=0, reg_penalty=K.abs):
def _loss(y_true, y_pred):
actual_loss = K.mean(mask * weights * loss(y_true, y_pred), axis=-1)
norm_mask = (K.mean(mask) + K.epsilon()) if norm_by_mask else 1
if reg_weight > 0:
reg_loss = K.mean((1-mask) * reg_penalty(y_pred), axis=-1)
return actual_loss / norm_mask + reg_weight * reg_loss
else:
return actual_loss / norm_mask
return _loss
def masked_loss(mask, penalty, reg_weight, norm_by_mask):
loss = lambda y_true, y_pred: penalty(y_true - y_pred)
return generic_masked_loss(mask, loss, reg_weight=reg_weight, norm_by_mask=norm_by_mask)
# TODO: should we use norm_by_mask=True in the loss or only in a metric?
# previous 2D behavior was norm_by_mask=False
# same question for reg_weight? use 1e-4 (as in 3D) or 0 (as in 2D)?
def masked_loss_mae(mask, reg_weight=0, norm_by_mask=True):
return masked_loss(mask, K.abs, reg_weight=reg_weight, norm_by_mask=norm_by_mask)
def masked_loss_mse(mask, reg_weight=0, norm_by_mask=True):
return masked_loss(mask, K.square, reg_weight=reg_weight, norm_by_mask=norm_by_mask)
def masked_metric_mae(mask):
def relevant_mae(y_true, y_pred):
return masked_loss(mask, K.abs, reg_weight=0, norm_by_mask=True)(y_true, y_pred)
return relevant_mae
def masked_metric_mse(mask):
def relevant_mse(y_true, y_pred):
return masked_loss(mask, K.square, reg_weight=0, norm_by_mask=True)(y_true, y_pred)
return relevant_mse
def kld(y_true, y_pred):
y_true = K.clip(y_true, K.epsilon(), 1)
y_pred = K.clip(y_pred, K.epsilon(), 1)
return K.mean(K.binary_crossentropy(y_true, y_pred) - K.binary_crossentropy(y_true, y_true), axis=-1)
def masked_loss_iou(mask, reg_weight=0, norm_by_mask=True):
def iou_loss(y_true, y_pred):
axis = -1 if backend_channels_last() else 1
# y_pred can be negative (since not constrained) -> 'inter' can be very large for y_pred << 0
# - clipping y_pred values at 0 can lead to vanishing gradients
# - 'K.sign(y_pred)' term fixes issue by enforcing that y_pred values >= 0 always lead to larger 'inter' (lower loss)
inter = K.mean(K.sign(y_pred)*K.square(K.minimum(y_true,y_pred)), axis=axis)
union = K.mean(K.square(K.maximum(y_true,y_pred)), axis=axis)
iou = inter/(union+K.epsilon())
iou = K.expand_dims(iou,axis)
loss = 1. - iou # + 0.005*K.abs(y_true-y_pred)
return loss
return generic_masked_loss(mask, iou_loss, reg_weight=reg_weight, norm_by_mask=norm_by_mask)
def masked_metric_iou(mask, reg_weight=0, norm_by_mask=True):
def iou_metric(y_true, y_pred):
axis = -1 if backend_channels_last() else 1
y_pred = K.maximum(0., y_pred)
inter = K.mean(K.square(K.minimum(y_true,y_pred)), axis=axis)
union = K.mean(K.square(K.maximum(y_true,y_pred)), axis=axis)
iou = inter/(union+K.epsilon())
loss = K.expand_dims(iou,axis)
return loss
return generic_masked_loss(mask, iou_metric, reg_weight=reg_weight, norm_by_mask=norm_by_mask)
def weighted_categorical_crossentropy(weights, ndim):
""" ndim = (2,3) """
axis = -1 if backend_channels_last() else 1
shape = [1]*(ndim+2)
shape[axis] = len(weights)
weights = np.broadcast_to(weights, shape)
weights = K.constant(weights)
def weighted_cce(y_true, y_pred):
# ignore pixels that have y_true (prob_class) < 0
mask = K.cast(y_true>=0, K.floatx())
y_pred /= K.sum(y_pred+K.epsilon(), axis=axis, keepdims=True)
y_pred = K.clip(y_pred, K.epsilon(), 1. - K.epsilon())
loss = - K.sum(weights*mask*y_true*K.log(y_pred), axis = axis)
return loss
return weighted_cce
class StarDistDataBase(RollingSequence):
def __init__(self, X, Y, n_rays, grid, batch_size, patch_size, length,
n_classes=None, classes=None,
use_gpu=False, sample_ind_cache=True, maxfilter_patch_size=None, augmenter=None, foreground_prob=0):
super().__init__(data_size=len(X), batch_size=batch_size, length=length, shuffle=True)
if isinstance(X, (np.ndarray, tuple, list)):
X = [x.astype(np.float32, copy=False) for x in X]
# sanity checks
len(X)==len(Y) and len(X)>0 or _raise(ValueError("X and Y can't be empty and must have same length"))
if classes is None:
# set classes to None for all images (i.e. defaults to every object instance assigned the same class)
classes = (None,)*len(X)
else:
n_classes is not None or warnings.warn("Ignoring classes since n_classes is None")
len(classes)==len(X) or _raise(ValueError("X and classes must have same length"))
self.n_classes, self.classes = n_classes, classes
nD = len(patch_size)
assert nD in (2,3)
x_ndim = X[0].ndim
assert x_ndim in (nD,nD+1)
if isinstance(X, (np.ndarray, tuple, list)) and \
isinstance(Y, (np.ndarray, tuple, list)):
all(y.ndim==nD and x.ndim==x_ndim and x.shape[:nD]==y.shape for x,y in zip(X,Y)) or _raise(ValueError("images and masks should have corresponding shapes/dimensions"))
all(x.shape[:nD]>=tuple(patch_size) for x in X) or _raise(ValueError("Some images are too small for given patch_size {patch_size}".format(patch_size=patch_size)))
if x_ndim == nD:
self.n_channel = None
else:
self.n_channel = X[0].shape[-1]
if isinstance(X, (np.ndarray, tuple, list)):
assert all(x.shape[-1]==self.n_channel for x in X)
assert 0 <= foreground_prob <= 1
self.X, self.Y = X, Y
# self.batch_size = batch_size
self.n_rays = n_rays
self.patch_size = patch_size
self.ss_grid = (slice(None),) + tuple(slice(0, None, g) for g in grid)
self.grid = tuple(grid)
self.use_gpu = bool(use_gpu)
if augmenter is None:
augmenter = lambda *args: args
callable(augmenter) or _raise(ValueError("augmenter must be None or callable"))
self.augmenter = augmenter
self.foreground_prob = foreground_prob
if self.use_gpu:
from gputools import max_filter
self.max_filter = lambda y, patch_size: max_filter(y.astype(np.float32), patch_size)
else:
from scipy.ndimage.filters import maximum_filter
self.max_filter = lambda y, patch_size: maximum_filter(y, patch_size, mode='constant')
self.maxfilter_patch_size = maxfilter_patch_size if maxfilter_patch_size is not None else self.patch_size
self.sample_ind_cache = sample_ind_cache
self._ind_cache_fg = {}
self._ind_cache_all = {}
self.lock = threading.Lock()
def get_valid_inds(self, k, foreground_prob=None):
if foreground_prob is None:
foreground_prob = self.foreground_prob
foreground_only = np.random.uniform() < foreground_prob
_ind_cache = self._ind_cache_fg if foreground_only else self._ind_cache_all
if k in _ind_cache:
inds = _ind_cache[k]
else:
patch_filter = (lambda y,p: self.max_filter(y, self.maxfilter_patch_size) > 0) if foreground_only else None
inds = get_valid_inds(self.Y[k], self.patch_size, patch_filter=patch_filter)
if self.sample_ind_cache:
with self.lock:
_ind_cache[k] = inds
if foreground_only and len(inds[0])==0:
# no foreground pixels available
return self.get_valid_inds(k, foreground_prob=0)
return inds
def channels_as_tuple(self, x):
if self.n_channel is None:
return (x,)
else:
return tuple(x[...,i] for i in range(self.n_channel))
class StarDistBase(BaseModel):
def __init__(self, config, name=None, basedir='.'):
super().__init__(config=config, name=name, basedir=basedir)
threshs = dict(prob=None, nms=None)
if basedir is not None:
try:
threshs = load_json(str(self.logdir / 'thresholds.json'))
print("Loading thresholds from 'thresholds.json'.")
if threshs.get('prob') is None or not (0 < threshs.get('prob') < 1):
print("- Invalid 'prob' threshold (%s), using default value." % str(threshs.get('prob')))
threshs['prob'] = None
if threshs.get('nms') is None or not (0 < threshs.get('nms') < 1):
print("- Invalid 'nms' threshold (%s), using default value." % str(threshs.get('nms')))
threshs['nms'] = None
except FileNotFoundError:
if config is None and len(tuple(self.logdir.glob('*.h5'))) > 0:
print("Couldn't load thresholds from 'thresholds.json', using default values. "
"(Call 'optimize_thresholds' to change that.)")
self.thresholds = dict (
prob = 0.5 if threshs['prob'] is None else threshs['prob'],
nms = 0.4 if threshs['nms'] is None else threshs['nms'],
)
print("Using default values: prob_thresh={prob:g}, nms_thresh={nms:g}.".format(prob=self.thresholds.prob, nms=self.thresholds.nms))
@property
def thresholds(self):
return self._thresholds
def _is_multiclass(self):
return (self.config.n_classes is not None)
def _parse_classes_arg(self, classes, length):
""" creates a proper classes tuple from different possible "classes" arguments in model.train()
classes can be
"auto" -> all objects will be assigned to the first foreground class (unless n_classes is None)
single integer -> all objects will be assigned that class
tuple, list, ndarray -> do nothing (needs to be of given length)
returns a tuple of given length
"""
if isinstance(classes, str):
classes == "auto" or _raise(ValueError(f"classes = '{classes}': only 'auto' supported as string argument for classes"))
if self.config.n_classes is None:
classes = None
elif self.config.n_classes == 1:
classes = (1,)*length
else:
raise ValueError("using classes = 'auto' for n_classes > 1 not supported")
elif isinstance(classes, (tuple, list, np.ndarray)):
len(classes) == length or _raise(ValueError(f"len(classes) should be {length}!"))
else:
raise ValueError("classes should either be 'auto' or a list of scalars/label dicts")
return classes
@thresholds.setter
def thresholds(self, d):
self._thresholds = namedtuple('Thresholds',d.keys())(*d.values())
def prepare_for_training(self, optimizer=None):
"""Prepare for neural network training.
Compiles the model and creates
`Keras Callbacks `_ to be used for training.
Note that this method will be implicitly called once by :func:`train`
(with default arguments) if not done so explicitly beforehand.
Parameters
----------
optimizer : obj or None
Instance of a `Keras Optimizer `_ to be used for training.
If ``None`` (default), uses ``Adam`` with the learning rate specified in ``config``.
"""
if optimizer is None:
optimizer = Adam(self.config.train_learning_rate)
masked_dist_loss = {'mse': masked_loss_mse,
'mae': masked_loss_mae,
'iou': masked_loss_iou,
}[self.config.train_dist_loss]
prob_loss = 'binary_crossentropy'
def split_dist_true_mask(dist_true_mask):
return tf.split(dist_true_mask, num_or_size_splits=[self.config.n_rays,-1], axis=-1)
def dist_loss(dist_true_mask, dist_pred):
dist_true, dist_mask = split_dist_true_mask(dist_true_mask)
return masked_dist_loss(dist_mask, reg_weight=self.config.train_background_reg)(dist_true, dist_pred)
def dist_iou_metric(dist_true_mask, dist_pred):
dist_true, dist_mask = split_dist_true_mask(dist_true_mask)
return masked_metric_iou(dist_mask, reg_weight=0)(dist_true, dist_pred)
def relevant_mae(dist_true_mask, dist_pred):
dist_true, dist_mask = split_dist_true_mask(dist_true_mask)
return masked_metric_mae(dist_mask)(dist_true, dist_pred)
def relevant_mse(dist_true_mask, dist_pred):
dist_true, dist_mask = split_dist_true_mask(dist_true_mask)
return masked_metric_mse(dist_mask)(dist_true, dist_pred)
if self._is_multiclass():
prob_class_loss = weighted_categorical_crossentropy(self.config.train_class_weights, ndim=self.config.n_dim)
loss = [prob_loss, dist_loss, prob_class_loss]
else:
loss = [prob_loss, dist_loss]
self.keras_model.compile(optimizer, loss = loss,
loss_weights = list(self.config.train_loss_weights),
metrics = {'prob': kld,
'dist': [relevant_mae, relevant_mse, dist_iou_metric]})
self.callbacks = []
if self.basedir is not None:
self.callbacks += self._checkpoint_callbacks()
if self.config.train_tensorboard:
if IS_TF_1:
self.callbacks.append(CARETensorBoard(log_dir=str(self.logdir), prefix_with_timestamp=False, n_images=3, write_images=True, prob_out=False))
else:
self.callbacks.append(TensorBoard(log_dir=str(self.logdir/'logs'), write_graph=False, profile_batch=0))
if self.config.train_reduce_lr is not None:
rlrop_params = self.config.train_reduce_lr
if 'verbose' not in rlrop_params:
rlrop_params['verbose'] = True
# TF2: add as first callback to put 'lr' in the logs for TensorBoard
self.callbacks.insert(0,ReduceLROnPlateau(**rlrop_params))
self._model_prepared = True
def _predict_setup(self, img, axes, normalizer, n_tiles, show_tile_progress, predict_kwargs):
""" Shared setup code between `predict` and `predict_sparse` """
if n_tiles is None:
n_tiles = [1]*img.ndim
try:
n_tiles = tuple(n_tiles)
img.ndim == len(n_tiles) or _raise(TypeError())
except TypeError:
raise ValueError("n_tiles must be an iterable of length %d" % img.ndim)
all(np.isscalar(t) and 1<=t and int(t)==t for t in n_tiles) or _raise(
ValueError("all values of n_tiles must be integer values >= 1"))
n_tiles = tuple(map(int,n_tiles))
axes = self._normalize_axes(img, axes)
axes_net = self.config.axes
_permute_axes = self._make_permute_axes(axes, axes_net)
x = _permute_axes(img) # x has axes_net semantics
channel = axes_dict(axes_net)['C']
self.config.n_channel_in == x.shape[channel] or _raise(ValueError())
axes_net_div_by = self._axes_div_by(axes_net)
grid = tuple(self.config.grid)
len(grid) == len(axes_net)-1 or _raise(ValueError())
grid_dict = dict(zip(axes_net.replace('C',''),grid))
normalizer = self._check_normalizer_resizer(normalizer, None)[0]
resizer = StarDistPadAndCropResizer(grid=grid_dict)
x = normalizer.before(x, axes_net)
x = resizer.before(x, axes_net, axes_net_div_by)
if not _is_floatarray(x):
warnings.warn("Predicting on non-float input... ( forgot to normalize? )")
def predict_direct(x):
ys = self.keras_model.predict(x[np.newaxis], **predict_kwargs)
return tuple(y[0] for y in ys)
def tiling_setup():
assert np.prod(n_tiles) > 1
tiling_axes = axes_net.replace('C','') # axes eligible for tiling
x_tiling_axis = tuple(axes_dict(axes_net)[a] for a in tiling_axes) # numerical axis ids for x
axes_net_tile_overlaps = self._axes_tile_overlap(axes_net)
# hack: permute tiling axis in the same way as img -> x was permuted
_n_tiles = _permute_axes(np.empty(n_tiles,bool)).shape
(all(_n_tiles[i] == 1 for i in range(x.ndim) if i not in x_tiling_axis) or
_raise(ValueError("entry of n_tiles > 1 only allowed for axes '%s'" % tiling_axes)))
sh = [s//grid_dict.get(a,1) for a,s in zip(axes_net,x.shape)]
sh[channel] = None
def create_empty_output(n_channel, dtype=np.float32):
sh[channel] = n_channel
return np.empty(sh,dtype)
if callable(show_tile_progress):
progress, _show_tile_progress = show_tile_progress, True
else:
progress, _show_tile_progress = tqdm, show_tile_progress
n_block_overlaps = [int(np.ceil(overlap/blocksize)) for overlap, blocksize
in zip(axes_net_tile_overlaps, axes_net_div_by)]
num_tiles_used = total_n_tiles(x, _n_tiles, block_sizes=axes_net_div_by, n_block_overlaps=n_block_overlaps)
tile_generator = progress(tile_iterator(x, _n_tiles, block_sizes=axes_net_div_by, n_block_overlaps=n_block_overlaps),
disable=(not _show_tile_progress), total=num_tiles_used)
return tile_generator, tuple(sh), create_empty_output
return x, axes, axes_net, axes_net_div_by, _permute_axes, resizer, n_tiles, grid, grid_dict, channel, predict_direct, tiling_setup
def _predict_generator(self, img, axes=None, normalizer=None, n_tiles=None, show_tile_progress=True, **predict_kwargs):
"""Predict.
Parameters
----------
img : :class:`numpy.ndarray`
Input image
axes : str or None
Axes of the input ``img``.
``None`` denotes that axes of img are the same as denoted in the config.
normalizer : :class:`csbdeep.data.Normalizer` or None
(Optional) normalization of input image before prediction.
Note that the default (``None``) assumes ``img`` to be already normalized.
n_tiles : iterable or None
Out of memory (OOM) errors can occur if the input image is too large.
To avoid this problem, the input image is broken up into (overlapping) tiles
that are processed independently and re-assembled.
This parameter denotes a tuple of the number of tiles for every image axis (see ``axes``).
``None`` denotes that no tiling should be used.
show_tile_progress: bool or callable
If boolean, indicates whether to show progress (via tqdm) during tiled prediction.
If callable, must be a drop-in replacement for tqdm.
show_tile_progress: bool
Whether to show progress during tiled prediction.
predict_kwargs: dict
Keyword arguments for ``predict`` function of Keras model.
Returns
-------
(:class:`numpy.ndarray`, :class:`numpy.ndarray`, [:class:`numpy.ndarray`])
Returns the tuple (`prob`, `dist`, [`prob_class`]) of per-pixel object probabilities and star-convex polygon/polyhedra distances.
In multiclass prediction mode, `prob_class` is the probability map for each of the 1+'n_classes' classes (first class is background)
"""
x, axes, axes_net, axes_net_div_by, _permute_axes, resizer, n_tiles, grid, grid_dict, channel, predict_direct, tiling_setup = \
self._predict_setup(img, axes, normalizer, n_tiles, show_tile_progress, predict_kwargs)
if np.prod(n_tiles) > 1:
tile_generator, output_shape, create_empty_output = tiling_setup()
prob = create_empty_output(1)
dist = create_empty_output(self.config.n_rays)
if self._is_multiclass():
prob_class = create_empty_output(self.config.n_classes+1)
result = (prob, dist, prob_class)
else:
result = (prob, dist)
for tile, s_src, s_dst in tile_generator:
# predict_direct -> prob, dist, [prob_class if multi_class]
result_tile = predict_direct(tile)
# account for grid
s_src = [slice(s.start//grid_dict.get(a,1),s.stop//grid_dict.get(a,1)) for s,a in zip(s_src,axes_net)]
s_dst = [slice(s.start//grid_dict.get(a,1),s.stop//grid_dict.get(a,1)) for s,a in zip(s_dst,axes_net)]
# prob and dist have different channel dimensionality than image x
s_src[channel] = slice(None)
s_dst[channel] = slice(None)
s_src, s_dst = tuple(s_src), tuple(s_dst)
# print(s_src,s_dst)
for part, part_tile in zip(result, result_tile):
part[s_dst] = part_tile[s_src]
yield # yield None after each processed tile
else:
# predict_direct -> prob, dist, [prob_class if multi_class]
result = predict_direct(x)
result = [resizer.after(part, axes_net) for part in result]
# result = (prob, dist) for legacy or (prob, dist, prob_class) for multiclass
# prob
result[0] = np.take(result[0],0,axis=channel)
# dist
result[1] = np.maximum(1e-3, result[1]) # avoid small dist values to prevent problems with Qhull
result[1] = np.moveaxis(result[1],channel,-1)
if self._is_multiclass():
# prob_class
result[2] = np.moveaxis(result[2],channel,-1)
# last "yield" is the actual output that would have been "return"ed if this was a regular function
yield tuple(result)
@functools.wraps(_predict_generator)
def predict(self, *args, **kwargs):
# return last "yield"ed value of generator
r = None
for r in self._predict_generator(*args, **kwargs):
pass
return r
def _predict_sparse_generator(self, img, prob_thresh=None, axes=None, normalizer=None, n_tiles=None, show_tile_progress=True, b=2, **predict_kwargs):
""" Sparse version of model.predict()
Returns
-------
(prob, dist, [prob_class], points) flat list of probs, dists, (optional prob_class) and points
"""
if prob_thresh is None: prob_thresh = self.thresholds.prob
x, axes, axes_net, axes_net_div_by, _permute_axes, resizer, n_tiles, grid, grid_dict, channel, predict_direct, tiling_setup = \
self._predict_setup(img, axes, normalizer, n_tiles, show_tile_progress, predict_kwargs)
def _prep(prob, dist):
prob = np.take(prob,0,axis=channel)
dist = np.moveaxis(dist,channel,-1)
dist = np.maximum(1e-3, dist)
return prob, dist
proba, dista, pointsa, prob_class = [],[],[], []
if np.prod(n_tiles) > 1:
tile_generator, output_shape, create_empty_output = tiling_setup()
sh = list(output_shape)
sh[channel] = 1;
proba, dista, pointsa, prob_classa = [], [], [], []
for tile, s_src, s_dst in tile_generator:
results_tile = predict_direct(tile)
# account for grid
s_src = [slice(s.start//grid_dict.get(a,1),s.stop//grid_dict.get(a,1)) for s,a in zip(s_src,axes_net)]
s_dst = [slice(s.start//grid_dict.get(a,1),s.stop//grid_dict.get(a,1)) for s,a in zip(s_dst,axes_net)]
s_src[channel] = slice(None)
s_dst[channel] = slice(None)
s_src, s_dst = tuple(s_src), tuple(s_dst)
prob_tile, dist_tile = results_tile[:2]
prob_tile, dist_tile = _prep(prob_tile[s_src], dist_tile[s_src])
bs = list((b if s.start==0 else -1, b if s.stop==_sh else -1) for s,_sh in zip(s_dst, sh))
bs.pop(channel)
inds = _ind_prob_thresh(prob_tile, prob_thresh, b=bs)
proba.extend(prob_tile[inds].copy())
dista.extend(dist_tile[inds].copy())
_points = np.stack(np.where(inds), axis=1)
offset = list(s.start for i,s in enumerate(s_dst))
offset.pop(channel)
_points = _points + np.array(offset).reshape((1,len(offset)))
_points = _points * np.array(self.config.grid).reshape((1,len(self.config.grid)))
pointsa.extend(_points)
if self._is_multiclass():
p = results_tile[2][s_src].copy()
p = np.moveaxis(p,channel,-1)
prob_classa.extend(p[inds])
yield # yield None after each processed tile
else:
# predict_direct -> prob, dist, [prob_class if multi_class]
results = predict_direct(x)
prob, dist = results[:2]
prob, dist = _prep(prob, dist)
inds = _ind_prob_thresh(prob, prob_thresh, b=b)
proba = prob[inds].copy()
dista = dist[inds].copy()
_points = np.stack(np.where(inds), axis=1)
pointsa = (_points * np.array(self.config.grid).reshape((1,len(self.config.grid))))
if self._is_multiclass():
p = np.moveaxis(results[2],channel,-1)
prob_classa = p[inds].copy()
proba = np.asarray(proba)
dista = np.asarray(dista).reshape((-1,self.config.n_rays))
pointsa = np.asarray(pointsa).reshape((-1,self.config.n_dim))
idx = resizer.filter_points(x.ndim, pointsa, axes_net)
proba = proba[idx]
dista = dista[idx]
pointsa = pointsa[idx]
# last "yield" is the actual output that would have been "return"ed if this was a regular function
if self._is_multiclass():
prob_classa = np.asarray(prob_classa).reshape((-1,self.config.n_classes+1))
prob_classa = prob_classa[idx]
yield proba, dista, prob_classa, pointsa
else:
prob_classa = None
yield proba, dista, pointsa
@functools.wraps(_predict_sparse_generator)
def predict_sparse(self, *args, **kwargs):
# return last "yield"ed value of generator
r = None
for r in self._predict_sparse_generator(*args, **kwargs):
pass
return r
def _predict_instances_generator(self, img, axes=None, normalizer=None,
sparse=True,
prob_thresh=None, nms_thresh=None,
scale=None,
n_tiles=None, show_tile_progress=True,
verbose=False,
return_labels=True,
predict_kwargs=None, nms_kwargs=None,
overlap_label=None, return_predict=False):
"""Predict instance segmentation from input image.
Parameters
----------
img : :class:`numpy.ndarray`
Input image
axes : str or None
Axes of the input ``img``.
``None`` denotes that axes of img are the same as denoted in the config.
normalizer : :class:`csbdeep.data.Normalizer` or None
(Optional) normalization of input image before prediction.
Note that the default (``None``) assumes ``img`` to be already normalized.
sparse: bool
If true, aggregate probabilities/distances sparsely during tiled
prediction to save memory (recommended).
prob_thresh : float or None
Consider only object candidates from pixels with predicted object probability
above this threshold (also see `optimize_thresholds`).
nms_thresh : float or None
Perform non-maximum suppression that considers two objects to be the same
when their area/surface overlap exceeds this threshold (also see `optimize_thresholds`).
scale: None or float or iterable
Scale the input image internally by this factor and rescale the output accordingly.
All spatial axes (X,Y,Z) will be scaled if a scalar value is provided.
Alternatively, multiple scale values (compatible with input `axes`) can be used
for more fine-grained control (scale values for non-spatial axes must be 1).
n_tiles : iterable or None
Out of memory (OOM) errors can occur if the input image is too large.
To avoid this problem, the input image is broken up into (overlapping) tiles
that are processed independently and re-assembled.
This parameter denotes a tuple of the number of tiles for every image axis (see ``axes``).
``None`` denotes that no tiling should be used.
show_tile_progress: bool
Whether to show progress during tiled prediction.
verbose: bool
Whether to print some info messages.
return_labels: bool
Whether to create a label image, otherwise return None in its place.
predict_kwargs: dict
Keyword arguments for ``predict`` function of Keras model.
nms_kwargs: dict
Keyword arguments for non-maximum suppression.
overlap_label: scalar or None
if not None, label the regions where polygons overlap with that value
return_predict: bool
Also return the outputs of :func:`predict` (in a separate tuple)
If True, implies sparse = False
Returns
-------
(:class:`numpy.ndarray`, dict), (optional: return tuple of :func:`predict`)
Returns a tuple of the label instances image and also
a dictionary with the details (coordinates, etc.) of all remaining polygons/polyhedra.
"""
if predict_kwargs is None:
predict_kwargs = {}
if nms_kwargs is None:
nms_kwargs = {}
if return_predict and sparse:
sparse = False
warnings.warn("Setting sparse to False because return_predict is True")
nms_kwargs.setdefault("verbose", verbose)
_axes = self._normalize_axes(img, axes)
_axes_net = self.config.axes
_permute_axes = self._make_permute_axes(_axes, _axes_net)
_shape_inst = tuple(s for s,a in zip(_permute_axes(img).shape, _axes_net) if a != 'C')
if scale is not None:
if isinstance(scale, numbers.Number):
scale = tuple(scale if a in 'XYZ' else 1 for a in _axes)
scale = tuple(scale)
len(scale) == len(_axes) or _raise(ValueError(f"scale {scale} must be of length {len(_axes)}, i.e. one value for each of the axes {_axes}"))
for s,a in zip(scale,_axes):
s > 0 or _raise(ValueError("scale values must be greater than 0"))
(s in (1,None) or a in 'XYZ') or warnings.warn(f"replacing scale value {s} for non-spatial axis {a} with 1")
scale = tuple(s if a in 'XYZ' else 1 for s,a in zip(scale,_axes))
verbose and print(f"scaling image by factors {scale} for axes {_axes}")
img = ndi.zoom(img, scale, order=1)
yield 'predict' # indicate that prediction is starting
res = None
if sparse:
for res in self._predict_sparse_generator(img, axes=axes, normalizer=normalizer, n_tiles=n_tiles,
prob_thresh=prob_thresh, show_tile_progress=show_tile_progress, **predict_kwargs):
if res is None:
yield 'tile' # yield 'tile' each time a tile has been processed
else:
for res in self._predict_generator(img, axes=axes, normalizer=normalizer, n_tiles=n_tiles,
show_tile_progress=show_tile_progress, **predict_kwargs):
if res is None:
yield 'tile' # yield 'tile' each time a tile has been processed
res = tuple(res) + (None,)
if self._is_multiclass():
prob, dist, prob_class, points = res
else:
prob, dist, points = res
prob_class = None
yield 'nms' # indicate that non-maximum suppression is starting
res_instances = self._instances_from_prediction(_shape_inst, prob, dist,
points=points,
prob_class=prob_class,
prob_thresh=prob_thresh,
nms_thresh=nms_thresh,
scale=(None if scale is None else dict(zip(_axes,scale))),
return_labels=return_labels,
overlap_label=overlap_label,
**nms_kwargs)
# last "yield" is the actual output that would have been "return"ed if this was a regular function
if return_predict:
yield res_instances, tuple(res[:-1])
else:
yield res_instances
@functools.wraps(_predict_instances_generator)
def predict_instances(self, *args, **kwargs):
# the reason why the actual computation happens as a generator function
# (in '_predict_instances_generator') is that the generator is called
# from the stardist napari plugin, which has its benefits regarding
# control flow and progress display. however, typical use cases should
# almost always use this function ('predict_instances'), and shouldn't
# even notice (thanks to @functools.wraps) that it wraps the generator
# function. note that similar reasoning applies to 'predict' and
# 'predict_sparse'.
# return last "yield"ed value of generator
r = None
for r in self._predict_instances_generator(*args, **kwargs):
pass
return r
# def _predict_instances_old(self, img, axes=None, normalizer=None,
# sparse = False,
# prob_thresh=None, nms_thresh=None,
# n_tiles=None, show_tile_progress=True,
# verbose = False,
# predict_kwargs=None, nms_kwargs=None, overlap_label=None):
# """
# old version, should be removed....
# """
# if predict_kwargs is None:
# predict_kwargs = {}
# if nms_kwargs is None:
# nms_kwargs = {}
# nms_kwargs.setdefault("verbose", verbose)
# _axes = self._normalize_axes(img, axes)
# _axes_net = self.config.axes
# _permute_axes = self._make_permute_axes(_axes, _axes_net)
# _shape_inst = tuple(s for s,a in zip(_permute_axes(img).shape, _axes_net) if a != 'C')
# res = self.predict(img, axes=axes, normalizer=normalizer,
# n_tiles=n_tiles,
# show_tile_progress=show_tile_progress,
# **predict_kwargs)
# res = tuple(res) + (None,)
# if self._is_multiclass():
# prob, dist, prob_class, points = res
# else:
# prob, dist, points = res
# prob_class = None
# return self._instances_from_prediction_old(_shape_inst, prob, dist,
# points = points,
# prob_class = prob_class,
# prob_thresh=prob_thresh,
# nms_thresh=nms_thresh,
# overlap_label=overlap_label,
# **nms_kwargs)
def predict_instances_big(self, img, axes, block_size, min_overlap, context=None,
labels_out=None, labels_out_dtype=np.int32, show_progress=True, **kwargs):
"""Predict instance segmentation from very large input images.
Intended to be used when `predict_instances` cannot be used due to memory limitations.
This function will break the input image into blocks and process them individually
via `predict_instances` and assemble all the partial results. If used as intended, the result
should be the same as if `predict_instances` was used directly on the whole image.
**Important**: The crucial assumption is that all predicted object instances are smaller than
the provided `min_overlap`. Also, it must hold that: min_overlap + 2*context < block_size.
Example
-------
>>> img.shape
(20000, 20000)
>>> labels, polys = model.predict_instances_big(img, axes='YX', block_size=4096,
min_overlap=128, context=128, n_tiles=(4,4))
Parameters
----------
img: :class:`numpy.ndarray` or similar
Input image
axes: str
Axes of the input ``img`` (such as 'YX', 'ZYX', 'YXC', etc.)
block_size: int or iterable of int
Process input image in blocks of the provided shape.
(If a scalar value is given, it is used for all spatial image dimensions.)
min_overlap: int or iterable of int
Amount of guaranteed overlap between blocks.
(If a scalar value is given, it is used for all spatial image dimensions.)
context: int or iterable of int, or None
Amount of image context on all sides of a block, which is discarded.
If None, uses an automatic estimate that should work in many cases.
(If a scalar value is given, it is used for all spatial image dimensions.)
labels_out: :class:`numpy.ndarray` or similar, or None, or False
numpy array or similar (must be of correct shape) to which the label image is written.
If None, will allocate a numpy array of the correct shape and data type ``labels_out_dtype``.
If False, will not write the label image (useful if only the dictionary is needed).
labels_out_dtype: str or dtype
Data type of returned label image if ``labels_out=None`` (has no effect otherwise).
show_progress: bool
Show progress bar for block processing.
kwargs: dict
Keyword arguments for ``predict_instances``.
Returns
-------
(:class:`numpy.ndarray` or False, dict)
Returns the label image and a dictionary with the details (coordinates, etc.) of the polygons/polyhedra.
"""
from ..big import _grid_divisible, BlockND, OBJECT_KEYS#, repaint_labels
from ..matching import relabel_sequential
n = img.ndim
axes = axes_check_and_normalize(axes, length=n)
grid = self._axes_div_by(axes)
axes_out = self._axes_out.replace('C','')
shape_dict = dict(zip(axes,img.shape))
shape_out = tuple(shape_dict[a] for a in axes_out)
if context is None:
context = self._axes_tile_overlap(axes)
if np.isscalar(block_size): block_size = n*[block_size]
if np.isscalar(min_overlap): min_overlap = n*[min_overlap]
if np.isscalar(context): context = n*[context]
block_size, min_overlap, context = list(block_size), list(min_overlap), list(context)
assert n == len(block_size) == len(min_overlap) == len(context)
if 'C' in axes:
# single block for channel axis
i = axes_dict(axes)['C']
# if (block_size[i], min_overlap[i], context[i]) != (None, None, None):
# print("Ignoring values of 'block_size', 'min_overlap', and 'context' for channel axis " +
# "(set to 'None' to avoid this warning).", file=sys.stderr, flush=True)
block_size[i] = img.shape[i]
min_overlap[i] = context[i] = 0
block_size = tuple(_grid_divisible(g, v, name='block_size', verbose=False) for v,g,a in zip(block_size, grid,axes))
min_overlap = tuple(_grid_divisible(g, v, name='min_overlap', verbose=False) for v,g,a in zip(min_overlap,grid,axes))
context = tuple(_grid_divisible(g, v, name='context', verbose=False) for v,g,a in zip(context, grid,axes))
# print(f"input: shape {img.shape} with axes {axes}")
print(f'effective: block_size={block_size}, min_overlap={min_overlap}, context={context}', flush=True)
for a,c,o in zip(axes,context,self._axes_tile_overlap(axes)):
if c < o:
print(f"{a}: context of {c} is small, recommended to use at least {o}", flush=True)
# create block cover
blocks = BlockND.cover(img.shape, axes, block_size, min_overlap, context, grid)
if np.isscalar(labels_out) and bool(labels_out) is False:
labels_out = None
else:
if labels_out is None:
labels_out = np.zeros(shape_out, dtype=labels_out_dtype)
else:
labels_out.shape == shape_out or _raise(ValueError(f"'labels_out' must have shape {shape_out} (axes {axes_out})."))
polys_all = {}
# problem_ids = []
label_offset = 1
kwargs_override = dict(axes=axes, overlap_label=None, return_labels=True, return_predict=False)
if show_progress:
kwargs_override['show_tile_progress'] = False # disable progress for predict_instances
for k,v in kwargs_override.items():
if k in kwargs: print(f"changing '{k}' from {kwargs[k]} to {v}", flush=True)
kwargs[k] = v
blocks = tqdm(blocks, disable=(not show_progress))
# actual computation
for block in blocks:
labels, polys = self.predict_instances(block.read(img, axes=axes), **kwargs)
labels = block.crop_context(labels, axes=axes_out)
labels, polys = block.filter_objects(labels, polys, axes=axes_out)
# TODO: relabel_sequential is not very memory-efficient (will allocate memory proportional to label_offset)
# this should not change the order of labels
labels = relabel_sequential(labels, label_offset)[0]
# labels, fwd_map, _ = relabel_sequential(labels, label_offset)
# if len(incomplete) > 0:
# problem_ids.extend([fwd_map[i] for i in incomplete])
# if show_progress:
# blocks.set_postfix_str(f"found {len(problem_ids)} problematic {'object' if len(problem_ids)==1 else 'objects'}")
if labels_out is not None:
block.write(labels_out, labels, axes=axes_out)
for k,v in polys.items():
polys_all.setdefault(k,[]).append(v)
label_offset += len(polys['prob'])
del labels
polys_all = {k: (np.concatenate(v) if k in OBJECT_KEYS else v[0]) for k,v in polys_all.items()}
# if labels_out is not None and len(problem_ids) > 0:
# # if show_progress:
# # blocks.write('')
# # print(f"Found {len(problem_ids)} objects that violate the 'min_overlap' assumption.", file=sys.stderr, flush=True)
# repaint_labels(labels_out, problem_ids, polys_all, show_progress=False)
return labels_out, polys_all#, tuple(problem_ids)
def optimize_thresholds(self, X_val, Y_val, nms_threshs=[0.3,0.4,0.5], iou_threshs=[0.3,0.5,0.7], predict_kwargs=None, optimize_kwargs=None, save_to_json=True):
"""Optimize two thresholds (probability, NMS overlap) necessary for predicting object instances.
Note that the default thresholds yield good results in many cases, but optimizing
the thresholds for a particular dataset can further improve performance.
The optimized thresholds are automatically used for all further predictions
and also written to the model directory.
See ``utils.optimize_threshold`` for details and possible choices for ``optimize_kwargs``.
Parameters
----------
X_val : list of ndarray
(Validation) input images (must be normalized) to use for threshold tuning.
Y_val : list of ndarray
(Validation) label images to use for threshold tuning.
nms_threshs : list of float
List of overlap thresholds to be considered for NMS.
For each value in this list, optimization is run to find a corresponding prob_thresh value.
iou_threshs : list of float
List of intersection over union (IOU) thresholds for which
the (average) matching performance is considered to tune the thresholds.
predict_kwargs: dict
Keyword arguments for ``predict`` function of this class.
(If not provided, will guess value for `n_tiles` to prevent out of memory errors.)
optimize_kwargs: dict
Keyword arguments for ``utils.optimize_threshold`` function.
"""
if predict_kwargs is None:
predict_kwargs = {}
if optimize_kwargs is None:
optimize_kwargs = {}
def _predict_kwargs(x):
if 'n_tiles' in predict_kwargs:
return predict_kwargs
else:
return {**predict_kwargs, 'n_tiles': self._guess_n_tiles(x), 'show_tile_progress': False}
# only take first two elements of predict in case multi class is activated
Yhat_val = [self.predict(x, **_predict_kwargs(x))[:2] for x in X_val]
opt_prob_thresh, opt_measure, opt_nms_thresh = None, -np.inf, None
for _opt_nms_thresh in nms_threshs:
_opt_prob_thresh, _opt_measure = optimize_threshold(Y_val, Yhat_val, model=self, nms_thresh=_opt_nms_thresh, iou_threshs=iou_threshs, **optimize_kwargs)
if _opt_measure > opt_measure:
opt_prob_thresh, opt_measure, opt_nms_thresh = _opt_prob_thresh, _opt_measure, _opt_nms_thresh
opt_threshs = dict(prob=opt_prob_thresh, nms=opt_nms_thresh)
self.thresholds = opt_threshs
print(end='', file=sys.stderr, flush=True)
print("Using optimized values: prob_thresh={prob:g}, nms_thresh={nms:g}.".format(prob=self.thresholds.prob, nms=self.thresholds.nms))
if save_to_json and self.basedir is not None:
print("Saving to 'thresholds.json'.")
save_json(opt_threshs, str(self.logdir / 'thresholds.json'))
return opt_threshs
def _guess_n_tiles(self, img):
axes = self._normalize_axes(img, axes=None)
shape = list(img.shape)
if 'C' in axes:
del shape[axes_dict(axes)['C']]
b = self.config.train_batch_size**(1.0/self.config.n_dim)
n_tiles = [int(np.ceil(s/(p*b))) for s,p in zip(shape,self.config.train_patch_size)]
if 'C' in axes:
n_tiles.insert(axes_dict(axes)['C'],1)
return tuple(n_tiles)
def _normalize_axes(self, img, axes):
if axes is None:
axes = self.config.axes
assert 'C' in axes
if img.ndim == len(axes)-1 and self.config.n_channel_in == 1:
# img has no dedicated channel axis, but 'C' always part of config axes
axes = axes.replace('C','')
return axes_check_and_normalize(axes, img.ndim)
def _compute_receptive_field(self, img_size=None):
# TODO: good enough?
from scipy.ndimage import zoom
if img_size is None:
img_size = tuple(g*(128 if self.config.n_dim==2 else 64) for g in self.config.grid)
if np.isscalar(img_size):
img_size = (img_size,) * self.config.n_dim
img_size = tuple(img_size)
# print(img_size)
assert all(_is_power_of_2(s) for s in img_size)
mid = tuple(s//2 for s in img_size)
x = np.zeros((1,)+img_size+(self.config.n_channel_in,), dtype=np.float32)
z = np.zeros_like(x)
x[(0,)+mid+(slice(None),)] = 1
y = self.keras_model.predict(x)[0][0,...,0]
y0 = self.keras_model.predict(z)[0][0,...,0]
grid = tuple((np.array(x.shape[1:-1])/np.array(y.shape)).astype(int))
assert grid == self.config.grid
y = zoom(y, grid,order=0)
y0 = zoom(y0,grid,order=0)
ind = np.where(np.abs(y-y0)>0)
return [(m-np.min(i), np.max(i)-m) for (m,i) in zip(mid,ind)]
def _axes_tile_overlap(self, query_axes):
query_axes = axes_check_and_normalize(query_axes)
try:
self._tile_overlap
except AttributeError:
self._tile_overlap = self._compute_receptive_field()
overlap = dict(zip(
self.config.axes.replace('C',''),
tuple(max(rf) for rf in self._tile_overlap)
))
return tuple(overlap.get(a,0) for a in query_axes)
def export_TF(self, fname=None, single_output=True, upsample_grid=True):
"""Export model to TensorFlow's SavedModel format that can be used e.g. in the Fiji plugin
Parameters
----------
fname : str
Path of the zip file to store the model
If None, the default path "/TF_SavedModel.zip" is used
single_output: bool
If set, concatenates the two model outputs into a single output (note: this is currently mandatory for further use in Fiji)
upsample_grid: bool
If set, upsamples the output to the input shape (note: this is currently mandatory for further use in Fiji)
"""
Concatenate, UpSampling2D, UpSampling3D, Conv2DTranspose, Conv3DTranspose = keras_import('layers', 'Concatenate', 'UpSampling2D', 'UpSampling3D', 'Conv2DTranspose', 'Conv3DTranspose')
Model = keras_import('models', 'Model')
if self.basedir is None and fname is None:
raise ValueError("Need explicit 'fname', since model directory not available (basedir=None).")
if self._is_multiclass():
warnings.warn("multi-class mode not supported yet, removing classification output from exported model")
grid = self.config.grid
prob = self.keras_model.outputs[0]
dist = self.keras_model.outputs[1]
assert self.config.n_dim in (2,3)
if upsample_grid and any(g>1 for g in grid):
# CSBDeep Fiji plugin needs same size input/output
# -> we need to upsample the outputs if grid > (1,1)
# note: upsampling prob with a transposed convolution creates sparse
# prob output with less candidates than with standard upsampling
conv_transpose = Conv2DTranspose if self.config.n_dim==2 else Conv3DTranspose
upsampling = UpSampling2D if self.config.n_dim==2 else UpSampling3D
prob = conv_transpose(1, (1,)*self.config.n_dim,
strides=grid, padding='same',
kernel_initializer='ones', use_bias=False)(prob)
dist = upsampling(grid)(dist)
inputs = self.keras_model.inputs[0]
outputs = Concatenate()([prob,dist]) if single_output else [prob,dist]
csbdeep_model = Model(inputs, outputs)
fname = (self.logdir / 'TF_SavedModel.zip') if fname is None else Path(fname)
export_SavedModel(csbdeep_model, str(fname))
return csbdeep_model
class StarDistPadAndCropResizer(Resizer):
# TODO: check correctness
def __init__(self, grid, mode='reflect', **kwargs):
assert isinstance(grid, dict)
self.mode = mode
self.grid = grid
self.kwargs = kwargs
def before(self, x, axes, axes_div_by):
assert all(a%g==0 for g,a in zip((self.grid.get(a,1) for a in axes), axes_div_by))
axes = axes_check_and_normalize(axes,x.ndim)
def _split(v):
return 0, v # only pad at the end
self.pad = {
a : _split((div_n-s%div_n)%div_n)
for a, div_n, s in zip(axes, axes_div_by, x.shape)
}
x_pad = np.pad(x, tuple(self.pad[a] for a in axes), mode=self.mode, **self.kwargs)
self.padded_shape = dict(zip(axes,x_pad.shape))
if 'C' in self.padded_shape: del self.padded_shape['C']
return x_pad
def after(self, x, axes):
# axes can include 'C', which may not have been present in before()
axes = axes_check_and_normalize(axes,x.ndim)
assert all(s_pad == s * g for s,s_pad,g in zip(x.shape,
(self.padded_shape.get(a,_s) for a,_s in zip(axes,x.shape)),
(self.grid.get(a,1) for a in axes)))
# print(self.padded_shape)
# print(self.pad)
# print(self.grid)
crop = tuple (
slice(0, -(math.floor(p[1]/g)) if p[1]>=g else None)
for p,g in zip((self.pad.get(a,(0,0)) for a in axes),(self.grid.get(a,1) for a in axes))
)
# print(crop)
return x[crop]
def filter_points(self, ndim, points, axes):
""" returns indices of points inside crop region """
assert points.ndim==2
axes = axes_check_and_normalize(axes,ndim)
bounds = np.array(tuple(self.padded_shape[a]-self.pad[a][1] for a in axes if a.lower() in ('z','y','x')))
idx = np.where(np.all(points< bounds, 1))
return idx
def _tf_version_at_least(version_string="1.0.0"):
from packaging import version
return version.parse(tf.__version__) >= version.parse(version_string)