import os, sys, time, shutil, tempfile, datetime, pathlib, subprocess
from pathlib import Path
import numpy as np
from tqdm import trange, tqdm
from urllib.parse import urlparse
import torch
import logging
models_logger = logging.getLogger(__name__)
from . import transforms, dynamics, utils, plot
from .core import UnetModel, assign_device, check_mkl, parse_model_string
_MODEL_URL = 'https://www.cellpose.org/models'
_MODEL_DIR_ENV = os.environ.get("CELLPOSE_LOCAL_MODELS_PATH")
_MODEL_DIR_DEFAULT = pathlib.Path.home().joinpath('.cellpose', 'models')
MODEL_DIR = pathlib.Path(_MODEL_DIR_ENV) if _MODEL_DIR_ENV else _MODEL_DIR_DEFAULT
MODEL_NAMES = ['cyto','nuclei','tissuenet','livecell', 'cyto2', 'general',
'CP', 'CPx', 'TN1', 'TN2', 'TN3', 'LC1', 'LC2', 'LC3', 'LC4']
MODEL_LIST_PATH = os.fspath(MODEL_DIR.joinpath('gui_models.txt'))
def model_path(model_type, model_index, use_torch=True):
torch_str = 'torch'
if model_type=='cyto' or model_type=='cyto2' or model_type=='nuclei':
basename = '%s%s_%d' % (model_type, torch_str, model_index)
else:
basename = model_type
return cache_model_path(basename)
def size_model_path(model_type, use_torch=True):
torch_str = 'torch'
basename = 'size_%s%s_0.npy' % (model_type, torch_str)
return cache_model_path(basename)
def cache_model_path(basename):
MODEL_DIR.mkdir(parents=True, exist_ok=True)
url = f'{_MODEL_URL}/{basename}'
cached_file = os.fspath(MODEL_DIR.joinpath(basename))
if not os.path.exists(cached_file):
models_logger.info('Downloading: "{}" to {}\n'.format(url, cached_file))
utils.download_url_to_file(url, cached_file, progress=True)
return cached_file
def get_user_models():
model_strings = []
if os.path.exists(MODEL_LIST_PATH):
with open(MODEL_LIST_PATH, 'r') as textfile:
lines = [line.rstrip() for line in textfile]
if len(lines) > 0:
model_strings.extend(lines)
return model_strings
[docs]class Cellpose():
""" main model which combines SizeModel and CellposeModel
Parameters
----------
gpu: bool (optional, default False)
whether or not to use GPU, will check if GPU available
model_type: str (optional, default 'cyto')
'cyto'=cytoplasm model; 'nuclei'=nucleus model; 'cyto2'=cytoplasm model with additional user images
net_avg: bool (optional, default False)
loads the 4 built-in networks and averages them if True, loads one network if False
device: torch device (optional, default None)
device used for model running / training
(torch.device('cuda') or torch.device('cpu')), overrides gpu input,
recommended if you want to use a specific GPU (e.g. torch.device('cuda:1'))
"""
def __init__(self, gpu=False, model_type='cyto', net_avg=False, device=None):
super(Cellpose, self).__init__()
self.torch = True
# assign device (GPU or CPU)
sdevice, gpu = assign_device(self.torch, gpu)
self.device = device if device is not None else sdevice
self.gpu = gpu
model_type = 'cyto' if model_type is None else model_type
self.diam_mean = 30. #default for any cyto model
nuclear = 'nuclei' in model_type
if nuclear:
self.diam_mean = 17.
self.cp = CellposeModel(device=self.device, gpu=self.gpu,
model_type=model_type,
diam_mean=self.diam_mean,
net_avg=net_avg)
self.cp.model_type = model_type
# size model not used for bacterial model
self.pretrained_size = size_model_path(model_type, self.torch)
self.sz = SizeModel(device=self.device, pretrained_size=self.pretrained_size,
cp_model=self.cp)
self.sz.model_type = model_type
[docs] def eval(self, x, batch_size=8, channels=None, channel_axis=None, z_axis=None,
invert=False, normalize=True, diameter=30., do_3D=False, anisotropy=None,
net_avg=False, augment=False, tile=True, tile_overlap=0.1, resample=True, interp=True,
flow_threshold=0.4, cellprob_threshold=0.0, min_size=15, stitch_threshold=0.0,
rescale=None, progress=None, model_loaded=False):
""" run cellpose and get masks
Parameters
----------
x: list or array of images
can be list of 2D/3D images, or array of 2D/3D images, or 4D image array
batch_size: int (optional, default 8)
number of 224x224 patches to run simultaneously on the GPU
(can make smaller or bigger depending on GPU memory usage)
channels: list (optional, default None)
list of channels, either of length 2 or of length number of images by 2.
First element of list is the channel to segment (0=grayscale, 1=red, 2=green, 3=blue).
Second element of list is the optional nuclear channel (0=none, 1=red, 2=green, 3=blue).
For instance, to segment grayscale images, input [0,0]. To segment images with cells
in green and nuclei in blue, input [2,3]. To segment one grayscale image and one
image with cells in green and nuclei in blue, input [[0,0], [2,3]].
channel_axis: int (optional, default None)
if None, channels dimension is attempted to be automatically determined
z_axis: int (optional, default None)
if None, z dimension is attempted to be automatically determined
invert: bool (optional, default False)
invert image pixel intensity before running network (if True, image is also normalized)
normalize: bool (optional, default True)
normalize data so 0.0=1st percentile and 1.0=99th percentile of image intensities in each channel
diameter: float (optional, default 30.)
if set to None, then diameter is automatically estimated if size model is loaded
do_3D: bool (optional, default False)
set to True to run 3D segmentation on 4D image input
anisotropy: float (optional, default None)
for 3D segmentation, optional rescaling factor (e.g. set to 2.0 if Z is sampled half as dense as X or Y)
net_avg: bool (optional, default False)
runs the 4 built-in networks and averages them if True, runs one network if False
augment: bool (optional, default False)
tiles image with overlapping tiles and flips overlapped regions to augment
tile: bool (optional, default True)
tiles image to ensure GPU/CPU memory usage limited (recommended)
tile_overlap: float (optional, default 0.1)
fraction of overlap of tiles when computing flows
resample: bool (optional, default True)
run dynamics at original image size (will be slower but create more accurate boundaries)
interp: bool (optional, default True)
interpolate during 2D dynamics (not available in 3D)
(in previous versions it was False)
flow_threshold: float (optional, default 0.4)
flow error threshold (all cells with errors below threshold are kept) (not used for 3D)
cellprob_threshold: float (optional, default 0.0)
all pixels with value above threshold kept for masks, decrease to find more and larger masks
min_size: int (optional, default 15)
minimum number of pixels per mask, can turn off with -1
stitch_threshold: float (optional, default 0.0)
if stitch_threshold>0.0 and not do_3D and equal image sizes, masks are stitched in 3D to return volume segmentation
rescale: float (optional, default None)
if diameter is set to None, and rescale is not None, then rescale is used instead of diameter for resizing image
progress: pyqt progress bar (optional, default None)
to return progress bar status to GUI
model_loaded: bool (optional, default False)
internal variable for determining if model has been loaded, used in __main__.py
Returns
-------
masks: list of 2D arrays, or single 3D array (if do_3D=True)
labelled image, where 0=no masks; 1,2,...=mask labels
flows: list of lists 2D arrays, or list of 3D arrays (if do_3D=True)
flows[k][0] = XY flow in HSV 0-255
flows[k][1] = XY flows at each pixel
flows[k][2] = cell probability (if > cellprob_threshold, pixel used for dynamics)
flows[k][3] = final pixel locations after Euler integration
styles: list of 1D arrays of length 256, or single 1D array (if do_3D=True)
style vector summarizing each image, also used to estimate size of objects in image
diams: list of diameters, or float (if do_3D=True)
"""
tic0 = time.time()
channels = [0,0] if channels is None else channels # why not just make this a default in the function header?
estimate_size = True if (diameter is None or diameter==0) else False
if estimate_size and self.pretrained_size is not None and not do_3D and x[0].ndim < 4:
tic = time.time()
models_logger.info('~~~ ESTIMATING CELL DIAMETER(S) ~~~')
diams, _ = self.sz.eval(x, channels=channels, channel_axis=channel_axis, invert=invert, batch_size=batch_size,
augment=augment, tile=tile, normalize=normalize)
rescale = self.diam_mean / np.array(diams)
diameter = None
models_logger.info('estimated cell diameter(s) in %0.2f sec'%(time.time()-tic))
models_logger.info('>>> diameter(s) = ')
if isinstance(diams, list) or isinstance(diams, np.ndarray):
diam_string = '[' + ''.join(['%0.2f, '%d for d in diams]) + ']'
else:
diam_string = '[ %0.2f ]'%diams
models_logger.info(diam_string)
elif estimate_size:
if self.pretrained_size is None:
reason = 'no pretrained size model specified in model Cellpose'
else:
reason = 'does not work on non-2D images'
models_logger.warning(f'could not estimate diameter, {reason}')
diams = self.diam_mean
else:
diams = diameter
tic = time.time()
models_logger.info('~~~ FINDING MASKS ~~~')
masks, flows, styles = self.cp.eval(x,
batch_size=batch_size,
invert=invert,
normalize=normalize,
diameter=diameter,
rescale=rescale,
anisotropy=anisotropy,
channels=channels,
channel_axis=channel_axis,
z_axis=z_axis,
augment=augment,
tile=tile,
do_3D=do_3D,
net_avg=net_avg,
progress=progress,
tile_overlap=tile_overlap,
resample=resample,
interp=interp,
flow_threshold=flow_threshold,
cellprob_threshold=cellprob_threshold,
min_size=min_size,
stitch_threshold=stitch_threshold,
model_loaded=model_loaded)
models_logger.info('>>>> TOTAL TIME %0.2f sec'%(time.time()-tic0))
return masks, flows, styles, diams
[docs]class CellposeModel(UnetModel):
"""
Parameters
-------------------
gpu: bool (optional, default False)
whether or not to save model to GPU, will check if GPU available
pretrained_model: str or list of strings (optional, default False)
full path to pretrained cellpose model(s), if None or False, no model loaded
model_type: str (optional, default None)
any model that is available in the GUI, use name in GUI e.g. 'livecell'
(can be user-trained or model zoo)
net_avg: bool (optional, default False)
loads the 4 built-in networks and averages them if True, loads one network if False
diam_mean: float (optional, default 30.)
mean 'diameter', 30. is built in value for 'cyto' model; 17. is built in value for 'nuclei' model;
if saved in custom model file (cellpose>=2.0) then it will be loaded automatically and overwrite this value
device: torch device (optional, default None)
device used for model running / training
(torch.device('cuda') or torch.device('cpu')), overrides gpu input,
recommended if you want to use a specific GPU (e.g. torch.device('cuda:1'))
residual_on: bool (optional, default True)
use 4 conv blocks with skip connections per layer instead of 2 conv blocks
like conventional u-nets
style_on: bool (optional, default True)
use skip connections from style vector to all upsampling layers
concatenation: bool (optional, default False)
if True, concatentate downsampling block outputs with upsampling block inputs;
default is to add
nchan: int (optional, default 2)
number of channels to use as input to network, default is 2
(cyto + nuclei) or (nuclei + zeros)
"""
def __init__(self, gpu=False, pretrained_model=False,
model_type=None, net_avg=False,
diam_mean=30., device=None,
residual_on=True, style_on=True, concatenation=False,
nchan=2):
self.torch = True
if isinstance(pretrained_model, np.ndarray):
pretrained_model = list(pretrained_model)
elif isinstance(pretrained_model, str):
pretrained_model = [pretrained_model]
self.diam_mean = diam_mean
builtin = True
if model_type is not None or (pretrained_model and not os.path.exists(pretrained_model[0])):
pretrained_model_string = model_type if model_type is not None else 'cyto'
model_strings = get_user_models()
all_models = MODEL_NAMES.copy()
all_models.extend(model_strings)
if ~np.any([pretrained_model_string == s for s in MODEL_NAMES]):
builtin = False
elif ~np.any([pretrained_model_string == s for s in all_models]):
pretrained_model_string = 'cyto'
if (pretrained_model and not os.path.exists(pretrained_model[0])):
models_logger.warning('pretrained model has incorrect path')
models_logger.info(f'>> {pretrained_model_string} << model set to be used')
if pretrained_model_string=='nuclei':
self.diam_mean = 17.
else:
self.diam_mean = 30.
model_range = range(4) if net_avg else range(1)
pretrained_model = [model_path(pretrained_model_string, j, self.torch) for j in model_range]
residual_on, style_on, concatenation = True, True, False
else:
builtin = False
if pretrained_model:
pretrained_model_string = pretrained_model[0]
params = parse_model_string(pretrained_model_string)
if params is not None:
_, residual_on, style_on, concatenation = params
models_logger.info(f'>>>> loading model {pretrained_model_string}')
# initialize network
super().__init__(gpu=gpu, pretrained_model=False,
diam_mean=self.diam_mean, net_avg=net_avg, device=device,
residual_on=residual_on, style_on=style_on, concatenation=concatenation,
nchan=nchan)
self.unet = False
self.pretrained_model = pretrained_model
if self.pretrained_model:
self.net.load_model(self.pretrained_model[0], device=self.device)
self.diam_mean = self.net.diam_mean.data.cpu().numpy()[0]
self.diam_labels = self.net.diam_labels.data.cpu().numpy()[0]
models_logger.info(f'>>>> model diam_mean = {self.diam_mean: .3f} (ROIs rescaled to this size during training)')
if not builtin:
models_logger.info(f'>>>> model diam_labels = {self.diam_labels: .3f} (mean diameter of training ROIs)')
ostr = ['off', 'on']
self.net_type = 'cellpose_residual_{}_style_{}_concatenation_{}'.format(ostr[residual_on],
ostr[style_on],
ostr[concatenation]
)
[docs] def eval(self, x, batch_size=8, channels=None, channel_axis=None,
z_axis=None, normalize=True, invert=False,
rescale=None, diameter=None, do_3D=False, anisotropy=None, net_avg=False,
augment=False, tile=True, tile_overlap=0.1,
resample=True, interp=True,
flow_threshold=0.4, cellprob_threshold=0.0,
compute_masks=True, min_size=15, stitch_threshold=0.0, progress=None,
loop_run=False, model_loaded=False):
"""
segment list of images x, or 4D array - Z x nchan x Y x X
Parameters
----------
x: list or array of images
can be list of 2D/3D/4D images, or array of 2D/3D/4D images
batch_size: int (optional, default 8)
number of 224x224 patches to run simultaneously on the GPU
(can make smaller or bigger depending on GPU memory usage)
channels: list (optional, default None)
list of channels, either of length 2 or of length number of images by 2.
First element of list is the channel to segment (0=grayscale, 1=red, 2=green, 3=blue).
Second element of list is the optional nuclear channel (0=none, 1=red, 2=green, 3=blue).
For instance, to segment grayscale images, input [0,0]. To segment images with cells
in green and nuclei in blue, input [2,3]. To segment one grayscale image and one
image with cells in green and nuclei in blue, input [[0,0], [2,3]].
channel_axis: int (optional, default None)
if None, channels dimension is attempted to be automatically determined
z_axis: int (optional, default None)
if None, z dimension is attempted to be automatically determined
normalize: bool (default, True)
normalize data so 0.0=1st percentile and 1.0=99th percentile of image intensities in each channel
invert: bool (optional, default False)
invert image pixel intensity before running network
diameter: float (optional, default None)
diameter for each image,
if diameter is None, set to diam_mean or diam_train if available
rescale: float (optional, default None)
resize factor for each image, if None, set to 1.0;
(only used if diameter is None)
do_3D: bool (optional, default False)
set to True to run 3D segmentation on 4D image input
anisotropy: float (optional, default None)
for 3D segmentation, optional rescaling factor (e.g. set to 2.0 if Z is sampled half as dense as X or Y)
net_avg: bool (optional, default False)
runs the 4 built-in networks and averages them if True, runs one network if False
augment: bool (optional, default False)
tiles image with overlapping tiles and flips overlapped regions to augment
tile: bool (optional, default True)
tiles image to ensure GPU/CPU memory usage limited (recommended)
tile_overlap: float (optional, default 0.1)
fraction of overlap of tiles when computing flows
resample: bool (optional, default True)
run dynamics at original image size (will be slower but create more accurate boundaries)
interp: bool (optional, default True)
interpolate during 2D dynamics (not available in 3D)
(in previous versions it was False)
flow_threshold: float (optional, default 0.4)
flow error threshold (all cells with errors below threshold are kept) (not used for 3D)
cellprob_threshold: float (optional, default 0.0)
all pixels with value above threshold kept for masks, decrease to find more and larger masks
compute_masks: bool (optional, default True)
Whether or not to compute dynamics and return masks.
This is set to False when retrieving the styles for the size model.
min_size: int (optional, default 15)
minimum number of pixels per mask, can turn off with -1
stitch_threshold: float (optional, default 0.0)
if stitch_threshold>0.0 and not do_3D, masks are stitched in 3D to return volume segmentation
progress: pyqt progress bar (optional, default None)
to return progress bar status to GUI
loop_run: bool (optional, default False)
internal variable for determining if model has been loaded, stops model loading in loop over images
model_loaded: bool (optional, default False)
internal variable for determining if model has been loaded, used in __main__.py
Returns
-------
masks: list of 2D arrays, or single 3D array (if do_3D=True)
labelled image, where 0=no masks; 1,2,...=mask labels
flows: list of lists 2D arrays, or list of 3D arrays (if do_3D=True)
flows[k][0] = XY flow in HSV 0-255
flows[k][1] = XY flows at each pixel
flows[k][2] = cell probability (if > cellprob_threshold, pixel used for dynamics)
flows[k][3] = final pixel locations after Euler integration
styles: list of 1D arrays of length 64, or single 1D array (if do_3D=True)
style vector summarizing each image, also used to estimate size of objects in image
"""
if isinstance(x, list) or x.squeeze().ndim==5:
masks, styles, flows = [], [], []
tqdm_out = utils.TqdmToLogger(models_logger, level=logging.INFO)
nimg = len(x)
iterator = trange(nimg, file=tqdm_out) if nimg>1 else range(nimg)
for i in iterator:
maski, flowi, stylei = self.eval(x[i],
batch_size=batch_size,
channels=channels[i] if (len(channels)==len(x) and
(isinstance(channels[i], list) or isinstance(channels[i], np.ndarray)) and
len(channels[i])==2) else channels,
channel_axis=channel_axis,
z_axis=z_axis,
normalize=normalize,
invert=invert,
rescale=rescale[i] if isinstance(rescale, list) or isinstance(rescale, np.ndarray) else rescale,
diameter=diameter[i] if isinstance(diameter, list) or isinstance(diameter, np.ndarray) else diameter,
do_3D=do_3D,
anisotropy=anisotropy,
net_avg=net_avg,
augment=augment,
tile=tile,
tile_overlap=tile_overlap,
resample=resample,
interp=interp,
flow_threshold=flow_threshold,
cellprob_threshold=cellprob_threshold,
compute_masks=compute_masks,
min_size=min_size,
stitch_threshold=stitch_threshold,
progress=progress,
loop_run=(i>0),
model_loaded=model_loaded)
masks.append(maski)
flows.append(flowi)
styles.append(stylei)
return masks, flows, styles
else:
if not model_loaded and (isinstance(self.pretrained_model, list) and not net_avg and not loop_run):
self.net.load_model(self.pretrained_model[0], device=self.device)
# reshape image (normalization happens in _run_cp)
x = transforms.convert_image(x, channels, channel_axis=channel_axis, z_axis=z_axis,
do_3D=(do_3D or stitch_threshold>0),
normalize=False, invert=False, nchan=self.nchan)
if x.ndim < 4:
x = x[np.newaxis,...]
self.batch_size = batch_size
if diameter is not None and diameter > 0:
rescale = self.diam_mean / diameter
elif rescale is None:
diameter = self.diam_labels
rescale = self.diam_mean / diameter
masks, styles, dP, cellprob, p = self._run_cp(x,
compute_masks=compute_masks,
normalize=normalize,
invert=invert,
rescale=rescale,
net_avg=net_avg,
resample=resample,
augment=augment,
tile=tile,
tile_overlap=tile_overlap,
flow_threshold=flow_threshold,
cellprob_threshold=cellprob_threshold,
interp=interp,
min_size=min_size,
do_3D=do_3D,
anisotropy=anisotropy,
stitch_threshold=stitch_threshold,
)
flows = [plot.dx_to_circ(dP), dP, cellprob, p]
return masks, flows, styles
def _run_cp(self, x, compute_masks=True, normalize=True, invert=False,
rescale=1.0, net_avg=False, resample=True,
augment=False, tile=True, tile_overlap=0.1,
cellprob_threshold=0.0,
flow_threshold=0.4, min_size=15,
interp=True, anisotropy=1.0, do_3D=False, stitch_threshold=0.0,
):
tic = time.time()
shape = x.shape
nimg = shape[0]
bd, tr = None, None
if do_3D:
img = np.asarray(x)
if normalize or invert:
img = transforms.normalize_img(img, invert=invert)
yf, styles = self._run_3D(img, rsz=rescale, anisotropy=anisotropy,
net_avg=net_avg, augment=augment, tile=tile,
tile_overlap=tile_overlap)
cellprob = yf[0][-1] + yf[1][-1] + yf[2][-1]
dP = np.stack((yf[1][0] + yf[2][0], yf[0][0] + yf[2][1], yf[0][1] + yf[1][1]),
axis=0) # (dZ, dY, dX)
del yf
else:
tqdm_out = utils.TqdmToLogger(models_logger, level=logging.INFO)
iterator = trange(nimg, file=tqdm_out) if nimg>1 else range(nimg)
styles = np.zeros((nimg, self.nbase[-1]), np.float32)
if resample:
dP = np.zeros((2, nimg, shape[1], shape[2]), np.float32)
cellprob = np.zeros((nimg, shape[1], shape[2]), np.float32)
else:
dP = np.zeros((2, nimg, int(shape[1]*rescale), int(shape[2]*rescale)), np.float32)
cellprob = np.zeros((nimg, int(shape[1]*rescale), int(shape[2]*rescale)), np.float32)
for i in iterator:
img = np.asarray(x[i])
if normalize or invert:
img = transforms.normalize_img(img, invert=invert)
if rescale != 1.0:
img = transforms.resize_image(img, rsz=rescale)
yf, style = self._run_nets(img, net_avg=net_avg,
augment=augment, tile=tile,
tile_overlap=tile_overlap)
if resample:
yf = transforms.resize_image(yf, shape[1], shape[2])
cellprob[i] = yf[:,:,2]
dP[:, i] = yf[:,:,:2].transpose((2,0,1))
if self.nclasses == 4:
if i==0:
bd = np.zeros_like(cellprob)
bd[i] = yf[:,:,3]
styles[i] = style
del yf, style
styles = styles.squeeze()
net_time = time.time() - tic
if nimg > 1:
models_logger.info('network run in %2.2fs'%(net_time))
if compute_masks:
tic=time.time()
niter = 200 if (do_3D and not resample) else (1 / rescale * 200)
if do_3D:
masks, p = dynamics.compute_masks(dP, cellprob, niter=niter,
cellprob_threshold=cellprob_threshold,
flow_threshold=flow_threshold,
interp=interp, do_3D=do_3D, min_size=min_size,
resize=None,
use_gpu=self.gpu, device=self.device
)
else:
masks, p = [], []
resize = [shape[1], shape[2]] if not resample else None
for i in iterator:
outputs = dynamics.compute_masks(dP[:,i], cellprob[i], niter=niter, cellprob_threshold=cellprob_threshold,
flow_threshold=flow_threshold, interp=interp, resize=resize,
use_gpu=self.gpu, device=self.device)
masks.append(outputs[0])
p.append(outputs[1])
masks = np.array(masks)
p = np.array(p)
if stitch_threshold > 0 and nimg > 1:
models_logger.info(f'stitching {nimg} planes using stitch_threshold={stitch_threshold:0.3f} to make 3D masks')
masks = utils.stitch3D(masks, stitch_threshold=stitch_threshold)
masks = utils.fill_holes_and_remove_small_masks(masks, min_size=min_size)
flow_time = time.time() - tic
if nimg > 1:
models_logger.info('masks created in %2.2fs'%(flow_time))
masks, dP, cellprob, p = masks.squeeze(), dP.squeeze(), cellprob.squeeze(), p.squeeze()
else:
masks, p = np.zeros(0), np.zeros(0) #pass back zeros if not compute_masks
return masks, styles, dP, cellprob, p
[docs] def loss_fn(self, lbl, y):
""" loss function between true labels lbl and prediction y """
veci = 5. * self._to_device(lbl[:,1:])
lbl = self._to_device(lbl[:,0]>.5)
loss = self.criterion(y[:,:2] , veci)
loss /= 2.
loss2 = self.criterion2(y[:,2] , lbl)
loss = loss + loss2
return loss
[docs] def train(self, train_data, train_labels, train_files=None,
test_data=None, test_labels=None, test_files=None,
channels=None, normalize=True,
save_path=None, save_every=100, save_each=False,
learning_rate=0.2, n_epochs=500, momentum=0.9, SGD=True,
weight_decay=0.00001, batch_size=8, nimg_per_epoch=None,
rescale=True, min_train_masks=5,
model_name=None):
""" train network with images train_data
Parameters
------------------
train_data: list of arrays (2D or 3D)
images for training
train_labels: list of arrays (2D or 3D)
labels for train_data, where 0=no masks; 1,2,...=mask labels
can include flows as additional images
train_files: list of strings
file names for images in train_data (to save flows for future runs)
test_data: list of arrays (2D or 3D)
images for testing
test_labels: list of arrays (2D or 3D)
labels for test_data, where 0=no masks; 1,2,...=mask labels;
can include flows as additional images
test_files: list of strings
file names for images in test_data (to save flows for future runs)
channels: list of ints (default, None)
channels to use for training
normalize: bool (default, True)
normalize data so 0.0=1st percentile and 1.0=99th percentile of image intensities in each channel
save_path: string (default, None)
where to save trained model, if None it is not saved
save_every: int (default, 100)
save network every [save_every] epochs
learning_rate: float or list/np.ndarray (default, 0.2)
learning rate for training, if list, must be same length as n_epochs
n_epochs: int (default, 500)
how many times to go through whole training set during training
weight_decay: float (default, 0.00001)
SGD: bool (default, True)
use SGD as optimization instead of RAdam
batch_size: int (optional, default 8)
number of 224x224 patches to run simultaneously on the GPU
(can make smaller or bigger depending on GPU memory usage)
nimg_per_epoch: int (optional, default None)
minimum number of images to train on per epoch,
with a small training set (< 8 images) it may help to set to 8
rescale: bool (default, True)
whether or not to rescale images to diam_mean during training,
if True it assumes you will fit a size model after training or resize your images accordingly,
if False it will try to train the model to be scale-invariant (works worse)
min_train_masks: int (default, 5)
minimum number of masks an image must have to use in training set
model_name: str (default, None)
name of network, otherwise saved with name as params + training start time
"""
train_data, train_labels, test_data, test_labels, run_test = transforms.reshape_train_test(train_data, train_labels,
test_data, test_labels,
channels, normalize)
# check if train_labels have flows
# if not, flows computed, returned with labels as train_flows[i][0]
train_flows = dynamics.labels_to_flows(train_labels, files=train_files, use_gpu=self.gpu, device=self.device)
if run_test:
test_flows = dynamics.labels_to_flows(test_labels, files=test_files, use_gpu=self.gpu, device=self.device)
else:
test_flows = None
nmasks = np.array([label[0].max() for label in train_flows])
nremove = (nmasks < min_train_masks).sum()
if nremove > 0:
models_logger.warning(f'{nremove} train images with number of masks less than min_train_masks ({min_train_masks}), removing from train set')
ikeep = np.nonzero(nmasks >= min_train_masks)[0]
train_data = [train_data[i] for i in ikeep]
train_flows = [train_flows[i] for i in ikeep]
if channels is None:
models_logger.warning('channels is set to None, input must therefore have nchan channels (default is 2)')
model_path = self._train_net(train_data, train_flows,
test_data=test_data, test_labels=test_flows,
save_path=save_path, save_every=save_every, save_each=save_each,
learning_rate=learning_rate, n_epochs=n_epochs,
momentum=momentum, weight_decay=weight_decay,
SGD=SGD, batch_size=batch_size, nimg_per_epoch=nimg_per_epoch,
rescale=rescale, model_name=model_name)
self.pretrained_model = model_path
return model_path
[docs]class SizeModel():
""" linear regression model for determining the size of objects in image
used to rescale before input to cp_model
uses styles from cp_model
Parameters
-------------------
cp_model: UnetModel or CellposeModel
model from which to get styles
device: torch device (optional, default None)
device used for model running / training
(torch.device('cuda') or torch.device('cpu')), overrides gpu input,
recommended if you want to use a specific GPU (e.g. torch.device('cuda:1'))
pretrained_size: str
path to pretrained size model
"""
def __init__(self, cp_model, device=None, pretrained_size=None, **kwargs):
super(SizeModel, self).__init__(**kwargs)
self.pretrained_size = pretrained_size
self.cp = cp_model
self.device = self.cp.device
self.diam_mean = self.cp.diam_mean
self.torch = True
if pretrained_size is not None:
self.params = np.load(self.pretrained_size, allow_pickle=True).item()
self.diam_mean = self.params['diam_mean']
if not hasattr(self.cp, 'pretrained_model'):
error_message = 'no pretrained cellpose model specified, cannot compute size'
models_logger.critical(error_message)
raise ValueError(error_message)
[docs] def eval(self, x, channels=None, channel_axis=None,
normalize=True, invert=False, augment=False, tile=True,
batch_size=8, progress=None, interp=True):
""" use images x to produce style or use style input to predict size of objects in image
Object size estimation is done in two steps:
1. use a linear regression model to predict size from style in image
2. resize image to predicted size and run CellposeModel to get output masks.
Take the median object size of the predicted masks as the final predicted size.
Parameters
-------------------
x: list or array of images
can be list of 2D/3D images, or array of 2D/3D images
channels: list (optional, default None)
list of channels, either of length 2 or of length number of images by 2.
First element of list is the channel to segment (0=grayscale, 1=red, 2=green, 3=blue).
Second element of list is the optional nuclear channel (0=none, 1=red, 2=green, 3=blue).
For instance, to segment grayscale images, input [0,0]. To segment images with cells
in green and nuclei in blue, input [2,3]. To segment one grayscale image and one
image with cells in green and nuclei in blue, input [[0,0], [2,3]].
channel_axis: int (optional, default None)
if None, channels dimension is attempted to be automatically determined
normalize: bool (default, True)
normalize data so 0.0=1st percentile and 1.0=99th percentile of image intensities in each channel
invert: bool (optional, default False)
invert image pixel intensity before running network
augment: bool (optional, default False)
tiles image with overlapping tiles and flips overlapped regions to augment
tile: bool (optional, default True)
tiles image to ensure GPU/CPU memory usage limited (recommended)
progress: pyqt progress bar (optional, default None)
to return progress bar status to GUI
Returns
-------
diam: array, float
final estimated diameters from images x or styles style after running both steps
diam_style: array, float
estimated diameters from style alone
"""
if isinstance(x, list):
diams, diams_style = [], []
nimg = len(x)
tqdm_out = utils.TqdmToLogger(models_logger, level=logging.INFO)
iterator = trange(nimg, file=tqdm_out) if nimg>1 else range(nimg)
for i in iterator:
diam, diam_style = self.eval(x[i],
channels=channels[i] if (channels is not None and len(channels)==len(x) and
(isinstance(channels[i], list) or isinstance(channels[i], np.ndarray)) and
len(channels[i])==2) else channels,
channel_axis=channel_axis,
normalize=normalize,
invert=invert,
augment=augment,
tile=tile,
batch_size=batch_size,
progress=progress,
)
diams.append(diam)
diams_style.append(diam_style)
return diams, diams_style
if x.squeeze().ndim > 3:
models_logger.warning('image is not 2D cannot compute diameter')
return self.diam_mean, self.diam_mean
styles = self.cp.eval(x,
channels=channels,
channel_axis=channel_axis,
normalize=normalize,
invert=invert,
augment=augment,
tile=tile,
batch_size=batch_size,
net_avg=False,
resample=False,
compute_masks=False)[-1]
diam_style = self._size_estimation(np.array(styles))
diam_style = self.diam_mean if (diam_style==0 or np.isnan(diam_style)) else diam_style
masks = self.cp.eval(x,
compute_masks=True,
channels=channels,
channel_axis=channel_axis,
normalize=normalize,
invert=invert,
augment=augment,
tile=tile,
batch_size=batch_size,
net_avg=False,
resample=False,
rescale = self.diam_mean / diam_style if self.diam_mean>0 else 1,
#flow_threshold=0,
diameter=None,
interp=False,
)[0]
diam = utils.diameters(masks)[0]
diam = self.diam_mean if (diam==0 or np.isnan(diam)) else diam
return diam, diam_style
def _size_estimation(self, style):
""" linear regression from style to size
sizes were estimated using "diameters" from square estimates not circles;
therefore a conversion factor is included (to be removed)
"""
szest = np.exp(self.params['A'] @ (style - self.params['smean']).T +
np.log(self.diam_mean) + self.params['ymean'])
szest = np.maximum(5., szest)
return szest
[docs] def train(self, train_data, train_labels,
test_data=None, test_labels=None,
channels=None, normalize=True,
learning_rate=0.2, n_epochs=10,
l2_regularization=1.0, batch_size=8,
):
""" train size model with images train_data to estimate linear model from styles to diameters
Parameters
------------------
train_data: list of arrays (2D or 3D)
images for training
train_labels: list of arrays (2D or 3D)
labels for train_data, where 0=no masks; 1,2,...=mask labels
can include flows as additional images
channels: list of ints (default, None)
channels to use for training
normalize: bool (default, True)
normalize data so 0.0=1st percentile and 1.0=99th percentile of image intensities in each channel
n_epochs: int (default, 10)
how many times to go through whole training set (taking random patches) for styles for diameter estimation
l2_regularization: float (default, 1.0)
regularize linear model from styles to diameters
batch_size: int (optional, default 8)
number of 224x224 patches to run simultaneously on the GPU
(can make smaller or bigger depending on GPU memory usage)
"""
batch_size /= 2 # reduce batch_size by factor of 2 to use larger tiles
batch_size = int(max(1, batch_size))
self.cp.batch_size = batch_size
train_data, train_labels, test_data, test_labels, run_test = transforms.reshape_train_test(train_data, train_labels,
test_data, test_labels,
channels, normalize)
if isinstance(self.cp.pretrained_model, list):
cp_model_path = self.cp.pretrained_model[0]
self.cp.net.load_model(cp_model_path, device=self.cp.device)
else:
cp_model_path = self.cp.pretrained_model
diam_train = np.array([utils.diameters(lbl)[0] for lbl in train_labels])
if run_test:
diam_test = np.array([utils.diameters(lbl)[0] for lbl in test_labels])
# remove images with no masks
for i in range(len(diam_train)):
if diam_train[i]==0.0:
del train_data[i]
del train_labels[i]
if run_test:
for i in range(len(diam_test)):
if diam_test[i]==0.0:
del test_data[i]
del test_labels[i]
nimg = len(train_data)
styles = np.zeros((n_epochs*nimg, 256), np.float32)
diams = np.zeros((n_epochs*nimg,), np.float32)
tic = time.time()
for iepoch in range(n_epochs):
iall = np.arange(0,nimg,1,int)
for ibatch in range(0,nimg,batch_size):
inds = iall[ibatch:ibatch+batch_size]
imgi,lbl,scale = transforms.random_rotate_and_resize([train_data[i] for i in inds],
Y=[train_labels[i].astype(np.int16) for i in inds],
scale_range=1, xy=(512,512))
feat = self.cp.network(imgi)[1]
styles[inds+nimg*iepoch] = feat
diams[inds+nimg*iepoch] = np.log(diam_train[inds]) - np.log(self.diam_mean) + np.log(scale)
del feat
if (iepoch+1)%2==0:
models_logger.info('ran %d epochs in %0.3f sec'%(iepoch+1, time.time()-tic))
# create model
smean = styles.mean(axis=0)
X = ((styles - smean).T).copy()
ymean = diams.mean()
y = diams - ymean
A = np.linalg.solve(X@X.T + l2_regularization*np.eye(X.shape[0]), X @ y)
ypred = A @ X
models_logger.info('train correlation: %0.4f'%np.corrcoef(y, ypred)[0,1])
if run_test:
nimg_test = len(test_data)
styles_test = np.zeros((nimg_test, 256), np.float32)
for i in range(nimg_test):
styles_test[i] = self.cp._run_net(test_data[i].transpose((1,2,0)))[1]
diam_test_pred = np.exp(A @ (styles_test - smean).T + np.log(self.diam_mean) + ymean)
diam_test_pred = np.maximum(5., diam_test_pred)
models_logger.info('test correlation: %0.4f'%np.corrcoef(diam_test, diam_test_pred)[0,1])
self.pretrained_size = cp_model_path+'_size.npy'
self.params = {'A': A, 'smean': smean, 'diam_mean': self.diam_mean, 'ymean': ymean}
np.save(self.pretrained_size, self.params)
models_logger.info('model saved to '+self.pretrained_size)
return self.params