Source code for cellpose.core

import os, sys, time, shutil, tempfile, datetime, pathlib, subprocess
import logging
import numpy as np
from tqdm import trange, tqdm
from urllib.parse import urlparse
import tempfile
import cv2
from scipy.stats import mode
import fastremap
from . import transforms, dynamics, utils, plot, metrics

import torch
#     from GPUtil import showUtilization as gpu_usage #for gpu memory debugging 
from torch import nn
from torch.utils import mkldnn as mkldnn_utils
from . import resnet_torch
TORCH_ENABLED = True

core_logger = logging.getLogger(__name__)
tqdm_out = utils.TqdmToLogger(core_logger, level=logging.INFO)

def parse_model_string(pretrained_model):
    if isinstance(pretrained_model, list):
        model_str = os.path.split(pretrained_model[0])[-1]
    else:
        model_str = os.path.split(pretrained_model)[-1]
    if len(model_str)>3 and model_str[:4]=='unet':
        cp = False
        nclasses = max(2, int(model_str[4]))
    elif len(model_str)>7 and model_str[:8]=='cellpose':
        cp = True
        nclasses = 3
    else:
        return 3, True, True, False
    
    if 'residual' in model_str and 'style' in model_str and 'concatentation' in model_str:
        ostrs = model_str.split('_')[2::2]
        residual_on = ostrs[0]=='on'
        style_on = ostrs[1]=='on'
        concatenation = ostrs[2]=='on'
        return nclasses, residual_on, style_on, concatenation
    else:
        if cp:
            return 3, True, True, False
        else:
            return nclasses, False, False, True

[docs]def use_gpu(gpu_number=0, use_torch=True): """ check if gpu works """ if use_torch: return _use_gpu_torch(gpu_number) else: raise ValueError('cellpose only runs with pytorch now')
def _use_gpu_torch(gpu_number=0): try: device = torch.device('cuda:' + str(gpu_number)) _ = torch.zeros([1, 2, 3]).to(device) core_logger.info('** TORCH CUDA version installed and working. **') return True except: core_logger.info('TORCH CUDA version not installed/working.') return False def assign_device(use_torch=True, gpu=False, device=0): mac = False cpu = True if isinstance(device, str): if device=='mps': mac = True else: device = int(device) if gpu and use_gpu(use_torch=True): device = torch.device(f'cuda:{device}') gpu=True cpu=False core_logger.info('>>>> using GPU') elif mac: try: device = torch.device('mps') gpu=True core_logger.info('>>>> using GPU') except: cpu = True gpu = False if cpu: device = torch.device('cpu') core_logger.info('>>>> using CPU') gpu=False return device, gpu def check_mkl(use_torch=True): #core_logger.info('Running test snippet to check if MKL-DNN working') mkl_enabled = torch.backends.mkldnn.is_available() if mkl_enabled: mkl_enabled = True #core_logger.info('MKL version working - CPU version is sped up.') else: core_logger.info('WARNING: MKL version on torch not working/installed - CPU version will be slightly slower.') core_logger.info('see https://pytorch.org/docs/stable/backends.html?highlight=mkl') return mkl_enabled class UnetModel(): def __init__(self, gpu=False, pretrained_model=False, diam_mean=30., net_avg=False, device=None, residual_on=False, style_on=False, concatenation=True, nclasses=3, nchan=2): self.unet = True self.torch = True self.mkldnn = None if device is None: sdevice, gpu = assign_device(self.torch, gpu) self.device = device if device is not None else sdevice if device is not None: device_gpu = self.device.type=='cuda' self.gpu = gpu if device is None else device_gpu if not self.gpu: self.mkldnn = check_mkl(True) self.pretrained_model = pretrained_model self.diam_mean = diam_mean ostr = ['off', 'on'] self.net_type = 'unet{}_residual_{}_style_{}_concatenation_{}'.format(nclasses, ostr[residual_on], ostr[style_on], ostr[concatenation]) if pretrained_model: core_logger.info(f'u-net net type: {self.net_type}') # create network self.nclasses = nclasses self.nbase = [32,64,128,256] self.nchan = nchan self.nbase = [nchan, 32, 64, 128, 256] self.net = resnet_torch.CPnet(self.nbase, self.nclasses, sz=3, residual_on=residual_on, style_on=style_on, concatenation=concatenation, mkldnn=self.mkldnn, diam_mean=diam_mean).to(self.device) if pretrained_model is not None and isinstance(pretrained_model, str): self.net.load_model(pretrained_model, cpu=(not self.gpu)) def eval(self, x, batch_size=8, channels=None, channels_last=False, invert=False, normalize=True, rescale=None, do_3D=False, anisotropy=None, net_avg=False, augment=False, channel_axis=None, z_axis=None, nolist=False, tile=True, cell_threshold=None, boundary_threshold=None, min_size=15, compute_masks=True): """ segment list of images x 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 normalize: bool (optional, default True) normalize data so 0.0=1st percentile and 1.0=99th percentile of image intensities in each channel rescale: float (optional, default None) resize factor for each image, if None, set to 1.0 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) cell_threshold: float (optional, default 0.0) cell probability threshold (all pixels with prob above threshold kept for masks) boundary_threshold: float (optional, default 0.0) cell probability threshold (all pixels with prob above threshold kept for masks) min_size: int (optional, default 15) minimum number of pixels per mask, can turn off with -1 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] = flows at each pixel flows[k][2] = the cell distance field flows[k][3] = the cell boundary 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 """ x = [transforms.convert_image(xi, channels, channel_axis, z_axis, do_3D, normalize, invert, nchan=self.nchan) for xi in x] nimg = len(x) self.batch_size = batch_size styles = [] flows = [] masks = [] if rescale is None: rescale = np.ones(nimg) elif isinstance(rescale, float): rescale = rescale * np.ones(nimg) if nimg > 1: iterator = trange(nimg, file=tqdm_out) else: iterator = range(nimg) if isinstance(self.pretrained_model, list): model_path = self.pretrained_model[0] if not net_avg: self.net.load_model(self.pretrained_model[0]) else: model_path = self.pretrained_model if cell_threshold is None or boundary_threshold is None: try: thresholds = np.load(model_path+'_cell_boundary_threshold.npy') cell_threshold, boundary_threshold = thresholds core_logger.info('>>>> found saved thresholds from validation set') except: core_logger.warning('WARNING: no thresholds found, using default / user input') cell_threshold = 2.0 if cell_threshold is None else cell_threshold boundary_threshold = 0.5 if boundary_threshold is None else boundary_threshold if not do_3D: for i in iterator: img = x[i].copy() shape = img.shape # rescale image for flow computation img = transforms.resize_image(img, rsz=rescale[i]) y, style = self._run_nets(img, net_avg=net_avg, augment=augment, tile=tile) if compute_masks: maski = utils.get_masks_unet(y, cell_threshold, boundary_threshold) maski = utils.fill_holes_and_remove_small_masks(maski, min_size=min_size) maski = transforms.resize_image(maski, shape[-3], shape[-2], interpolation=cv2.INTER_NEAREST) else: maski = None masks.append(maski) styles.append(style) else: for i in iterator: tic=time.time() yf, style = self._run_3D(x[i], rsz=rescale[i], anisotropy=anisotropy, net_avg=net_avg, augment=augment, tile=tile) yf = yf.mean(axis=0) core_logger.info('probabilities computed %2.2fs'%(time.time()-tic)) if compute_masks: maski = utils.get_masks_unet(yf.transpose((1,2,3,0)), cell_threshold, boundary_threshold) maski = utils.fill_holes_and_remove_small_masks(maski, min_size=min_size) else: maski = None masks.append(maski) styles.append(style) core_logger.info('masks computed %2.2fs'%(time.time()-tic)) flows.append(yf) if nolist: masks, flows, styles = masks[0], flows[0], styles[0] return masks, flows, styles def _to_device(self, x): X = torch.from_numpy(x).float().to(self.device) return X def _from_device(self, X): x = X.detach().cpu().numpy() return x def network(self, x, return_conv=False): """ convert imgs to torch and run network model and return numpy """ X = self._to_device(x) self.net.eval() if self.mkldnn: self.net = mkldnn_utils.to_mkldnn(self.net) with torch.no_grad(): y, style = self.net(X) del X y = self._from_device(y) style = self._from_device(style) if return_conv: conv = self._from_device(conv) y = np.concatenate((y, conv), axis=1) return y, style def _run_nets(self, img, net_avg=False, augment=False, tile=True, tile_overlap=0.1, bsize=224, return_conv=False, progress=None): """ run network (if more than one, loop over networks and average results Parameters -------------- img: float, [Ly x Lx x nchan] or [Lz x Ly x Lx x nchan] 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 memory usage limited (recommended) tile_overlap: float (optional, default 0.1) fraction of overlap of tiles when computing flows progress: pyqt progress bar (optional, default None) to return progress bar status to GUI Returns ------------------ y: array [3 x Ly x Lx] or [3 x Lz x Ly x Lx] y is output (averaged over networks); y[0] is Y flow; y[1] is X flow; y[2] is cell probability style: array [64] 1D array summarizing the style of the image, if tiled it is averaged over tiles, but not averaged over networks. """ if isinstance(self.pretrained_model, str) or not net_avg: y, style = self._run_net(img, augment=augment, tile=tile, tile_overlap=tile_overlap, bsize=bsize, return_conv=return_conv) else: for j in range(len(self.pretrained_model)): self.net.load_model(self.pretrained_model[j], cpu=(not self.gpu)) y0, style = self._run_net(img, augment=augment, tile=tile, tile_overlap=tile_overlap, bsize=bsize, return_conv=return_conv) if j==0: y = y0 else: y += y0 if progress is not None: progress.setValue(10 + 10*j) y = y / len(self.pretrained_model) return y, style def _run_net(self, imgs, augment=False, tile=True, tile_overlap=0.1, bsize=224, return_conv=False): """ run network on image or stack of images (faster if augment is False) Parameters -------------- imgs: array [Ly x Lx x nchan] or [Lz x Ly x Lx x nchan] rsz: float (optional, default 1.0) resize coefficient(s) for image 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); cannot be turned off for 3D segmentation tile_overlap: float (optional, default 0.1) fraction of overlap of tiles when computing flows bsize: int (optional, default 224) size of tiles to use in pixels [bsize x bsize] Returns ------------------ y: array [Ly x Lx x 3] or [Lz x Ly x Lx x 3] y[...,0] is Y flow; y[...,1] is X flow; y[...,2] is cell probability style: array [64] 1D array summarizing the style of the image, if tiled it is averaged over tiles """ if imgs.ndim==4: # make image Lz x nchan x Ly x Lx for net imgs = np.transpose(imgs, (0,3,1,2)) detranspose = (0,2,3,1) return_conv = False else: # make image nchan x Ly x Lx for net imgs = np.transpose(imgs, (2,0,1)) detranspose = (1,2,0) # pad image for net so Ly and Lx are divisible by 4 imgs, ysub, xsub = transforms.pad_image_ND(imgs) # slices from padding # slc = [slice(0, self.nclasses) for n in range(imgs.ndim)] # changed from imgs.shape[n]+1 for first slice size slc = [slice(0, imgs.shape[n]+1) for n in range(imgs.ndim)] slc[-3] = slice(0, self.nclasses + 32*return_conv + 1) slc[-2] = slice(ysub[0], ysub[-1]+1) slc[-1] = slice(xsub[0], xsub[-1]+1) slc = tuple(slc) # run network if tile or augment or imgs.ndim==4: y, style = self._run_tiled(imgs, augment=augment, bsize=bsize, tile_overlap=tile_overlap, return_conv=return_conv) else: imgs = np.expand_dims(imgs, axis=0) y, style = self.network(imgs, return_conv=return_conv) y, style = y[0], style[0] style /= (style**2).sum()**0.5 # slice out padding y = y[slc] # transpose so channels axis is last again y = np.transpose(y, detranspose) return y, style def _run_tiled(self, imgi, augment=False, bsize=224, tile_overlap=0.1, return_conv=False): """ run network in tiles of size [bsize x bsize] First image is split into overlapping tiles of size [bsize x bsize]. If augment, tiles have 50% overlap and are flipped at overlaps. The average of the network output over tiles is returned. Parameters -------------- imgi: array [nchan x Ly x Lx] or [Lz x nchan x Ly x Lx] augment: bool (optional, default False) tiles image with overlapping tiles and flips overlapped regions to augment bsize: int (optional, default 224) size of tiles to use in pixels [bsize x bsize] tile_overlap: float (optional, default 0.1) fraction of overlap of tiles when computing flows Returns ------------------ yf: array [3 x Ly x Lx] or [Lz x 3 x Ly x Lx] yf is averaged over tiles yf[0] is Y flow; yf[1] is X flow; yf[2] is cell probability styles: array [64] 1D array summarizing the style of the image, averaged over tiles """ if imgi.ndim==4: batch_size = self.batch_size Lz, nchan = imgi.shape[:2] IMG, ysub, xsub, Ly, Lx = transforms.make_tiles(imgi[0], bsize=bsize, augment=augment, tile_overlap=tile_overlap) ny, nx, nchan, ly, lx = IMG.shape batch_size *= max(4, (bsize**2 // (ly*lx))**0.5) yf = np.zeros((Lz, self.nclasses, imgi.shape[-2], imgi.shape[-1]), np.float32) styles = [] if ny*nx > batch_size: ziterator = trange(Lz, file=tqdm_out) for i in ziterator: yfi, stylei = self._run_tiled(imgi[i], augment=augment, bsize=bsize, tile_overlap=tile_overlap) yf[i] = yfi styles.append(stylei) else: # run multiple slices at the same time ntiles = ny*nx nimgs = max(2, int(np.round(batch_size / ntiles))) niter = int(np.ceil(Lz/nimgs)) ziterator = trange(niter, file=tqdm_out) for k in ziterator: IMGa = np.zeros((ntiles*nimgs, nchan, ly, lx), np.float32) for i in range(min(Lz-k*nimgs, nimgs)): IMG, ysub, xsub, Ly, Lx = transforms.make_tiles(imgi[k*nimgs+i], bsize=bsize, augment=augment, tile_overlap=tile_overlap) IMGa[i*ntiles:(i+1)*ntiles] = np.reshape(IMG, (ny*nx, nchan, ly, lx)) ya, stylea = self.network(IMGa) for i in range(min(Lz-k*nimgs, nimgs)): y = ya[i*ntiles:(i+1)*ntiles] if augment: y = np.reshape(y, (ny, nx, 3, ly, lx)) y = transforms.unaugment_tiles(y, self.unet) y = np.reshape(y, (-1, 3, ly, lx)) yfi = transforms.average_tiles(y, ysub, xsub, Ly, Lx) yfi = yfi[:,:imgi.shape[2],:imgi.shape[3]] yf[k*nimgs+i] = yfi stylei = stylea[i*ntiles:(i+1)*ntiles].sum(axis=0) stylei /= (stylei**2).sum()**0.5 styles.append(stylei) return yf, np.array(styles) else: IMG, ysub, xsub, Ly, Lx = transforms.make_tiles(imgi, bsize=bsize, augment=augment, tile_overlap=tile_overlap) ny, nx, nchan, ly, lx = IMG.shape IMG = np.reshape(IMG, (ny*nx, nchan, ly, lx)) batch_size = self.batch_size niter = int(np.ceil(IMG.shape[0] / batch_size)) nout = self.nclasses + 32*return_conv y = np.zeros((IMG.shape[0], nout, ly, lx)) for k in range(niter): irange = np.arange(batch_size*k, min(IMG.shape[0], batch_size*k+batch_size)) y0, style = self.network(IMG[irange], return_conv=return_conv) y[irange] = y0.reshape(len(irange), y0.shape[-3], y0.shape[-2], y0.shape[-1]) if k==0: styles = style[0] styles += style.sum(axis=0) styles /= IMG.shape[0] if augment: y = np.reshape(y, (ny, nx, nout, bsize, bsize)) y = transforms.unaugment_tiles(y, self.unet) y = np.reshape(y, (-1, nout, bsize, bsize)) yf = transforms.average_tiles(y, ysub, xsub, Ly, Lx) yf = yf[:,:imgi.shape[1],:imgi.shape[2]] styles /= (styles**2).sum()**0.5 return yf, styles def _run_3D(self, imgs, rsz=1.0, anisotropy=None, net_avg=False, augment=False, tile=True, tile_overlap=0.1, bsize=224, progress=None): """ run network on stack of images (faster if augment is False) Parameters -------------- imgs: array [Lz x Ly x Lx x nchan] rsz: float (optional, default 1.0) resize coefficient(s) for image 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); cannot be turned off for 3D segmentation tile_overlap: float (optional, default 0.1) fraction of overlap of tiles when computing flows bsize: int (optional, default 224) size of tiles to use in pixels [bsize x bsize] progress: pyqt progress bar (optional, default None) to return progress bar status to GUI Returns ------------------ yf: array [Lz x Ly x Lx x 3] y[...,0] is Y flow; y[...,1] is X flow; y[...,2] is cell probability style: array [64] 1D array summarizing the style of the image, if tiled it is averaged over tiles """ sstr = ['YX', 'ZY', 'ZX'] if anisotropy is not None: rescaling = [[rsz, rsz], [rsz*anisotropy, rsz], [rsz*anisotropy, rsz]] else: rescaling = [rsz] * 3 pm = [(0,1,2,3), (1,0,2,3), (2,0,1,3)] ipm = [(3,0,1,2), (3,1,0,2), (3,1,2,0)] yf = np.zeros((3, self.nclasses, imgs.shape[0], imgs.shape[1], imgs.shape[2]), np.float32) for p in range(3 - 2*self.unet): xsl = imgs.copy().transpose(pm[p]) # rescale image for flow computation shape = xsl.shape xsl = transforms.resize_image(xsl, rsz=rescaling[p]) # per image core_logger.info('running %s: %d planes of size (%d, %d)'%(sstr[p], shape[0], shape[1], shape[2])) y, style = self._run_nets(xsl, net_avg=net_avg, augment=augment, tile=tile, bsize=bsize, tile_overlap=tile_overlap) y = transforms.resize_image(y, shape[1], shape[2]) yf[p] = y.transpose(ipm[p]) if progress is not None: progress.setValue(25+15*p) return yf, style def loss_fn(self, lbl, y): """ loss function between true labels lbl and prediction y """ # if available set boundary pixels to 2 if lbl.shape[1]>1 and self.nclasses>2: boundary = lbl[:,1]<=4 lbl = lbl[:,0] lbl[boundary] *= 2 else: lbl = lbl[:,0] lbl = self._to_device(lbl).long() loss = 8 * 1./self.nclasses * self.criterion(y, lbl) return loss 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, weight_decay=0.00001, batch_size=8, nimg_per_epoch=None, min_train_masks=5, rescale=False, model_name=None): """ train function uses 0-1 mask label and boundary pixels for training """ nimg = len(train_data) train_data, train_labels, test_data, test_labels, run_test = transforms.reshape_train_test(train_data, train_labels, test_data, test_labels, channels, normalize) train_labels = [fastremap.renumber(label, in_place=True)[0] for label in train_labels] # add dist_to_bound to labels if self.nclasses==3: core_logger.info('computing boundary pixels for training data') train_classes = [np.stack((label, label>0, utils.distance_to_boundary(label)), axis=0).astype(np.float32) for label in tqdm(train_labels, file=tqdm_out)] else: train_classes = [np.stack((label, label>0), axis=0).astype(np.float32) for label in tqdm(train_labels, file=tqdm_out)] if run_test: test_labels = [fastremap.renumber(label, in_place=True)[0] for label in test_labels] if self.nclasses==3: core_logger.info('computing boundary pixels for test data') test_classes = [np.stack((label, label>0, utils.distance_to_boundary(label)), axis=0).astype(np.float32) for label in tqdm(test_labels, file=tqdm_out)] else: test_classes = [np.stack((label, label>0), axis=0).astype(np.float32) for label in tqdm(test_labels, file=tqdm_out)] else: test_classes = None nmasks = np.array([label[0].max()-1 for label in train_classes]) nremove = (nmasks < min_train_masks).sum() if nremove > 0: core_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_classes = [train_classes[i] for i in ikeep] train_labels = [train_labels[i] for i in ikeep] # split train data into train and val val_data = train_data[::8] val_classes = train_classes[::8] val_labels = train_labels[::8] del train_data[::8], train_classes[::8], train_labels[::8] model_path = self._train_net(train_data, train_classes, test_data, test_classes, 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=True, batch_size=batch_size, nimg_per_epoch=nimg_per_epoch, rescale=rescale, model_name=model_name) # find threshold using validation set core_logger.info('>>>> finding best thresholds using validation set') cell_threshold, boundary_threshold = self.threshold_validation(val_data, val_labels) np.save(model_path+'_cell_boundary_threshold.npy', np.array([cell_threshold, boundary_threshold])) return model_path def threshold_validation(self, val_data, val_labels): cell_thresholds = np.arange(-4.0, 4.25, 0.5) if self.nclasses==3: boundary_thresholds = np.arange(-2, 2.25, 1.0) else: boundary_thresholds = np.zeros(1) aps = np.zeros((cell_thresholds.size, boundary_thresholds.size, 3)) for j,cell_threshold in enumerate(cell_thresholds): for k,boundary_threshold in enumerate(boundary_thresholds): masks = [] for data in val_data: output,style = self._run_net(data.transpose(1,2,0), augment=False) masks.append(utils.get_masks_unet(output, cell_threshold, boundary_threshold)) ap = metrics.average_precision(val_labels, masks)[0] ap0 = ap.mean(axis=0) aps[j,k] = ap0 if self.nclasses==3: kbest = aps[j].mean(axis=-1).argmax() else: kbest = 0 if j%4==0: core_logger.info('best threshold at cell_threshold = {} => boundary_threshold = {}, ap @ 0.5 = {}'.format(cell_threshold, boundary_thresholds[kbest], aps[j,kbest,0])) if self.nclasses==3: jbest, kbest = np.unravel_index(aps.mean(axis=-1).argmax(), aps.shape[:2]) else: jbest = aps.squeeze().mean(axis=-1).argmax() kbest = 0 cell_threshold, boundary_threshold = cell_thresholds[jbest], boundary_thresholds[kbest] core_logger.info('>>>> best overall thresholds: (cell_threshold = {}, boundary_threshold = {}); ap @ 0.5 = {}'.format(cell_threshold, boundary_threshold, aps[jbest,kbest,0])) return cell_threshold, boundary_threshold def _train_step(self, x, lbl): X = self._to_device(x) self.optimizer.zero_grad() #if self.gpu: # self.net.train() #.cuda() #else: self.net.train() y = self.net(X)[0] del X loss = self.loss_fn(lbl,y) loss.backward() train_loss = loss.item() self.optimizer.step() train_loss *= len(x) return train_loss def _test_eval(self, x, lbl): X = self._to_device(x) self.net.eval() with torch.no_grad(): y, style = self.net(X) del X loss = self.loss_fn(lbl,y) test_loss = loss.item() test_loss *= len(x) return test_loss def _set_optimizer(self, learning_rate, momentum, weight_decay, SGD=False): if SGD: self.optimizer = torch.optim.SGD(self.net.parameters(), lr=learning_rate, momentum=momentum, weight_decay=weight_decay) else: import torch_optimizer as optim # for RADAM optimizer self.optimizer = optim.RAdam(self.net.parameters(), lr=learning_rate, betas=(0.95, 0.999), #changed to .95 eps=1e-08, weight_decay=weight_decay) core_logger.info('>>> Using RAdam optimizer') self.optimizer.current_lr = learning_rate def _set_learning_rate(self, lr): for param_group in self.optimizer.param_groups: param_group['lr'] = lr def _set_criterion(self): if self.unet: self.criterion = nn.CrossEntropyLoss(reduction='mean') else: self.criterion = nn.MSELoss(reduction='mean') self.criterion2 = nn.BCEWithLogitsLoss(reduction='mean') def _train_net(self, train_data, train_labels, test_data=None, test_labels=None, save_path=None, save_every=100, save_each=False, learning_rate=0.2, n_epochs=500, momentum=0.9, weight_decay=0.00001, SGD=True, batch_size=8, nimg_per_epoch=None, rescale=True, model_name=None): """ train function uses loss function self.loss_fn in models.py""" d = datetime.datetime.now() self.n_epochs = n_epochs if isinstance(learning_rate, (list, np.ndarray)): if isinstance(learning_rate, np.ndarray) and learning_rate.ndim > 1: raise ValueError('learning_rate.ndim must equal 1') elif len(learning_rate) != n_epochs: raise ValueError('if learning_rate given as list or np.ndarray it must have length n_epochs') self.learning_rate = learning_rate self.learning_rate_const = mode(learning_rate)[0][0] else: self.learning_rate_const = learning_rate # set learning rate schedule if SGD: LR = np.linspace(0, self.learning_rate_const, 10) if self.n_epochs > 250: LR = np.append(LR, self.learning_rate_const*np.ones(self.n_epochs-100)) for i in range(10): LR = np.append(LR, LR[-1]/2 * np.ones(10)) else: LR = np.append(LR, self.learning_rate_const*np.ones(max(0,self.n_epochs-10))) else: LR = self.learning_rate_const * np.ones(self.n_epochs) self.learning_rate = LR self.batch_size = batch_size self._set_optimizer(self.learning_rate[0], momentum, weight_decay, SGD) self._set_criterion() nimg = len(train_data) # compute average cell diameter diam_train = np.array([utils.diameters(train_labels[k][0])[0] for k in range(len(train_labels))]) diam_train_mean = diam_train[diam_train > 0].mean() self.diam_labels = diam_train_mean if rescale: diam_train[diam_train<5] = 5. if test_data is not None: diam_test = np.array([utils.diameters(test_labels[k][0])[0] for k in range(len(test_labels))]) diam_test[diam_test<5] = 5. scale_range = 0.5 core_logger.info('>>>> median diameter set to = %d'%self.diam_mean) else: scale_range = 1.0 core_logger.info(f'>>>> mean of training label mask diameters (saved to model) {diam_train_mean:.3f}') self.net.diam_labels.data = torch.ones(1, device=self.device) * diam_train_mean nchan = train_data[0].shape[0] core_logger.info('>>>> training network with %d channel input <<<<'%nchan) core_logger.info('>>>> LR: %0.5f, batch_size: %d, weight_decay: %0.5f'%(self.learning_rate_const, self.batch_size, weight_decay)) if test_data is not None: core_logger.info(f'>>>> ntrain = {nimg}, ntest = {len(test_data)}') else: core_logger.info(f'>>>> ntrain = {nimg}') tic = time.time() lavg, nsum = 0, 0 if save_path is not None: _, file_label = os.path.split(save_path) file_path = os.path.join(save_path, 'models/') if not os.path.exists(file_path): os.makedirs(file_path) else: core_logger.warning('WARNING: no save_path given, model not saving') ksave = 0 rsc = 1.0 # cannot train with mkldnn self.net.mkldnn = False # get indices for each epoch for training np.random.seed(0) inds_all = np.zeros((0,), 'int32') if nimg_per_epoch is None or nimg > nimg_per_epoch: nimg_per_epoch = nimg core_logger.info(f'>>>> nimg_per_epoch = {nimg_per_epoch}') while len(inds_all) < n_epochs * nimg_per_epoch: rperm = np.random.permutation(nimg) inds_all = np.hstack((inds_all, rperm)) for iepoch in range(self.n_epochs): if SGD: self._set_learning_rate(self.learning_rate[iepoch]) np.random.seed(iepoch) rperm = inds_all[iepoch*nimg_per_epoch:(iepoch+1)*nimg_per_epoch] for ibatch in range(0,nimg_per_epoch,batch_size): inds = rperm[ibatch:ibatch+batch_size] rsc = diam_train[inds] / self.diam_mean if rescale else np.ones(len(inds), np.float32) # now passing in the full train array, need the labels for distance field imgi, lbl, scale = transforms.random_rotate_and_resize( [train_data[i] for i in inds], Y=[train_labels[i][1:] for i in inds], rescale=rsc, scale_range=scale_range, unet=self.unet) if self.unet and lbl.shape[1]>1 and rescale: lbl[:,1] *= scale[:,np.newaxis,np.newaxis]**2#diam_batch[:,np.newaxis,np.newaxis]**2 train_loss = self._train_step(imgi, lbl) lavg += train_loss nsum += len(imgi) if iepoch%10==0 or iepoch==5: lavg = lavg / nsum if test_data is not None: lavgt, nsum = 0., 0 np.random.seed(42) rperm = np.arange(0, len(test_data), 1, int) for ibatch in range(0,len(test_data),batch_size): inds = rperm[ibatch:ibatch+batch_size] rsc = diam_test[inds] / self.diam_mean if rescale else np.ones(len(inds), np.float32) imgi, lbl, scale = transforms.random_rotate_and_resize( [test_data[i] for i in inds], Y=[test_labels[i][1:] for i in inds], scale_range=0., rescale=rsc, unet=self.unet) if self.unet and lbl.shape[1]>1 and rescale: lbl[:,1] *= scale[:,np.newaxis,np.newaxis]**2 test_loss = self._test_eval(imgi, lbl) lavgt += test_loss nsum += len(imgi) core_logger.info('Epoch %d, Time %4.1fs, Loss %2.4f, Loss Test %2.4f, LR %2.4f'% (iepoch, time.time()-tic, lavg, lavgt/nsum, self.learning_rate[iepoch])) else: core_logger.info('Epoch %d, Time %4.1fs, Loss %2.4f, LR %2.4f'% (iepoch, time.time()-tic, lavg, self.learning_rate[iepoch])) lavg, nsum = 0, 0 if save_path is not None: if iepoch==self.n_epochs-1 or iepoch%save_every==1: # save model at the end if save_each: #separate files as model progresses if model_name is None: file_name = '{}_{}_{}_{}'.format(self.net_type, file_label, d.strftime("%Y_%m_%d_%H_%M_%S.%f"), 'epoch_'+str(iepoch)) else: file_name = '{}_{}'.format(model_name, 'epoch_'+str(iepoch)) else: if model_name is None: file_name = '{}_{}_{}'.format(self.net_type, file_label, d.strftime("%Y_%m_%d_%H_%M_%S.%f")) else: file_name = model_name file_name = os.path.join(file_path, file_name) ksave += 1 core_logger.info(f'saving network parameters to {file_name}') self.net.save_model(file_name) else: file_name = save_path # reset to mkldnn if available self.net.mkldnn = self.mkldnn return file_name