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UNet3D_cellpose.py
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UNet3D_cellpose.py
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# -*- coding: utf-8 -*-
"""
# 3D Cellpose Extension.
# Copyright (C) 2021 D. Eschweiler, J. Stegmaier
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the Liceense at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Please refer to the documentation for more information about the software
# as well as for installation instructions.
"""
import json
import numpy as np
import torch
import torch.nn.functional as F
import torchvision
import pytorch_lightning as pl
from argparse import ArgumentParser, Namespace
from collections import OrderedDict
from torch.utils.data import DataLoader
from dataloader.h5_dataloader import MeristemH5Dataset
from ThirdParty.radam import RAdam
from models.UNet3D import UNet3D_module
class UNet3D_cellpose(pl.LightningModule):
def __init__(self, hparams):
super(UNet3D_cellpose, self).__init__()
if type(hparams) is dict:
hparams = Namespace(**hparams)
self.hparams = hparams
self.augmentation_dict = {}
# networks
self.network = UNet3D_module(patch_size=hparams.patch_size, in_channels=hparams.in_channels, out_channels=hparams.out_channels, feat_channels=hparams.feat_channels, out_activation=hparams.out_activation, norm_method=hparams.norm_method)
# cache for generated images
self.last_predictions = None
self.last_imgs = None
self.last_masks = None
def forward(self, z):
return self.network(z)
def load_pretrained(self, pretrained_file, strict=True, verbose=True):
if isinstance(pretrained_file, (list,tuple)):
pretrained_file = pretrained_file[0]
# Load the state dict
state_dict = torch.load(pretrained_file)['state_dict']
# Make sure to have a weight dict
if not isinstance(state_dict, dict):
state_dict = dict(state_dict)
# Get parameter dict of current model
param_dict = dict(self.network.named_parameters())
layers = []
for layer in param_dict:
if strict and not 'network.'+layer in state_dict:
if verbose:
print('Could not find weights for layer "{0}"'.format(layer))
continue
try:
param_dict[layer].data.copy_(state_dict['network.'+layer].data)
layers.append(layer)
except (RuntimeError, KeyError) as e:
print('Error at layer {0}:\n{1}'.format(layer, e))
self.network.load_state_dict(param_dict)
if verbose:
print('Loaded weights for the following layers:\n{0}'.format(layers))
def background_loss(self, y_hat, y):
return F.l1_loss(y_hat, y)
def flow_loss(self, y_hat, y, mask):
loss = F.mse_loss(y_hat, y, reduction='none')
weight = torch.clamp(mask, min=0.01, max=1.0)
loss = torch.mul(loss, weight)
loss = torch.sum(loss)
loss = torch.div(loss, torch.clamp(torch.sum(weight), 1, mask.numel()))
return loss
def training_step(self, batch, batch_idx):
# Get image ans mask of current batch
self.last_imgs, self.last_masks = batch['image'], batch['mask']
# generate images
self.predictions = self.forward(self.last_imgs)
# get the losses
loss_bg = self.background_loss(self.predictions[:,0,...], self.last_masks[:,0,...])
loss_flowx = self.flow_loss(self.predictions[:,1,...], self.last_masks[:,1,...], self.last_masks[:,0,...])
loss_flowy = self.flow_loss(self.predictions[:,2,...], self.last_masks[:,2,...], self.last_masks[:,0,...])
loss_flowz = self.flow_loss(self.predictions[:,3,...], self.last_masks[:,3,...], self.last_masks[:,0,...])
loss_flow = (loss_flowx + loss_flowy + loss_flowz)/3
loss = self.hparams.background_weight * loss_bg + \
self.hparams.flow_weight * loss_flow
tqdm_dict = {'bg_loss': loss_bg, 'flow_loss':loss_flow, 'epoch': self.current_epoch}
output = OrderedDict({
'loss': loss,
'progress_bar': tqdm_dict,
'log': tqdm_dict
})
return output
def test_step(self, batch, batch_idx):
x, y = batch['image'], batch['mask']
y_hat = self.forward(x)
return {'test_loss': F.mse_loss(y_hat, y)}
def test_end(self, outputs):
avg_loss = torch.stack([x['test_loss'] for x in outputs]).mean()
tensorboard_logs = {'test_loss': avg_loss}
return {'avg_test_loss': avg_loss, 'log': tensorboard_logs}
def validation_step(self, batch, batch_idx):
x, y = batch['image'], batch['mask']
y_hat = self.forward(x)
return {'val_loss': F.mse_loss(y_hat, y)}
def validation_end(self, outputs):
avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
tensorboard_logs = {'val_loss': avg_loss}
return {'avg_val_loss': avg_loss, 'log': tensorboard_logs}
def configure_optimizers(self):
opt = RAdam(self.network.parameters(), lr=self.hparams.learning_rate)
return [opt], []
def train_dataloader(self):
if self.hparams.train_list is None:
return None
else:
dataset = MeristemH5Dataset(self.hparams.train_list, self.hparams.data_root, patch_size=self.hparams.patch_size,\
image_groups=self.hparams.image_groups, mask_groups=self.hparams.mask_groups, augmentation_dict=self.augmentation_dict,\
dist_handling=self.hparams.dist_handling, norm_method=self.hparams.data_norm, sample_per_epoch=self.hparams.samples_per_epoch)
return DataLoader(dataset, batch_size=self.hparams.batch_size, shuffle=True, drop_last=True)
def test_dataloader(self):
if self.hparams.test_list is None:
return None
else:
dataset = MeristemH5Dataset(self.hparams.test_list, self.hparams.data_root, patch_size=self.hparams.patch_size,\
image_groups=self.hparams.image_groups, mask_groups=self.hparams.mask_groups, augmentation_dict={},\
dist_handling=self.hparams.dist_handling, norm_method=self.hparams.data_norm)
return DataLoader(dataset, batch_size=self.hparams.batch_size)
def val_dataloader(self):
if self.hparams.val_list is None:
return None
else:
dataset = MeristemH5Dataset(self.hparams.val_list, self.hparams.data_root, patch_size=self.hparams.patch_size,\
image_groups=self.hparams.image_groups, mask_groups=self.hparams.mask_groups, augmentation_dict={},\
dist_handling=self.hparams.dist_handling, norm_method=self.hparams.data_norm)
return DataLoader(dataset, batch_size=self.hparams.batch_size)
def on_epoch_end(self):
# log sampled images
predictions = self.forward(self.last_imgs)
prediction_grid = torchvision.utils.make_grid(predictions[0,:,np.newaxis,int(self.hparams.patch_size[0]//2),:,:])
self.logger.experiment.add_image('generated_images', prediction_grid, self.current_epoch)
img_grid = torchvision.utils.make_grid(self.last_imgs[0,:,np.newaxis,int(self.hparams.patch_size[0]//2),:,:])
self.logger.experiment.add_image('raw_images', img_grid, self.current_epoch)
mask_grid = torchvision.utils.make_grid(self.last_masks[0,:,np.newaxis,int(self.hparams.patch_size[0]//2),:,:])
self.logger.experiment.add_image('input_masks', mask_grid, self.current_epoch)
def set_augmentations(self, augmentation_dict_file):
self.augmentation_dict = json.load(open(augmentation_dict_file))
@staticmethod
def add_model_specific_args(parent_parser):
"""
Parameters you define here will be available to your model through self.hparams
"""
parser = ArgumentParser(parents=[parent_parser])
# network params
parser.add_argument('--in_channels', default=1, type=int)
parser.add_argument('--out_channels', default=4, type=int)
parser.add_argument('--feat_channels', default=16, type=int)
parser.add_argument('--patch_size', default=(64,128,128), type=int, nargs='+')
parser.add_argument('--out_activation', default='tanh', type=str)
parser.add_argument('--norm_method', default='instance', type=str)
# data
parser.add_argument('--data_norm', default='percentile', type=str)
#parser.add_argument('--data_root', default=r'D:\LfB\pytorchRepo\data\Stardist_model', type=str)
#parser.add_argument('--train_list', default=r'D:\LfB\pytorchRepo\data\Dimitris_h5.csv', type=str)
#parser.add_argument('--test_list', default=r'D:\LfB\pytorchRepo\data\Dimitris_h5_test.csv', type=str)
#parser.add_argument('--val_list', default=r'D:\LfB\pytorchRepo\data\Dimitris_h5.csv', type=str)
#parser.add_argument('--data_root', default=r'D:\LfB\pytorchRepo\data', type=str)
#parser.add_argument('--train_list', default=r'D:\LfB\pytorchRepo\data\MembranesNYU.csv', type=str)
#parser.add_argument('--test_list', default=r'D:\LfB\pytorchRepo\data\MembranesNYU.csv', type=str)
#parser.add_argument('--val_list', default=r'D:\LfB\pytorchRepo\data\MembranesNYU.csv', type=str)
parser.add_argument('--data_root', default=r'D:\LfB\pytorchRepo\data\PNAS', type=str)
parser.add_argument('--train_list', default=r'D:\LfB\pytorchRepo\data\PNAS_boundary_plant_split1_train.csv', type=str)
parser.add_argument('--test_list', default=r'D:\LfB\pytorchRepo\data\PNAS_boundary_plant_split1_test.csv', type=str)
#parser.add_argument('--test_list', default=r'D:\LfB\pytorchRepo\data\PNAS_boundary_plant_split1_syn10_test.csv', type=str)
parser.add_argument('--val_list', default=r'D:\LfB\pytorchRepo\data\PNAS_boundary_plant_split1_val.csv', type=str)
parser.add_argument('--image_groups', default=('data/image',), type=str, nargs='+')
parser.add_argument('--mask_groups', default=('data/distance', 'data/flow_x', 'data/flow_y', 'data/flow_z'), type=str, nargs='+')
parser.add_argument('--dist_handling', default='bool_inv', type=str)
# training params (opt)
parser.add_argument('--samples_per_epoch', default=-1, type=int)
parser.add_argument('--batch_size', default=1, type=int)
parser.add_argument('--learning_rate', default=0.001, type=float)
parser.add_argument('--background_weight', default=1, type=float)
parser.add_argument('--flow_weight', default=1, type=float)
return parser