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main.py
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main.py
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#!/usr/bin/env python
# Copyright 2018 Division of Medical Image Computing, German Cancer Research Center (DKFZ).
#
# 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 License 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.
# ==============================================================================
"""execution script."""
import argparse
import os
import time
import torch
import torch.nn.functional as F
import torch.nn as nn
import numpy as np
import utils.exp_utils as utils
from evaluator import Evaluator
from predictor import Predictor
from plotting import plot_batch_prediction, save_monitor_valuse,save_test_image
from tensorboardX import SummaryWriter
from utils.exp_utils import save_models
import utils.model_utils as mutils
import pickle
from models.mrcnn import compute_vnet_seg_loss
def train(logger):
"""
perform the training routine for a given fold. saves plots and selected parameters to the experiment dir
specified in the configs.
"""
logger.info('performing training in {}D over fold {} on experiment {} with model {}'.format(
cf.dim, cf.fold, cf.exp_dir, cf.model))
writer = SummaryWriter(os.path.join(cf.exp_dir,'tensorboard'))
net = model.net(cf, logger).cuda()
#optimizer = torch.optim.Adam(net.parameters(), lr=cf.learning_rate[0], weight_decay=cf.weight_decay)
optimizer = torch.optim.Adam(net.parameters(), lr=cf.initial_learning_rate, weight_decay=cf.weight_decay)
model_selector = utils.ModelSelector(cf, logger)
train_evaluator = Evaluator(cf, logger, mode='train')
val_evaluator = Evaluator(cf, logger, mode=cf.val_mode)#val_sampling
starting_epoch = 1
# prepare monitoring
if cf.resume_to_checkpoint:#default: False
lastepochpth = cf.resume_to_checkpoint + 'last_checkpoint/'
best_epoch = np.load(lastepochpth + 'epoch_ranking.npy')[0]
df = open(lastepochpth+'monitor_metrics.pickle','rb')
monitor_metrics = pickle.load(df)
df.close()
starting_epoch = utils.load_checkpoint(lastepochpth, net, optimizer)
logger.info('resumed to checkpoint {} at epoch {}'.format(cf.resume_to_checkpoint, starting_epoch))
num_batch = starting_epoch * cf.num_train_batches+1
num_val = starting_epoch * cf.num_val_batches+1
else:
monitor_metrics = utils.prepare_monitoring(cf)
num_batch = 0#for show loss
num_val = 0
logger.info('loading dataset and initializing batch generators...')
batch_gen = data_loader.get_train_generators(cf, logger)
best_train_recall,best_val_recall = 0,0
lr_now = cf.initial_learning_rate
for epoch in range(starting_epoch, cf.num_epochs + 1):
logger.info('starting training epoch {}'.format(epoch))
for param_group in optimizer.param_groups:
#param_group['lr'] = cf.learning_rate[epoch - 1]
print('lr_now',lr_now)
lr_next = utils.learning_rate_decreasing(cf,epoch,lr_now,mode='step')#cf.learning_rate[epoch - 1]
print('lr_next',lr_next)
param_group['lr'] = lr_next#learning_rate_decreasing(cf,epoch,lr_now,mode='step')#cf.learning_rate[epoch - 1]
lr_now = lr_next
start_time = time.time()
net.train()
train_results_list = []#this batch
train_results_list_seg = []
for bix in range(cf.num_train_batches):#200
num_batch += 1
batch = next(batch_gen['train'])#data,seg,pid,class_target,bb_target,roi_masks,roi_labels
for ii,i in enumerate(batch['roi_labels']):
if i[0] > 0:
batch['roi_labels'][ii] = [1]
else:
batch['roi_labels'][ii] = [-1]
tic_fw = time.time()
results_dict = net.train_forward(batch)
tic_bw = time.time()
optimizer.zero_grad()
results_dict['torch_loss'].backward()#total loss
optimizer.step()
if (num_batch) % cf.show_train_images == 0:
fig = plot_batch_prediction(batch, results_dict, cf,'train')
writer.add_figure('/Train/results',fig,num_batch)
fig.clear()
print('model',cf.exp_dir.split('/')[-2])
logger.info('tr. batch {0}/{1} (ep. {2}) fw {3:.3f}s / bw {4:.3f}s / total {5:.3f}s || '
.format(bix + 1, cf.num_train_batches, epoch, tic_bw - tic_fw,
time.time() - tic_bw, time.time() - tic_fw))
#writer.add_scalar('Train/total_loss',results_dict['torch_loss'].item(),num_batch)
#writer.add_scalar('Train/rpn_class_loss',results_dict['monitor_losses']['rpn_class_loss'],num_batch)
#writer.add_scalar('Train/rpn_bbox_loss',results_dict['monitor_losses']['rpn_bbox_loss'],num_batch)
#writer.add_scalar('Train/mrcnn_class_loss',results_dict['monitor_losses']['mrcnn_class_loss'],num_batch)
#writer.add_scalar('Train/mrcnn_bbox_loss',results_dict['monitor_losses']['mrcnn_bbox_loss'],num_batch)
#writer.add_scalar('Train/mrcnn_mask_loss',results_dict['monitor_losses']['mrcnn_mask_loss'],num_batch)
#writer.add_scalar('Train/seg_dice_loss',results_dict['monitor_losses']['seg_loss_dice'],num_batch)
#writer.add_scalar('Train/fusion_dice_loss',results_dict['monitor_losses']['fusion_loss_dice'],num_batch)
train_results_list.append([results_dict['boxes'], batch['pid']])#just gt and det
monitor_metrics['train']['monitor_values'][epoch].append(results_dict['monitor_losses'])
count_train = train_evaluator.evaluate_predictions(train_results_list,epoch,cf,flag = 'train')
precision = count_train[0]/ (count_train[0]+count_train[2]+0.01)
recall = count_train[0]/ (count_train[3])
print('tp_patient {}, tp_roi {}, fp_roi {}, total_num {}'.format(count_train[0],count_train[1],count_train[2],count_train[3]))
print('precision:{}, recall:{}'.format(precision,recall))
monitor_metrics['train']['train_recall'].append(recall)
monitor_metrics['train']['train_percision'].append(precision)
writer.add_scalar('Train/train_precision',precision,epoch)
writer.add_scalar('Train/train_recall',recall,epoch)
train_time = time.time() - start_time
logger.info('starting validation in mode {}.'.format(cf.val_mode))
with torch.no_grad():
net.eval()
if cf.do_validation:
val_results_list = []
val_predictor = Predictor(cf, net, logger, mode='val')
dice_val_seg,dice_val_mask,dice_val_fusion = [], [], []
for _ in range(batch_gen['n_val']):#50
num_val += 1
batch = next(batch_gen[cf.val_mode])
print('eval',batch['pid'])
for ii,i in enumerate(batch['roi_labels']):
if i[0] > 0:
batch['roi_labels'][ii] = [1]
else:
batch['roi_labels'][ii] = [-1]
if cf.val_mode == 'val_patient':
results_dict = val_predictor.predict_patient(batch)#result of one patient
elif cf.val_mode == 'val_sampling':
results_dict = net.train_forward(batch, is_validation=True)
if (num_val) % cf.show_val_images == 0:
fig = plot_batch_prediction(batch, results_dict, cf, cf.val_mode)
writer.add_figure('Val/results',fig,num_val)
fig.clear()
# compute dice for vnet
this_batch_seg_label = torch.FloatTensor(mutils.get_one_hot_encoding(batch['seg'], cf.num_seg_classes+1)).cuda()
if cf.fusion_feature_method == 'after':
this_batch_dice_seg = mutils.dice_val(results_dict['seg_logits'],this_batch_seg_label)
else:
this_batch_dice_seg = mutils.dice_val(F.softmax(results_dict['seg_logits'],dim=1),this_batch_seg_label)
dice_val_seg.append(this_batch_dice_seg)
# compute dice for mask
#mask_map = torch.from_numpy(results_dict['seg_preds']).cuda()
if cf.fusion_feature_method == 'after':
this_batch_dice_mask = mutils.dice_val(results_dict['seg_preds'],this_batch_seg_label)
else:
this_batch_dice_mask = mutils.dice_val(F.softmax(results_dict['seg_preds'], dim=1),this_batch_seg_label)
dice_val_mask.append(this_batch_dice_mask)
# compute dice for fusion
if cf.fusion_feature_method == 'after':
this_batch_dice_fusion = mutils.dice_val(results_dict['fusion_map'],this_batch_seg_label)
else:
this_batch_dice_fusion = mutils.dice_val(F.softmax(results_dict['fusion_map'], dim=1),this_batch_seg_label)
dice_val_fusion.append(this_batch_dice_fusion)
val_results_list.append([results_dict['boxes'], batch['pid']])
monitor_metrics['val']['monitor_values'][epoch].append(results_dict['monitor_values'])
count_val = val_evaluator.evaluate_predictions(val_results_list,epoch,cf,flag = 'val')
precision = count_val[0]/ (count_val[0]+count_val[2]+0.01)
recall = count_val[0]/ (count_val[3])
print('tp_patient {}, tp_roi {}, fp_roi {}, total_num {}'.format(count_val[0],count_val[1],count_val[2],count_val[3]))
print('precision:{}, recall:{}'.format(precision,recall))
val_dice_seg = sum(dice_val_seg)/float(len(dice_val_seg))
val_dice_mask = sum(dice_val_mask)/float(len(dice_val_mask))
val_dice_fusion = sum(dice_val_fusion)/float(len(dice_val_fusion))
monitor_metrics['val']['val_recall'].append(recall)
monitor_metrics['val']['val_precision'].append(precision)
monitor_metrics['val']['val_dice_seg'].append(val_dice_seg)
monitor_metrics['val']['val_dice_mask'].append(val_dice_mask)
monitor_metrics['val']['val_dice_fusion'].append(val_dice_fusion)
writer.add_scalar('Val/val_precision',precision,epoch)
writer.add_scalar('Val/val_recall',recall,epoch)
writer.add_scalar('Val/val_dice_seg',val_dice_seg,epoch)
writer.add_scalar('Val/val_dice_mask',val_dice_mask,epoch)
writer.add_scalar('Val/val_dice_fusion',val_dice_fusion,epoch)
model_selector.run_model_selection(net, optimizer, monitor_metrics, epoch)
# update monitoring and prediction plots
#TrainingPlot.update_and_save(monitor_metrics, epoch)
epoch_time = time.time() - start_time
logger.info('trained epoch {}: took {} sec. ({} train / {} val)'.format(
epoch, epoch_time, train_time, epoch_time-train_time))
writer.close()
def test(logger):
"""
perform testing for a given fold (or hold out set). save stats in evaluator.
"""
logger.info('starting testing model of fold {} in exp {}'.format(cf.fold, cf.exp_dir))
net = model.net(cf, logger).cuda()
test_predictor = Predictor(cf, net, logger, mode='test')
test_evaluator = Evaluator(cf, logger, mode='test')
batch_gen = data_loader.get_test_generator(cf, logger)
test_results_list,test_results_list_mask,test_results_list_seg ,test_results_list_fusion,testing_epoch= test_predictor.predict_test_set(batch_gen,cf, return_results=True)
count = test_evaluator.evaluate_predictions(test_results_list,testing_epoch,cf,pth = cf.test_dir,flag = 'test')
print('tp_patient {}, tp_roi {}, fp_roi {}'.format(count[0],count[1],count[2]))
save_test_image(test_results_list,test_results_list_mask,test_results_list_seg,test_results_list_fusion,testing_epoch,cf,cf.plot_dir)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--mode', type=str, default='test',
help='one out of: train / test / train_test / analysis / create_exp')
parser.add_argument('-f','--folds', nargs='+', type=int, default=[1],
help='None runs over all folds in CV. otherwise specify list of folds.')
parser.add_argument('--exp_dir', type=str, default='/data/yuezhou/newdata/0720_mrcnn_seg_fusprob_opt_weight_newdata/',
help='path to experiment dir. will be created if non existent.')
parser.add_argument('--resume_to_checkpoint', type=str, default='/data/yuezhou/newdata/0720_mrcnn_seg_fusprob_opt_weight_newdata/fold_1/',
help='if resuming to checkpoint, the desired fold still needs to be parsed via --folds.')
parser.add_argument('--exp_source', type=str, default='experiments/abus_exp/',
help='specifies, from which source experiment to load configs and data_loader.')
args = parser.parse_args()
folds = args.folds
resume_to_checkpoint = args.resume_to_checkpoint
if args.mode == 'train':
cf = utils.prep_exp(args.exp_dir, is_training=True)
cf.resume_to_checkpoint = resume_to_checkpoint#default:None
model = utils.import_module('model', cf.model_path)
data_loader = utils.import_module('dl', os.path.join(args.exp_source, 'data_loader.py'))
for fold in folds:
cf.fold_dir = os.path.join(cf.exp_dir, 'fold_{}'.format(fold))#path to save results
cf.fold = fold
if not os.path.exists(cf.fold_dir):
os.mkdir(cf.fold_dir)
logger = utils.get_logger(cf.fold_dir)#loginfo for this fold
train(logger)
cf.resume_to_checkpoint = None
if args.mode == 'train_test':
test(logger)
for hdlr in logger.handlers:
hdlr.close()
logger.handlers = []
elif args.mode == 'test':
cf = utils.prep_exp(args.exp_dir, is_training=False)
model = utils.import_module('model', cf.model_path)
data_loader = utils.import_module('dl', os.path.join(args.exp_source, 'data_loader.py'))
for fold in folds:
cf.fold_dir = os.path.join(cf.exp_dir, 'fold_{}'.format(fold))
logger = utils.get_logger(cf.fold_dir)
cf.fold = fold
test(logger)
for hdlr in logger.handlers:
hdlr.close()
logger.handlers = []