File size: 11,266 Bytes
970a7a2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 |
# Experimenter Class is responsible for mainly four things:
# 1. Configuration - Done
# 2. Logging using the AdvancedLogger class - Almost Done
# 3. Model Handling, including loading and saving models - Done(Upgrades Left)
# 4. Running Different Variants Paralelly/Sequentially of experiments
# 5. Combining frcnn training followed by bilateral training and final froc calculation - Done
# 6. Version Control
from advanced_config import AdvancedConfig
from advanced_logger import AdvancedLogger, LogPriority
import os
from os.path import join
from plot_froc import plot_froc
from train_frcnn import main as TRAIN_FRCNN
from train_bilateral import main as TRAIN_BILATERAL
import torch
from model_utils import generate_predictions, generate_predictions_bilateral
import argparse
from dataloaders import get_dict
from utils import create_backup
from torch.utils.tensorboard import SummaryWriter
class Experimenter:
def __init__(self, cfg_file, BASE_DIR = 'experiments'):
self.cfg_file = cfg_file
self.con = AdvancedConfig(cfg_file)
self.config = self.con.config
self.exp_dir = join(BASE_DIR,self.config['EXP_NAME'])
os.makedirs(self.exp_dir, exist_ok=True)
self.con.save(join(self.exp_dir,'config.cfg'))
self.logger = AdvancedLogger(self.exp_dir)
self.logger.log('Experiment:',self.config['EXP_NAME'],priority = LogPriority.STATS)
self.logger.log('Experiment Description:', self.config['EXP_DESC'], priority = LogPriority.STATS)
self.logger.log('Config File:',self.cfg_file, priority = LogPriority.STATS)
self.logger.log('Experiment started', priority = LogPriority.LOW)
self.losses = dict()
self.frocs = dict()
self.writer = SummaryWriter(join(self.exp_dir,'tensor_logs'))
create_backup(backup_dir=join(self.exp_dir,'scripts'))
def log(self, *args, **kwargs):
self.logger.log(*args, **kwargs)
def init_losses(self,mode):
if mode == 'FRCNN' or mode == 'FRCNN_BILATERAL':
self.losses['frcnn_loss'] = []
self.frocs['frcnn_froc'] = []
elif mode == 'BILATERAL' or mode == 'FRCNN_BILATERAL':
self.losses['bilateral_loss'] = []
self.frocs['bilateral_froc'] = []
def start_epoch(self):
self.curr_epoch += 1
self.logger.log('Epoch:',self.curr_epoch, priority = LogPriority.MEDIUM)
def end_epoch(self, loss, model = None, device = None):
if self.curr_mode == 'FRCNN':
self.losses['frcnn_loss'].append(loss)
self.best_loss = min(self.losses['frcnn_loss'])
if self.config['EVAL_METHOD'] == 'FROC':
exp_name = self.config['EXP_NAME']
_, val_path, _ = self.init_paths()
generate_predictions(model,device,val_path,f'preds_frcnn_{exp_name}')
from froc_by_pranjal import get_froc_points
senses, _ = get_froc_points(f'preds_frcnn_{exp_name}', root_fol= join(self.config['DATA_DIR'],self.config['AIIMS_DATA'], self.config['AIIMS_VAL_SPLIT']), fps_req = [0.2])
self.frocs['frcnn_froc'].append(senses[0])
self.best_froc = max(self.frocs['frcnn_froc'])
self.logger.log(f'Val FROC: {senses[0]}', LogPriority.MEDIUM)
self.logger.log(f'Best FROC: {self.best_froc}')
elif self.curr_mode == 'BILATERAL':
self.losses['bilateral_loss'].append(loss)
self.best_loss = min(self.losses['bilateral_loss'])
if self.config['EVAL_METHOD'] == 'FROC':
exp_name = self.config['EXP_NAME']
_, val_path, _ = self.init_paths()
data_dir = self.config['DATA_DIR']
print('Generating')
generate_predictions_bilateral(model,device,val_path,get_dict(data_dir,self.abs_path(self.config['AIIMS_CORRS_LIST'])),preds_folder = f'preds_bilateral_{exp_name}')
print('Generation Done')
from froc_by_pranjal import get_froc_points
senses, _ = get_froc_points(f'preds_bilateral_{exp_name}', root_fol= join(self.config['DATA_DIR'],self.config['AIIMS_DATA'], self.config['AIIMS_VAL_SPLIT']), fps_req = [0.1])
print('Reading Sens from',f'preds_bilateral_{exp_name}', join(self.config['DATA_DIR'],self.config['AIIMS_DATA'], self.config['AIIMS_VAL_SPLIT']),)
self.frocs['bilateral_froc'].append(senses[0])
self.best_froc = max(self.frocs['bilateral_froc'])
self.logger.log(f'Val FROC: {senses[0]}', priority = LogPriority.MEDIUM)
self.logger.log(f'Best FROC: {self.best_froc}')
self.writer.add_scalar(f"{self.curr_mode}/Loss/Valid", loss, self.curr_epoch)
def save_model(self, model):
if self.curr_mode == 'FRCNN':
self.logger.log('Saving FRCNN Model', priority = LogPriority.LOW)
model_file = join(self.exp_dir,'frcnn_models',f'frcnn_model.pth')
if self.config['EVAL_METHOD']:
SAVE = self.best_froc == self.frocs['frcnn_froc'][-1]
else:
SAVE = self.best_loss == self.losses['frcnn_loss'][-1]
elif self.curr_mode == 'BILATERAL':
self.logger.log('Saving Bilateral Model', priority = LogPriority.LOW)
model_file = join(self.exp_dir,'bilateral_models',f'bilateral_model.pth')
if self.config['EVAL_METHOD'] == 'FROC':
SAVE = self.best_froc == self.frocs['bilateral_froc'][-1]
else:
SAVE = self.best_loss == self.losses['bilateral_loss'][-1]
os.makedirs(os.path.split(model_file)[0], exist_ok=True)
if SAVE:
torch.save(model.state_dict(), model_file)
torch.save(model.state_dict(), f'{model_file[:-4]}_{self.curr_epoch}.pth')
def init_paths(self,):
train_path = join(self.config['DATA_DIR'], self.config['AIIMS_DATA'], self.config['AIIMS_TRAIN_SPLIT'])
val_path = join(self.config['DATA_DIR'], self.config['AIIMS_DATA'], self.config['AIIMS_VAL_SPLIT'])
test_path = join(self.config['DATA_DIR'], self.config['AIIMS_DATA'], self.config['AIIMS_TEST_SPLIT'])
return train_path, val_path, test_path
def abs_path(self, path):
return join(self.config['DATA_DIR'], path)
# Impure Function, upadtes the model with best state dicts
def generate_predictions(self,model, device):
self.logger.log('Generating Predictions')
self.logger.flush()
exp_name = self.config['EXP_NAME']
train_path, val_path, test_path = self.init_paths()
# Load the best val_loss model's state dicts
if self.curr_mode == 'FRCNN':
model_file = join(self.exp_dir,'frcnn_models','frcnn_model.pth')
elif self.curr_mode == 'BILATERAL':
model_file = join(self.exp_dir,'bilateral_models','bilateral_model.pth')
model.load_state_dict(torch.load(model_file))
if self.curr_mode == 'FRCNN':
generate_predictions(model,device,train_path,f'preds_frcnn_{exp_name}')
generate_predictions(model,device,val_path,f'preds_frcnn_{exp_name}')
generate_predictions(model,device,test_path,f'preds_frcnn_{exp_name}')
elif self.curr_mode == 'BILATERAL':
data_dir = self.config['DATA_DIR']
generate_predictions_bilateral(model,device,train_path,get_dict(data_dir,self.abs_path(self.config['AIIMS_CORRS_LIST'])),'aiims',f'preds_bilateral_{exp_name}')
generate_predictions_bilateral(model,device,val_path,get_dict(data_dir,self.abs_path(self.config['AIIMS_CORRS_LIST'])),'aiims',f'preds_bilateral_{exp_name}')
generate_predictions_bilateral(model,device,test_path,get_dict(data_dir,self.abs_path(self.config['AIIMS_CORRS_LIST'])),'aiims',f'preds_bilateral_{exp_name}')
test_path = join(self.config['DATA_DIR'], self.config['AIIMS_DATA'], self.config['AIIMS_TEST_SPLIT'])
def run_experiment(self):
# First Determine the mode of running the experiment
mode = self.config['MODE']
self.init_losses(mode)
self.curr_mode = 'FRCNN'
self.curr_epoch = -1
self.best_loss = 999999
self.best_froc = 0
if mode == 'FRCNN':
TRAIN_FRCNN(self.config['FRCNN'], self)
elif mode == 'BILATERAL':
self.curr_mode = 'BILATERAL'
TRAIN_BILATERAL(self.config['BILATERAL'], self)
elif mode == 'FRCNN_BILATERAL':
TRAIN_FRCNN(self.config['FRCNN'], self)
self.curr_mode = 'BILATERAL'
self.curr_epoch = -1
self.best_loss = 999999
# Note the path to frcnn model must be the same as that dictated by experiment
self.config['BILATERAL']['FRCNN_MODEL_PATH'] = join(self.exp_dir,'frcnn_models','frcnn_model.pth')
TRAIN_BILATERAL(self.config['BILATERAL'], self)
self.logger.log(f'Best Loss: {self.best_loss}', priority= LogPriority.STATS)
self.logger.log('Experiment Training and Generation Ended', priority = LogPriority.MEDIUM)
# Now evaluate the results
frcnn_file = join(self.exp_dir, 'senses_fps_frcnn.txt')
bilateral_file = join(self.exp_dir, 'senses_fps_bilateral.txt')
from froc_by_pranjal import get_froc_points
exp_name = self.config['EXP_NAME']
if mode == 'FRCNN' or mode == 'FRCNN_BILATERAL':
senses, fps = get_froc_points(f'preds_frcnn_{exp_name}', root_fol= join(self.config['DATA_DIR'],self.config['AIIMS_DATA'], self.config['AIIMS_TEST_SPLIT']), save_to = frcnn_file)
self.logger.log('FRCNN RESULTS', priority = LogPriority.STATS)
for s,f in zip(senses, fps):
self.logger.log(f'Sensitivty at {f}: {s}', priority = LogPriority.STATS)
if mode == 'BILATERAL' or mode == 'FRCNN_BILATERAL':
senses, fps = get_froc_points(f'preds_bilateral_{exp_name}', root_fol= join(self.config['DATA_DIR'],self.config['AIIMS_DATA'], self.config['AIIMS_TEST_SPLIT']), save_to = bilateral_file)
self.logger.log('BILATERAL RESULTS', priority = LogPriority.STATS)
for s,f in zip(senses, fps):
self.logger.log(f'Sensitivty at {f}: {s}', priority = LogPriority.STATS)
# Now draw the graphs.... If FRCNN and BILATERAL both done, draw them on one graph
# Else draw single graphs only
if mode == 'FRCNN':
plot_froc({frcnn_file : 'FRCNN'}, join(self.exp_dir,'plot.png'), TITLE = 'FRCNN FROC')
elif mode == 'BILATERAL':
plot_froc({bilateral_file : 'BILATERAL'}, join(self.exp_dir,'plot.png'), TITLE = 'BILATERAL FROC')
elif mode == 'FRCNN_BILATERAL':
plot_froc({frcnn_file : 'FRCNN', bilateral_file : 'BILATERAL'}, join(self.exp_dir,'plot.png'), TITLE = 'FRCNN vs BILATERAL FROC')
self.logger.flush()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--cfg_file', type=str, default='configs/AIIMS_C1.cfg')
args = parser.parse_args()
exp = Experimenter(args.cfg_file)
exp.run_experiment() |