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Running on Zero
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d43892c | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 | import numpy as np
import matplotlib.pyplot as plt
import os
import utils
from models.networks import *
import torch
import torch.optim as optim
from misc.metric_tool import ConfuseMatrixMeter
from models.losses import cross_entropy
import models.losses as losses
from misc.logger_tool import Logger, Timer
from utils import de_norm
class CDTrainer():
def __init__(self, args, dataloaders):
self.dataloaders = dataloaders
self.n_class = args.n_class
# define G
self.net_G = define_G(args=args, gpu_ids=args.gpu_ids)
self.device = torch.device("cuda:%s" % args.gpu_ids[0] if torch.cuda.is_available() and len(args.gpu_ids)>0
else "cpu")
print(self.device)
# Learning rate and Beta1 for Adam optimizers
self.lr = args.lr
# define optimizers
self.optimizer_G = optim.SGD(self.net_G.parameters(), lr=self.lr,
momentum=0.9,
weight_decay=5e-4)
# define lr schedulers
self.exp_lr_scheduler_G = get_scheduler(self.optimizer_G, args)
self.running_metric = ConfuseMatrixMeter(n_class=2)
# define logger file
logger_path = os.path.join(args.checkpoint_dir, 'log.txt')
self.logger = Logger(logger_path)
self.logger.write_dict_str(args.__dict__)
# define timer
self.timer = Timer()
self.batch_size = args.batch_size
# training log
self.epoch_acc = 0
self.best_val_acc = 0.0
self.best_epoch_id = 0
self.epoch_to_start = 0
self.max_num_epochs = args.max_epochs
self.global_step = 0
self.steps_per_epoch = len(dataloaders['train'])
self.total_steps = (self.max_num_epochs - self.epoch_to_start)*self.steps_per_epoch
self.G_pred = None
self.pred_vis = None
self.batch = None
self.G_loss = None
self.is_training = False
self.batch_id = 0
self.epoch_id = 0
self.checkpoint_dir = args.checkpoint_dir
self.vis_dir = args.vis_dir
# define the loss functions
if args.loss == 'ce':
self._pxl_loss = cross_entropy
elif args.loss == 'bce':
self._pxl_loss = losses.binary_ce
else:
raise NotImplemented(args.loss)
self.VAL_ACC = np.array([], np.float32)
if os.path.exists(os.path.join(self.checkpoint_dir, 'val_acc.npy')):
self.VAL_ACC = np.load(os.path.join(self.checkpoint_dir, 'val_acc.npy'))
self.TRAIN_ACC = np.array([], np.float32)
if os.path.exists(os.path.join(self.checkpoint_dir, 'train_acc.npy')):
self.TRAIN_ACC = np.load(os.path.join(self.checkpoint_dir, 'train_acc.npy'))
# check and create model dir
if os.path.exists(self.checkpoint_dir) is False:
os.mkdir(self.checkpoint_dir)
if os.path.exists(self.vis_dir) is False:
os.mkdir(self.vis_dir)
def _load_checkpoint(self, ckpt_name='last_ckpt.pt'):
if os.path.exists(os.path.join(self.checkpoint_dir, ckpt_name)):
self.logger.write('loading last checkpoint...\n')
# load the entire checkpoint
checkpoint = torch.load(os.path.join(self.checkpoint_dir, ckpt_name),
map_location=self.device)
# update net_G states
self.net_G.load_state_dict(checkpoint['model_G_state_dict'])
self.optimizer_G.load_state_dict(checkpoint['optimizer_G_state_dict'])
self.exp_lr_scheduler_G.load_state_dict(
checkpoint['exp_lr_scheduler_G_state_dict'])
self.net_G.to(self.device)
# update some other states
self.epoch_to_start = checkpoint['epoch_id'] + 1
self.best_val_acc = checkpoint['best_val_acc']
self.best_epoch_id = checkpoint['best_epoch_id']
self.total_steps = (self.max_num_epochs - self.epoch_to_start)*self.steps_per_epoch
self.logger.write('Epoch_to_start = %d, Historical_best_acc = %.4f (at epoch %d)\n' %
(self.epoch_to_start, self.best_val_acc, self.best_epoch_id))
self.logger.write('\n')
else:
print('training from scratch...')
def _timer_update(self):
self.global_step = (self.epoch_id-self.epoch_to_start) * self.steps_per_epoch + self.batch_id
self.timer.update_progress((self.global_step + 1) / self.total_steps)
est = self.timer.estimated_remaining()
imps = (self.global_step + 1) * self.batch_size / self.timer.get_stage_elapsed()
return imps, est
def _visualize_pred(self):
pred = torch.argmax(self.G_pred, dim=1, keepdim=True)
pred_vis = pred * 255
return pred_vis
def _save_checkpoint(self, ckpt_name):
torch.save({
'epoch_id': self.epoch_id,
'best_val_acc': self.best_val_acc,
'best_epoch_id': self.best_epoch_id,
'model_G_state_dict': self.net_G.state_dict(),
'optimizer_G_state_dict': self.optimizer_G.state_dict(),
'exp_lr_scheduler_G_state_dict': self.exp_lr_scheduler_G.state_dict(),
}, os.path.join(self.checkpoint_dir, ckpt_name))
def _update_lr_schedulers(self):
self.exp_lr_scheduler_G.step()
def _update_metric(self):
"""
update metric
"""
target = self.batch['L'].to(self.device).detach()
G_pred = self.G_pred.detach()
G_pred = torch.argmax(G_pred, dim=1)
current_score = self.running_metric.update_cm(pr=G_pred.cpu().numpy(), gt=target.cpu().numpy())
return current_score
def _collect_running_batch_states(self):
running_acc = self._update_metric()
m = len(self.dataloaders['train'])
if self.is_training is False:
m = len(self.dataloaders['val'])
imps, est = self._timer_update()
if np.mod(self.batch_id, 100) == 1:
message = 'Is_training: %s. [%d,%d][%d,%d], imps: %.2f, est: %.2fh, G_loss: %.5f, running_mf1: %.5f\n' %\
(self.is_training, self.epoch_id, self.max_num_epochs-1, self.batch_id, m,
imps*self.batch_size, est,
self.G_loss.item(), running_acc)
self.logger.write(message)
if np.mod(self.batch_id, 500) == 1:
vis_input = utils.make_numpy_grid(de_norm(self.batch['A']))
vis_input2 = utils.make_numpy_grid(de_norm(self.batch['B']))
vis_pred = utils.make_numpy_grid(self._visualize_pred())
vis_gt = utils.make_numpy_grid(self.batch['L'])
vis = np.concatenate([vis_input, vis_input2, vis_pred, vis_gt], axis=0)
vis = np.clip(vis, a_min=0.0, a_max=1.0)
file_name = os.path.join(
self.vis_dir, 'istrain_'+str(self.is_training)+'_'+
str(self.epoch_id)+'_'+str(self.batch_id)+'.jpg')
plt.imsave(file_name, vis)
def _collect_epoch_states(self):
scores = self.running_metric.get_scores()
self.epoch_acc = scores['mf1']
self.logger.write('Is_training: %s. Epoch %d / %d, epoch_mF1= %.5f\n' %
(self.is_training, self.epoch_id, self.max_num_epochs-1, self.epoch_acc))
message = ''
for k, v in scores.items():
message += '%s: %.5f ' % (k, v)
self.logger.write(message+'\n')
self.logger.write('\n')
def _update_checkpoints(self):
# save current model
self._save_checkpoint(ckpt_name='last_ckpt.pt')
self.logger.write('Lastest model updated. Epoch_acc=%.4f, Historical_best_acc=%.4f (at epoch %d)\n'
% (self.epoch_acc, self.best_val_acc, self.best_epoch_id))
self.logger.write('\n')
# update the best model (based on eval acc)
if self.epoch_acc > self.best_val_acc:
self.best_val_acc = self.epoch_acc
self.best_epoch_id = self.epoch_id
self._save_checkpoint(ckpt_name='best_ckpt.pt')
self.logger.write('*' * 10 + 'Best model updated!\n')
self.logger.write('\n')
def _update_training_acc_curve(self):
# update train acc curve
self.TRAIN_ACC = np.append(self.TRAIN_ACC, [self.epoch_acc])
np.save(os.path.join(self.checkpoint_dir, 'train_acc.npy'), self.TRAIN_ACC)
def _update_val_acc_curve(self):
# update val acc curve
self.VAL_ACC = np.append(self.VAL_ACC, [self.epoch_acc])
np.save(os.path.join(self.checkpoint_dir, 'val_acc.npy'), self.VAL_ACC)
def _clear_cache(self):
self.running_metric.clear()
def _forward_pass(self, batch):
self.batch = batch
img_in1 = batch['A'].to(self.device)
img_in2 = batch['B'].to(self.device)
self.G_pred = self.net_G(img_in1, img_in2)
def _backward_G(self):
gt = self.batch['L'].to(self.device).long()
self.G_loss = self._pxl_loss(self.G_pred, gt)
self.G_loss.backward()
def train_models(self):
self._load_checkpoint()
# loop over the dataset multiple times
for self.epoch_id in range(self.epoch_to_start, self.max_num_epochs):
################## train #################
##########################################
self._clear_cache()
self.is_training = True
self.net_G.train() # Set model to training mode
# Iterate over data.
self.logger.write('lr: %0.7f\n' % self.optimizer_G.param_groups[0]['lr'])
for self.batch_id, batch in enumerate(self.dataloaders['train'], 0):
self._forward_pass(batch)
# update G
self.optimizer_G.zero_grad()
self._backward_G()
self.optimizer_G.step()
self._collect_running_batch_states()
self._timer_update()
self._collect_epoch_states()
self._update_training_acc_curve()
self._update_lr_schedulers()
################## Eval ##################
##########################################
self.logger.write('Begin evaluation...\n')
self._clear_cache()
self.is_training = False
self.net_G.eval()
# Iterate over data.
for self.batch_id, batch in enumerate(self.dataloaders['val'], 0):
with torch.no_grad():
self._forward_pass(batch)
self._collect_running_batch_states()
self._collect_epoch_states()
########### Update_Checkpoints ###########
##########################################
self._update_val_acc_curve()
self._update_checkpoints()
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