forked from peterhpark/neuroclear
-
Notifications
You must be signed in to change notification settings - Fork 0
/
train.py
115 lines (92 loc) · 6.63 KB
/
train.py
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
"""General-purpose training script for image-to-image translation.
This script works for various models (with option '--model': e.g., pix2pix, cyclegan, colorization) and
different datasets (with option '--dataset_mode': e.g., aligned, unaligned, single, colorization).
You need to specify the dataset ('--dataroot'), experiment name ('--name'), and model ('--model').
It first creates model, dataset, and visualizer given the option.
It then does standard network training. During the training, it also visualize/save the images, print/save the loss plot, and save models.
The script supports continue/resume training. Use '--continue_train' to resume your previous training.
Example:
Train a CycleGAN model:
python train.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan
Train a pix2pix model:
python train.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --direction BtoA
See options/base_options.py and options/train_options.py for more training options.
See training and test tips at: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/tips.md
See frequently asked questions at: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/qa.md
### Train script that follows the original cycleGAN training routine, with no repetition.###
"""
import time
from options.train_options import TrainOptions
from data import create_dataset
from models import create_model
from util.visualizer import Visualizer
if __name__ == '__main__':
opt = TrainOptions().parse() # get training options
## DEBUG FLAG
if opt.debug:
print ("DEBUG MODE ACTIVATED.")
import pydevd_pycharm
Host_IP_address = '143.248.31.79'
print ("For debug, listening to...{}".format(Host_IP_address))
# pydevd_pycharm.settrace('143.248.31.79', port=5678, stdoutToServer=True, stderrToServer=True)
pydevd_pycharm.settrace(Host_IP_address, port=5678, stdoutToServer=True, stderrToServer=True)
##
dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options
dataset_size = len(dataset) # get the number of images in the dataset.
print('The number of training images = %d' % dataset_size)
model = create_model(opt) # create a model given opt.model and other options
model.setup(opt) # regular setup: load and print networks; create schedulers
visualizer = Visualizer(opt) # create a visualizer that display/save images and plots
total_iters = 0 # the total number of training iterations
visualizer.display_model_hyperparameters()
print ("Model hyperparameters documented on tensorboard.")
print ("start the epoch training...")
for epoch in range(opt.epoch_count, opt.n_epochs + opt.n_epochs_decay + 1): # outer loop for different epochs; we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>
epoch_start_time = time.time() # timer for entire epoch
iter_data_time = time.time() # timer for data loading per iteration
epoch_iter = 0 # the number of training iterations in current epoch, reset to 0 every epoch
visualizer.reset() # reset the visualizer: make sure it saves the results to HTML at least once every epoch
for i, data in enumerate(dataset): # inner loop within one epoch
iter_start_time = time.time() # timer for computation per iteration
if total_iters % opt.print_freq == 0:
t_data = iter_start_time - iter_data_time
total_iters += opt.batch_size
epoch_iter += opt.batch_size
model.set_input(data) # unpack data from dataset and apply preprocessing
model.optimize_parameters() # calculate loss functions, get gradients, update network weights
if total_iters % opt.display_freq == 0: # display images on tensorboard
save_result = total_iters % opt.update_html_freq == 0
model.compute_visuals()
epoch_progress = round(float(epoch_iter/dataset_size),2) * 100
epoch_count = (epoch-1) * 100 #display the progress in percent: for example 30% past two epochs is 230.
visualizer.display_current_results(model.get_current_visuals(), epoch_count+epoch_progress)
visualizer.display_current_histogram(model.get_current_visuals(), epoch)
if total_iters % opt.print_freq == 0: # print training losses and save logging information to the disk
print ("----------------------------------")
print ("exp name: " + str(opt.name) + ", gpu_id:"+str(opt.gpu_ids))
print ("----------------------------------")
losses = model.get_current_losses()
t_comp = (time.time() - iter_start_time) / opt.batch_size
epoch_progress = round(float(epoch_iter / dataset_size), 2) * 100
epoch_count = (epoch - 1) * 100 # display the progress in percent: for example 30% past two epochs is 230.
visualizer.print_current_losses(epoch, epoch_progress, losses, t_comp, t_data)
if opt.display_id > 0:
visualizer.plot_current_losses(epoch_count+epoch_progress, losses, is_epoch = False)
if total_iters % opt.save_latest_freq == 0: # cache our latest model every <save_latest_freq> iterations
epoch_progress = round(float(epoch_iter / dataset_size), 2) * 100
epoch_count = (epoch - 1) * 100 # display the progress in percent: for example 30% past two epochs is 230.
print('saving the latest model (epoch %d, epoch_progress %d%%)' % (epoch, epoch_progress))
save_suffix = 'iter_%d' % total_iters if opt.save_by_iter else 'latest'
model.save_networks(save_suffix)
iter_data_time = time.time()
# display the image histogram per epoch.
visualizer.display_current_histogram(model.get_current_visuals(), epoch)
losses = model.get_current_losses()
visualizer.plot_current_losses(epoch, losses, is_epoch=True)
if epoch % opt.save_epoch_freq == 0: # cache our model every <save_epoch_freq> epochs
print('saving the model at the end of epoch %d, iters %d' % (epoch, total_iters))
model.save_networks('latest')
model.save_networks(epoch)
visualizer.save_current_visuals(model.get_current_visuals(), epoch)
model.update_learning_rate()
print('End of epoch %d / %d \t Time Taken: %d sec' % (epoch, opt.n_epochs + opt.n_epochs_decay, time.time() - epoch_start_time))