different spacing

train.py

@@ -1,78 +1,101 @@
-"""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.
+"""General training script for image-to-image translation
 
 Example:
     Train a CycleGAN model:
-        python train.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan
+        python train --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
+        python train --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --direction BtoA
+
+Options:
+    Available models ('--model')
+        pix2pix, cyclegan, colorization
+
+    Available datasets ('--dataset_mode')
+       aligned, unaligned, single, colorization
+
+Required parameters:
+    You need to specify the dataset ('--dataroot'), experiment name ('--name'), and model ('--model').
 
-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
+Features:
+    Loads networks, dataset, etc
+    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.
+
+References:
+    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
 """
 import time
-from options.train_options import TrainOptions
+
 from data import create_dataset
 from models import create_model
+from options.train_options import TrainOptions
 from util.visualizer import Visualizer
 
 if __name__ == '__main__':
-    opt = TrainOptions().parse()   # get training options
-    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.
+    # Get training options
+    opt = TrainOptions().parse()
+
+    dataset = create_dataset(opt)
+    dataset_size = len(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
+    model = create_model(opt)
+    model.setup(opt)  # regular stuff; load and print networks; create schedulers
+    visualizer = Visualizer(opt)  # create a visualiser that displays/saves images and plots
 
-    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>
+    total_iters = 0
+
+    # Apparently training is easy enough that we'll do it inline!
+    for epoch in range(opt.epoch_count, opt.n_epochs + opt.n_epochs_decay + 1):
         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
+        iter_data_time = time.time()  # timer for data loading
+
+        epoch_iter = 0
+        visualizer.reset()
+
+        for i, data in enumerate(dataset):
+            iter_start_time = time.time()  # timer for computation
 
-        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 visdom and save images to a HTML file
+            # this is equivalent to metrics = train(x,y) in the BigGAN code
+            model.set_input(data)  # unpack from the dataset
+            model.optimize_parameters()
+
+            if total_iters % opt.display_freq == 0:
                 save_result = total_iters % opt.update_html_freq == 0
+
                 model.compute_visuals()
                 visualizer.display_current_results(model.get_current_visuals(), epoch, save_result)
 
-            if total_iters % opt.print_freq == 0:    # print training losses and save logging information to the disk
+            if total_iters % opt.print_freq == 0:  # print training losses and save logging information to the disk
                 losses = model.get_current_losses()
                 t_comp = (time.time() - iter_start_time) / opt.batch_size
+
                 visualizer.print_current_losses(epoch, epoch_iter, losses, t_comp, t_data)
                 if opt.display_id > 0:
                     visualizer.plot_current_losses(epoch, float(epoch_iter) / dataset_size, losses)
 
-            if total_iters % opt.save_latest_freq == 0:   # cache our latest model every <save_latest_freq> iterations
+            if total_iters % opt.save_latest_freq == 0:  # cache our latest model every <save_latest_freq> iterations
                 print('saving the latest model (epoch %d, total_iters %d)' % (epoch, total_iters))
+
                 save_suffix = 'iter_%d' % total_iters if opt.save_by_iter else 'latest'
                 model.save_networks(save_suffix)
 
             iter_data_time = time.time()
-        if epoch % opt.save_epoch_freq == 0:              # cache our model every <save_epoch_freq> epochs
+
+        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)
 
-        print('End of epoch %d / %d \t Time Taken: %d sec' % (epoch, opt.n_epochs + opt.n_epochs_decay, time.time() - epoch_start_time))
-        model.update_learning_rate()                     # update learning rates at the end of every epoch.
+        print('End of epoch %d / %d \t Time Taken: %d sec' % (
+            epoch, opt.n_epochs + opt.n_epochs_decay, time.time() - epoch_start_time))
+        model.update_learning_rate()  # update learning rates at the end of every epoch.