Major refactor, small fixes

simclr.py

@@ -1,14 +1,14 @@
 import logging
 import os
-import shutil
 import sys
 
 import torch
 import torch.nn.functional as F
-import yaml
 from torch.utils.tensorboard import SummaryWriter
 from tqdm import tqdm
 
+from utils import save_config_file, accuracy, save_checkpoint
+
 torch.manual_seed(0)
 
 apex_support = False
@@ -22,36 +22,6 @@
     apex_support = False
 
 
-def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
-    torch.save(state, filename)
-    if is_best:
-        shutil.copyfile(filename, 'model_best.pth.tar')
-
-
-def _save_config_file(model_checkpoints_folder, args):
-    if not os.path.exists(model_checkpoints_folder):
-        os.makedirs(model_checkpoints_folder)
-        with open(os.path.join(model_checkpoints_folder, 'config.yml'), 'w') as outfile:
-            yaml.dump(args, outfile, default_flow_style=False)
-
-
-def accuracy(output, target, topk=(1,)):
-    """Computes the accuracy over the k top predictions for the specified values of k"""
-    with torch.no_grad():
-        maxk = max(topk)
-        batch_size = target.size(0)
-
-        _, pred = output.topk(maxk, 1, True, True)
-        pred = pred.t()
-        correct = pred.eq(target.view(1, -1).expand_as(pred))
-
-        res = []
-        for k in topk:
-            correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
-            res.append(correct_k.mul_(100.0 / batch_size))
-        return res
-
-
 class SimCLR(object):
 
     def __init__(self, *args, **kwargs):
@@ -86,7 +56,7 @@ def train(self, train_loader):
                                                         opt_level='O2',
                                                         keep_batchnorm_fp32=True)
         # save config file
-        _save_config_file(self.writer.log_dir, self.args)
+        save_config_file(self.writer.log_dir, self.args)
 
         n_iter = 0
         logging.info(f"Start SimCLR training for {self.args.epochs} epochs.")

utils.py

@@ -0,0 +1,35 @@
+import os
+import shutil
+
+import torch
+import yaml
+
+
+def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
+    torch.save(state, filename)
+    if is_best:
+        shutil.copyfile(filename, 'model_best.pth.tar')
+
+
+def save_config_file(model_checkpoints_folder, args):
+    if not os.path.exists(model_checkpoints_folder):
+        os.makedirs(model_checkpoints_folder)
+        with open(os.path.join(model_checkpoints_folder, 'config.yml'), 'w') as outfile:
+            yaml.dump(args, outfile, default_flow_style=False)
+
+
+def accuracy(output, target, topk=(1,)):
+    """Computes the accuracy over the k top predictions for the specified values of k"""
+    with torch.no_grad():
+        maxk = max(topk)
+        batch_size = target.size(0)
+
+        _, pred = output.topk(maxk, 1, True, True)
+        pred = pred.t()
+        correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+        res = []
+        for k in topk:
+            correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
+            res.append(correct_k.mul_(100.0 / batch_size))
+        return res