Update torch_utils.py (#1652)

utils/torch_utils.py

@@ -3,9 +3,11 @@
 import logging
 import math
 import os
+import subprocess
 import time
 from contextlib import contextmanager
 from copy import deepcopy
+from pathlib import Path
 
 import torch
 import torch.backends.cudnn as cudnn
@@ -41,9 +43,17 @@ def init_torch_seeds(seed=0):
         cudnn.benchmark, cudnn.deterministic = True, False
 
 
+def git_describe():
+    # return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe
+    if Path('.git').exists():
+        return subprocess.check_output('git describe --tags --long --always', shell=True).decode('utf-8')[:-1]
+    else:
+        return ''
+
+
 def select_device(device='', batch_size=None):
     # device = 'cpu' or '0' or '0,1,2,3'
-    s = f'Using torch {torch.__version__} '  # string
+    s = f'YOLOv3 {git_describe()} torch {torch.__version__} '  # string
     cpu = device.lower() == 'cpu'
     if cpu:
         os.environ['CUDA_VISIBLE_DEVICES'] = '-1'  # force torch.cuda.is_available() = False
@@ -61,9 +71,9 @@ def select_device(device='', batch_size=None):
             p = torch.cuda.get_device_properties(i)
             s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / 1024 ** 2}MB)\n"  # bytes to MB
     else:
-        s += 'CPU'
+        s += 'CPU\n'
 
-    logger.info(f'{s}\n')  # skip a line
+    logger.info(s)  # skip a line
     return torch.device('cuda:0' if cuda else 'cpu')
 
 
@@ -225,8 +235,8 @@ def load_classifier(name='resnet101', n=2):
     return model
 
 
-def scale_img(img, ratio=1.0, same_shape=False):  # img(16,3,256,416), r=ratio
-    # scales img(bs,3,y,x) by ratio
+def scale_img(img, ratio=1.0, same_shape=False, gs=32):  # img(16,3,256,416)
+    # scales img(bs,3,y,x) by ratio constrained to gs-multiple
     if ratio == 1.0:
         return img
     else:
@@ -234,7 +244,6 @@ def scale_img(img, ratio=1.0, same_shape=False):  # img(16,3,256,416), r=ratio
         s = (int(h * ratio), int(w * ratio))  # new size
         img = F.interpolate(img, size=s, mode='bilinear', align_corners=False)  # resize
         if not same_shape:  # pad/crop img
-            gs = 32  # (pixels) grid size
             h, w = [math.ceil(x * ratio / gs) * gs for x in (h, w)]
         return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447)  # value = imagenet mean