Update EMA decay tau

utils/torch_utils.py

@@ -32,9 +32,7 @@
 
 @contextmanager
 def torch_distributed_zero_first(local_rank: int):
-    """
-    Decorator to make all processes in distributed training wait for each local_master to do something.
-    """
+    # Decorator to make all processes in distributed training wait for each local_master to do something
     if local_rank not in [-1, 0]:
         dist.barrier(device_ids=[local_rank])
     yield
@@ -43,13 +41,13 @@ def torch_distributed_zero_first(local_rank: int):
 
 
 def date_modified(path=__file__):
-    # return human-readable file modification date, i.e. '2021-3-26'
+    # Return human-readable file modification date, i.e. '2021-3-26'
     t = datetime.datetime.fromtimestamp(Path(path).stat().st_mtime)
     return f'{t.year}-{t.month}-{t.day}'
 
 
 def git_describe(path=Path(__file__).parent):  # path must be a directory
-    # return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe
+    # Return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe
     s = f'git -C {path} describe --tags --long --always'
     try:
         return subprocess.check_output(s, shell=True, stderr=subprocess.STDOUT).decode()[:-1]
@@ -99,7 +97,7 @@ def select_device(device='', batch_size=0, newline=True):
 
 
 def time_sync():
-    # pytorch-accurate time
+    # PyTorch-accurate time
     if torch.cuda.is_available():
         torch.cuda.synchronize()
     return time.time()
@@ -205,7 +203,7 @@ def prune(model, amount=0.3):
 
 
 def fuse_conv_and_bn(conv, bn):
-    # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/
+    # Fuse Conv2d() and BatchNorm2d() layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/
     fusedconv = nn.Conv2d(conv.in_channels,
                           conv.out_channels,
                           kernel_size=conv.kernel_size,
@@ -214,12 +212,12 @@ def fuse_conv_and_bn(conv, bn):
                           groups=conv.groups,
                           bias=True).requires_grad_(False).to(conv.weight.device)
 
-    # prepare filters
+    # Prepare filters
     w_conv = conv.weight.clone().view(conv.out_channels, -1)
     w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
     fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))
 
-    # prepare spatial bias
+    # Prepare spatial bias
     b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
     b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
     fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
@@ -252,7 +250,7 @@ def model_info(model, verbose=False, img_size=640):
 
 
 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
+    # Scales img(bs,3,y,x) by ratio constrained to gs-multiple
     if ratio == 1.0:
         return img
     else:
@@ -302,13 +300,13 @@ class ModelEMA:
     For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
     """
 
-    def __init__(self, model, decay=0.9999, updates=0):
+    def __init__(self, model, decay=0.9999, tau=2000, updates=0):
         # Create EMA
         self.ema = deepcopy(de_parallel(model)).eval()  # FP32 EMA
         # if next(model.parameters()).device.type != 'cpu':
         #     self.ema.half()  # FP16 EMA
         self.updates = updates  # number of EMA updates
-        self.decay = lambda x: decay * (1 - math.exp(-x / 2000))  # decay exponential ramp (to help early epochs)
+        self.decay = lambda x: decay * (1 - math.exp(-x / tau))  # decay exponential ramp (to help early epochs)
         for p in self.ema.parameters():
             p.requires_grad_(False)