Fix redundant outputs via Logging in DDP training (#500)

* Change print to logging

* Clean function set_logging

* Add line spacing

* Change leftover prints to log

* Fix scanning labels output

* Fix rank naming

* Change leftover print to logging

* Reorganized DDP variables

* Fix type error

* Make quotes consistent

* Fix spelling

* Clean function call

* Add line spacing

* Update datasets.py

* Update train.py

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

models/yolo.py

@@ -1,5 +1,6 @@
 import argparse
 import math
+import logging
 from copy import deepcopy
 from pathlib import Path
 
@@ -12,6 +13,7 @@ from utils.general import check_anchor_order, make_divisible, check_file
 from utils.torch_utils import (
     time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, select_device)
 
+logger = logging.getLogger(__name__)
 
 class Detect(nn.Module):
     def __init__(self, nc=80, anchors=(), ch=()):  # detection layer
@@ -169,7 +171,7 @@ class Model(nn.Module):
 
 
 def parse_model(d, ch):  # model_dict, input_channels(3)
-    print('\n%3s%18s%3s%10s  %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
+    logger.info('\n%3s%18s%3s%10s  %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
     anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
     na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
     no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)
@@ -224,7 +226,7 @@ def parse_model(d, ch):  # model_dict, input_channels(3)
         t = str(m)[8:-2].replace('__main__.', '')  # module type
         np = sum([x.numel() for x in m_.parameters()])  # number params
         m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
-        print('%3s%18s%3s%10.0f  %-40s%-30s' % (i, f, n, np, t, args))  # print
+        logger.info('%3s%18s%3s%10.0f  %-40s%-30s' % (i, f, n, np, t, args))  # print
         save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
         layers.append(m_)
         ch.append(c2)