CLI fire prep updates

detect.py

@@ -238,7 +238,7 @@ def parse_opt():
     parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
     opt = parser.parse_args()
     opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
-    print_args(FILE.stem, opt)
+    print_args(vars(opt))
     return opt
 
 

export.py

@@ -566,7 +566,7 @@ def parse_opt():
                         default=['torchscript', 'onnx'],
                         help='torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs')
     opt = parser.parse_args()
-    print_args(FILE.stem, opt)
+    print_args(vars(opt))
     return opt
 
 

models/tf.py

@@ -480,7 +480,7 @@ def parse_opt():
     parser.add_argument('--dynamic', action='store_true', help='dynamic batch size')
     opt = parser.parse_args()
     opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
-    print_args(FILE.stem, opt)
+    print_args(vars(opt))
     return opt
 
 

models/yolo.py

@@ -308,7 +308,7 @@ def parse_model(d, ch):  # model_dict, input_channels(3)
     parser.add_argument('--test', action='store_true', help='test all yolo*.yaml')
     opt = parser.parse_args()
     opt.cfg = check_yaml(opt.cfg)  # check YAML
-    print_args(FILE.stem, opt)
+    print_args(vars(opt))
     device = select_device(opt.device)
 
     # Create model

train.py

@@ -515,7 +515,7 @@ def parse_opt(known=False):
 def main(opt, callbacks=Callbacks()):
     # Checks
     if RANK in [-1, 0]:
-        print_args(FILE.stem, opt)
+        print_args(vars(opt))
         check_git_status()
         check_requirements(exclude=['thop'])
 

utils/benchmarks.py

@@ -92,7 +92,7 @@ def parse_opt():
     parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
     parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
     opt = parser.parse_args()
-    print_args(FILE.stem, opt)
+    print_args(vars(opt))
     return opt
 
 

utils/general.py

@@ -5,6 +5,7 @@
 
 import contextlib
 import glob
+import inspect
 import logging
 import math
 import os
@@ -20,6 +21,7 @@
 from multiprocessing.pool import ThreadPool
 from pathlib import Path
 from subprocess import check_output
+from typing import Optional
 from zipfile import ZipFile
 
 import cv2
@@ -163,9 +165,15 @@ def methods(instance):
     return [f for f in dir(instance) if callable(getattr(instance, f)) and not f.startswith("__")]
 
 
-def print_args(name, opt):
-    # Print argparser arguments
-    LOGGER.info(colorstr(f'{name}: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items()))
+def print_args(args: Optional[dict] = None, show_file=True, show_fcn=False):
+    # Print function arguments (optional args dict)
+    x = inspect.currentframe().f_back  # previous frame
+    file, _, fcn, _, _ = inspect.getframeinfo(x)
+    if args is None:  # get args automatically
+        args, _, _, frm = inspect.getargvalues(x)
+        args = {k: v for k, v in frm.items() if k in args}
+    s = (f'{Path(file).stem}: ' if show_file else '') + (f'{fcn}: ' if show_fcn else '')
+    LOGGER.info(colorstr(s) + ', '.join(f'{k}={v}' for k, v in args.items()))
 
 
 def init_seeds(seed=0):
@@ -346,6 +354,7 @@ def check_img_size(imgsz, s=32, floor=0):
     if isinstance(imgsz, int):  # integer i.e. img_size=640
         new_size = max(make_divisible(imgsz, int(s)), floor)
     else:  # list i.e. img_size=[640, 480]
+        imgsz = list(imgsz)  # convert to list if tuple
         new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz]
     if new_size != imgsz:
         LOGGER.warning(f'WARNING: --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}')

val.py

@@ -350,7 +350,7 @@ def parse_opt():
     opt.data = check_yaml(opt.data)  # check YAML
     opt.save_json |= opt.data.endswith('coco.yaml')
     opt.save_txt |= opt.save_hybrid
-    print_args(FILE.stem, opt)
+    print_args(vars(opt))
     return opt