fix(plugins): update workflow and fix bug of kwargs in nano (Megvii-B…

…aseDetection#844)

.github/workflows/format_check.sh

@@ -4,12 +4,12 @@ set -e
 
 export PYTHONPATH=$PWD:$PYTHONPATH
 
-flake8 yolox || flake8_ret=$?
+flake8 yolox exps tools || flake8_ret=$?
 if [ "$flake8_ret" ]; then
     exit $flake8_ret
 fi
 echo "All flake check passed!"
-isort --check-only -rc yolox || isort_ret=$?
+isort --check-only -rc yolox exps || isort_ret=$?
 if [ "$isort_ret" ]; then
     exit $isort_ret
 fi

.gitignore

@@ -4,6 +4,8 @@
 # user experiments directory
 YOLOX_outputs/
 datasets/
+# do not ignore datasets under yolox/data
+!*yolox/data/datasets/
 
 # temporary files which can be created if a process still has a handle open of a deleted file
 .fuse_hidden*

exps/default/nano.py

@@ -33,8 +33,14 @@ def init_yolo(M):
             from yolox.models import YOLOX, YOLOPAFPN, YOLOXHead
             in_channels = [256, 512, 1024]
             # NANO model use depthwise = True, which is main difference.
-            backbone = YOLOPAFPN(self.depth, self.width, in_channels=in_channels, self.act, depthwise=True)
-            head = YOLOXHead(self.num_classes, self.width, in_channels=in_channels, self.act, depthwise=True)
+            backbone = YOLOPAFPN(
+                self.depth, self.width, in_channels=in_channels,
+                act=self.act, depthwise=True,
+            )
+            head = YOLOXHead(
+                self.num_classes, self.width, in_channels=in_channels,
+                act=self.act, depthwise=True
+            )
             self.model = YOLOX(backbone, head)
 
         self.model.apply(init_yolo)

exps/default/yolov3.py

@@ -4,7 +4,6 @@
 
 import os
 
-import torch
 import torch.nn as nn
 
 from yolox.exp import Exp as MyExp
@@ -32,4 +31,3 @@ def init_yolo(M):
         self.model.head.initialize_biases(1e-2)
 
         return self.model
-

tools/demo.py

@@ -298,7 +298,10 @@ def main(exp, args):
         trt_file = None
         decoder = None
 
-    predictor = Predictor(model, exp, COCO_CLASSES, trt_file, decoder, args.device, args.fp16, args.legacy)
+    predictor = Predictor(
+        model, exp, COCO_CLASSES, trt_file, decoder,
+        args.device, args.fp16, args.legacy,
+    )
     current_time = time.localtime()
     if args.demo == "image":
         image_demo(predictor, vis_folder, args.path, current_time, args.save_result)

tools/export_onnx.py

@@ -101,7 +101,7 @@ def main():
         from onnxsim import simplify
 
         input_shapes = {args.input: list(dummy_input.shape)} if args.dynamic else None
-        
+
         # use onnxsimplify to reduce reduent model.
         onnx_model = onnx.load(args.output_name)
         model_simp, check = simplify(onnx_model,

tools/export_torchscript.py

@@ -7,7 +7,6 @@
 from loguru import logger
 
 import torch
-from torch import nn
 
 from yolox.exp import get_exp
 

tools/trt.py

@@ -27,7 +27,9 @@ def make_parser():
         help="pls input your expriment description file",
     )
     parser.add_argument("-c", "--ckpt", default=None, type=str, help="ckpt path")
-    parser.add_argument("-w", '--workspace', type=int, default=32, help='max workspace size in detect')
+    parser.add_argument(
+        "-w", '--workspace', type=int, default=32, help='max workspace size in detect'
+    )
     parser.add_argument("-b", '--batch', type=int, default=1, help='max batch size in detect')
     return parser