Merge pull request #136 from wenh06/master

put validation set in use

cfg.py

@@ -10,12 +10,16 @@
     @Detail    :
 
 '''
+import os
 from easydict import EasyDict
 
+
+_BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+
 Cfg = EasyDict()
 
 Cfg.use_darknet_cfg = True
-Cfg.cfgfile = 'cfg/yolov4.cfg'
+Cfg.cfgfile = os.path.join(_BASE_DIR, 'cfg', 'yolov4.cfg')
 
 Cfg.batch = 64
 Cfg.subdivisions = 16
@@ -50,8 +54,8 @@
 Cfg.gaussian = 0
 Cfg.boxes = 60  # box num
 Cfg.TRAIN_EPOCHS = 300
-Cfg.train_label = 'data/train.txt'
-Cfg.val_label = 'data/val.txt'
+Cfg.train_label = os.path.join(_BASE_DIR, 'data', 'train.txt')
+Cfg.val_label = os.path.join(_BASE_DIR, 'data' ,'val.txt')
 Cfg.TRAIN_OPTIMIZER = 'adam'
 '''
 image_path1 x1,y1,x2,y2,id x1,y1,x2,y2,id x1,y1,x2,y2,id ...
@@ -66,5 +70,9 @@
 elif Cfg.mosaic:
     Cfg.mixup = 3
 
-Cfg.checkpoints = 'checkpoints'
-Cfg.TRAIN_TENSORBOARD_DIR = 'log'
+Cfg.checkpoints = os.path.join(_BASE_DIR, 'checkpoints')
+Cfg.TRAIN_TENSORBOARD_DIR = os.path.join(_BASE_DIR, 'log')
+
+Cfg.iou_type = 'iou'  # 'giou', 'diou', 'ciou'
+
+Cfg.keep_checkpoint_max = 10

dataset.py

@@ -239,7 +239,7 @@ def draw_box(img, bboxes):
 
 
 class Yolo_dataset(Dataset):
-    def __init__(self, lable_path, cfg):
+    def __init__(self, lable_path, cfg, train=True):
         super(Yolo_dataset, self).__init__()
         if cfg.mixup == 2:
             print("cutmix=1 - isn't supported for Detector")
@@ -249,6 +249,7 @@ def __init__(self, lable_path, cfg):
             raise
 
         self.cfg = cfg
+        self.train = train
 
         truth = {}
         f = open(lable_path, 'r', encoding='utf-8')
@@ -264,6 +265,8 @@ def __len__(self):
         return len(self.truth.keys())
 
     def __getitem__(self, index):
+        if not self.train:
+            return self._get_val_item(index)
         img_path = list(self.truth.keys())[index]
         bboxes = np.array(self.truth.get(img_path), dtype=np.float)
         img_path = os.path.join(self.cfg.dataset_dir, img_path)
@@ -381,6 +384,37 @@ def __getitem__(self, index):
         out_bboxes1[:min(out_bboxes.shape[0], self.cfg.boxes)] = out_bboxes[:min(out_bboxes.shape[0], self.cfg.boxes)]
         return out_img, out_bboxes1
 
+    def _get_val_item(self, index):
+        """
+        """
+        img_path = self.imgs[index]
+        bboxes_with_cls_id = np.array(self.truth.get(img_path), dtype=np.float)
+        img = cv2.imread(os.path.join(self.cfg.dataset_dir, img_path))
+        # img_height, img_width = img.shape[:2]
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        # img = cv2.resize(img, (self.cfg.w, self.cfg.h))
+        # img = torch.from_numpy(img.transpose(2, 0, 1)).float().div(255.0).unsqueeze(0)
+        num_objs = len(bboxes_with_cls_id)
+        target = {}
+        # boxes to coco format
+        boxes = bboxes_with_cls_id[...,:4]
+        boxes[..., 2:] = boxes[..., 2:] - boxes[..., :2]  # box width, box height
+        target['boxes'] = torch.as_tensor(boxes, dtype=torch.float32)
+        target['labels'] = torch.as_tensor(bboxes_with_cls_id[...,-1].flatten(), dtype=torch.int64)
+        target['image_id'] = torch.tensor([get_image_id(img_path)])
+        target['area'] = (target['boxes'][:,3])*(target['boxes'][:,2])
+        target['iscrowd'] = torch.zeros((num_objs,), dtype=torch.int64)
+        return img, target
+
+
+def get_image_id(filename:str) -> int:
+    """Convert a string to a integer."""
+    raise NotImplementedError("Create your own 'get_image_id' function")
+    lv, no = os.path.splitext(os.path.basename(filename))[0].split("_")
+    lv = lv.replace("level", "")
+    no = f"{int(no):04d}"
+    return int(lv+no)
+
 
 if __name__ == "__main__":
     from cfg import Cfg

tool/darknet2pytorch.py

@@ -132,8 +132,10 @@ def forward(self, x):
 
 # support route shortcut and reorg
 class Darknet(nn.Module):
-    def __init__(self, cfgfile):
+    def __init__(self, cfgfile, inference=False):
         super(Darknet, self).__init__()
+        self.inference = inference
+        self.training = not self.inference
 
         self.blocks = parse_cfg(cfgfile)
         self.width = int(self.blocks[0]['width'])

tool/tv_reference/README.md

@@ -0,0 +1,45 @@
+# Object detection reference training scripts
+
+This folder contains reference training scripts for object detection.
+They serve as a log of how to train specific models, to provide baseline
+training and evaluation scripts to quickly bootstrap research.
+
+To execute the example commands below you must install the following:
+
+```
+cython
+pycocotools
+matplotlib
+```
+
+You must modify the following flags:
+
+`--data-path=/path/to/coco/dataset`
+
+`--nproc_per_node=<number_of_gpus_available>`
+
+Except otherwise noted, all models have been trained on 8x V100 GPUs. 
+
+### Faster R-CNN
+```
+python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py\
+    --dataset coco --model fasterrcnn_resnet50_fpn --epochs 26\
+    --lr-steps 16 22 --aspect-ratio-group-factor 3
+```
+
+
+### Mask R-CNN
+```
+python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py\
+    --dataset coco --model maskrcnn_resnet50_fpn --epochs 26\
+    --lr-steps 16 22 --aspect-ratio-group-factor 3
+```
+
+
+### Keypoint R-CNN
+```
+python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py\
+    --dataset coco_kp --model keypointrcnn_resnet50_fpn --epochs 46\
+    --lr-steps 36 43 --aspect-ratio-group-factor 3
+```
+