update eval

eval/mmpose_custom/painter_inference_pose.py

@@ -44,7 +44,7 @@ def get_args_parser():
                         default='000000000165_box0')
     parser.add_argument('--input_size', type=int, default=448)
     parser.add_argument('--flip_test', action='store_true', help='use offline bbox')
-
+    parser.add_argument('--data_dir', type=str, default='datasets')
     # distributed training parameters
     parser.add_argument('--world_size', default=1, type=int,
                         help='number of distributed processes')
@@ -93,7 +93,7 @@ def run_one_image(img, tgt, size, model, out_path, device):
 
 
 if __name__ == '__main__':
-    dataset_dir = "datasets/"
+
     args = get_args_parser()
     args = ddp_utils.init_distributed_mode(args)
     device = torch.device("cuda")
@@ -118,7 +118,7 @@ if __name__ == '__main__':
     model_painter = prepare_model(ckpt_path, model, args)
     print('Model loaded.')
 
-    img_src_dir = dataset_dir + "coco_pose/data_pair/test_256x192"
+    img_src_dir = "{}/coco_pose/data_pair/test_256x192".format(args.data_dir)
     if args.flip_test:
         img_src_dir += "_flip"
 
@@ -128,8 +128,8 @@ if __name__ == '__main__':
     data_loader_val = DataLoader(dataset_val, batch_size=1, sampler=sampler_val,
                                  drop_last=False, collate_fn=ddp_utils.collate_fn, num_workers=2)
 
-    img2_path = dataset_dir + "coco_pose/data_pair/train_256x192_aug0/{}_image.png".format(prompt)
-    tgt2_path = dataset_dir + "coco_pose/data_pair/train_256x192_aug0/{}_label.png".format(prompt)
+    img2_path = "{}/coco_pose/data_pair/train_256x192_aug0/{}_image.png".format(args.data_dir, prompt)
+    tgt2_path = "{}/coco_pose/data_pair/train_256x192_aug0/{}_label.png".format(args.data_dir, prompt)
 
     # load the shared prompt image pair
     img2 = Image.open(img2_path).convert("RGB")

eval/nyuv2_depth/eval.sh

@@ -12,10 +12,10 @@ DST_DIR="models_inference/${JOB_NAME}/nyuv2_depth_inference_${CKPT_FILE}_${PROMP
 DATA_DIR="datasets"
 # inference
 python eval/nyuv2_depth/painter_inference_depth.py \
+  --data_dir ${DATA_DIR} \
   --ckpt_path ${CKPT_PATH} --model ${MODEL} --prompt ${PROMPT}
 
 python eval/nyuv2_depth/eval_with_pngs.py \
   --pred_path ${DST_DIR} \
   --gt_path ${DATA_DIR}/nyu_depth_v2/official_splits/test/ \
-  --data_dir ${DATA_DIR} \
   --dataset nyu --min_depth_eval 1e-3 --max_depth_eval 10 --eigen_crop