release commit
authorManu Mathew <a0393608@ti.com>
Tue, 28 Jan 2020 12:42:43 +0000 (18:12 +0530)
committerManu Mathew <a0393608@ti.com>
Tue, 28 Jan 2020 12:42:43 +0000 (18:12 +0530)
run_depth.sh
scripts/train_depth_main.py

index f96d1afefc010b755927e6f788793cffa67937ab..861b29496f1e67dc3ff8cc872e354cacc49d554c 100755 (executable)
@@ -5,8 +5,8 @@
 ## Training
 ## =====================================================================================
 #### KITTI Depth (Manual Download) - Training with MobileNetV2+DeeplabV3Lite
-#python ./scripts/train_depth_main.py --dataset_name kitti_depth --model_name deeplabv3lite_mobilenetv2_tv --data_path ./data/datasets/kitti/kitti_depth/data --img_resize 384 768 --output_size 374 1242 \
-#--pretrained https://download.pytorch.org/models/mobilenet_v2-b0353104.pth
+python ./scripts/train_depth_main.py --dataset_name kitti_depth --model_name deeplabv3lite_mobilenetv2_tv --data_path ./data/datasets/kitti/kitti_depth/data --img_resize 384 768 --output_size 374 1242 \
+--pretrained https://download.pytorch.org/models/mobilenet_v2-b0353104.pth
 
 #### KITTI Depth (Manual Download) - Training with ResNet50+FPN
 #python ./scripts/train_depth_main.py --dataset_name kitti_depth --model_name fpn_pixel2pixel_aspp_resnet50 --data_path ./data/datasets/kitti/kitti_depth/data --img_resize 384 768 --output_size 374 1242 \
index f0f7e786a7a1957d6ea2bb4d78768727aa6415f6..e5db9066de4452c241e1ef8ce62bb2cde7c81fd4 100755 (executable)
@@ -83,8 +83,8 @@ args.pretrained = './data/modelzoo/semantic_segmentation/cityscapes/deeplabv3lit
 args.model_config.input_channels = (3,)      # [3,3]
 args.model_config.output_type = ['depth']
 args.model_config.output_channels = [1]
-args.model_config.output_range = [(0,64)] # important note: set this output_range parameter in the inference script as well
-                                          # this is an important difference from the semantic segmentation script.
+args.model_config.output_range = [(0,128)] # important note: set this output_range parameter in the inference script as well
+                                           # this is an important difference from the semantic segmentation script.
 
 args.losses = [['supervised_loss', 'scale_loss', 'supervised_error_var']] #[['supervised_loss', 'scale_loss']]
 args.loss_mult_factors = [[0.125, 0.125, 4.0]]