diff --git a/run_segmentation.sh b/run_segmentation.sh
index fef2a5efb62d3e1081bbfd535cf7bd4245b06003..d25a5d8e1971f7b7807cb75494e07fc7776bd06b 100755 (executable)
--- a/run_segmentation.sh
+++ b/run_segmentation.sh
# Summary of commands - uncomment one and run this script
#### Manual Download: It is expected that the dataset is manually downloaded and kept in the folder specified agaianst the --data_path option.
+## =====================================================================================
+# Models Supported:
+## =====================================================================================
+# deeplabv3lite_mobilenetv2_tv: deeplabv3lite decoder
+# fpnlite_pixel2pixel_aspp_mobilenetv2_tv: fpn decoder
+# unetlite_pixel2pixel_aspp_mobilenetv2_tv: unet decoder
+# deeplabv3lite_mobilenetv2_tv_fd: low complexity model with fast downsampling strategy
+# fpnlite_pixel2pixel_aspp_mobilenetv2_tv_fd: low complexity model with fast downsampling strategy
+# unetlite_pixel2pixel_aspp_mobilenetv2_tv_fd: low complexity model with fast downsampling strategy
+#
+# deeplabv3lite_resnet50: uses resnet50 encoder
+# deeplabv3lite_resnet50_p5: low complexity model - uses resnet50 encoder with half the number of channels (1/4 the complexity). note this need specially trained resnet50 pretrained weights
+# fpnlite_pixel2pixel_aspp_resnet50_fd: low complexity model - with fast downsampling strategy
+
+
## =====================================================================================
## Training
## =====================================================================================
#python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name deeplabv3lite_mobilenetv2_tv --data_path ./data/datasets/cityscapes/data --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 \
#--pretrained https://download.pytorch.org/models/mobilenet_v2-b0353104.pth
-#### Cityscapes Semantic Segmentation - original fpn - no aspp model, stride 64 model - Low Complexity Model
-#python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name fpn_pixel2pixel_aspp_mobilenetv2_tv_es64 --data_path ./data/datasets/cityscapes/data --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 \
-#--pretrained https://download.pytorch.org/models/mobilenet_v2-b0353104.pth
-
#### Cityscapes Semantic Segmentation - Training with MobileNetV2+DeeplabV3Lite, Higher Resolution
#python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name deeplabv3lite_mobilenetv2_tv --data_path ./data/datasets/cityscapes/data --img_resize 768 1536 --rand_crop 512 1024 --output_size 1024 2048 --gpus 0 1 \
#--pretrained https://download.pytorch.org/models/mobilenet_v2-b0353104.pth
#### Cityscapes Semantic Segmentation - original fpn - no aspp model, stride 64 model, Higher Resolution - Low Complexity Model
-#python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name fpn_pixel2pixel_aspp_mobilenetv2_tv_es64 --data_path ./data/datasets/cityscapes/data --img_resize 768 1536 --rand_crop 512 1024 --output_size 1024 2048 --gpus 0 1 \
+#python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name deeplabv3lite_mobilenetv2_tv_fd --data_path ./data/datasets/cityscapes/data --img_resize 768 1536 --rand_crop 512 1024 --output_size 1024 2048 --gpus 0 1 \
#--pretrained https://download.pytorch.org/models/mobilenet_v2-b0353104.pth
-#### Cityscapes Semantic Segmentation - Training with ResNet50+FPN
-#python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name fpn_pixel2pixel_aspp_resnet50 --data_path ./data/datasets/cityscapes/data --img_resize 768 1536 --rand_crop 512 1024 --output_size 1024 2048 --gpus 0 1 \
+
+
+##--ResNet50 + deeplabv3lite
+#### Cityscapes Semantic Segmentation - Training with ResNet50+DeeplabV3Lite
+#python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name deeplabv3lite_resnet50 --data_path ./data/datasets/cityscapes/data --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 \
+#--pretrained https://download.pytorch.org/models/resnet50-19c8e357.pth
+
+#### Cityscapes Semantic Segmentation - Training with FD-ResNet50+FPN - High Resolution - Low Complexity Model
+#python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name deeplabv3lite_resnet50_fd --data_path ./data/datasets/cityscapes/data --img_resize 768 1536 --rand_crop 512 1024 --output_size 1024 2048 --gpus 0 1 \
#--pretrained https://download.pytorch.org/models/resnet50-19c8e357.pth
+#--ResNet50 encoder with half the channels + deeplabv3lite
+#### Cityscapes Semantic Segmentation - Training with ResNet50_p5+DeeplabV3Lite (ResNet50 encoder with half the channels): deeplabv3lite_resnet50_p5 & deeplabv3lite_resnet50_p5_fd
+#python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name deeplabv3lite_resnet50_p5 --data_path ./data/datasets/cityscapes/data --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 \
+#--pretrained "./data/modelzoo/pretrained/pytorch/imagenet_classification/jacinto_ai/resnet50-0.5_2018-07-23_12-10-23.pth"
+
+
+#-- VOC Segmentation
#### VOC Segmentation - Training with MobileNetV2+DeeplabV3Lite
#python ./scripts/train_segmentation_main.py --dataset_name voc_segmentation --model_name deeplabv3lite_mobilenetv2_tv --data_path ./data/datasets/voc --img_resize 512 512 --output_size 512 512 --gpus 0 1 \
#--pretrained https://download.pytorch.org/models/mobilenet_v2-b0353104.pth
## Validation
## =====================================================================================
#### Validation - Cityscapes Semantic Segmentation - Validation with MobileNetV2+DeeplabV3Lite - populate the pretrained filename in ??
-#python ./scripts/train_segmentation_main.py --evaluate True --dataset_name cityscapes_segmentation --model_name deeplabv3lite_mobilenetv2_tv --data_path ./data/datasets/cityscapes/data --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 \
+#python ./scripts/train_segmentation_main.py --phase validation --dataset_name cityscapes_segmentation --model_name deeplabv3lite_mobilenetv2_tv --data_path ./data/datasets/cityscapes/data --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 \
#--pretrained ??
#### Inference - Cityscapes Semantic Segmentation - Inference with MobileNetV2+DeeplabV3Lite - populate the pretrained filename in ??
#--pretrained ???
#### Validation - VOC Segmentation - Validation with MobileNetV2+DeeplabV3Lite - populate the pretrained filename in ??
-#python ./scripts/train_segmentation_main.py --evaluate True --dataset_name voc_segmentation_measure --model_name deeplabv3lite_mobilenetv2_tv --data_path ./data/datasets/voc --img_resize 512 512 --output_size 512 512 --gpus 0 1
-#--evaluate True --pretrained ???
+#python ./scripts/train_segmentation_main.py --phase validation --dataset_name voc_segmentation_measure --model_name deeplabv3lite_mobilenetv2_tv --data_path ./data/datasets/voc --img_resize 512 512 --output_size 512 512 --gpus 0 1
+#--phase validation --pretrained ???