diff --git a/run_segmentation.sh b/run_segmentation.sh
index e2b6970e3586c9e8af84318f590e1731a148d15c..d667c734d2d147c76a9c5e60cd6004a58a1d1a4a 100755 (executable)
--- a/run_segmentation.sh
+++ b/run_segmentation.sh
+#################################################################################
+# Copyright (c) 2018-2021, Texas Instruments Incorporated - http://www.ti.com
+# All Rights Reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
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+# * Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
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+# * Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
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+# this software without specific prior written permission.
+#
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+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+#################################################################################
+
# 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.
+## =====================================================================================
+# Extra Flags
+## =====================================================================================
+EXTRA_OPTIONS="" #"--enable_fp16 True"
+
## =====================================================================================
# Models Supported:
## =====================================================================================
+# fpnlite_pixel2pixel_aspp_regnetx800mf: uses regnetx800mf encoder and group_width according to that (even in the decoder)
+# unetlite_pixel2pixel_aspp_regnetx800mf: uses regnetx800mf encoder and group_width according to that (even in the decoder)
+# deeplabv3lite_regnetx800mf: uses regnetx800mf encoder and group_width according to that (even in the decoder)
+#
# deeplabv3lite_mobilenetv2_tv: deeplabv3lite decoder
# fpnlite_pixel2pixel_aspp_mobilenetv2_tv: fpn decoder
# unetlite_pixel2pixel_aspp_mobilenetv2_tv: unet decoder
# note this need specially trained resnet50 pretrained weights
# fpnlite_pixel2pixel_aspp_resnet50_fd: low complexity model - with fast downsampling strategy
-# unetlite_pixel2pixel_aspp_regnetx800mf: uses regnetx800mf encoder and group_width according to that (even in the decoder)
-# fpnlite_pixel2pixel_aspp_regnetx800mf: uses regnetx800mf encoder and group_width according to that (even in the decoder)
-# deeplabv3lite_regnetx800mf: uses regnetx800mf encoder and group_width according to that (even in the decoder)
-
## =====================================================================================
## Training
## =====================================================================================
+#RegNetX based Models
+#Note: to use BGR input, set: --input_channel_reverse True, for RGB input ommit this argument or set it to False.
+#------------------------
+## Cityscapes Semantic Segmentation - Training with RegNetX800MF+DeeplabV3Lite
+#python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name deeplabv3lite_regnetx800mf \
+#--data_path ./data/datasets/cityscapes/data --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 2 3 \
+#--pretrained https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906036/RegNetX-800MF_dds_8gpu.pyth --batch_size 16 \
+#--optimizer sgd --scheduler cosine --lr 1e-1 ${EXTRA_OPTIONS}
+
+### Cityscapes Semantic Segmentation - Training with RegNetX800MF+UNetLite
+#python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name unetlite_pixel2pixel_aspp_regnetx800mf \
+#--data_path ./data/datasets/cityscapes/data --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 2 3 \
+#--pretrained https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906036/RegNetX-800MF_dds_8gpu.pyth --batch_size 16 \
+#--optimizer sgd --scheduler cosine --lr 1e-1 ${EXTRA_OPTIONS}
+
+### Cityscapes Semantic Segmentation - Training Training with RegNetX400MF+FPNLite
+#python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name fpnlite_pixel2pixel_aspp_regnetx400mf \
+#--data_path ./data/datasets/cityscapes/data --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 2 3 \
+#--pretrained https://dl.fbaipublicfiles.com/pycls/dds_baselines/160905967/RegNetX-400MF_dds_8gpu.pyth --batch_size 16 \
+#--optimizer sgd --scheduler cosine --lr 1e-1 ${EXTRA_OPTIONS}
+
+### Cityscapes Semantic Segmentation - Training with RegNetX800MF+FPNLite
+#python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name fpnlite_pixel2pixel_aspp_regnetx800mf \
+#--data_path ./data/datasets/cityscapes/data --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 2 3 \
+#--pretrained https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906036/RegNetX-800MF_dds_8gpu.pyth --batch_size 16 \
+#--optimizer sgd --scheduler cosine --lr 1e-1 ${EXTRA_OPTIONS}
+
+## Higher Resolution - 1024x512 - regnetx1.6gf
+#python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name fpnlite_pixel2pixel_aspp_regnetx1p6gf \
+#--data_path ./data/datasets/cityscapes/data --img_resize 512 1024 --output_size 1024 2048 --gpus 0 1 2 3 \
+#--pretrained https://dl.fbaipublicfiles.com/pycls/dds_baselines/160990626/RegNetX-1.6GF_dds_8gpu.pyth --batch_size 16 \
+#--optimizer sgd --scheduler cosine --lr 1e-1 ${EXTRA_OPTIONS}
+#
+## Higher Resolution - 1024x512 - regnetx3.2gf
+#python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name fpnlite_pixel2pixel_aspp_regnetx3p2gf \
+#--data_path ./data/datasets/cityscapes/data --img_resize 512 1024 --output_size 1024 2048 --gpus 0 1 2 3 \
+#--pretrained https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906139/RegNetX-3.2GF_dds_8gpu.pyth --batch_size 16 \
+#--optimizer sgd --scheduler cosine --lr 1e-1 ${EXTRA_OPTIONS}
+
+## Higher Resolution - 1536x768 - regnetx400mf
+#python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name fpnlite_pixel2pixel_aspp_regnetx400mf \
+#--data_path ./data/datasets/cityscapes/data --img_resize 768 1536 --rand_crop 512 1024 --output_size 1024 2048 --gpus 0 1 2 3 \
+#--pretrained https://dl.fbaipublicfiles.com/pycls/dds_baselines/160905967/RegNetX-400MF_dds_8gpu.pyth --batch_size 16 \
+#--optimizer sgd --scheduler cosine --lr 1e-1 ${EXTRA_OPTIONS}
+
+## Higher Resolution - 1536x768 - regnetx3.2gf
+#python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name fpnlite_pixel2pixel_aspp_regnetx3p2gf \
+#--data_path ./data/datasets/cityscapes/data --img_resize 768 1536 --rand_crop 512 1024 --output_size 1024 2048 --gpus 0 1 2 3 \
+#--pretrained https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906139/RegNetX-3.2GF_dds_8gpu.pyth --batch_size 16 \
+#--optimizer sgd --scheduler cosine --lr 1e-1 ${EXTRA_OPTIONS}
+
+
+
#MobileNetV2 based Models
#------------------------
#### Cityscapes Semantic Segmentation - Training with MobileNetV2+DeeplabV3Lite
#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
+#--pretrained https://download.pytorch.org/models/mobilenet_v2-b0353104.pth ${EXTRA_OPTIONS}
#### Cityscapes Semantic Segmentation - Training with MobileNetV2+FPNLite
#python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name fpnlite_pixel2pixel_aspp_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
+#--pretrained https://download.pytorch.org/models/mobilenet_v2-b0353104.pth ${EXTRA_OPTIONS}
#### Cityscapes Semantic Segmentation - Training with MobileNetV2+UNetLite
#python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name unetlite_pixel2pixel_aspp_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
+#--pretrained https://download.pytorch.org/models/mobilenet_v2-b0353104.pth ${EXTRA_OPTIONS}
#Higher Resolution
#------------------------
#### Cityscapes Semantic Segmentation - Training with MobileNetV2+DeeplabV3Lite, Higher Resolution
-#python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name deeplabv3lite_mobilenetv2_tv \
+#python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name fpnlite_pixel2pixel_aspp_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
+#--pretrained https://download.pytorch.org/models/mobilenet_v2-b0353104.pth ${EXTRA_OPTIONS}
#### 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 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
-
-
-
-#RegNetX based Models
-#------------------------
-## Cityscapes Semantic Segmentation - Training with RegNetX800MF+DeeplabV3Lite
-#Note: to use BGR input, set: --input_channel_reverse True, for RGB input ommit this argument or set it to False.
-#python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name deeplabv3lite_regnetx800mf \
-#--data_path ./data/datasets/cityscapes/data --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 \
-#--pretrained https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906036/RegNetX-800MF_dds_8gpu.pyth
-
-### Cityscapes Semantic Segmentation - Training with RegNetX800MF+FPNLite
-#Note: to use BGR input, set: --input_channel_reverse True, for RGB input ommit this argument or set it to False.
-#python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name fpnlite_pixel2pixel_aspp_regnetx800mf \
-#--data_path ./data/datasets/cityscapes/data --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 \
-#--pretrained https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906036/RegNetX-800MF_dds_8gpu.pyth
-
-### Cityscapes Semantic Segmentation - Training with RegNetX800MF+UNetLite
-#Note: to use BGR input, set: --input_channel_reverse True, for RGB input ommit this argument or set it to False.
-#python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name unetlite_pixel2pixel_aspp_regnetx800mf \
-#--data_path ./data/datasets/cityscapes/data --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 \
-#--pretrained https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906036/RegNetX-800MF_dds_8gpu.pyth
+#--pretrained https://download.pytorch.org/models/mobilenet_v2-b0353104.pth ${EXTRA_OPTIONS}
#ResNet50 based Models
#### 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
+#--pretrained https://download.pytorch.org/models/resnet50-19c8e357.pth ${EXTRA_OPTIONS}
#### 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
+#--pretrained https://download.pytorch.org/models/resnet50-19c8e357.pth ${EXTRA_OPTIONS}
#### Cityscapes Semantic Segmentation - Training with ResNet50_p5+DeeplabV3Lite (ResNet50 encoder with half the channels):
#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"
+#--pretrained "./data/modelzoo/pretrained/pytorch/imagenet_classification/jacinto_ai/resnet50-0.5_2018-07-23_12-10-23.pth" ${EXTRA_OPTIONS}
+
+# ADE20K Segmentation training
+#------------------------
+#python ./scripts/train_segmentation_main.py --dataset_name ade20k_seg_class32 --model_name deeplabv3lite_mobilenetv2_tv \
+#--data_path ./data/datasets/ADEChallengeData2016 --img_resize 512 512 --output_size 768 768 --gpus 0 1 2 3 \
+#--pretrained https://download.pytorch.org/models/mobilenet_v2-b0353104.pth \
+#--weight_decay 4e-5 --batch_size 32 --epochs 60 --milestones 30 45 --optimizer sgd --scheduler cosine --lr 5e-2 ${EXTRA_OPTIONS}
+#python ./scripts/train_segmentation_main.py --dataset_name ade20k_seg_class32 --model_name fpnlite_pixel2pixel_aspp_mobilenetv2_tv \
+#--data_path ./data/datasets/ADEChallengeData2016 --img_resize 512 512 --output_size 768 768 --gpus 0 1 2 3 \
+#--pretrained https://download.pytorch.org/models/mobilenet_v2-b0353104.pth \
+#--weight_decay 4e-5 --batch_size 32 --epochs 60 --milestones 30 45 --optimizer sgd --scheduler cosine --lr 5e-2 ${EXTRA_OPTIONS}
+#python ./scripts/train_segmentation_main.py --dataset_name ade20k_seg_class32 --model_name unetlite_pixel2pixel_aspp_mobilenetv2_tv \
+#--data_path ./data/datasets/ADEChallengeData2016 --img_resize 512 512 --output_size 768 768 --gpus 0 1 2 3 \
+#--pretrained https://download.pytorch.org/models/mobilenet_v2-b0353104.pth \
+#--weight_decay 4e-5 --batch_size 32 --epochs 60 --milestones 30 45 --optimizer sgd --scheduler cosine --lr 5e-2 ${EXTRA_OPTIONS}
-#-- 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
+#python ./scripts/train_segmentation_main.py --dataset_name ade20k_seg_class32 --model_name fpnlite_pixel2pixel_aspp_regnetx400mf \
+#--data_path ./data/datasets/ADEChallengeData2016 --img_resize 384 384 --output_size 768 768 --gpus 0 1 2 3 \
+#--pretrained https://dl.fbaipublicfiles.com/pycls/dds_baselines/160905967/RegNetX-400MF_dds_8gpu.pyth \
+#--weight_decay 4e-5 --batch_size 32 --epochs 60 --milestones 30 45 --optimizer sgd --scheduler cosine --lr 1e-1 ${EXTRA_OPTIONS}
+
+#python ./scripts/train_segmentation_main.py --dataset_name ade20k_seg_class32 --model_name fpnlite_pixel2pixel_aspp_regnetx800mf \
+#--data_path ./data/datasets/ADEChallengeData2016 --img_resize 512 512 --output_size 768 768 --gpus 0 1 2 3 \
+#--pretrained https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906036/RegNetX-800MF_dds_8gpu.pyth \
+#--weight_decay 4e-5 --batch_size 32 --epochs 60 --milestones 30 45 --optimizer sgd --scheduler cosine --lr 1e-1 ${EXTRA_OPTIONS}
+
+#python ./scripts/train_segmentation_main.py --dataset_name ade20k_seg_class32 --model_name fpnlite_pixel2pixel_aspp_regnetx1p6gf \
+#--data_path ./data/datasets/ADEChallengeData2016 --img_resize 512 512 --output_size 768 768 --gpus 0 1 2 3 \
+#--pretrained https://dl.fbaipublicfiles.com/pycls/dds_baselines/160990626/RegNetX-1.6GF_dds_8gpu.pyth \
+#--weight_decay 4e-5 --batch_size 32 --epochs 60 --milestones 30 45 --optimizer sgd --scheduler cosine --lr 1e-1 ${EXTRA_OPTIONS}
+
+#python ./scripts/train_segmentation_main.py --dataset_name ade20k_seg_class32 --model_name fpnlite_pixel2pixel_aspp_regnetx3p2gf \
+#--data_path ./data/datasets/ADEChallengeData2016 --img_resize 256 256 --output_size 768 768 --gpus 0 1 2 3 \
+#--pretrained https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906139/RegNetX-3.2GF_dds_8gpu.pyth \
+#--weight_decay 4e-5 --batch_size 32 --epochs 60 --milestones 30 45 --optimizer sgd --scheduler cosine --lr 1e-1 ${EXTRA_OPTIONS}
## =====================================================================================
## Validation
## =====================================================================================
-#### Validation - Cityscapes Semantic Segmentation - Validation with MobileNetV2+DeeplabV3Lite - populate the pretrained filename in ??
+##### Validation - Cityscapes Semantic Segmentation - Validation with RegNetX+DeeplabV3Lite - replace the pretrained checkpoint with what you have.
+#python ./scripts/train_segmentation_main.py --phase validation --dataset_name cityscapes_segmentation --model_name deeplabv3lite_regnetx800mf \
+#--data_path ./data/datasets/cityscapes/data --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 \
+#--pretrained ./data/modelzoo/jai-modelzoo/pytorch/vision/segmentation/jai-devkit/cityscapes/deeplabv3lite_regnetx800mf_768x384_768x384_2020-08-08_18-47-29_checkpoint.pth
+#
+##### Validation - Cityscapes Semantic Segmentation - Inference with RegNetX+FPNLite - replace the pretrained checkpoint with what you have.
+#python ./scripts/train_segmentation_main.py --phase validation --dataset_name cityscapes_segmentation --model_name fpnlite_pixel2pixel_aspp_regnetx800mf \
+#--data_path ./data/datasets/cityscapes/data --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 \
+#--pretrained ./data/modelzoo/jai-modelzoo/pytorch/vision/segmentation/jai-devkit/cityscapes/fpnlite_aspp_regnetx800mf_768x384_2020-08-04_21-14-35_checkpoint.pth
+#
+##### Validation - VOC Segmentation - Validation with RegNetX+UNetLite - replace the pretrained checkpoint with what you have.
+#python ./scripts/train_segmentation_main.py --phase validation --dataset_name cityscapes_segmentation --model_name unetlite_pixel2pixel_aspp_mobilenetv2_tv \
+#--data_path ./data/datasets/cityscapes/data --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 \
+#--pretrained ./data/modelzoo/jai-modelzoo/pytorch/vision/segmentation/jai-devkit/cityscapes/unetlite_aspp_regnetx800mf_768x384_768x384_2020-08-08_18-47-54_checkpoint.pth
+#
+##### Validation - Cityscapes Semantic Segmentation - Validation with MobileNetV2+DeeplabV3Lite - replace the pretrained checkpoint with what you have.
#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 ??
+#--pretrained ./data/modelzoo/jai-modelzoo/pytorch/vision/segmentation/jai-devkit/cityscapes/deeplabv3lite_mobilenetv2_tv_768x384_2019-06-26-08-59-32_checkpoint.pth
+#
+##### Validation - Cityscapes Semantic Segmentation - Inference with MobileNetV2+FPNLite - replace the pretrained checkpoint with what you have.
+#python ./scripts/train_segmentation_main.py --phase validation --dataset_name cityscapes_segmentation --model_name fpnlite_pixel2pixel_aspp_mobilenetv2_tv \
+#--data_path ./data/datasets/cityscapes/data --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 \
+#--pretrained ./data/modelzoo/jai-modelzoo/pytorch/vision/segmentation/jai-devkit/cityscapes/fpnlite_aspp_mobilenetv2_tv_768x384_2020-01-20_13-57-01_checkpoint.pth
+#
+##### Validation - VOC Segmentation - Validation with MobileNetV2+UNetLite - replace the pretrained checkpoint with what you have.
+#python ./scripts/train_segmentation_main.py --phase validation --dataset_name cityscapes_segmentation --model_name unetlite_pixel2pixel_aspp_mobilenetv2_tv \
+#--data_path ./data/datasets/cityscapes/data --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 \
+#--pretrained ./data/modelzoo/jai-modelzoo/pytorch/vision/segmentation/jai-devkit/cityscapes/unetlite_aspp_mobilenetv2_tv_768x384_2020-01-29_16-43-40_checkpoint.pth
-#### Inference - Cityscapes Semantic Segmentation - Inference with MobileNetV2+DeeplabV3Lite - populate the pretrained filename in ??
-#python ./scripts/infer_segmentation_main.py --dataset_name cityscapes_segmentation_measure --model_name deeplabv3lite_mobilenetv2_tv \
+
+## =====================================================================================
+## Inference
+## =====================================================================================
+##### Inference - Cityscapes Semantic Segmentation - Inference with RegNetX+DeeplabV3Lite - replace the pretrained checkpoint with what you have.
+#python ./scripts/infer_segmentation_main.py --dataset_name cityscapes_segmentation_measure --model_name fpnlite_pixel2pixel_aspp_regnetx800mf \
#--data_path ./data/datasets/cityscapes/data --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 \
-#--pretrained ???
+#--pretrained ./data/modelzoo/jai-modelzoo/pytorch/vision/segmentation/jai-devkit/cityscapes/fpnlite_aspp_regnetx800mf_768x384_2020-08-04_21-14-35_checkpoint.pth
+
+
+## =====================================================================================
+# VOC Segmentation Training
+## =====================================================================================
+#### VOC Segmentation - Training with RegNetX+DeeplabV3Lite
+#python ./scripts/train_segmentation_main.py --dataset_name voc_segmentation --model_name fpnlite_pixel2pixel_aspp_regnetx800mf \
+#--data_path ./data/datasets/voc --img_resize 512 512 --output_size 512 512 --gpus 0 1 \
+#--phase validation --pretrained https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906036/RegNetX-800MF_dds_8gpu.pyth
+
+
+## =====================================================================================
+# COCO 80 class Segmentation Training
+## =====================================================================================
+# compute accuracy at resized resolution - to speedup training
+#python ./scripts/train_segmentation_main.py --dataset_name coco_segmentation --model_name fpnlite_pixel2pixel_aspp_regnetx800mf \
+#--data_path ./data/datasets/coco --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 2 3 \
+#--pretrained https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906036/RegNetX-800MF_dds_8gpu.pyth --batch_size 16 \
+#--optimizer sgd --scheduler cosine --lr 1e-1 --epochs 30 ${EXTRA_OPTIONS}
+
+
+## =====================================================================================
+# COCO 21 class Segmentation Training
+## =====================================================================================
+# compute accuracy at resized resolution - to speedup training
+#python ./scripts/train_segmentation_main.py --dataset_name coco_seg21 --model_name deeplabv3lite_mobilenetv2_tv \
+#--data_path ./data/datasets/coco --img_resize 512 512 --output_size 1024 1024 --gpus 0 1 2 3 \
+#--pretrained https://download.pytorch.org/models/mobilenet_v2-b0353104.pth \
+#--weight_decay 4e-5 --batch_size 32 --epochs 60 --milestones 30 45 --optimizer sgd --scheduler cosine --lr 5e-2 \
+#--interpolation 1 ${EXTRA_OPTIONS}
+
+#python ./scripts/train_segmentation_main.py --dataset_name coco_seg21 --model_name fpnlite_pixel2pixel_aspp_mobilenetv2_tv \
+#--data_path ./data/datasets/coco --img_resize 512 512 --output_size 1024 1024 --gpus 0 1 2 3 \
+#--pretrained https://download.pytorch.org/models/mobilenet_v2-b0353104.pth \
+#--weight_decay 4e-5 --batch_size 32 --epochs 60 --milestones 30 45 --optimizer sgd --scheduler cosine --lr 5e-2 \
+#--interpolation 1 ${EXTRA_OPTIONS}
+
+#python ./scripts/train_segmentation_main.py --dataset_name coco_seg21 --model_name unetlite_pixel2pixel_aspp_mobilenetv2_tv \
+#--data_path ./data/datasets/coco --img_resize 512 512 --output_size 1024 1024 --gpus 0 1 2 3 \
+#--pretrained https://download.pytorch.org/models/mobilenet_v2-b0353104.pth \
+#--weight_decay 4e-5 --batch_size 32 --epochs 60 --milestones 30 45 --optimizer sgd --scheduler cosine --lr 5e-2 \
+#--interpolation 1 ${EXTRA_OPTIONS}
+
+#python ./scripts/train_segmentation_main.py --dataset_name coco_seg21 --model_name fpnlite_pixel2pixel_aspp_regnetx400mf \
+#--data_path ./data/datasets/coco --img_resize 384 384 --output_size 1024 1024 --gpus 0 1 2 3 \
+#--pretrained https://dl.fbaipublicfiles.com/pycls/dds_baselines/160905967/RegNetX-400MF_dds_8gpu.pyth \
+#--weight_decay 4e-5 --batch_size 32 --epochs 60 --milestones 30 45 --optimizer sgd --scheduler cosine --lr 1e-1 \
+#--interpolation 1 ${EXTRA_OPTIONS}
+
+#python ./scripts/train_segmentation_main.py --dataset_name coco_seg21 --model_name fpnlite_pixel2pixel_aspp_regnetx800mf \
+#--data_path ./data/datasets/coco --img_resize 512 512 --output_size 1024 1024 --gpus 0 1 2 3 \
+#--pretrained https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906036/RegNetX-800MF_dds_8gpu.pyth \
+#--weight_decay 4e-5 --batch_size 32 --epochs 60 --milestones 30 45 --optimizer sgd --scheduler cosine --lr 1e-1 \
+#--interpolation 1 ${EXTRA_OPTIONS}
-#### Validation - VOC Segmentation - Validation with MobileNetV2+DeeplabV3Lite - populate the pretrained filename in ??
-#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 ???
+#python ./scripts/train_segmentation_main.py --dataset_name coco_seg21 --model_name fpnlite_pixel2pixel_aspp_regnetx1p6gf \
+#--data_path ./data/datasets/coco --img_resize 768 768 --output_size 1024 1024 --gpus 0 1 2 3 \
+#--pretrained https://dl.fbaipublicfiles.com/pycls/dds_baselines/160990626/RegNetX-1.6GF_dds_8gpu.pyth \
+#--weight_decay 4e-5 --batch_size 16 --epochs 60 --milestones 30 45 --optimizer sgd --scheduler cosine --lr 1e-1 \
+#--interpolation 1 ${EXTRA_OPTIONS}
+#python ./scripts/train_segmentation_main.py --dataset_name coco_seg21 --model_name fpnlite_pixel2pixel_aspp_regnetx3p2gf \
+#--data_path ./data/datasets/coco --img_resize 1024 1024 --rand_crop 768 768 --output_size 1024 1024 --gpus 0 1 2 3 \
+#--pretrained https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906139/RegNetX-3.2GF_dds_8gpu.pyth \
+#--weight_decay 4e-5 --batch_size 16 --epochs 60 --milestones 30 45 --optimizer sgd --scheduler cosine --lr 1e-1 \
+#--interpolation 1 ${EXTRA_OPTIONS}