diff --git a/run_quantization.sh b/run_quantization.sh
index 63ea151c5740ccd34443779744b50e745e90214a..f9882afd0d9dbb4caf7d63f9f9100b6afa8d0cb0 100755 (executable)
--- a/run_quantization.sh
+++ b/run_quantization.sh
# Quantization
## =====================================================================================
-## Trained Quantization
+## Quantization Aware Training
## =====================================================================================
#
-#### Image Classification - Trained Quantization - MobileNetV2
+#### Image Classification - Quantization Aware Training - MobileNetV2
#python ./scripts/train_classification_main.py --dataset_name image_folder_classification --model_name mobilenetv2_tv_x1 --data_path ./data/datasets/image_folder_classification \
#--pretrained https://download.pytorch.org/models/mobilenet_v2-b0353104.pth \
#--batch_size 64 --quantize True --epochs 25 --epoch_size 1000 --lr 1e-5 --evaluate_start False
#
#
-#### Image Classification - Trained Quantization - MobileNetV2(Shicai) - a TOUGH MobileNetV2 pretrained model
+#### Image Classification - Quantization Aware Training - MobileNetV2(Shicai) - a TOUGH MobileNetV2 pretrained model
#python ./scripts/train_classification_main.py --dataset_name image_folder_classification --model_name mobilenetv2_shicai_x1 --data_path ./data/datasets/image_folder_classification \
-#--pretrained ./data/modelzoo/pretrained/pytorch/others/shicai/MobileNet-Caffe/mobilenetv2_shicai_rgb.tar \
+#--pretrained ./data/modelzoo/experimental/pytorch/others/shicai/MobileNet-Caffe/mobilenetv2_shicai_rgb.tar \
#--batch_size 64 --quantize True --epochs 25 --epoch_size 1000 --lr 1e-5 --evaluate_start False
#
#
-#### Semantic Segmentation - Trained Quantization for MobileNetV2+DeeplabV3Lite
+#### Semantic Segmentation - Quantization Aware Training for 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 ./data/modelzoo/semantic_segmentation/cityscapes/deeplabv3lite-mobilenetv2/cityscapes_segmentation_deeplabv3lite-mobilenetv2_2019-06-26-08-59-32.pth \
-#--batch_size 12 --quantize True --epochs 150 --lr 1e-5 --evaluate_start False
-
+#--pretrained ./data/modelzoo/pytorch/semantic_seg/cityscapes/jacinto_ai/deeplabv3lite_mobilenetv2_tv_768x384_best.pth.tar \
+#--batch_size 6 --quantize True --epochs 150 --lr 1e-5 --evaluate_start False
+#
+#
+#### Semantic Segmentation - Quantization Aware Training for 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 ./data/modelzoo/pytorch/semantic_seg/cityscapes/jacinto_ai/unet_aspp_mobilenetv2_tv_768x384_best.pth.tar \
+#--batch_size 6 --quantize True --epochs 150 --lr 1e-5 --evaluate_start False
## =====================================================================================
## Acuracy Evaluation with Post Training Quantization - cannot save quantized model - only accuracy evaluation
#### Image Classification - Accuracy Estimation with Post Training Quantization - A TOUGH MobileNetV2 pretrained model
#python ./scripts/train_classification_main.py --phase validation --dataset_name image_folder_classification --model_name mobilenetv2_shicai_x1 --data_path ./data/datasets/image_folder_classification \
-#--pretrained ./data/modelzoo/pretrained/pytorch/others/shicai/MobileNet-Caffe/mobilenetv2_shicai_rgb.tar \
+#--pretrained ./data/modelzoo/pytorch/semantic_seg/cityscapes/jacinto_ai/deeplabv3lite_mobilenetv2_tv_768x384_best.pth \
#--batch_size 64 --quantize True
-#### Semantic Segmentation - Accuracy Estimation with Post Training Quantization
+#### Semantic Segmentation - Accuracy Estimation with Post Training Quantization - MobileNetV2+DeeplabV3Lite
#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 './data/modelzoo/semantic_segmentation/cityscapes/deeplabv3lite-mobilenetv2/cityscapes_segmentation_deeplabv3lite-mobilenetv2_2019-06-26-08-59-32.pth' \
+#--pretrained './data/modelzoo/pytorch/semantic_seg/cityscapes/jacinto_ai/deeplabv3lite_mobilenetv2_tv_768x384_best.pth' \
+#--batch_size 1 --quantize True
+
+#### Semantic Segmentation - Accuracy Estimation with Post Training Quantization - MobileNetV2+UNetLite
+#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/pytorch/semantic_seg/cityscapes/jacinto_ai/unet_aspp_mobilenetv2_tv_768x384_best.pth \
#--batch_size 1 --quantize True
#### Image Classification - Post Training Calibration & Quantization - ResNet50
#python ./scripts/train_classification_main.py --phase calibration --dataset_name image_folder_classification --model_name resnet50_x1 --data_path ./data/datasets/image_folder_classification \
#--pretrained https://download.pytorch.org/models/resnet50-19c8e357.pth \
-#--batch_size 64 --quantize True --epochs 1 --epoch_size 100
+#--batch_size 64 --quantize True --epochs 1 --epoch_size 100 --evaluate_start False
#
#
#### Image Classification - Post Training Calibration & Quantization - MobileNetV2
#python ./scripts/train_classification_main.py --phase calibration --dataset_name image_folder_classification --model_name mobilenetv2_tv_x1 --data_path ./data/datasets/image_folder_classification \
#--pretrained https://download.pytorch.org/models/mobilenet_v2-b0353104.pth \
-#--batch_size 64 --quantize True --epochs 1 --epoch_size 100
+#--batch_size 64 --quantize True --epochs 1 --epoch_size 100 --evaluate_start False
#
#
#### Image Classification - Post Training Calibration & Quantization for a TOUGH MobileNetV2 pretrained model
#python ./scripts/train_classification_main.py --phase calibration --dataset_name image_folder_classification --model_name mobilenetv2_shicai_x1 --data_path ./data/datasets/image_folder_classification \
-#--pretrained ./data/modelzoo/pretrained/pytorch/others/shicai/MobileNet-Caffe/mobilenetv2_shicai_rgb.tar \
-#--batch_size 64 --quantize True --epochs 1 --epoch_size 100
+#--pretrained ./data/modelzoo/pytorch/image_classification/imagenet1k/shicai/mobilenetv2_shicai_rgb.pth \
+#--batch_size 64 --quantize True --epochs 1 --epoch_size 100 --evaluate_start False
#
#
-#### Semantic Segmentation - Post Training Calibration & Quantization for MobileNetV2+DeeplabV3Lite
+### Semantic Segmentation - Post Training Calibration & Quantization for MobileNetV2+DeeplabV3Lite
#python ./scripts/train_segmentation_main.py --phase calibration --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 ./data/modelzoo/semantic_segmentation/cityscapes/deeplabv3lite-mobilenetv2/cityscapes_segmentation_deeplabv3lite-mobilenetv2_2019-06-26-08-59-32.pth \
-#--batch_size 12 --quantize True --epochs 1 --epoch_size 100
-
-
+#--pretrained ./data/modelzoo/pytorch/semantic_seg/cityscapes/jacinto_ai/deeplabv3lite_mobilenetv2_tv_768x384_best.pth \
+#--batch_size 12 --quantize True --epochs 1 --epoch_size 100 --evaluate_start False
+#
+#
+### Semantic Segmentation - Post Training Calibration & Quantization for MobileNetV2+UNetLite
+#python ./scripts/train_segmentation_main.py --phase calibration --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/pytorch/semantic_seg/cityscapes/jacinto_ai/unet_aspp_mobilenetv2_tv_768x384_best.pth \
+#--batch_size 12 --quantize True --epochs 1 --epoch_size 100 --evaluate_start False
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