Fix Conflicts

Author: PkuRainBow

README.md

@@ -16,7 +16,11 @@
 
 - 2021/03/12 The late ACCPET is finally here, our "OCNet: Object context network for scene parsing" has been accepted by IJCV-2021, which consists of two of our previous technical reports: [OCNet](https://arxiv.org/pdf/1809.00916.pdf) and [ISA](https://arxiv.org/abs/1907.12273.pdf). Congratulations to all the co-authors!
 
-- 2021/02/16 Support **pytorch-1.7, mixed-precision, and distributed training**. Based on the [PaddleClas](https://github.com/PaddlePaddle/PaddleClas) ImageNet pretrained weights, we achieve **59.62%** on PASCAL-Context val (**new SOTA**) and **45.20%** on COCO-Stuff val (**new SOTA**). Check [Model Zoo](https://github.com/openseg-group/openseg.pytorch/blob/pytorch-1.7/MODEL_ZOO.md) for more details. **The current released checkpoints are previously trained with Pytorch-0.4.1 and we will release the main checkpoints with SOTA performance trained with Pytorch-1.7 soon.**
+
+- 2021/02/16 Support **pytorch-1.7, mixed-precision, and distributed training**. Based on the [PaddleClas](https://github.com/PaddlePaddle/PaddleClas) ImageNet pretrained weights, we achieve **83.22%** on Cityscapes val, **59.62%** on PASCAL-Context val (**new SOTA**), **45.20%** on COCO-Stuff val (**new SOTA**), **58.21%** on LIP val and  **47.98%** on ADE20K val. Please checkout [branch pytorch-1.7](https://github.com/openseg-group/openseg.pytorch/tree/pytorch-1.7) for more details.
+
+
+- 2020/12/07 [PaddleSeg](https://github.com/PaddlePaddle/PaddleSeg) has supported our ISA and HRNet + OCR. [Jittor](https://github.com/Jittor/segmentation-jittor) also has supported our ResNet-101 + OCR.
 
 - 2020/08/16 [MMSegmentation](https://github.com/open-mmlab/mmsegmentation) has supported our HRNet + OCR.
 

lib/datasets/preprocess/cityscapes/dt_offset_generator.py

@@ -62,7 +62,7 @@ def process(inp):
     depth_map = np.zeros(labelmap.shape, dtype=np.float32)
     dir_map = np.zeros((*labelmap.shape, 2), dtype=np.float32)
 
-    for id in range(1, 20):
+    for id in range(1, len(label_list) + 1):
         labelmap_i = labelmap.copy()
         labelmap_i[labelmap_i != id] = 0
         labelmap_i[labelmap_i == id] = 1

scripts/cityscapes/fcn/run_r_101_d_8_fcn_train.sh

@@ -63,7 +63,7 @@ elif [ "$1"x == "resume"x ]; then
 
 
 elif [ "$1"x == "val"x ]; then
-  ${PYTHON} -u main.py --configs ${CONFIGS} --drop_last y \
+  ${PYTHON} -u main.py --configs ${CONFIGS} --drop_last y --data_dir ${DATA_DIR} \
                        --backbone ${BACKBONE} --model_name ${MODEL_NAME} --checkpoints_name ${CHECKPOINTS_NAME} \
                        --phase test --gpu 0 1 2 3 --resume ./checkpoints/cityscapes/${CHECKPOINTS_NAME}_latest.pth \
                        --loss_type ${LOSS_TYPE} --test_dir ${DATA_DIR}/val/image \

scripts/cityscapes/fcn/run_r_101_d_8_fcn_wo_dsn_train.sh

@@ -65,7 +65,7 @@ elif [ "$1"x == "resume"x ]; then
 
 
 elif [ "$1"x == "val"x ]; then
-  ${PYTHON} -u main.py --configs ${CONFIGS} --drop_last y \
+  ${PYTHON} -u main.py --configs ${CONFIGS} --drop_last y --data_dir ${DATA_DIR} \
                        --backbone ${BACKBONE} --model_name ${MODEL_NAME} --checkpoints_name ${CHECKPOINTS_NAME} \
                        --phase test --gpu 0 1 2 3 --resume ./checkpoints/cityscapes/${CHECKPOINTS_NAME}_latest.pth \
                        --loss_type ${LOSS_TYPE} --test_dir ${DATA_DIR}/val/image \

scripts/cityscapes/hrnet/run_h_48_d_4.sh

@@ -68,7 +68,7 @@ elif [ "$1"x == "resume"x ]; then
 
 
 elif [ "$1"x == "val"x ]; then
-  ${PYTHON} -u main.py --configs ${CONFIGS} --drop_last y \
+  ${PYTHON} -u main.py --configs ${CONFIGS} --drop_last y --data_dir ${DATA_DIR} \
                        --backbone ${BACKBONE} --model_name ${MODEL_NAME} --checkpoints_name ${CHECKPOINTS_NAME} \
                        --phase test --gpu 0 1 2 3 --resume ./checkpoints/cityscapes/${CHECKPOINTS_NAME}_latest.pth \
                        --loss_type ${LOSS_TYPE} --test_dir ${DATA_DIR}/val/image \

scripts/cityscapes/hrnet/run_h_48_d_4_ocr.sh

@@ -69,7 +69,7 @@ elif [ "$1"x == "resume"x ]; then
 
 
 elif [ "$1"x == "val"x ]; then
-  ${PYTHON} -u main.py --configs ${CONFIGS} --drop_last y \
+  ${PYTHON} -u main.py --configs ${CONFIGS} --drop_last y --data_dir ${DATA_DIR} \
                        --backbone ${BACKBONE} --model_name ${MODEL_NAME} --checkpoints_name ${CHECKPOINTS_NAME} \
                        --phase test --gpu 0 1 2 3 --resume ./checkpoints/cityscapes/${CHECKPOINTS_NAME}_latest.pth \
                        --loss_type ${LOSS_TYPE} --test_dir ${DATA_DIR}/val/image \

scripts/cityscapes/hrnet/run_h_48_d_4_ocr_b.sh

@@ -69,7 +69,7 @@ elif [ "$1"x == "resume"x ]; then
 
 
 elif [ "$1"x == "val"x ]; then
-  ${PYTHON} -u main.py --configs ${CONFIGS} --drop_last y \
+  ${PYTHON} -u main.py --configs ${CONFIGS} --drop_last y --data_dir ${DATA_DIR} \
                        --backbone ${BACKBONE} --model_name ${MODEL_NAME} --checkpoints_name ${CHECKPOINTS_NAME} \
                        --phase test --gpu 0 1 2 3 --resume ./checkpoints/cityscapes/${CHECKPOINTS_NAME}_latest.pth \
                        --loss_type ${LOSS_TYPE} --test_dir ${DATA_DIR}/val/image \

scripts/cityscapes/hrnet/run_h_48_d_4_ocr_b_mapillary_trainval_coarse_ohem.sh

@@ -57,7 +57,7 @@ elif [ "$1"x == "resume"x ]; then
 
 
 elif [ "$1"x == "val"x ]; then
-  ${PYTHON} -u main.py --configs ${CONFIGS} --drop_last y --train_batch_size ${BATCH_SIZE} \
+  ${PYTHON} -u main.py --configs ${CONFIGS} --drop_last y --train_batch_size ${BATCH_SIZE} --data_dir ${DATA_DIR} \
                        --backbone ${BACKBONE} --model_name ${MODEL_NAME} --checkpoints_name ${CHECKPOINTS_NAME} \
                        --phase test --gpu 0 --resume ./checkpoints/cityscapes/${CHECKPOINTS_NAME}_latest.pth \
                        --test_dir ${DATA_DIR}/val/image --log_to_file n --out_dir val 2>&1 | tee -a ${LOG_FILE}

scripts/cityscapes/hrnet/run_h_48_d_4_ocr_b_mapillary_trainval_coarse_trainval_ohem.sh

@@ -58,7 +58,7 @@ elif [ "$1"x == "resume"x ]; then
 
 
 elif [ "$1"x == "val"x ]; then
-  ${PYTHON} -u main.py --configs ${CONFIGS} --drop_last y --train_batch_size ${BATCH_SIZE} \
+  ${PYTHON} -u main.py --configs ${CONFIGS} --drop_last y --train_batch_size ${BATCH_SIZE} --data_dir ${DATA_DIR} \
                        --backbone ${BACKBONE} --model_name ${MODEL_NAME} --checkpoints_name ${CHECKPOINTS_NAME} \
                        --phase test --gpu 0 --resume ./checkpoints/cityscapes/${CHECKPOINTS_NAME}_latest.pth \
                        --test_dir ${DATA_DIR}/val/image --log_to_file n --out_dir val 2>&1 | tee -a ${LOG_FILE}

scripts/cityscapes/hrnet/run_h_48_d_4_ocr_b_mapillary_trainval_ohem.sh

@@ -56,7 +56,7 @@ elif [ "$1"x == "resume"x ]; then
 
 
 elif [ "$1"x == "val"x ]; then
-  ${PYTHON} -u main.py --configs ${CONFIGS} --drop_last y --train_batch_size ${BATCH_SIZE} \
+  ${PYTHON} -u main.py --configs ${CONFIGS} --drop_last y --train_batch_size ${BATCH_SIZE} --data_dir ${DATA_DIR} \
                        --backbone ${BACKBONE} --model_name ${MODEL_NAME} --checkpoints_name ${CHECKPOINTS_NAME} \
                        --phase test --gpu 0 --resume ./checkpoints/cityscapes/${CHECKPOINTS_NAME}_latest.pth \
                        --test_dir ${DATA_DIR}/val/image --log_to_file n --out_dir val 2>&1 | tee -a ${LOG_FILE}