I trained SFNet with default setting of torchcv/scripts/seg/cityscapes/run_sfnet_res18_cityscapes.sh except for NGPUS (I changed NGPUS from 8 to 4, and --train_batch_size from 2 to 4). But I got about 73% of mIoU for single scale inference. It may be because pretrained model, 3x3resnet18-imagenet.pth, is not given. Although I also trained SFNet (ResNet-101) using pretrained model, 3x3resnet101-imagenet.pth, given by this repository, I got about 78% of mIoU for multi-scale inference.

How can I reproduce the paper results?

作者你好，看资料应该是北京大学的，中文肯定是好的，我就用中文了。 我想用yolov3来跑coco2017的数据，看项目的结构需要写一个scripts文件，我也写了scripts文件。但是在加了之后开始训练的时候出现了错误。 scripts文件 #!/usr/bin/env bash

nvidia-smi PYTHON="python"

export PYTHONPATH="/home/dezheng/work/torchcv":$PYTHONPATH

cd ../../../

DATA_DIR="/home/dezheng/work/torchcv/data/torchcv_coco" MODEL_NAME="yolov3" LOSS_TYPE="yolov3loss" CHECKPOINTS_NAME="yolov3_darknet_coco_det"$2 PRETRAINED_MODEL="./pretrained_models/yolov3_darknet_caffe_pretrained.pth" HYPES_FILE='hypes/det/coco/yolov3_darknet_coco_det.json'

LOG_DIR="./log/det/coco/" LOG_FILE="${LOG_DIR}${CHECKPOINTS_NAME}.log"

if [[ ! -d ${LOG_DIR} ]]; then echo ${LOG_DIR}" not exists!!!" mkdir -p ${LOG_DIR} fi

if [[ "$1"x == "train"x ]]; then ${PYTHON} -u main.py --hypes ${HYPES_FILE} --phase train --log_to_file n --gpu 0 --cudnn n
--data_dir ${DATA_DIR} --loss_type ${LOSS_TYPE} --model_name ${MODEL_NAME}
--checkpoints_name ${CHECKPOINTS_NAME} --pretrained ${PRETRAINED_MODEL} 2>&1 | tee ${LOG_FILE}

最后报错如下： Traceback (most recent call last): File "main.py", line 186, in runner = method_selector.select_det_method() File "/home/dezheng/work/torchcv/methods/method_selector.py", line 93, in select_det_method return DET_METHOD_DICTkey File "/home/dezheng/work/torchcv/methods/det/yolov3.py", line 35, in init self.det_data_loader = DataLoader(configer) File "/home/dezheng/work/torchcv/datasets/det/data_loader.py", line 23, in init self.aug_train_transform = pil_aug_trans.PILAugCompose(self.configer, split='train') File "/home/dezheng/work/torchcv/datasets/tools/pil_aug_transforms.py", line 1187, in init self.transforms[trans] = PIL_AUGMENTATIONS_DICTtrans TypeError: init() got an unexpected keyword argument 'center_jitter' 这个怎么改呢？

Hi, thanks for your great work and contribution. I'm interested in your work SFNet and trained a model (SFNet with ResNet18) and achieved a comparable validation result. However, I'm confused about how to reach the inference speed presented in your paper (18/26FPS) with this codebase. I inserted a timer to segmentation_test.py and obtain the inference speed of SFNet (single scale, ResNet18): 0.143s/image, which is much slower than that mentioned in the paper. Could you provide some clues to obtain the inference speed presented in the paper? (without TensorRT)

Traceback (most recent call last): File "main.py", line 185, in Controller.test(runner) File "/home/jerry/torchcv/runner/tools/controller.py", line 81, in test runner.test(test_dir, out_dir) File "/home/jerry/torchcv/runner/seg/fcn_segmentor_test.py", line 48, in test total_logits = self.ss_test(data_dict) File "/home/jerry/torchcv/runner/seg/fcn_segmentor_test.py", line 85, in ss_test data_dict = self.blob_helper.get_blob(in_data_dict, scale=1.0) File "/home/jerry/torchcv/runner/tools/blob_helper.py", line 25, in get_blob for image, meta in zip(DCHelper.tolist(data_dict['img']), DCHelper.tolist(data_dict['meta'])): File "/home/jerry/torchcv/tools/helper/dc_helper.py", line 19, in tolist assert isinstance(dc, DataContainer), type(dc) AssertionError: <class 'torch.Tensor'>

When I run test on my own dataset, the error is thrown.

Hi, I want try to train denseaspp_model on your code. Can I just change the .sh? Or, could you have the script about denseaspp on cityscapes? Thank you.

When I try to download trained models of DeepLabv3, there is a 404 error. Is there anything wrong to the hyperlink? Hope this problem to be corrected, THX :D

Hi, donnyyou. After downloading the pretrained .pth ssd512 model, I run bash run_ssd512_vgg16_voc_det.sh train tag Then I got:

2019-05-23 10:23:17,616 INFO [vgg512_ssd.py, 60] Loading pretrained model:./pretrained_models/ssd_vgg512_voc_0.808.pth 2019-05-23 10:23:20,996 INFO [vgg512_ssd.py, 63] Pretrained Keys: dict_keys(['config_dict', 'state_dict']) 2019-05-23 10:23:20,997 INFO [vgg512_ssd.py, 65] Model Keys: odict_keys(['features.0.weight', 'features.0.bias', 'features.2.weight', 'features.2.bias', 'features.5.weight', 'features.5.bias', 'features.7.weight', 'features.7.bias', 'features.10.weight', 'features.10.bias', 'features.12.weight', 'features.12.bias', 'features.14.weight', 'features.14.bias', 'features.17.weight', 'features.17.bias', 'features.19.weight', 'features.19.bias', 'features.21.weight', 'features.21.bias', 'features.24.weight', 'features.24.bias', 'features.26.weight', 'features.26.bias', 'features.28.weight', 'features.28.bias', 'features.31.weight', 'features.31.bias', 'features.33.weight', 'features.33.bias']) 2019-05-23 10:23:20,997 INFO [vgg512_ssd.py, 75] Matched Keys: dict_keys([]) 2019-05-23 10:23:21,067 ERROR [configer.py, 69] ssd_detection_layer.py, 16 KeyError: ('gt', 'num_anchor_list').

Since your ssd script is 404 not found, I modify the faster rcnn script for ssd as below:

#!/usr/bin/env bash

#check the enviroment info nvidia-smi PYTHON="python"

export PYTHONPATH="/home/ruijin/Work/python/torchcv-master":$PYTHONPATH

cd ../../../

DATA_DIR="/home/donny/DataSet/VOC07_DET" MODEL_NAME="vgg512_ssd" LOSS_TYPE="ssd_multibox_loss" CHECKPOINTS_NAME="ssd_vgg16_voc_det"$2 PRETRAINED_MODEL="./pretrained_models/ssd_vgg512_voc_0.808.pth" HYPES_FILE='hypes/det/voc/ssd512_vgg16_voc_det.json'

LOG_DIR="./log/det/voc/" LOG_FILE="${LOG_DIR}${CHECKPOINTS_NAME}.log"

if [[ ! -d ${LOG_DIR} ]]; then echo ${LOG_DIR}" not exists!!!" mkdir -p ${LOG_DIR} fi

if [[ "$1"x == "train"x ]]; then ${PYTHON} -u main.py --hypes ${HYPES_FILE} --phase train --log_to_file n --gpu 0 --cudnn n
--data_dir ${DATA_DIR} --loss_type ${LOSS_TYPE} --model_name ${MODEL_NAME}
--checkpoints_name ${CHECKPOINTS_NAME} --pretrained ${PRETRAINED_MODEL} 2>&1 | tee ${LOG_FILE}

elif [[ "$1"x == "resume"x ]]; then ${PYTHON} -u main.py --hypes ${HYPES_FILE} --phase train --log_to_file n --gpu 0 --cudnn n
--data_dir ${DATA_DIR} --loss_type ${LOSS_TYPE} --model_name ${MODEL_NAME}
--resume_continue y --resume ./checkpoints/cityscapes/${CHECKPOINTS_NAME}_latest.pth
--checkpoints_name ${CHECKPOINTS_NAME} --pretrained ${PRETRAINED_MODEL} 2>&1 | tee -a ${LOG_FILE}

elif [[ "$1"x == "debug"x ]]; then ${PYTHON} -u main.py --hypes ${HYPES_FILE} --phase debug --gpu 0 --log_to_file n 2>&1 | tee ${LOG_FILE}

elif [[ "$1"x == "val"x ]]; then ${PYTHON} -u main.py --hypes ${HYPES_FILE} --phase test --log_to_file n --model_name ${MODEL_NAME}
--phase test --gpu 0 --resume ./checkpoints/cityscapes/${CHECKPOINTS_NAME}_latest.pth
--test_dir ${DATA_DIR}/val/image --out_dir val 2>&1 | tee -a ${LOG_FILE} cd metrics/det/ ${PYTHON} -u voc_evaluator.py --hypes "../../../"${HYPES_FILE}
--json_dir ../../../out/results/voc/test_dir/${CHECKPOINTS_NAME}/val/label
--gt_dir ${DATA_DIR}/val/label 2>&1 | tee -a "../../"${LOG_FILE}

else echo "$1"x" is invalid..." fi

Hope you can help me. Thanks!

Hi,

Great job! It is a great framework to learn pytorch and latest networks.

But when I am training the openpose network, it shows No module named 'extensions.layers.nms.src.cython_nms'. And I check with the files, finding the file is missing.

Is there anything I am missing?

in utils/layers/det/yolo_detection_layer.py : I believe line 42(if self.configer.get('phase') != 'debug':) should be removed, because values passed to BCE loss should be in the range of (0, 1) in the training phase.

In hypes/seg/cityscape/*_cityscape_seg.json, "ignore_index": 19 But in datasets/seg/fs_data_loader.py encoded_labelmap = np.ones(shape=(shape[0], shape[1]), dtype=np.float32) * 255 so labels treated as void will be equel to 255. My question is that should ignore_index be equel to 255? Looking forward to your reply.

In the .sh file, I find a parameter PRETRAINED_MODEL refering to a pretrained file. But I don't find this file in repo. So where is the pretrained.pth file?

RuntimeError: Given groups=1, weight of size [512, 1024, 3, 3], expected input[1, 256, 64, 64] to have 1024 channels, but got 256 channels instead 请问训练时，这个问题如何解决

data = ImageClassifierData.from_paths(PATH, tfms=tfms_from_model(arch, size), bs=batch_size) try: learn = ConvLearner.pretrained(arch, data, precompute=True)
except: learn = ConvLearner.pretrained(arch, data, precompute=True)

This was the code. Could anyone help me to address this issue!

https://github.com/donnyyou/torchcv/blob/1875b7905f4aa0aed7d6cf49ae0ed4512d5f6ae7/model/seg/nets/sfnet.py#L189

而且，ResSFNet 和 AlignHead 都有self.fpn_out，是否是重复的？（虽然这两个fpn_out有一点点差别）

RuntimeError: Given groups=1, weight of size [512, 1024, 3, 3], expected input[8, 256, 64, 64] to have 1024 channels, but got 256 channels instead

Command;

python main.py --config_file configs/seg/cityscapes/sfnet_res18_cityscapes_seg.conf --gpu 0