This page walks through the steps required to run DeepLab on Cityscapes on a local machine.
We have prepared the script (under the folder datasets
) to convert Cityscapes
dataset to TFRecord. The users are required to download the dataset beforehand
by registering the website.
# From the tensorflow/models/research/deeplab/datasets directory.
sh convert_cityscapes.sh
The converted dataset will be saved at ./deeplab/datasets/cityscapes/tfrecord.
+ datasets
+ cityscapes
+ leftImg8bit
+ gtFine
+ tfrecord
+ exp
+ train_on_train_set
+ train
+ eval
+ vis
where the folder train_on_train_set
stores the train/eval/vis events and
results (when training DeepLab on the Cityscapes train set).
A local training job using xception_65
can be run with the following command:
# From tensorflow/models/research/
python deeplab/train.py \
--logtostderr \
--training_number_of_steps=90000 \
--train_split="train" \
--model_variant="xception_65" \
--atrous_rates=6 \
--atrous_rates=12 \
--atrous_rates=18 \
--output_stride=16 \
--decoder_output_stride=4 \
--train_crop_size="769,769" \
--train_batch_size=1 \
--dataset="cityscapes" \
--tf_initial_checkpoint=${PATH_TO_INITIAL_CHECKPOINT} \
--train_logdir=${PATH_TO_TRAIN_DIR} \
--dataset_dir=${PATH_TO_DATASET}
where
Note that for {train,eval,vis}.py:
-
In order to reproduce our results, one needs to use large batch size (> 8), and set fine_tune_batch_norm = True. Here, we simply use small batch size during training for the purpose of demonstration. If the users have limited GPU memory at hand, please fine-tune from our provided checkpoints whose batch norm parameters have been trained, and use smaller learning rate with fine_tune_batch_norm = False.
-
The users should change atrous_rates from [6, 12, 18] to [12, 24, 36] if setting output_stride=8.
-
The users could skip the flag,
decoder_output_stride
, if you do not want to use the decoder structure. -
Change and add the following flags in order to use the provided dense prediction cell. Note we need to set decoder_output_stride if you want to use the provided checkpoints which include the decoder module.
--model_variant="xception_71"
--dense_prediction_cell_json="deeplab/core/dense_prediction_cell_branch5_top1_cityscapes.json"
--decoder_output_stride=4
A local evaluation job using xception_65
can be run with the following
command:
# From tensorflow/models/research/
python deeplab/eval.py \
--logtostderr \
--eval_split="val" \
--model_variant="xception_65" \
--atrous_rates=6 \
--atrous_rates=12 \
--atrous_rates=18 \
--output_stride=16 \
--decoder_output_stride=4 \
--eval_crop_size="1025,2049" \
--dataset="cityscapes" \
--checkpoint_dir=${PATH_TO_CHECKPOINT} \
--eval_logdir=${PATH_TO_EVAL_DIR} \
--dataset_dir=${PATH_TO_DATASET}
where
A local visualization job using xception_65
can be run with the following
command:
# From tensorflow/models/research/
python deeplab/vis.py \
--logtostderr \
--vis_split="val" \
--model_variant="xception_65" \
--atrous_rates=6 \
--atrous_rates=12 \
--atrous_rates=18 \
--output_stride=16 \
--decoder_output_stride=4 \
--vis_crop_size="1025,2049" \
--dataset="cityscapes" \
--colormap_type="cityscapes" \
--checkpoint_dir=${PATH_TO_CHECKPOINT} \
--vis_logdir=${PATH_TO_VIS_DIR} \
--dataset_dir=${PATH_TO_DATASET}
where
Progress for training and evaluation jobs can be inspected using Tensorboard. If using the recommended directory structure, Tensorboard can be run using the following command:
tensorboard --logdir=${PATH_TO_LOG_DIRECTORY}
where ${PATH_TO_LOG_DIRECTORY}
points to the directory that contains the
train, eval, and vis directories (e.g., the folder train_on_train_set
in the
above example). Please note it may take Tensorboard a couple minutes to populate
with data.