cityscapes.md

# Running DeepLab on Cityscapes Semantic Segmentation Dataset

This page walks through the steps required to run DeepLab on Cityscapes on a
local machine.

## Download dataset and convert to TFRecord

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](https://www.cityscapes-dataset.com/).

```bash
# From the tensorflow/models/research/deeplab/datasets directory.
sh convert_cityscapes.sh
```

The converted dataset will be saved at ./deeplab/datasets/cityscapes/tfrecord.

## Recommended Directory Structure for Training and Evaluation

```
+ 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).

## Running the train/eval/vis jobs

A local training job using `xception_65` can be run with the following command:

```bash
# From tensorflow/models/research/
python deeplab/train.py \
    --logtostderr \
<<<<<<< HEAD
=======
    --training_number_of_steps=90000 \
>>>>>>> origin/master
    --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 \
    --train_crop_size=769 \
    --train_batch_size=1 \
<<<<<<< HEAD
=======
    --dataset="cityscapes" \
    --train_split="train" \
>>>>>>> origin/master
    --tf_initial_checkpoints=${PATH_TO_INITIAL_CHECKPOINT} \
    --train_logdir=${PATH_TO_TRAIN_DIR} \
    --dataset_dir=${PATH_TO_DATASET}
```

where ${PATH_TO_INITIAL_CHECKPOINT} is the path to the initial checkpoint
(usually an ImageNet pretrained checkpoint), ${PATH_TO_TRAIN_DIR} is the
directory in which training checkpoints and events will be written to, and
${PATH_TO_DATASET} is the directory in which the Cityscapes dataset resides.

<<<<<<< HEAD
Note that for {train,eval,vis}.py:

1.  We use small batch size during training. The users could change it based on
    the available GPU memory and also set `fine_tune_batch_norm` to be False or
    True depending on the use case.
=======
**Note that for {train,eval,vis}.py**:

1.  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.
>>>>>>> origin/master

2.  The users should change atrous_rates from [6, 12, 18] to [12, 24, 36] if
    setting output_stride=8.

3.  The users could skip the flag, `decoder_output_stride`, if you do not want
    to use the decoder structure.

A local evaluation job using `xception_65` can be run with the following
command:

```bash
# 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 \
    --eval_crop_size=2049 \
<<<<<<< HEAD
=======
    --dataset="cityscapes" \
    --eval_split="val" \
>>>>>>> origin/master
    --checkpoint_dir=${PATH_TO_CHECKPOINT} \
    --eval_logdir=${PATH_TO_EVAL_DIR} \
    --dataset_dir=${PATH_TO_DATASET}
```

where ${PATH_TO_CHECKPOINT} is the path to the trained checkpoint (i.e., the
path to train_logdir), ${PATH_TO_EVAL_DIR} is the directory in which evaluation
events will be written to, and ${PATH_TO_DATASET} is the directory in which the
Cityscapes dataset resides.

A local visualization job using `xception_65` can be run with the following
command:

```bash
# 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 \
    --vis_crop_size=2049 \
<<<<<<< HEAD
=======
    --dataset="cityscapes" \
    --vis_split="val" \
>>>>>>> origin/master
    --colormap_type="cityscapes" \
    --checkpoint_dir=${PATH_TO_CHECKPOINT} \
    --vis_logdir=${PATH_TO_VIS_DIR} \
    --dataset_dir=${PATH_TO_DATASET}
```

where ${PATH_TO_CHECKPOINT} is the path to the trained checkpoint (i.e., the
path to train_logdir), ${PATH_TO_VIS_DIR} is the directory in which evaluation
events will be written to, and ${PATH_TO_DATASET} is the directory in which the
Cityscapes dataset resides. Note that if the users would like to save the
segmentation results for evaluation server, set also_save_raw_predictions =
True.

## Running Tensorboard

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:

```bash
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.