/
semantic_segmentation.py
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/
semantic_segmentation.py
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import os
from os.path import join
import rastervision as rv
from examples.utils import str_to_bool, save_image_crop
class PotsdamSemanticSegmentation(rv.ExperimentSet):
def exp_main(self, raw_uri, processed_uri, root_uri, test=False, use_tf=False):
"""Run an experiment on the ISPRS Potsdam dataset.
Uses Tensorflow Deeplab backend with Mobilenet architecture. Should get to
F1 score of ~0.86 (including clutter class) after 6 hours of training on a P3
instance.
Args:
raw_uri: (str) directory of raw data
root_uri: (str) root directory for experiment output
test: (bool) if True, run a very small experiment as a test and generate
debug output
use_tf: (bool) if True, use Tensorflow Deeplab backend.
"""
test = str_to_bool(test)
use_tf = str_to_bool(use_tf)
exp_id = 'potsdam-seg'
train_ids = ['2-10', '2-11', '3-10', '3-11', '4-10', '4-11', '4-12', '5-10',
'5-11', '5-12', '6-10', '6-11', '6-7', '6-9', '7-10', '7-11',
'7-12', '7-7', '7-8', '7-9']
val_ids = ['2-12', '3-12', '6-12']
# infrared, red, green
channel_order = [3, 0, 1]
debug = False
if test:
debug = True
train_ids = train_ids[0:1]
val_ids = val_ids[0:1]
exp_id += '-test'
classes = {
'Car': (1, '#ffff00'),
'Building': (2, '#0000ff'),
'Low Vegetation': (3, '#00ffff'),
'Tree': (4, '#00ff00'),
'Impervious': (5, "#ffffff"),
'Clutter': (6, "#ff0000")
}
task = rv.TaskConfig.builder(rv.SEMANTIC_SEGMENTATION) \
.with_chip_size(300) \
.with_classes(classes) \
.with_chip_options(window_method='sliding',
stride=300, debug_chip_probability=1.0) \
.build()
if use_tf:
batch_size = 8
num_steps = 100000
if test:
num_steps = 1
batch_size = 2
model_type = rv.MOBILENET_V2
backend = rv.BackendConfig.builder(rv.TF_DEEPLAB) \
.with_task(task) \
.with_model_defaults(model_type) \
.with_train_options(sync_interval=600) \
.with_num_steps(num_steps) \
.with_batch_size(batch_size) \
.with_debug(debug) \
.build()
else:
batch_size = 8
num_epochs = 10
if test:
batch_size = 2
num_epochs = 1
backend = rv.BackendConfig.builder(rv.PYTORCH_SEMANTIC_SEGMENTATION) \
.with_task(task) \
.with_train_options(
lr=1e-4,
batch_size=batch_size,
num_epochs=num_epochs,
model_arch='resnet50',
debug=debug) \
.build()
def make_scene(id):
id = id.replace('-', '_')
raster_uri = '{}/4_Ortho_RGBIR/top_potsdam_{}_RGBIR.tif'.format(
raw_uri, id)
label_uri = '{}/5_Labels_for_participants/top_potsdam_{}_label.tif'.format(
raw_uri, id)
if test:
crop_uri = join(
processed_uri, 'crops', os.path.basename(raster_uri))
save_image_crop(raster_uri, crop_uri, size=600)
raster_uri = crop_uri
# Using with_rgb_class_map because label TIFFs have classes encoded as RGB colors.
label_source = rv.LabelSourceConfig.builder(rv.SEMANTIC_SEGMENTATION) \
.with_rgb_class_map(task.class_map) \
.with_raster_source(label_uri) \
.build()
# URI will be injected by scene config.
# Using with_rgb(True) because we want prediction TIFFs to be in RGB format.
label_store = rv.LabelStoreConfig.builder(rv.SEMANTIC_SEGMENTATION_RASTER) \
.with_rgb(True) \
.build()
scene = rv.SceneConfig.builder() \
.with_task(task) \
.with_id(id) \
.with_raster_source(raster_uri,
channel_order=channel_order) \
.with_label_source(label_source) \
.with_label_store(label_store) \
.build()
return scene
train_scenes = [make_scene(id) for id in train_ids]
val_scenes = [make_scene(id) for id in val_ids]
dataset = rv.DatasetConfig.builder() \
.with_train_scenes(train_scenes) \
.with_validation_scenes(val_scenes) \
.build()
experiment = rv.ExperimentConfig.builder() \
.with_id(exp_id) \
.with_task(task) \
.with_backend(backend) \
.with_dataset(dataset) \
.with_root_uri(root_uri) \
.build()
return experiment
if __name__ == '__main__':
rv.main()