def main(flags):
nn_utils.set_gpu(GPU)
# define network
model = pspnet.PSPNet(flags.num_classes, flags.tile_size, suffix=flags.model_suffix, learn_rate=flags.learning_rate,
decay_step=flags.decay_step, decay_rate=flags.decay_rate, epochs=flags.epochs,
batch_size=flags.batch_size, weight_decay=flags.weight_decay, momentum=flags.momentum)
cm_train = cityscapes_reader.CollectionMakerCityscapes(flags.data_dir, flags.rgb_type, flags.gt_type, 'train',
flags.rgb_ext, flags.gt_ext, ['png', 'png'],
clc_name='{}_train'.format(flags.ds_name),
force_run=flags.force_run)
cm_valid = cityscapes_reader.CollectionMakerCityscapes(flags.data_dir, flags.rgb_type, flags.gt_type, 'val',
flags.rgb_ext, flags.gt_ext, ['png', 'png'],
clc_name='{}_valid'.format(flags.ds_name),
force_run=flags.force_run)
cm_train.print_meta_data()
cm_valid.print_meta_data()
resize_func = lambda img: resize_image(img, flags.tile_size)
train_init_op, valid_init_op, reader_op = dataReaderSegmentation.DataReaderSegmentationTrainValid(
flags.tile_size, cm_train.meta_data['file_list'], cm_valid.meta_data['file_list'],
flags.batch_size, cm_train.meta_data['chan_mean'], aug_func=[reader_utils.image_flipping_hori,
reader_utils.image_scaling_with_label],
random=True, has_gt=True, gt_dim=1, include_gt=True, valid_mult=flags.val_mult, global_func=resize_func)\
.read_op()
feature, label = reader_op
model.create_graph(feature)
model.load_resnet(flags.res_dir)
model.compile(feature, label, flags.n_train, flags.n_valid, flags.tile_size, ersaPath.PATH['model'],
par_dir=flags.model_par_dir, val_mult=flags.val_mult, loss_type='xent')
train_hook = hook.ValueSummaryHook(flags.verb_step, [model.loss, model.lr_op],
value_names=['train_loss', 'learning_rate'], print_val=[0])
model_save_hook = hook.ModelSaveHook(model.get_epoch_step()*flags.save_epoch, model.ckdir)
valid_loss_hook = hook.ValueSummaryHook(model.get_epoch_step(), [model.loss, model.loss_iou],
value_names=['valid_loss', 'valid_mIoU'], log_time=True,
run_time=model.n_valid, iou_pos=1)
image_hook = hook.ImageValidSummaryHook(model.input_size, model.get_epoch_step(), feature, label, model.output,
cityscapes_labels.image_summary, img_mean=cm_train.meta_data['chan_mean'])
start_time = time.time()
model.train(train_hooks=[train_hook, model_save_hook], valid_hooks=[valid_loss_hook, image_hook],
train_init=train_init_op, valid_init=valid_init_op)
print('Duration: {:.3f}'.format((time.time() - start_time)/3600))
ds = 60
dr = 0.1
epochs = 130
bs = 5
valid_mult = 5
gpu = 0
n_train = 785
n_valid = 500
verb_step = 50
save_epoch = 5
model_dir = r'/hdd6/Models/spca/psp101/pspnet_spca_PS(384, 384)_BS5_EP100_LR0.001_DS40_DR0.1'
nn_utils.set_gpu(gpu)
# define network
unet = pspnet.PSPNet(class_num, patch_size, suffix=suffix, learn_rate=lr, decay_step=ds, decay_rate=dr,
epochs=epochs, batch_size=bs, weight_decay=1e-3)
overlap = unet.get_overlap()
cm = collectionMaker.read_collection(raw_data_path=r'/home/lab/Documents/bohao/data/aemo',
field_name='aus10,aus30,aus50',
field_id='',
rgb_ext='.*rgb',
gt_ext='.*gt',
file_ext='tif',
force_run=False,
clc_name='aemo')
gt_d255 = collectionEditor.SingleChanMult(cm.clc_dir, 1/255, ['.*gt', 'gt_d255']).\
run(force_run=False, file_ext='tif', d_type=np.uint8,)
cm.replace_channel(gt_d255.files, True, ['gt', 'gt_d255'])
# hist matching
ref_file = r'/media/ei-edl01/data/uab_datasets/spca/data/Original_Tiles/Fresno1_RGB.jpg'
def main(flags):
nn_utils.set_gpu(GPU)
# define network
model = pspnet.PSPNet(flags.num_classes,
flags.patch_size,
suffix=flags.model_suffix,
learn_rate=flags.learning_rate,
decay_step=flags.decay_step,
decay_rate=flags.decay_rate,
epochs=flags.epochs,
batch_size=flags.batch_size,
weight_decay=flags.weight_decay,
momentum=flags.momentum)
overlap = model.get_overlap()
cm = collectionMaker.read_collection(raw_data_path=flags.data_dir,
field_name='Fresno,Modesto,Stockton',
field_id=','.join(
str(i) for i in range(663)),
rgb_ext='RGB',
gt_ext='GT',
file_ext='jpg,png',
force_run=False,
clc_name=flags.ds_name)
cm.print_meta_data()
file_list_train = cm.load_files(field_id=','.join(
str(i) for i in range(0, 250)),
field_ext='RGB,GT')
file_list_valid = cm.load_files(field_id=','.join(
str(i) for i in range(250, 500)),
field_ext='RGB,GT')
chan_mean = cm.meta_data['chan_mean'][:3]
patch_list_train = patchExtractor.PatchExtractor(flags.patch_size, flags.tile_size, flags.ds_name + '_train',
overlap, overlap // 2). \
run(file_list=file_list_train, file_exts=['jpg', 'png'], force_run=False).get_filelist()
patch_list_valid = patchExtractor.PatchExtractor(flags.patch_size, flags.tile_size, flags.ds_name + '_valid',
overlap, overlap // 2). \
run(file_list=file_list_valid, file_exts=['jpg', 'png'], force_run=False).get_filelist()
train_init_op, valid_init_op, reader_op = \
dataReaderSegmentation.DataReaderSegmentationTrainValid(
flags.patch_size, patch_list_train, patch_list_valid, batch_size=flags.batch_size, chan_mean=chan_mean,
aug_func=[reader_utils.image_flipping, reader_utils.image_rotating],
random=True, has_gt=True, gt_dim=1, include_gt=True, valid_mult=flags.val_mult).read_op()
feature, label = reader_op
model.create_graph(feature)
model.load_resnet(flags.res_dir, keep_last=False)
model.compile(feature,
label,
flags.n_train,
flags.n_valid,
flags.patch_size,
ersaPath.PATH['model'],
par_dir=flags.model_par_dir,
val_mult=flags.val_mult,
loss_type='xent')
train_hook = hook.ValueSummaryHook(
flags.verb_step, [model.loss, model.lr_op],
value_names=['train_loss', 'learning_rate'],
print_val=[0])
model_save_hook = hook.ModelSaveHook(
model.get_epoch_step() * flags.save_epoch, model.ckdir)
valid_loss_hook = hook.ValueSummaryHookIters(
model.get_epoch_step(), [model.loss_xent, model.loss_iou],
value_names=['valid_loss', 'valid_mIoU'],
log_time=True,
run_time=model.n_valid)
image_hook = hook.ImageValidSummaryHook(model.input_size,
model.get_epoch_step(),
feature,
label,
model.output,
nn_utils.image_summary,
img_mean=cm.meta_data['chan_mean'])
start_time = time.time()
model.train(train_hooks=[train_hook, model_save_hook],
valid_hooks=[valid_loss_hook, image_hook],
train_init=train_init_op,
valid_init=valid_init_op)
print('Duration: {:.3f}'.format((time.time() - start_time) / 3600))
def main(flags):
nn_utils.set_gpu(GPU)
# define network
model = pspnet.PSPNet(flags.num_classes,
flags.tile_size,
batch_size=flags.batch_size)
cm_train = cityscapes_reader.CollectionMakerCityscapes(
flags.data_dir,
flags.rgb_type,
flags.gt_type,
'train',
flags.rgb_ext,
flags.gt_ext, ['png', 'png'],
clc_name='{}_train'.format(flags.ds_name),
force_run=False)
cm_test = cityscapes_reader.CollectionMakerCityscapes(
flags.data_dir,
flags.rgb_type,
flags.gt_type,
'val',
flags.rgb_ext,
flags.gt_ext, ['png', 'png'],
clc_name='{}_valid'.format(flags.ds_name),
force_run=False)
cm_test.print_meta_data()
resize_func_train = lambda img: skimage.transform.resize(
img, flags.tile_size, mode='reflect')
resize_func_test = lambda img: skimage.transform.resize(
img,
cm_test.meta_data['tile_dim'],
order=0,
preserve_range=True,
mode='reflect')
init_op, reader_op = dataReaderSegmentation.DataReaderSegmentation(
flags.tile_size,
cm_test.meta_data['file_list'],
batch_size=flags.batch_size,
random=False,
chan_mean=cm_train.meta_data['chan_mean'],
is_train=False,
has_gt=True,
gt_dim=1,
include_gt=True,
global_func=resize_func_train).read_op()
estimator = nn_processor.NNEstimatorSegmentScene(
model,
cm_test.meta_data['file_list'],
flags.res_dir,
init_op,
reader_op,
ds_name='city_scapes',
save_result_parent_dir='Cityscapes',
gpu=flags.GPU,
score_result=True,
split_char='.',
post_func=resize_func_test,
save_func=make_general_id_map,
ignore_label=(-1, 255))
estimator.run(force_run=flags.force_run)
suffix = 'spca'
batch_size = 5
data_dir = r'/media/ei-edl01/data/uab_datasets/spca/data/Original_Tiles'
ds_name = 'spca'
gpu = 1
model_dirs = [ #r'/hdd6/Models/spca/psp101/pspnet_spca_PS(384, 384)_BS5_EP100_LR0.0001_DS40_DR0.1',
#r'/hdd6/Models/spca/psp101/pspnet_spca_PS(384, 384)_BS5_EP100_LR0.0001_DS100_DR0.1',
r'/hdd6/Models/spca/psp101/pspnet_spca_PS(384, 384)_BS5_EP100_LR0.001_DS40_DR0.1'
]
# define network
for model_dir in model_dirs:
tf.reset_default_graph()
model = pspnet.PSPNet(class_num,
patch_size,
suffix=suffix,
batch_size=batch_size)
overlap = model.get_overlap()
cm = collectionMaker.read_collection(raw_data_path=data_dir,
field_name='Fresno,Modesto,Stockton',
field_id=','.join(
str(i) for i in range(663)),
rgb_ext='RGB',
gt_ext='GT',
file_ext='jpg,png',
force_run=False,
clc_name=ds_name)
cm.print_meta_data()
file_list_train = cm.load_files(field_id=','.join(
str(i) for i in range(0, 250)),