def main(flags):
nn_utils.set_gpu(GPU)
# define network
model = unet.UNet(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)
overlap = model.get_overlap()
cm = collectionMaker.read_collection(raw_data_path=flags.data_dir,
field_name='austin,chicago,kitsap,tyrol-w,vienna',
field_id=','.join(str(i) for i in range(37)),
rgb_ext='RGB',
gt_ext='GT',
file_ext='tif',
force_run=False,
clc_name=flags.ds_name)
gt_d255 = collectionEditor.SingleChanMult(cm.clc_dir, 1 / 255, ['GT', 'gt_d255']). \
run(force_run=False, file_ext='png', d_type=np.uint8, )
cm.replace_channel(gt_d255.files, True, ['GT', 'gt_d255'])
cm.print_meta_data()
file_list_train = cm.load_files(field_id=','.join(str(i) for i in range(6, 37)), field_ext='RGB,gt_d255')
file_list_valid = cm.load_files(field_id=','.join(str(i) for i in range(6)), field_ext='RGB,gt_d255')
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.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.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,
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, 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))
file_list_valid = cm.load_files(field_name='aus50', field_id='', field_ext='.*rgb_hist,.*gt_d255')
chan_mean = cm.meta_data['chan_mean'][-3:]
patch_list_train = patchExtractor.PatchExtractor(patch_size, tile_size, 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(patch_size, tile_size, 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(
patch_size, patch_list_train, patch_list_valid, batch_size=bs, 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=valid_mult).read_op()
feature, label = reader_op
unet.create_graph(feature)
unet.compile(feature, label, n_train, n_valid, patch_size, ersaPath.PATH['model'], par_dir=ds_name, loss_type='xent')
train_hook = hook.ValueSummaryHook(verb_step, [unet.loss, unet.lr_op], value_names=['train_loss', 'learning_rate'],
print_val=[0])
model_save_hook = hook.ModelSaveHook(unet.get_epoch_step()*save_epoch, unet.ckdir)
valid_loss_hook = hook.ValueSummaryHook(unet.get_epoch_step(), [unet.loss, unet.loss_iou],
value_names=['valid_loss', 'IoU'], log_time=True, run_time=unet.n_valid,
iou_pos=1)
image_hook = hook.ImageValidSummaryHook(unet.input_size, unet.get_epoch_step(), feature, label, unet.pred,
nn_utils.image_summary, img_mean=chan_mean)
start_time = time.time()
unet.load(model_dir)
unet.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.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(flags.GPU)
np.random.seed(flags.run_id)
tf.set_random_seed(flags.run_id)
# define network
model = unet.UNet(flags.num_classes,
flags.patch_size,
suffix=flags.suffix,
learn_rate=flags.learn_rate,
decay_step=flags.decay_step,
decay_rate=flags.decay_rate,
epochs=flags.epochs,
batch_size=flags.batch_size)
overlap = model.get_overlap()
cm = collectionMaker.read_collection(
raw_data_path=flags.data_dir,
field_name='Tucson,Colwich,Clyde,Wilmington',
field_id=','.join(str(i) for i in range(1, 16)),
rgb_ext='RGB',
gt_ext='GT',
file_ext='tif,png',
force_run=False,
clc_name=flags.ds_name)
gt_d255 = collectionEditor.SingleChanSwitch(cm.clc_dir, {
2: 0,
3: 1,
4: 0,
5: 0,
6: 0,
7: 0
}, ['GT', 'GT_switch'], 'tower_only').run(
force_run=False,
file_ext='png',
d_type=np.uint8,
)
cm.replace_channel(gt_d255.files, True, ['GT', 'GT_switch'])
cm.print_meta_data()
file_list_train = cm.load_files(
field_name='Tucson,Colwich,Clyde,Wilmington',
field_id=','.join(str(i) for i in range(4, 16)),
field_ext='RGB,GT_switch')
file_list_valid = cm.load_files(
field_name='Tucson,Colwich,Clyde,Wilmington',
field_id='1,2,3',
field_ext='RGB,GT_switch')
patch_list_train = patchExtractor.PatchExtractor(flags.patch_size,
ds_name=flags.ds_name + '_tower_only',
overlap=overlap, pad=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,
ds_name=flags.ds_name + '_tower_only',
overlap=overlap, pad=overlap // 2). \
run(file_list=file_list_valid, file_exts=['jpg', 'png'], force_run=False).get_filelist()
chan_mean = cm.meta_data['chan_mean']
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.compile(feature,
label,
flags.n_train,
flags.n_valid,
flags.patch_size,
ersaPath.PATH['model'],
par_dir=flags.par_dir,
loss_type='xent',
pos_weight=flags.pos_weight)
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_iou, model.loss_xent],
value_names=['IoU', 'valid_loss'],
log_time=True,
run_time=model.n_valid)
image_hook = hook.ImageValidSummaryHook(model.input_size,
model.get_epoch_step(),
feature,
label,
model.pred,
partial(
nn_utils.image_summary,
label_num=flags.num_classes),
img_mean=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 = stn.STN(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)
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()
train_pred_dir = r'/home/lab/Documents/bohao/data/deeplab_model/vis_train/raw_segmentation_results'
valid_pred_dir = r'/home/lab/Documents/bohao/data/deeplab_model/vis/raw_segmentation_results'
file_list_train = get_image_list(flags.data_dir, train_pred_dir, 'train')
file_list_valid = get_image_list(flags.data_dir, valid_pred_dir, 'val')
resize_func = lambda img: resize_image(img, flags.tile_size)
train_init_op, valid_init_op, reader_op = DataReaderSegmentationTrainValid(
flags.tile_size, file_list_train, file_list_valid,
flags.batch_size, cm_train.meta_data['chan_mean'], aug_func=[reader_utils.image_flipping_hori],
random=True, has_gt=True, gt_dim=1, include_gt=True, valid_mult=flags.val_mult, global_func=resize_func)\
.read_op()
feature, label, pred = reader_op
train_init_op_valid, _, reader_op = DataReaderSegmentationTrainValid(
flags.tile_size, file_list_valid, file_list_train,
flags.batch_size, cm_valid.meta_data['chan_mean'], aug_func=[reader_utils.image_flipping_hori],
random=True, has_gt=True, gt_dim=1, include_gt=True, valid_mult=flags.val_mult, global_func=resize_func) \
.read_op()
_, _, pred_valid = reader_op
model.create_graph(pred, feature_valid=pred_valid, rgb=feature)
model.compile(pred,
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.g_loss, model.d_loss, model.lr_op[0],
model.lr_op[1], model.lr_op[2]
],
value_names=['seg_loss', 'g_loss', 'd_loss', 'lr_seg', 'lr_g', 'lr_d'],
print_val=[0, 1, 2])
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.refine,
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, train_init_op_valid],
valid_init=valid_init_op)
print('Duration: {:.3f}'.format((time.time() - start_time) / 3600))
def main(flags):
nn_utils.set_gpu(flags.GPU)
for start_layer in flags.start_layer:
if start_layer >= 10:
suffix_base = 'aemo_newloss'
else:
suffix_base = 'aemo_newloss_up{}'.format(start_layer)
if flags.from_scratch:
suffix_base += '_scratch'
for lr in flags.learn_rate:
for run_id in range(4):
suffix = '{}_{}'.format(suffix_base, run_id)
tf.reset_default_graph()
np.random.seed(run_id)
tf.set_random_seed(run_id)
# define network
model = unet.UNet(flags.num_classes, flags.patch_size, suffix=suffix, learn_rate=lr,
decay_step=flags.decay_step, decay_rate=flags.decay_rate,
epochs=flags.epochs, batch_size=flags.batch_size)
overlap = model.get_overlap()
cm = collectionMaker.read_collection(raw_data_path=flags.data_dir,
field_name='aus10,aus30,aus50',
field_id='',
rgb_ext='.*rgb',
gt_ext='.*gt',
file_ext='tif',
force_run=False,
clc_name=flags.ds_name)
cm.print_meta_data()
file_list_train = cm.load_files(field_name='aus10,aus30', field_id='', field_ext='.*rgb,.*gt')
file_list_valid = cm.load_files(field_name='aus50', field_id='', field_ext='.*rgb,.*gt')
patch_list_train = patchExtractor.PatchExtractor(flags.patch_size, flags.tile_size,
flags.ds_name + '_train_hist',
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_hist',
overlap, overlap // 2). \
run(file_list=file_list_valid, file_exts=['jpg', 'png'], force_run=False).get_filelist()
chan_mean = cm.meta_data['chan_mean']
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)
if start_layer >= 10:
model.compile(feature, label, flags.n_train, flags.n_valid, flags.patch_size, ersaPath.PATH['model'],
par_dir=flags.par_dir, loss_type='xent')
else:
model.compile(feature, label, flags.n_train, flags.n_valid, flags.patch_size, ersaPath.PATH['model'],
par_dir=flags.par_dir, loss_type='xent',
train_var_filter=['layerup{}'.format(i) for i in range(start_layer, 10)])
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', 'IoU'], log_time=True,
run_time=model.n_valid)
image_hook = hook.ImageValidSummaryHook(model.input_size, model.get_epoch_step(), feature, label,
model.pred,
nn_utils.image_summary, img_mean=chan_mean)
start_time = time.time()
if not flags.from_scratch:
model.load(flags.model_dir)
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(flags.GPU)
for start_layer in flags.start_layer:
if start_layer >= 10:
suffix_base = 'aemo'
else:
suffix_base = 'aemo_up{}'.format(start_layer)
if flags.from_scratch:
suffix_base += '_scratch'
for lr in flags.learn_rate:
for run_id in range(1):
suffix = '{}_{}'.format(suffix_base, run_id)
tf.reset_default_graph()
np.random.seed(run_id)
tf.set_random_seed(run_id)
# define network
model = unet.UNet(flags.num_classes,
flags.patch_size,
suffix=suffix,
learn_rate=lr,
decay_step=flags.decay_step,
decay_rate=flags.decay_rate,
epochs=flags.epochs,
batch_size=flags.batch_size)
file_list = os.path.join(flags.data_dir, 'file_list.txt')
lines = ersa_utils.load_file(file_list)
patch_list_train = []
patch_list_valid = []
train_tile_names = ['aus10', 'aus30']
valid_tile_names = ['aus50']
for line in lines:
tile_name = os.path.basename(
line.split(' ')[0]).split('_')[0].strip()
if tile_name in train_tile_names:
patch_list_train.append(line.strip().split(' '))
elif tile_name in valid_tile_names:
patch_list_valid.append(line.strip().split(' '))
else:
raise ValueError
cm = collectionMaker.read_collection('aemo_align')
chan_mean = cm.meta_data['chan_mean']
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)
if start_layer >= 10:
model.compile(feature,
label,
flags.n_train,
flags.n_valid,
flags.patch_size,
ersaPath.PATH['model'],
par_dir=flags.par_dir,
loss_type='xent')
else:
model.compile(feature,
label,
flags.n_train,
flags.n_valid,
flags.patch_size,
ersaPath.PATH['model'],
par_dir=flags.par_dir,
loss_type='xent',
train_var_filter=[
'layerup{}'.format(i)
for i in range(start_layer, 10)
])
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', 'IoU'],
log_time=True,
run_time=model.n_valid)
image_hook = hook.ImageValidSummaryHook(model.input_size,
model.get_epoch_step(),
feature,
label,
model.pred,
nn_utils.image_summary,
img_mean=chan_mean)
start_time = time.time()
if not flags.from_scratch:
model.load(flags.model_dir)
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))
