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))
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'
ga = histMatching.HistMatching(ref_file, color_space='RGB', ds_name=suffix)
file_list = [f[0] for f in cm.meta_data['rgb_files']]
hist_match = ga.run(force_run=False, file_list=file_list)
cm.add_channel(hist_match.get_files(), '.*rgb_hist')
cm.print_meta_data()
file_list_train = cm.load_files(field_name='aus10,aus30', field_id='', field_ext='.*rgb_hist,.*gt_d255')
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()