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config.py
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config.py
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## author: xin luo,
## created: 2023.9.21, modify: xxxx
## des: configuration parameters
import torch
import torch.nn as nn
from dataloader.img_aug import rotate, flip, torch_noise, numpy2tensor
from dataloader.img_aug import colorjitter
# -------- root directory -------- #
dir_landsat = '/home/xin/Developer-luo/WatSet/landsat5789-ongoing'
dir_s2 = '/home/xin/Developer-luo/WatSet/sentinel-2'
dir_patch_val_ls = 'dset_val_patch/ls'
dir_patch_val_s2 = 'dset_val_patch/s2'
# ## --------- data loader -------- ##
bands_min = 0
bands_max = 10000
i_valset = [i for i in range(20)]
transforms_tra = [
colorjitter(prob=0.25, alpha=0.05, beta=0.05), # numpy-based, !!!beta should be small
rotate(prob=0.25), # numpy-based
flip(prob=0.25), # numpy-based
numpy2tensor(),
torch_noise(prob=0.25, std_min=0, std_max=0.1), # tensor-based
]
## ---------- model training ------- ##
# ----- parameter setting
lr = 0.002 # if use lr_scheduler;
batch_size = 32 ## selected
# ----- loss function
loss_ce = nn.CrossEntropyLoss()
loss_bce = nn.BCELoss() # selected for binary classification
# import torch.nn as nn
# from model.loss import FocalLoss
# from dataloader.img_aug import missing_band_p, rotate, flip, torch_noise, missing_region, numpy2tensor
# from dataloader.img_aug import missing_line, missing_band
# from dataloader.img_aug import colorjitter, bandjitter
# ## ------------- Path -------------- ##
# # -------- root directory -------- #
# root_tb_data = '/myDrive/tibet-water-data'
# root_proj = '/home/xin/Developer-luo/Tibet-Water-2020'
# # ------------ data directory -------------- #
# # --- scene dir path for training ---
# dir_as = root_proj + '/data/dset/s1_ascend_clean'
# dir_des = root_proj + '/data/dset/s1_descend_clean'
# dir_truth = root_proj + '/data/dset/s1_truth_clean'
# ## -------- train/validation data spliting --------
# # ### for visually asscessment (!!!our previous experiment)
# val_ids = ['01','02','03','04','05','06','07']
# tra_ids= ['08','09','10','11','12','13','14','15','16','17',
# '18','19','20','21','22','23','24','25','26','27',
# '28','29','30','31','32','33','34','35','36','37','38', '39']
# # ### for epoch-accuracy plots (!!!our latter experiment)
# # val_ids = ['03','06','07','11','15','16','18','24','31','39']
# # tra_ids= ['01','02','04','05','08','09','10','12','13','14',
# # '17','19','20','21','22','23','25','26','27','28',
# # '29','30','32','33','34','35','36','37','38']
# # --- patch dir for validation ---
# dir_patch_val = root_proj + '/data/dset/s1_val_patches'
# ## --------- data loader -------- ##
# s1_min = [-63.00, -70.37, -59.01, -69.94] # as-vv, as-vh, des-vv, des-vh
# s1_max = [30.61, 13.71, 29.28, 17.60] # as-vv, as-vh, des-vv, des-vh
# def missing_line_aug(prob=0.25): # implemented in the parallel_loader.py
# return missing_line(prob=prob)
# transforms_tra = [
# ### !!!note: line missing is in the paraller_loader.py
# colorjitter(prob=0.25, alpha=0.05, beta=0.05), # numpy-based, !!!beta should be small
# # bandjitter(prob=0.2), # numpy-based
# rotate(prob=0.25), # numpy-based
# flip(prob=0.25), # numpy-based
# missing_region(prob=0.25, ratio_max=0.2), # numpy-based
# missing_band_p(prob=0.25, ratio_max=0.2), # numpy-based
# numpy2tensor(),
# torch_noise(prob=0.25, std_min=0, std_max=0.1), # tensor-based
# ]
# ## ---------- model training ------- ##
# # ----- parameter setting
# lr = 0.0002 # if use lr_scheduler;
# batch_size = 32 ## selected
# # ----- loss function
# loss_ce = nn.CrossEntropyLoss()
# loss_bce = nn.BCELoss() # selected for binary classification
# loss_focal = FocalLoss()
## ----- label_smooth
def label_smooth(image_label, label_smooth = 0.1):
image_label = image_label + label_smooth
image_label = torch.clamp(image_label, label_smooth, 1-label_smooth)
return image_label