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finetune_all.py
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finetune_all.py
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import argparse
import logging
import os
import pickle
import warnings
from collections import OrderedDict, defaultdict
import numpy as np
import torch
from tensorboardX import SummaryWriter
from torch.nn.functional import mse_loss, l1_loss
from torch.utils.data import DataLoader
import model
# from x2_data.mydataset_patch import BigDataset_train
from data.mydataset_patch import SR3_Dataset_patch,SR3_Dataset_finetune_patch
from configs import Config
import matplotlib
import matplotlib.pyplot as plt
matplotlib.use('Agg')
import glob
from utils import dict2str, setup_logger, construct_and_save_wbd_plots, \
accumulate_statistics, \
get_optimizer, construct_mask, set_seeds,psnr
import random
warnings.filterwarnings("ignore")
if __name__ == "__main__":
set_seeds() # For reproducability.
parser = argparse.ArgumentParser()
parser.add_argument("-c", "--config", type=str, help="JSON file for configuration")
parser.add_argument("-p", "--phase", type=str, choices=["train", "val"],
help="Run either training or validation(inference).", default="train")
parser.add_argument("-gpu", "--gpu_ids", type=str, default=None)
parser.add_argument("-var", "--variable_name", type=str, default=None)
args = parser.parse_args()
variable_name=args.variable_name
configs = Config(args)
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
setup_logger(None, configs.log, "train", screen=True)
setup_logger("val", configs.log, "val")
logger = logging.getLogger("base")
val_logger = logging.getLogger("val")
logger.info(dict2str(configs.get_hyperparameters_as_dict()))
tb_logger = SummaryWriter(log_dir=configs.tb_logger)
target_paths = sorted(glob.glob("/home/data/downscaling/downscaling_1023/data/train_dataset/hr/*npy"))
land_01_path="/home/data/downscaling/downscaling_1023/data/land10.npy"
mask_path="/home/data/downscaling/downscaling_1023/data/mask10.npy"
# physical_paths= sorted(glob.glob("/home/data/downscaling/downscaling_1023/data/train_dataset/pl/*npy"))
lr_paths= sorted(glob.glob("/home/data/downscaling/downscaling_1023/data/train_dataset/sl/*npy"))
random_dataset_index= random.sample(range(0, len(target_paths)), 2)
data_index=np.arange(0,len(target_paths))
train_index=np.delete(data_index,random_dataset_index)
logger.info(f"split_random dataset is {random_dataset_index}" )
train_data = SR3_Dataset_finetune_patch(np.array(target_paths)[train_index],land_01_path,mask_path,lr_paths=np.array(lr_paths)[train_index],var=variable_name,patch_size=configs.height)
val_data=SR3_Dataset_finetune_patch(np.array(target_paths)[random_dataset_index],land_01_path,mask_path,lr_paths=np.array(lr_paths)[random_dataset_index],var=variable_name,patch_size=configs.height)
logger.info(f"Train size: {len(train_data)}, Val size: {len(val_data)}.")
train_loader = DataLoader(train_data, batch_size=configs.batch_size,shuffle=configs.use_shuffle, num_workers=configs.num_workers,drop_last=True)
val_loader = DataLoader(val_data, batch_size=np.int(configs.batch_size/12),shuffle=False, num_workers=configs.num_workers,drop_last=True)
logger.info("Training and Validation dataloaders are ready.")
# Defining the model.
optimizer = get_optimizer(configs.optimizer_type)
diffusion = model.create_model(in_channel=configs.in_channel, out_channel=configs.out_channel,
norm_groups=configs.norm_groups, inner_channel=configs.inner_channel,
channel_multiplier=configs.channel_multiplier, attn_res=configs.attn_res,
res_blocks=configs.res_blocks, dropout=configs.dropout,
diffusion_loss=configs.diffusion_loss, conditional=configs.conditional,
gpu_ids=configs.gpu_ids, distributed=configs.distributed,
init_method=configs.init_method, train_schedule=configs.train_schedule,
train_n_timestep=configs.train_n_timestep,
train_linear_start=configs.train_linear_start,
train_linear_end=configs.train_linear_end,
val_schedule=configs.val_schedule, val_n_timestep=configs.val_n_timestep,
val_linear_start=configs.val_linear_start, val_linear_end=configs.val_linear_end,
finetune_norm=configs.finetune_norm, optimizer=optimizer, amsgrad=configs.amsgrad,
learning_rate=configs.lr, checkpoint=configs.checkpoint,
resume_state=configs.resume_state,phase=configs.phase, height=configs.height)
logger.info("Model initialization is finished.")
current_step, current_epoch = diffusion.begin_step, diffusion.begin_epoch
if configs.resume_state:
logger.info(f"Resuming training from epoch: {current_epoch}, iter: {current_step}.")
logger.info("Starting the training.")
diffusion.register_schedule(beta_schedule=configs.train_schedule, timesteps=configs.train_n_timestep,
linear_start=configs.train_linear_start, linear_end=configs.train_linear_end)
accumulated_statistics = OrderedDict()
val_metrics_dict={"MSE": 0.0, "MAE": 0.0,"MAE_inter":0.0}
val_metrics_dict["PSNR_"+variable_name]=0.0
val_metrics_dict["PSNR_inter_"+variable_name]=0.0
val_metrics_dict["RMSE_"+variable_name]=0.0
val_metrics_dict["RMSE_inter_"+variable_name]=0.0
val_metrics = OrderedDict(val_metrics_dict)
# Training.
while current_step < configs.n_iter:
current_epoch += 1
for train_data in train_loader:
current_step += 1
if current_step > configs.n_iter:
break
# Training.
diffusion.feed_data(train_data)
diffusion.optimize_parameters()
diffusion.lr_scheduler_step() # For lr scheduler updates per iteration.
accumulate_statistics(diffusion.get_current_log(), accumulated_statistics)
# Logging the training information.
if current_step % configs.print_freq == 0:
message = f"Epoch: {current_epoch:5} | Iteration: {current_step:8}"
for metric, values in accumulated_statistics.items():
mean_value = np.mean(values)
message = f"{message} | {metric:s}: {mean_value:.5f}"
tb_logger.add_scalar(f"{metric}/train", mean_value, current_step)
logger.info(message)
# tb_logger.add_scalar(f"learning_rate", diffusion.get_lr(), current_step)
# Visualizing distributions of parameters.
# for name, param in diffusion.get_named_parameters():
# tb_logger.add_histogram(name, param.clone().cpu().data.numpy(), current_step)
accumulated_statistics = OrderedDict()
# Validation.
if current_step % configs.val_freq == 0:
logger.info("Starting validation.")
idx = 0
result_path = f"{configs.results}/{current_epoch}"
os.makedirs(result_path, exist_ok=True)
diffusion.register_schedule(beta_schedule=configs.val_schedule,
timesteps=configs.val_n_timestep,
linear_start=configs.val_linear_start,
linear_end=configs.val_linear_end)
# A dictionary for storing a list of mean temperatures for each month.
# month2mean_temperature = defaultdict(list)
for val_data in val_loader:
idx += 1
diffusion.feed_data(val_data)
#实验一采用了250，实验二用50
diffusion.test(continuous=False,use_ddim=True,ddim_steps=250,use_dpm_solver=False) # Continues=False to return only the last timesteps's outcome.
# Computing metrics on vlaidation data.
visuals = diffusion.get_current_visuals()
# Computing MSE and RMSE on original data.
mask=val_data["mask"]
mse_value = mse_loss(visuals["HR"]*mask, visuals["SR"]*mask)
val_metrics["MSE"] += mse_value
val_metrics["MAE"] += l1_loss(visuals["HR"]*mask, visuals["SR"]*mask)
val_metrics["MAE_inter"] += l1_loss(visuals["HR"]*mask, visuals["INTERPOLATED"]*mask)
val_metrics["RMSE_"+variable_name] += torch.sqrt(mse_loss(visuals["HR"]*mask, visuals["SR"]*mask))
val_metrics["RMSE_inter_"+variable_name] += torch.sqrt(mse_loss(visuals["HR"]*mask, visuals["INTERPOLATED"]*mask))
val_metrics["PSNR_"+variable_name] += psnr(visuals["HR"]*mask, visuals["SR"]*mask)
val_metrics["PSNR_inter_"+variable_name] += psnr(visuals["HR"]*mask, visuals["INTERPOLATED"]*mask)
if idx % configs.val_vis_freq == 0:
logger.info(f"[{idx//configs.val_vis_freq}] Visualizing and storing some examples.")
sr_candidates = diffusion.generate_multiple_candidates(n=configs.sample_size,ddim_steps=100,use_dpm_solver=False)
mean_candidate = sr_candidates.mean(dim=0) # [B, C, H, W]
std_candidate = sr_candidates.std(dim=0) # [B, C, H, W]
bias = mean_candidate - visuals["HR"]
mean_bias_over_pixels = bias.mean() # Scalar.
std_bias_over_pixels = bias.std() # Scalar.
# # Choosing the first n_val_vis number of samples to visualize.
# variable_id=0
random_idx=np.random.randint(0,np.int(configs.batch_size/12),5)
path = f"{result_path}/{current_epoch}_{current_step}_{idx}"
figure,axs=plt.subplots(5,9,figsize=(25,12))
if variable_name=="tp":
vmin=0
cmap="BrBG"
vmax=2
elif variable_name in ["t2m","sp","u","v"]:
vmin=0
cmap="RdBu_r"
vmax=1
else:
vmin=-2
cmap="RdBu_r"
vmax=2
for idx_i,num in enumerate(random_idx):
axs[idx_i,0].imshow(visuals["HR"][num,0],vmin=vmin,vmax=vmax,cmap=cmap)
axs[idx_i,1].imshow(visuals["SR"][num,0],vmin=vmin,vmax=vmax,cmap=cmap)
axs[idx_i,2].imshow(visuals["INTERPOLATED"][num,0],vmin=vmin,vmax=vmax,cmap=cmap)
axs[idx_i,3].imshow(mean_candidate[num,0],vmin=vmin,vmax=vmax,cmap=cmap)
axs[idx_i,4].imshow(std_candidate[num,0],vmin=0,vmax=2,cmap='Reds')
axs[idx_i,5].imshow(np.abs(visuals["HR"][num,0]-visuals["SR"][num,0]),vmin=0,vmax=2,cmap="Reds")
axs[idx_i,7].imshow(np.abs(visuals["HR"][num,0]-visuals["INTERPOLATED"][num,0]),vmin=0,vmax=2,cmap="Reds")
axs[idx_i,6].imshow(np.abs(bias)[num,0],vmin=0,vmax=2,cmap="Reds")
axs[idx_i,8].imshow(val_data['mask'][num,0],vmin=0,vmax=2,cmap="RdBu_r")
axs[idx_i,8].set_title("mean_mae:%.3f,inter_mae:%.3f,sr_mae:%.3f"%(np.abs(bias)[num,0].mean(),np.abs(visuals["HR"][num,0]-visuals["INTERPOLATED"][num,0]).mean(),np.abs(visuals["HR"][num,0]-visuals["SR"][num,0]).mean()))
for title , col in zip(["HR","Diffusion","INTERPOLATED","mean","std","mae_sr","mae_mean","mae_inter"],range(8)):
axs[0,col].set_title(title)
plt.savefig(f"{path}_.png", bbox_inches="tight")
plt.close("all")
val_metrics["MSE"] /= idx
val_metrics["MAE"] /= idx
val_metrics["MAE_inter"] /= idx
val_metrics["RMSE_"+variable_name] /= idx
val_metrics["RMSE_inter_"+variable_name] /= idx
val_metrics["PSNR_"+variable_name] /= idx
val_metrics["PSNR_inter_"+variable_name] /= idx
diffusion.register_schedule(beta_schedule=configs.train_schedule,
timesteps=configs.train_n_timestep,
linear_start=configs.train_linear_start,
linear_end=configs.train_linear_end)
message = f"Epoch: {current_epoch:5} | Iteration: {current_step:8}"
for metric, value in val_metrics.items():
message = f"{message} | {metric:s}: {value:.5f}"
tb_logger.add_scalar(f"{metric}/val", value, current_step)
val_logger.info(message)
val_metrics = val_metrics.fromkeys(val_metrics, 0.0) # Sets all metrics to zero.
if current_step % configs.save_checkpoint_freq == 0:
logger.info("Saving models and training states.")
diffusion.save_network(current_epoch, current_step)
tb_logger.close()
logger.info("End of training.")