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import os
import sys
import time
import glob
import h5py
import logging
import argparse
import numpy as np
from icecream import ic
from datetime import datetime
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.distributed as dist
from torchvision.utils import save_image
from torch.nn.parallel import DistributedDataParallel
sys.path.append(os.path.dirname(os.path.realpath(__file__)) + '/../')
from my_utils.YParams import YParams
from my_utils.data_loader_multifiles import get_data_loader
from my_utils import logging_utils
logging_utils.config_logger()
def load_model(model, params, checkpoint_file):
model.zero_grad()
checkpoint_fname = checkpoint_file
checkpoint = torch.load(checkpoint_fname)
try:
new_state_dict = OrderedDict()
for key, val in checkpoint['model_state'].items():
name = key[7:]
if name != 'ged':
new_state_dict[name] = val
model.load_state_dict(new_state_dict)
except:
model.load_state_dict(checkpoint['model_state'])
model.eval()
return model
def setup(params):
device = torch.cuda.current_device() if torch.cuda.is_available() else 'cpu'
# get data loader
valid_data_loader, valid_dataset = get_data_loader(params, params.test_data_path, dist.is_initialized(), train=False)
img_shape_x = valid_dataset.img_shape_x
img_shape_y = valid_dataset.img_shape_y
params.img_shape_x = img_shape_x
params.img_shape_y = img_shape_y
in_channels = np.array(params.in_channels)
out_channels = np.array(params.out_channels)
n_in_channels = len(in_channels)
n_out_channels = len(out_channels)
params['N_in_channels'] = n_in_channels
params['N_out_channels'] = n_out_channels
if params.normalization == 'zscore':
params.means = np.load(params.global_means_path)
params.stds = np.load(params.global_stds_path)
if params.nettype == 'Fourcastnet':
from networks.Fourcastnet import Fourcastnet as model
if params.nettype == 'Triton':
from networks.Triton import Triton as model
else:
raise Exception("not implemented")
checkpoint_file = params['best_checkpoint_path']
logging.info('Loading trained model checkpoint from {}'.format(checkpoint_file))
model = model(params).to(device)
model = load_model(model, params, checkpoint_file)
model = model.to(device)
files_paths = glob.glob(params.test_data_path + "/*.h5")
files_paths.sort()
# which year
yr = 0
logging.info('Loading inference data')
logging.info('Inference data from {}'.format(files_paths[yr]))
climate_mean = np.load('./data/climate_mean_s_t_ssh.npy')
valid_data_full = h5py.File(files_paths[yr], 'r')['fields'][:365, :, :, :]
valid_data_full = valid_data_full - climate_mean
return valid_data_full, model
def autoregressive_inference(params, init_condition, valid_data_full, model):
icd = int(init_condition)
device = torch.cuda.current_device() if torch.cuda.is_available() else 'cpu'
exp_dir = params['experiment_dir']
dt = int(params.dt)
prediction_length = int(params.prediction_length/dt)
n_history = params.n_history
img_shape_x = params.img_shape_x
img_shape_y = params.img_shape_y
in_channels = np.array(params.in_channels)
out_channels = np.array(params.out_channels)
atmos_channels = np.array(params.atmos_channels)
n_in_channels = len(in_channels)
n_out_channels = len(out_channels)
seq_real = torch.zeros((prediction_length, n_out_channels, img_shape_x, img_shape_y))
seq_pred = torch.zeros((prediction_length, n_out_channels, img_shape_x, img_shape_y))
valid_data = valid_data_full[icd:(icd+prediction_length*dt+n_history*dt):dt][:, params.in_channels][:,:,0:120]
logging.info(f'valid_data_full: {valid_data_full.shape}')
logging.info(f'valid_data: {valid_data.shape}')
if params.normalization == 'zscore':
valid_data = (valid_data - params.means[:,params.in_channels])/params.stds[:,params.in_channels]
valid_data = np.nan_to_num(valid_data, nan=0)
valid_data = torch.as_tensor(valid_data)
logging.info('Begin autoregressive inference')
with torch.no_grad():
for i in range(valid_data.shape[0]):
if i==0:
first = valid_data[0:n_history+1]
ic(valid_data.shape, first.shape)
future = valid_data[n_history+1]
ic(future.shape)
for h in range(n_history+1):
seq_real[h] = first[h*n_in_channels : (h+1)*n_in_channels, :93]
seq_pred[h] = seq_real[h]
first = first.to(device, dtype=torch.float)
first_ocean = first[:, params.ocean_channels, :, :]
ic(first_ocean.shape)
future_force = future[params.atmos_channels, :120, :240]
future_force = torch.unsqueeze(future_force, dim=0).to(device, dtype=torch.float)
model_input = torch.cat((first_ocean, future_force.cuda()), axis=1)
ic(model_input.shape)
future_pred = model(model_input)
else:
if i < prediction_length-1:
future = valid_data[n_history+i+1]
inf_one_step_start = time.time()
future_force = future[params.atmos_channels, :120, :240]
future_force = torch.unsqueeze(future_force, dim=0).to(device, dtype=torch.float)
future_pred = model(torch.cat((future_pred.cuda(), future_force), axis=1)) #autoregressive step
inf_one_step_time = time.time() - inf_one_step_start
logging.info(f'inference one step time: {inf_one_step_time}')
if i < prediction_length - 1:
with h5py.File(params.land_mask_path, 'r') as _f:
mask_data = torch.as_tensor(_f['fields'][:,out_channels, :120, :240], dtype=bool)
seq_pred[n_history+i+1] = torch.masked_fill(input=future_pred.cpu(), mask=~mask_data, value=0)
seq_real[n_history+i+1] = future[:93]
history_stack = seq_pred[i+1:i+2+n_history]
future_pred = history_stack
pred = torch.unsqueeze(seq_pred[i], 0)
tar = torch.unsqueeze(seq_real[i], 0)
with h5py.File(params.land_mask_path, 'r') as _f:
mask_data = torch.as_tensor(_f['fields'][:,out_channels, :120, :240], dtype=bool)
ic(mask_data.shape, pred.shape, tar.shape)
pred = torch.masked_fill(input=pred, mask=~mask_data, value=0)
tar = torch.masked_fill(input=tar, mask=~mask_data, value=0)
print(torch.mean((pred-tar)**2))
seq_real = seq_real * params.stds[:,params.out_channels] + params.means[:,params.out_channels]
seq_real = seq_real.numpy()
seq_pred = seq_pred * params.stds[:,params.out_channels] + params.means[:,params.out_channels]
seq_pred = seq_pred.numpy()
return (np.expand_dims(seq_real[n_history:], 0), # no mask
np.expand_dims(seq_pred[n_history:], 0) # no mask
)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--exp_dir", default='./exp', type=str)
parser.add_argument("--config", default='full_field', type=str)
parser.add_argument("--run_num", default='00', type=str)
parser.add_argument("--prediction_length", default=30, type=int)
parser.add_argument("--finetune_dir", default='', type=str)
parser.add_argument("--ics_type", default='default', type=str)
parser.add_argument("--year", default=2012, type=int)
args = parser.parse_args()
config_path = os.path.join(args.exp_dir, args.config, args.run_num, 'config.yaml')
params = YParams(config_path, args.config)
params['resuming'] = False
params['interp'] = 0
params['world_size'] = 1
params['local_rank'] = 0
params['global_batch_size'] = params.batch_size
params['prediction_length'] = args.prediction_length
params['multi_steps_finetune'] = 1
params['year'] = args.year
params['ics_type'] = args.ics_type
torch.cuda.set_device(0)
torch.backends.cudnn.benchmark = True
# Set up directory
if args.finetune_dir == '':
expDir = os.path.join(params.exp_dir, args.config, str(args.run_num))
else:
expDir = os.path.join(params.exp_dir, args.config, str(args.run_num), args.finetune_dir)
logging.info(f'expDir: {expDir}')
params['experiment_dir'] = expDir
params['best_checkpoint_path'] = os.path.join(expDir, 'training_checkpoints/best_ckpt.tar')
# set up logging
logging_utils.log_to_file(logger_name=None, log_filename=os.path.join(expDir, 'inference.log'))
logging_utils.log_versions()
params.log()
if params["ics_type"] == 'default':
ics = np.arange(0, 240, 1)
n_ics = len(ics)
print('init_condition:', ics)
n_ics = len(ics)
logging.info("Inference for {} initial conditions".format(n_ics))
try:
autoregressive_inference_filetag = params["inference_file_tag"]
except:
autoregressive_inference_filetag = ""
if params.interp > 0:
autoregressive_inference_filetag = "_coarse"
valid_data_full, model = setup(params)
seq_pred = []
seq_real = []
# run autoregressive inference for multiple initial conditions
for i, ic_ in enumerate(ics):
logging.info("Initial condition {} of {}".format(i+1, n_ics))
seq_real, seq_pred= autoregressive_inference(params, ic_, valid_data_full, model)
prediction_length = seq_real[0].shape[0]
n_out_channels = seq_real[0].shape[1]
img_shape_x = seq_real[0].shape[2]
img_shape_y = seq_real[0].shape[3]
save_path = os.path.join(params['experiment_dir'], 'autoregressive_predictions' + autoregressive_inference_filetag+ '_' + str(params.year) +'_dt1_120day.h5')
logging.info("Saving to {}".format(save_path))
print(f'saving to {save_path}')
if i==0:
f = h5py.File(save_path, 'w')
f.create_dataset(
"ground_truth",
data=seq_real,
maxshape=[None, prediction_length, n_out_channels, img_shape_x, img_shape_y],
dtype=np.float32)
f.create_dataset(
"predicted",
data=seq_pred,
maxshape=[None, prediction_length, n_out_channels, img_shape_x, img_shape_y],
dtype=np.float32)
f.close()
else:
f = h5py.File(save_path, 'a')
f["ground_truth"].resize((f["ground_truth"].shape[0] + 1), axis = 0)
f["ground_truth"][-1:] = seq_real
f["predicted"].resize((f["predicted"].shape[0] + 1), axis = 0)
f["predicted"][-1:] = seq_pred
f.close()
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