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cikm_inter_dst_predrnn_run.py
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import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import shutil
import argparse
import numpy as np
import torch
from core.models.model_factory import *
from core.utils import preprocess
import core.trainer as trainer
from data_provider.CIKM.data_iterator import *
import math
# -----------------------------------------------------------------------------
parser = argparse.ArgumentParser(description='PyTorch video prediction model - DST PredRNN')
# training/test
parser.add_argument('--is_training', type=int, default=1)
# parser.add_argument('--device', type=str, default='gpu:0')
# data
parser.add_argument('--dataset_name', type=str, default='radar')
parser.add_argument('--r', type=int, default=4)
parser.add_argument('--is_parallel', type=bool, default=False)
parser.add_argument('--save_dir', type=str, default='checkpoints/cikm_inter_dst_predrnn_r4')
parser.add_argument('--gen_frm_dir', type=str, default='/mnt/A/meteorological/2500_ref_seq/CIKM_inter_dst_predrnn_r4_/')
parser.add_argument('--input_length', type=int, default=5)
parser.add_argument('--total_length', type=int, default=15)
parser.add_argument('--img_width', type=int, default=128)
parser.add_argument('--img_channel', type=int, default=1)
# model
parser.add_argument('--model_name', type=str, default='interact_dst_predrnn')
parser.add_argument('--pretrained_model', type=str, default='')
parser.add_argument('--num_hidden', type=str, default='64,64,64,64')
parser.add_argument('--filter_size', type=int, default=5)
parser.add_argument('--stride', type=int, default=1)
parser.add_argument('--patch_size', type=int, default=4)
parser.add_argument('--layer_norm', type=int, default=1)
# scheduled sampling
parser.add_argument('--scheduled_sampling', type=int, default=1)
parser.add_argument('--sampling_stop_iter', type=int, default=50000)
parser.add_argument('--sampling_start_value', type=float, default=1.0)
parser.add_argument('--sampling_changing_rate', type=float, default=0.00002)
# optimization
parser.add_argument('--lr', type=float, default=0.001)
parser.add_argument('--reverse_input', type=int, default=1)
parser.add_argument('--batch_size', type=int, default=4)
parser.add_argument('--max_iterations', type=int, default=80000)
parser.add_argument('--display_interval', type=int, default=200)
parser.add_argument('--test_interval', type=int, default=2000)
parser.add_argument('--snapshot_interval', type=int, default=5000)
parser.add_argument('--num_save_samples', type=int, default=10)
parser.add_argument('--n_gpu', type=int, default=0)
args = parser.parse_args()
batch_size = args.batch_size
def padding_CIKM_data(frame_data):
shape = frame_data.shape
batch_size = shape[0]
seq_length = shape[1]
padding_frame_dat = np.zeros((batch_size,seq_length,args.img_width,args.img_width,args.img_channel))
padding_frame_dat[:,:,13:-14,13:-14,:] = frame_data
return padding_frame_dat
def unpadding_CIKM_data(padding_frame_dat):
return padding_frame_dat[:,:,13:-14,13:-14,:]
def schedule_sampling(eta, itr):
zeros = np.zeros((args.batch_size,
args.total_length - args.input_length - 1,
args.img_width // args.patch_size,
args.img_width // args.patch_size,
args.patch_size ** 2 * args.img_channel))
if not args.scheduled_sampling:
return 0.0, zeros
if itr < args.sampling_stop_iter:
eta -= args.sampling_changing_rate
else:
eta = 0.0
random_flip = np.random.random_sample(
(args.batch_size, args.total_length - args.input_length - 1))
true_token = (random_flip < eta)
ones = np.ones((args.img_width // args.patch_size,
args.img_width // args.patch_size,
args.patch_size ** 2 * args.img_channel))
zeros = np.zeros((args.img_width // args.patch_size,
args.img_width // args.patch_size,
args.patch_size ** 2 * args.img_channel))
real_input_flag = []
for i in range(args.batch_size):
for j in range(args.total_length - args.input_length - 1):
if true_token[i, j]:
real_input_flag.append(ones)
else:
real_input_flag.append(zeros)
real_input_flag = np.array(real_input_flag)
real_input_flag = np.reshape(real_input_flag,
(args.batch_size,
args.total_length - args.input_length - 1,
args.img_width // args.patch_size,
args.img_width // args.patch_size,
args.patch_size ** 2 * args.img_channel))
return eta, real_input_flag
def wrapper_test(model):
test_save_root = args.gen_frm_dir
clean_fold(test_save_root)
loss = 0
count = 0
index = 1
flag = True
img_mse, ssim = [], []
for i in range(args.total_length - args.input_length):
img_mse.append(0)
ssim.append(0)
real_input_flag = np.zeros(
(args.batch_size,
args.total_length - args.input_length - 1,
args.img_width // args.patch_size,
args.img_width // args.patch_size,
args.patch_size ** 2 * args.img_channel))
output_length = args.total_length - args.input_length
while flag:
dat, (index, b_cup) = sample(batch_size, data_type='test', index=index)
dat = nor(dat)
tars = dat[:, -output_length:]
ims = padding_CIKM_data(dat)
print('index is:', str(index))
ims = preprocess.reshape_patch(ims, args.patch_size)
img_gen,_ = model.test(ims, real_input_flag)
img_gen = preprocess.reshape_patch_back(img_gen, args.patch_size)
img_out = unpadding_CIKM_data(img_gen[:, -output_length:])
mse = np.mean(np.square(tars - img_out))
img_out = de_nor(img_out)
loss = loss + mse
count = count + 1
bat_ind = 0
for ind in range(index - batch_size, index, 1):
save_fold = test_save_root + 'sample_' + str(ind) + '/'
clean_fold(save_fold)
for t in range(6, 16, 1):
imsave(save_fold + 'img_' + str(t) + '.png', img_out[bat_ind, t - 6, :, :, 0])
bat_ind = bat_ind + 1
if b_cup == args.batch_size - 1:
pass
else:
flag = False
return loss / count
def wrapper_valid(model):
loss = 0
count = 0
index = 1
flag = True
img_mse, ssim = [], []
for i in range(args.total_length - args.input_length):
img_mse.append(0)
ssim.append(0)
real_input_flag = np.zeros(
(args.batch_size,
args.total_length - args.input_length - 1,
args.img_width // args.patch_size,
args.img_width // args.patch_size,
args.patch_size ** 2 * args.img_channel))
output_length = args.total_length - args.input_length
while flag:
dat, (index, b_cup) = sample(batch_size, data_type='validation', index=index)
dat = nor(dat)
tars = dat[:, -output_length:]
ims = padding_CIKM_data(dat)
ims = preprocess.reshape_patch(ims, args.patch_size)
img_gen,_ = model.test(ims, real_input_flag)
img_gen = preprocess.reshape_patch_back(img_gen, args.patch_size)
img_out = unpadding_CIKM_data(img_gen[:, -output_length:])
mse = np.mean(np.square(tars-img_out))
loss = loss+mse
count = count+1
if b_cup == args.batch_size-1:
pass
else:
flag = False
return loss/count
def wrapper_train(model):
if args.pretrained_model:
model.load(args.pretrained_model)
# load data
# train_input_handle, test_input_handle = datasets_factory.data_provider(
# args.dataset_name, args.train_data_paths, args.valid_data_paths, args.batch_size, args.img_width,
# seq_length=args.total_length, is_training=True)
eta = args.sampling_start_value
best_mse = math.inf
tolerate = 0
limit = 3
best_iter = None
for itr in range(1, args.max_iterations + 1):
ims = sample(
batch_size=batch_size
)
ims = padding_CIKM_data(ims)
ims = preprocess.reshape_patch(ims, args.patch_size)
ims = nor(ims)
eta, real_input_flag = schedule_sampling(eta, itr)
cost = trainer.train(model, ims, real_input_flag, args, itr)
if itr % args.display_interval == 0:
print('itr: ' + str(itr))
print('training loss: ' + str(cost))
if itr % args.test_interval == 0:
print('validation one ')
valid_mse = wrapper_valid(model)
print('validation mse is:',str(valid_mse))
if valid_mse<best_mse:
best_mse = valid_mse
best_iter = itr
tolerate = 0
model.save()
else:
tolerate = tolerate+1
if tolerate==limit:
model.load()
test_mse = wrapper_test(model)
print('the best valid mse is:',str(best_mse))
print('the test mse is ',str(test_mse))
break
# if os.path.exists(args.save_dir):
# shutil.rmtree(args.save_dir)
# os.makedirs(args.save_dir)
#
# if os.path.exists(args.gen_frm_dir):
# shutil.rmtree(args.gen_frm_dir)
# os.makedirs(args.gen_frm_dir)
# print(torch.cuda.device_count())
# gpu_list = np.asarray(os.environ.get('CUDA_VISIBLE_DEVICES', '-1').split(','), dtype=np.int32)
# args.n_gpu = len(gpu_list)
# print('the number gpu is:',str(len(gpu_list)))
print('Initializing models')
model = Model(args)
model.load()
test_mse = wrapper_test(model)
print('test mse is:',str(test_mse))
# if args.is_training:
# wrapper_train(model)
# else:
# wrapper_test(model)