def test(args):
assert (args.resume is True)
if cfg.MODEL.TEST.FINETUNE:
assert (cfg.MODEL.DECONVBASELINE.BN_GLOBAL_STATS is True)
base_dir = args.save_dir
logging_config(folder=base_dir, name="testing")
save_cfg(dir_path=base_dir, source=cfg.MODEL)
generator_net, loss_net, = construct_modules(args)
if args.dataset == "test":
pd_path = cfg.HKO_PD.RAINY_TEST
elif args.dataset == "valid":
pd_path = cfg.HKO_PD.RAINY_VALID
else:
raise NotImplementedError
hko_benchmark(
ctx=args.ctx[0],
generator_net=generator_net,
loss_net=loss_net,
sample_num=1,
save_dir=os.path.join(base_dir, "iter{}_{}_finetune{}".format(
cfg.MODEL.LOAD_ITER + 1, args.dataset, cfg.MODEL.TEST.FINETUNE)),
finetune=cfg.MODEL.TEST.FINETUNE,
mode=cfg.MODEL.TEST.MODE,
pd_path=pd_path)
def test_sst(args):
assert cfg.MODEL.FRAME_STACK == 1 and cfg.MODEL.FRAME_SKIP == 1
assert len(cfg.MODEL.LOAD_DIR) > 0
base_dir = args.save_dir
logging_config(folder=base_dir, name="testing")
save_cfg(dir_path=base_dir, source=cfg.MODEL)
sst_nowcasting_online = SSTNowcastingFactory(batch_size=1,
in_seq_len=cfg.MODEL.IN_LEN,
out_seq_len=cfg.MODEL.OUT_LEN)
t_encoder_net, t_forecaster_net, t_loss_net = encoder_forecaster_build_networks(
factory=sst_nowcasting_online, context=args.ctx, for_finetune=True)
t_encoder_net.summary()
t_forecaster_net.summary()
t_loss_net.summary()
load_encoder_forecaster_params(
load_dir=cfg.MODEL.LOAD_DIR,
load_iter=cfg.MODEL.LOAD_ITER,
encoder_net=t_encoder_net,
forecaster_net=t_forecaster_net,
)
if args.dataset == "test":
pd_path = cfg.SST_PD.RAINY_TEST
elif args.dataset == "valid":
pd_path = cfg.SST_PD.RAINY_VALID
else:
raise NotImplementedError
run_benchmark(
sst_factory=sst_nowcasting_online,
context=args.ctx[0],
encoder_net=t_encoder_net,
forecaster_net=t_forecaster_net,
loss_net=t_loss_net,
save_dir=os.path.join(
base_dir,
"iter%d_%s_finetune%d" %
(cfg.MODEL.LOAD_ITER + 1, args.dataset, cfg.MODEL.TEST.FINETUNE),
),
finetune=cfg.MODEL.TEST.FINETUNE,
mode=cfg.MODEL.TEST.MODE,
pd_path=pd_path,
)
def argument_parser():
parser = argparse.ArgumentParser(
description='Deconvolution baseline for HKO')
cfg.DATASET = "HKO"
mode_args(parser)
training_args(parser)
dataset_args(parser)
model_args(parser)
args = parser.parse_args()
parse_mode_args(args)
parse_training_args(args)
parse_model_args(args)
base_dir = get_base_dir(args)
logging_config(folder=base_dir, name="training")
save_cfg(base_dir, source=cfg.MODEL)
logging.info(args)
return args
def train(args):
assert cfg.MODEL.FRAME_STACK == 1 and cfg.MODEL.FRAME_SKIP == 1
base_dir = get_base_dir(args)
logging_config(folder=base_dir)
save_cfg(dir_path=base_dir, source=cfg.MODEL)
if cfg.MODEL.TRAIN.TBPTT:
# Create a set of sequent iterators with different starting point
train_hko_iters = []
train_hko_iter_restart = []
for _ in range(cfg.MODEL.TRAIN.BATCH_SIZE):
ele_iter = HKOIterator(pd_path=cfg.HKO_PD.RAINY_TRAIN,
sample_mode="sequent",
seq_len=cfg.MODEL.IN_LEN +
cfg.MODEL.OUT_LEN,
stride=cfg.MODEL.IN_LEN)
ele_iter.random_reset()
train_hko_iter_restart.append(True)
train_hko_iters.append(ele_iter)
else:
train_hko_iter = HKOIterator(pd_path=cfg.HKO_PD.RAINY_TRAIN,
sample_mode="random",
seq_len=cfg.MODEL.IN_LEN +
cfg.MODEL.OUT_LEN)
hko_nowcasting = HKONowcastingFactory(
batch_size=cfg.MODEL.TRAIN.BATCH_SIZE // len(args.ctx),
ctx_num=len(args.ctx),
in_seq_len=cfg.MODEL.IN_LEN,
out_seq_len=cfg.MODEL.OUT_LEN)
hko_nowcasting_online = HKONowcastingFactory(batch_size=1,
in_seq_len=cfg.MODEL.IN_LEN,
out_seq_len=cfg.MODEL.OUT_LEN)
encoder_net, forecaster_net, loss_net = \
encoder_forecaster_build_networks(
factory=hko_nowcasting,
context=args.ctx)
t_encoder_net, t_forecaster_net, t_loss_net = \
encoder_forecaster_build_networks(
factory=hko_nowcasting_online,
context=args.ctx[0],
shared_encoder_net=encoder_net,
shared_forecaster_net=forecaster_net,
shared_loss_net=loss_net,
for_finetune=True)
encoder_net.summary()
forecaster_net.summary()
loss_net.summary()
# Begin to load the model if load_dir is not empty
if len(cfg.MODEL.LOAD_DIR) > 0:
load_encoder_forecaster_params(load_dir=cfg.MODEL.LOAD_DIR,
load_iter=cfg.MODEL.LOAD_ITER,
encoder_net=encoder_net,
forecaster_net=forecaster_net)
states = EncoderForecasterStates(factory=hko_nowcasting, ctx=args.ctx[0])
for info in hko_nowcasting.init_encoder_state_info:
assert info["__layout__"].find(
'N') == 0, "Layout=%s is not supported!" % info["__layout__"]
for info in hko_nowcasting.init_forecaster_state_info:
assert info["__layout__"].find(
'N') == 0, "Layout=%s is not supported!" % info["__layout__"]
test_mode = "online" if cfg.MODEL.TRAIN.TBPTT else "fixed"
iter_id = 0
while iter_id < cfg.MODEL.TRAIN.MAX_ITER:
if not cfg.MODEL.TRAIN.TBPTT:
# We are not using TBPTT, we could directly sample a random minibatch
frame_dat, mask_dat, datetime_clips, _ = \
train_hko_iter.sample(batch_size=cfg.MODEL.TRAIN.BATCH_SIZE)
states.reset_all()
else:
# We are using TBPTT, we should sample minibatches from the iterators.
frame_dat_l = []
mask_dat_l = []
for i, ele_iter in enumerate(train_hko_iters):
if ele_iter.use_up:
states.reset_batch(batch_id=i)
ele_iter.random_reset()
train_hko_iter_restart[i] = True
if train_hko_iter_restart[
i] == False and ele_iter.check_new_start():
states.reset_batch(batch_id=i)
ele_iter.random_reset()
frame_dat, mask_dat, datetime_clips, _ = \
ele_iter.sample(batch_size=cfg.MODEL.TRAIN.BATCH_SIZE)
train_hko_iter_restart[i] = False
frame_dat_l.append(frame_dat)
mask_dat_l.append(mask_dat)
frame_dat = np.concatenate(frame_dat_l, axis=1)
mask_dat = np.concatenate(mask_dat_l, axis=1)
data_nd = mx.nd.array(frame_dat[0:cfg.MODEL.IN_LEN, ...],
ctx=args.ctx[0]) / 255.0
target_nd = mx.nd.array(frame_dat[cfg.MODEL.IN_LEN:(
cfg.MODEL.IN_LEN + cfg.MODEL.OUT_LEN), ...],
ctx=args.ctx[0]) / 255.0
mask_nd = mx.nd.array(mask_dat[cfg.MODEL.IN_LEN:(cfg.MODEL.IN_LEN +
cfg.MODEL.OUT_LEN),
...],
ctx=args.ctx[0])
states, _ = train_step(batch_size=cfg.MODEL.TRAIN.BATCH_SIZE,
encoder_net=encoder_net,
forecaster_net=forecaster_net,
loss_net=loss_net,
init_states=states,
data_nd=data_nd,
gt_nd=target_nd,
mask_nd=mask_nd,
iter_id=iter_id)
if (iter_id + 1) % cfg.MODEL.VALID_ITER == 0:
run_benchmark(hko_factory=hko_nowcasting_online,
context=args.ctx[0],
encoder_net=t_encoder_net,
forecaster_net=t_forecaster_net,
loss_net=t_loss_net,
save_dir=os.path.join(base_dir, "iter%d_valid" %
(iter_id + 1)),
mode=test_mode,
pd_path=cfg.HKO_PD.RAINY_VALID)
if (iter_id + 1) % cfg.MODEL.SAVE_ITER == 0:
encoder_net.save_checkpoint(prefix=os.path.join(
base_dir, "encoder_net"),
epoch=iter_id)
forecaster_net.save_checkpoint(prefix=os.path.join(
base_dir, "forecaster_net"),
epoch=iter_id)
iter_id += 1
def save_movingmnist_cfg(dir_path):
tmp_cfg = edict()
tmp_cfg.MOVINGMNIST = cfg.MOVINGMNIST
tmp_cfg.MODEL = cfg.MODEL
save_cfg(dir_path=dir_path, source=tmp_cfg)
def train(args):
base_dir = get_base_dir(args)
logging_config(folder=base_dir)
save_cfg(dir_path=base_dir, source=cfg.MODEL)
use_gan = (cfg.MODEL.PROBLEM_FORM == 'regression') and (cfg.MODEL.GAN_G_LAMBDA > 0.0)
if cfg.MODEL.FRAME_SKIP_OUT > 1:
# here assume the target span the last 30 frames
iter_outlen = cfg.MODEL.OUT_LEN
else:
iter_outlen = 30
model_outlen = cfg.MODEL.OUT_LEN # training on 30 costs too much memory
train_szo_iter = SZOIterator(rec_paths=cfg.SZO_TRAIN_DATA_PATHS,
in_len=cfg.MODEL.IN_LEN,
out_len=iter_outlen, # iterator has to provide full output sequence as target if needed
batch_size=cfg.MODEL.TRAIN.BATCH_SIZE,
frame_skip_in=cfg.MODEL.FRAME_SKIP_IN,
frame_skip_out=cfg.MODEL.FRAME_SKIP_OUT,
ctx=args.ctx)
valid_szo_iter = SZOIterator(rec_paths=cfg.SZO_TEST_DATA_PATHS,
in_len=cfg.MODEL.IN_LEN,
out_len=iter_outlen,
batch_size=cfg.MODEL.TRAIN.BATCH_SIZE,
frame_skip_in=cfg.MODEL.FRAME_SKIP_IN,
frame_skip_out=cfg.MODEL.FRAME_SKIP_OUT,
ctx=args.ctx)
szo_nowcasting = SZONowcastingFactory(batch_size=cfg.MODEL.TRAIN.BATCH_SIZE // len(args.ctx),
ctx_num=len(args.ctx),
in_seq_len=cfg.MODEL.IN_LEN,
out_seq_len=model_outlen, # model still generate cfg.MODEL.OUT_LEN number of outputs at a time
frame_stack=cfg.MODEL.FRAME_STACK)
# discrim_net, loss_D_net will be None if use_gan = False
encoder_net, forecaster_net, loss_net, discrim_net, loss_D_net = \
encoder_forecaster_build_networks(
factory=szo_nowcasting,
context=args.ctx)
encoder_net.summary()
forecaster_net.summary()
loss_net.summary()
if use_gan:
discrim_net.summary()
loss_D_net.summary()
if use_gan:
loss_types = ('mse','gdl','gan','dis')
else:
if cfg.MODEL.PROBLEM_FORM == 'regression':
loss_types = ('mse', 'gdl')
elif cfg.MODEL.PROBLEM_FORM == 'classification':
loss_types = ('ce',)
else:
raise NotImplementedError
# try to load checkpoint
if args.resume:
start_iter_id = latest_iter_id(base_dir)
encoder_net.load_params(os.path.join(base_dir, 'encoder_net'+'-%04d.params'%(start_iter_id)))
forecaster_net.load_params(os.path.join(base_dir, 'forecaster_net'+'-%04d.params'%(start_iter_id)))
synchronize_kvstore(encoder_net)
synchronize_kvstore(forecaster_net)
if not args.resume_param_only:
encoder_net.load_optimizer_states(os.path.join(base_dir, 'encoder_net'+'-%04d.states'%(start_iter_id)))
forecaster_net.load_optimizer_states(os.path.join(base_dir, 'forecaster_net'+'-%04d.states'%(start_iter_id)))
if use_gan:
discrim_net.load_params(os.path.join(base_dir, 'discrim_net'+'-%04d.params'%(start_iter_id)))
synchronize_kvstore(discrim_net)
if not args.resume_param_only:
discrim_net.load_optimizer_states(os.path.join(base_dir, 'discrim_net'+'-%04d.states'%(start_iter_id)))
synchronize_kvstore(discrim_net)
else:
start_iter_id = -1
if args.resume and (not args.resume_param_only):
with open(os.path.join(base_dir, 'train_loss_dicts.pkl'), 'rb') as f:
train_loss_dicts = pickle.load(f)
with open(os.path.join(base_dir, 'valid_loss_dicts.pkl'), 'rb') as f:
valid_loss_dicts = pickle.load(f)
for dicts in (train_loss_dicts, valid_loss_dicts):
keys_to_delete = []
keys_to_add = []
key_len = 0
for k in dicts.keys():
key_len = len(dicts[k])
if k not in loss_types:
keys_to_delete.append(k)
for k in keys_to_delete:
del dicts[k]
for k in loss_types:
if k not in dicts.keys():
dicts[k] = [0] * key_len
else:
train_loss_dicts = {}
valid_loss_dicts = {}
for dicts in (train_loss_dicts, valid_loss_dicts):
for typ in loss_types:
dicts[typ] = []
states = EncoderForecasterStates(factory=szo_nowcasting, ctx=args.ctx[0])
for info in szo_nowcasting.init_encoder_state_info:
assert info["__layout__"].find('N') == 0, "Layout=%s is not supported!" %info["__layout__"]
for info in szo_nowcasting.init_forecaster_state_info:
assert info["__layout__"].find('N') == 0, "Layout=%s is not supported!" % info["__layout__"]
cumulative_loss = {}
for k in train_loss_dicts.keys():
cumulative_loss[k] = 0.0
iter_id = start_iter_id + 1
fix_shift = True if cfg.MODEL.OPTFLOW_AS_INPUT else False
chan = cfg.SZO.DATA.IMAGE_CHANNEL if cfg.MODEL.OPTFLOW_AS_INPUT else 0
buffers = {}
buffers['fake'] = deque([], maxlen=cfg.MODEL.TRAIN.GEN_BUFFER_LEN)
buffers['true'] = deque([], maxlen=cfg.MODEL.TRAIN.GEN_BUFFER_LEN)
while iter_id < cfg.MODEL.TRAIN.MAX_ITER:
frame_dat = train_szo_iter.sample(fix_shift=fix_shift)
data_nd = frame_dat[0:cfg.MODEL.IN_LEN,:,:,:,:] / 255.0 # scale to [0,1]
# only take the channel of grey scale
target_nd = frame_dat[cfg.MODEL.IN_LEN:(cfg.MODEL.IN_LEN + cfg.MODEL.OUT_LEN),:,chan,:,:] / 255.0
target_nd = target_nd.expand_dims(axis=2)
states.reset_all()
if cfg.MODEL.ENCODER_FORECASTER.HAS_MASK:
mask_nd = target_nd < 1.0
else:
mask_nd = mx.nd.ones_like(target_nd)
states, loss_dict, pred_nd, buffers = train_step(batch_size=cfg.MODEL.TRAIN.BATCH_SIZE,
encoder_net=encoder_net, forecaster_net=forecaster_net,
loss_net=loss_net, discrim_net=discrim_net,
loss_D_net=loss_D_net, init_states=states,
data_nd=data_nd, gt_nd=target_nd, mask_nd=mask_nd,
factory=szo_nowcasting, iter_id=iter_id, buffers=buffers)
for k in cumulative_loss.keys():
loss = loss_dict[k+'_output']
cumulative_loss[k] += loss
if (iter_id+1) % cfg.MODEL.VALID_ITER == 0:
for i in range(cfg.MODEL.VALID_LOOP):
states.reset_all()
frame_dat_v = valid_szo_iter.sample(fix_shift=fix_shift)
data_nd_v = frame_dat_v[0:cfg.MODEL.IN_LEN,:,:,:,:] / 255.0
gt_nd_v = frame_dat_v[cfg.MODEL.IN_LEN:(cfg.MODEL.IN_LEN+cfg.MODEL.OUT_LEN),:,chan,:,:] / 255.0
gt_nd_v = gt_nd_v.expand_dims(axis=2)
if cfg.MODEL.ENCODER_FORECASTER.HAS_MASK:
mask_nd_v = gt_nd_v < 1.0
else:
mask_nd_v = mx.nd.ones_like(gt_nd_v)
states, new_valid_loss_dicts, pred_nd_v = valid_step(batch_size=cfg.MODEL.TRAIN.BATCH_SIZE,
encoder_net=encoder_net, forecaster_net=forecaster_net,
loss_net=loss_net, discrim_net=discrim_net,
loss_D_net=loss_D_net, init_states=states,
data_nd=data_nd_v, gt_nd=gt_nd_v, mask_nd=mask_nd_v,
valid_loss_dicts=valid_loss_dicts, factory=szo_nowcasting, iter_id=iter_id)
if i == 0:
for k in valid_loss_dicts.keys():
valid_loss_dicts[k].append(new_valid_loss_dicts[k])
else:
for k in valid_loss_dicts.keys():
valid_loss_dicts[k][-1] += new_valid_loss_dicts[k]
for k in valid_loss_dicts.keys():
valid_loss_dicts[k][-1] /= cfg.MODEL.VALID_LOOP
plot_loss_curve(os.path.join(base_dir, 'valid_'+k+'_loss'), valid_loss_dicts[k])
if (iter_id+1) % cfg.MODEL.DRAW_EVERY == 0:
for k in train_loss_dicts.keys():
avg_loss = cumulative_loss[k] / cfg.MODEL.DRAW_EVERY
if k=='gan':
avg_loss = avg_loss if avg_loss < 1.0 else 1.0
elif k == 'dis':
if avg_loss > 1.0:
avg_loss = 1.0
if avg_loss < 0:
avg_loss = 0
train_loss_dicts[k].append(avg_loss)
cumulative_loss[k] = 0.0
plot_loss_curve(os.path.join(base_dir, 'train_'+k+'_loss'), train_loss_dicts[k])
if (iter_id+1) % cfg.MODEL.DISPLAY_EVERY == 0:
new_frame_dat = train_szo_iter.sample(fix_shift=fix_shift)
data_nd_d = new_frame_dat[0:cfg.MODEL.IN_LEN,:,:,:,:] / 255.0
target_nd_d = new_frame_dat[cfg.MODEL.IN_LEN:(cfg.MODEL.IN_LEN + cfg.MODEL.OUT_LEN),:,chan,:,:] / 255.0
target_nd_d = target_nd_d.expand_dims(axis=2)
states.reset_all()
pred_nd_d = get_prediction(data_nd_d, states, encoder_net, forecaster_net)
if cfg.MODEL.PROBLEM_FORM == 'classification':
pred_nd_d = from_class_to_image(pred_nd_d)
display_path1 = os.path.join(base_dir, 'display_'+str(iter_id))
display_path2 = os.path.join(base_dir, 'display_'+str(iter_id)+'_')
if not os.path.exists(display_path1):
os.mkdir(display_path1)
if not os.path.exists(display_path2):
os.mkdir(display_path2)
data_nd_d = (data_nd_d*255.0).clip(0, 255.0)
target_nd_d = (target_nd_d*255.0).clip(0, 255.0)
pred_nd_d = (pred_nd_d*255.0).clip(0, 255.0)
save_prediction(data_nd_d[:,0,chan,:,:], target_nd_d[:,0,0,:,:], pred_nd_d[:,0,0,:,:], display_path1, default_as_0=True)
save_prediction(data_nd_d[:,0,chan,:,:], target_nd_d[:,0,0,:,:], pred_nd_d[:,0,0,:,:], display_path2, default_as_0=False)
if (iter_id+1) % cfg.MODEL.SAVE_ITER == 0:
encoder_net.save_checkpoint(
prefix=os.path.join(base_dir, "encoder_net",),
epoch=iter_id,
save_optimizer_states=True)
forecaster_net.save_checkpoint(
prefix=os.path.join(base_dir, "forecaster_net",),
epoch=iter_id,
save_optimizer_states=True)
if cfg.MODEL.GAN_G_LAMBDA > 0:
discrim_net.save_checkpoint(
prefix=os.path.join(base_dir, "discrim_net",),
epoch=iter_id,
save_optimizer_states=True)
path1 = os.path.join(base_dir, 'train_loss_dicts.pkl')
path2 = os.path.join(base_dir, 'valid_loss_dicts.pkl')
with open(path1, 'wb') as f:
pickle.dump(train_loss_dicts, f)
with open(path2, 'wb') as f:
pickle.dump(valid_loss_dicts, f)
iter_id += 1
def main(args):
assert cfg.MODEL.FRAME_STACK == 1 and cfg.MODEL.FRAME_SKIP == 1
base_dir = args.save_dir
logging_config(folder=base_dir, name="train")
save_cfg(dir_path=base_dir, source=cfg.MODEL)
metadata_file = os.path.join(
args.data_dir,
'hdf_metadata.csv') if args.data_csv is None else args.data_csv
all_data = h5py.File(os.path.join(args.data_dir, 'hdf_archives',
'all_data.hdf5'),
'r',
libver='latest')
outlier_mask = cv2.imread(os.path.join(args.data_dir, 'mask.png'), 0)
metadata = pd.read_csv(metadata_file, index_col='id')
metadata['start_datetime'] = pd.to_datetime(metadata['start_datetime'])
metadata['end_datetime'] = pd.to_datetime(metadata['end_datetime'])
if args.date_start is not None:
metadata = metadata.loc[metadata['start_datetime'] >= args.date_start]
if args.date_end is not None:
metadata = metadata.loc[metadata['start_datetime'] < args.date_end]
sort_meta = metadata.sample(frac=1)
split_idx = int(len(sort_meta) * 0.95)
train_meta = sort_meta.iloc[:split_idx]
test_meta = sort_meta.iloc[split_idx:]
logging.info("Initializing data iterator with filter threshold %s" %
cfg.HKO.ITERATOR.FILTER_RAINFALL_THRESHOLD)
train_model_iter = infinite_batcher(
all_data,
train_meta,
outlier_mask,
shuffle=False,
filter_threshold=cfg.HKO.ITERATOR.FILTER_RAINFALL_THRESHOLD)
model_nowcasting = RadarNowcastingFactory(
batch_size=cfg.MODEL.TRAIN.BATCH_SIZE // len(args.ctx),
ctx_num=len(args.ctx),
in_seq_len=cfg.MODEL.IN_LEN,
out_seq_len=cfg.MODEL.OUT_LEN)
model_nowcasting_online = RadarNowcastingFactory(
batch_size=1,
in_seq_len=cfg.MODEL.IN_LEN,
out_seq_len=cfg.MODEL.OUT_LEN)
encoder_net, forecaster_net, loss_net = \
encoder_forecaster_build_networks(
factory=model_nowcasting,
context=args.ctx)
t_encoder_net, t_forecaster_net, t_loss_net = \
encoder_forecaster_build_networks(
factory=model_nowcasting_online,
context=args.ctx[0],
shared_encoder_net=encoder_net,
shared_forecaster_net=forecaster_net,
shared_loss_net=loss_net,
for_finetune=True)
encoder_net.summary()
forecaster_net.summary()
loss_net.summary()
# Begin to load the model if load_dir is not empty
if len(cfg.MODEL.LOAD_DIR) > 0:
load_encoder_forecaster_params(load_dir=cfg.MODEL.LOAD_DIR,
load_iter=cfg.MODEL.LOAD_ITER,
encoder_net=encoder_net,
forecaster_net=forecaster_net)
states = EncoderForecasterStates(factory=model_nowcasting, ctx=args.ctx[0])
for info in model_nowcasting.init_encoder_state_info:
assert info["__layout__"].find(
'N') == 0, "Layout=%s is not supported!" % info["__layout__"]
for info in model_nowcasting.init_forecaster_state_info:
assert info["__layout__"].find(
'N') == 0, "Layout=%s is not supported!" % info["__layout__"]
test_mode = "online" if cfg.MODEL.TRAIN.TBPTT else "fixed"
iter_id = 0
while iter_id < cfg.MODEL.TRAIN.MAX_ITER:
# sample a random minibatch
try:
frame_dat, _, mask_dat = next(train_model_iter)
except StopIteration:
break
else:
states.reset_all()
data_nd = mx.nd.array(frame_dat[0:cfg.MODEL.IN_LEN, ...],
ctx=args.ctx[0])
target_nd = mx.nd.array(
frame_dat[cfg.MODEL.IN_LEN:(cfg.MODEL.IN_LEN +
cfg.MODEL.OUT_LEN), ...],
ctx=args.ctx[0])
mask_nd = mx.nd.array(
mask_dat[cfg.MODEL.IN_LEN:(cfg.MODEL.IN_LEN +
cfg.MODEL.OUT_LEN), ...],
ctx=args.ctx[0])
states, _ = train_step(batch_size=cfg.MODEL.TRAIN.BATCH_SIZE,
encoder_net=encoder_net,
forecaster_net=forecaster_net,
loss_net=loss_net,
init_states=states,
data_nd=data_nd,
gt_nd=target_nd,
mask_nd=mask_nd,
iter_id=iter_id)
if (iter_id + 1) % cfg.MODEL.SAVE_ITER == 0:
encoder_net.save_checkpoint(prefix=os.path.join(
base_dir, "encoder_net"),
epoch=iter_id)
forecaster_net.save_checkpoint(prefix=os.path.join(
base_dir, "forecaster_net"),
epoch=iter_id)
if (iter_id + 1) % cfg.MODEL.VALID_ITER == 0:
test_model_iter = HDFIterator(
all_data,
test_meta,
outlier_mask,
batch_size=1,
shuffle=False,
filter_threshold=cfg.HKO.ITERATOR.FILTER_RAINFALL_THRESHOLD
)
run_benchmark(model_factory=model_nowcasting_online,
context=args.ctx[0],
encoder_net=t_encoder_net,
forecaster_net=t_forecaster_net,
save_dir=os.path.join(
base_dir, "iter%d_valid" % (iter_id + 1)),
mode=test_mode,
batcher=test_model_iter)
iter_id += 1
def save_icdm_cfg(dir_path):
tmp_cfg = edict()
tmp_cfg.ICDM = cfg.ICDM
tmp_cfg.MODEL = cfg.MODEL
save_cfg(dir_path=dir_path, source=tmp_cfg)