def main():
results_dir = "../01_results/CNN2D"
createfolder(results_dir)
for input_frames in [10, 9, 8, 7, 6, 5]:
results_file = os.path.join(
results_dir, "BMSE_f.{:02d}_x.{:02d}.txt".format(18, input_frames))
print(results_file)
run(results_file=results_file,
input_frames=input_frames,
output_frames=18,
loss_function="BMSE",
max_epochs=100,
device=device1)
def main():
results_dir = "../01_results/CNN2D"
createfolder(results_dir)
for forecast_t in range(1, 4):
for x_tsteps in range(3, 10):
results_file = os.path.join(
results_dir,
"BMSE_f.{:02d}_x.{:02d}.txt".format(forecast_t, x_tsteps))
run(results_file=results_file,
x_tsteps=x_tsteps,
forecast_t=forecast_t,
loss_function="BMSE",
max_epochs=1,
device=device)
def main():
results_dir = "../01_results/CNN2D_En_De_Fc"
createfolder(results_dir)
for forecast_t in range(1, 4):
for x_tsteps in range(3, 7):
results_file = os.path.join(
results_dir,
"BMSE_x.{:02d}_f.{:02d}.txt".format(x_tsteps, forecast_t))
print(results_file)
run(results_file=results_file,
x_tsteps=x_tsteps,
forecast_t=forecast_t,
loss_function="BMSE",
max_epochs=50,
device=device1)
def main():
output_frames = 18
channel_factor = 2
input_frames = 10
results_dir = "../01_results/ConvGRUv2_c.{:d}".format(channel_factor)
createfolder(results_dir)
results_name = os.path.join(
results_dir,
"BMSE_f.{:02d}_x.{:02d}.txt".format(output_frames, input_frames))
print(results_name)
run(results_name,
channel_factor=channel_factor,
input_frames=input_frames,
output_frames=output_frames,
loss_function="BMSE",
max_epochs=100,
device=args.device)
def main():
output_frames = 18
for channel_factor in [1, 2, 4, 8]:
for input_frames in range(5, 11):
results_dir = "../01_results/ConvGRU_v1_c.{:d}".format(
channel_factor)
createfolder(results_dir)
results_name = os.path.join(
results_dir,
"BMSE_f.{:02d}_x.{:02d}.txt".format(output_frames,
input_frames))
print(results_name)
run(results_name,
channel_factor,
input_frames=input_frames,
output_frames=output_frames,
loss_function="BMSE",
max_epochs=50,
device=args.device)
def main():
if args.root_dir == None:
print("Please set the directory of root data ")
return
elif args.ty_list_file == None:
print("Please set typhoon list file")
return
elif args.result_dir == None:
print("Please set the directory of the result")
return
else:
# set the parameters of the experiment
output_frames = 18
channel_factor = 2
input_frames = 10
result_dir = os.path.join(args.result_dir,"I{:d}_F{:d}".format(args.I_shape[0],args.F_shape[0]),"convGRU_c.{:d}".format(channel_factor))
createfolder(result_dir)
result_name = os.path.join(result_dir,"BMSE_f{:02d}_x{:02d}_w{:.5f}.txt".format(output_frames,input_frames,args.weight_decay))
print(result_name)
run(result_name=result_name, channel_factor=channel_factor, input_frames=input_frames, output_frames=output_frames,
loss_function=BMSE, max_epochs=100, device=args.device)
decoder_stride = [1,2,2,2,2,1]
decoder_padding = [0,1,1,1,1,1]
Net = model(encoder_input, encoder_hidden, encoder_kernel, encoder_n_layer, encoder_stride, encoder_padding,
decoder_input, decoder_hidden, decoder_kernel, decoder_n_layer, decoder_stride, decoder_padding,
batch_norm=batch_norm).to(device)
# print(Net)
# Train process
info = "|CNN2D| Forecast frames: {:02d}, Input frames: {:02d} |".format(output_frames, input_frames)
print("="*len(info))
print(info)
print("="*len(info))
train(net=Net, trainloader=trainloader, testloader=testloader, result_name=result_name, max_epochs=max_epochs, loss_function=loss_function, device=device)
if __name__ == "__main__":
output_frames = args.output_frames
channel_factor = args.channel_factor
input_frames = args.input_frames
result_dir=os.path.join(args.result_dir,
'cnn2D_i{:d}_o{:d}_c{:d}'.format(input_frames,output_frames,channel_factor),
'I{:d}_F{:d}'.format(args.I_shape[0],args.F_shape[0]))
print(" [The path of the result folder]:", result_dir)
createfolder(result_dir)
result_name = os.path.join(result_dir,"BMSE_f.{:02d}_x.{:02d}.txt".format(output_frames, input_frames))
print(result_name)
run(result_name=result_name, channel_factor=channel_factor, input_frames=input_frames, output_frames=output_frames,
loss_function=BMSE, max_epochs=50, batch_norm=args.batch_norm, device=args.device)