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run.py
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from pytorch_lightning import Trainer
from pytorch_lightning import loggers as pl_loggers
from pytorch_lightning.strategies import DDPStrategy
import argparse
import os
import copy
from simulation import SimulationDataModule
from cromnet import CROMnet
from callbacks import *
def prepare_Trainer(args):
output_path = os.getcwd() + '/outputs'
time_string = getTime()
weightdir = output_path + '/weights/' + time_string
checkpoint_callback = CustomCheckPointCallback(verbose=True, dirpath=weightdir, save_last=True)
lr_monitor = LearningRateMonitor(logging_interval='step')
epoch_timer = EpochTimeCallback()
custom_progress_bar = LitProgressBar()
callbacks=[lr_monitor, checkpoint_callback, epoch_timer, custom_progress_bar]
logdir = output_path + '/logs'
logger = pl_loggers.TensorBoardLogger(save_dir=logdir, name='', version=time_string, log_graph=False)
trainer = Trainer.from_argparse_args(args, gpus=findEmptyCudaDeviceList(args.gpus), default_root_dir=output_path, callbacks=callbacks, logger=logger, max_epochs= np.sum(args.epo), log_every_n_steps=1, strategy=DDPStrategy(find_unused_parameters=False))
return trainer
def tune_MinMaxLR(net, dm):
dummy_net = copy.deepcopy(net)
dummy_net.verbose = False
tuner = Trainer(max_epochs=-1, gpus=findEmptyCudaDeviceList(1), log_every_n_steps=1)
lr_finder = tuner.tuner.lr_find(dummy_net, dm, min_lr=1e-08, max_lr=1e-02, early_stop_threshold=None)
import matplotlib.pyplot as plt
lrs = lr_finder.results["lr"]
losses = lr_finder.results["loss"]
skip_begin = 10
skip_end = 1
loss = np.array(lr_finder.results["loss"][skip_begin:-skip_end])
loss = loss[np.isfinite(loss)]
loss_grad = np.gradient(loss)
min_grad = loss_grad.argmin()
min_idx = min_grad
while min_idx >= 0 and loss_grad[min_idx] < 0:
min_idx -= 1
min_idx += skip_begin
max_idx = min_grad
while max_idx < len(loss_grad) - 1 and loss_grad[max_idx] < 0:
max_idx += 1
max_idx += skip_begin
fig, ax = plt.subplots()
# Plot loss as a function of the learning rate
ax.plot(lrs, losses)
if lr_finder.mode == "exponential":
ax.set_xscale("log")
ax.set_xlabel("Learning rate")
ax.set_ylabel("Loss")
ax.plot(lrs[min_idx], losses[min_idx], markersize=10, marker="o", color="red")
ax.plot(lrs[max_idx], losses[max_idx], markersize=10, marker="o", color="red")
fig.savefig('lr_plot.png')
min_lr = lr_finder.results["lr"][min_idx]
max_lr = lr_finder.results["lr"][max_idx]
net.min_lr = min_lr
net.max_lr = max_lr
return net, dm
def main():
parser = argparse.ArgumentParser(description='Neural Representation training')
# Mode for script
parser.add_argument('-mode', help='train or test',
type=str, required=True)
# Network arguments
parser.add_argument('-lbl', help='label length',
type=int, required=False, default=6)
parser.add_argument('-scale_mlp', help='scale mlp',
type=int, required=False, default=10)
parser.add_argument('-ks', help='scale mlp',
type=int, required=False, default=6)
parser.add_argument('-strides', help='scale mlp',
type=int, required=False, default=4)
parser.add_argument('-siren_dec', help='use siren - decoder',
action='store_true')
parser.add_argument('-siren_enc', help='use siren - encoder',
action='store_true')
parser.add_argument('-dec_omega_0', help='dec_omega_0',
type=float, required=False, default=30)
parser.add_argument('-enc_omega_0', help='enc_omega_0',
type=float, required=False, default=0.3)
# Network Training arguments
parser.add_argument('-m', help='path to weight',
type=str, required=False)
parser.add_argument('-d', help='path to the dataset',
type=str, required=False)
parser.add_argument('-verbose', help='verbose',
action='store_false')
parser.add_argument('-initial_lr', help='initial learning rate',
type=float, nargs=1, required=False, default=1e-4)
parser.add_argument('-lr', help='adaptive learning rates',
type=float, nargs='*', required=False, default=[10,5])
parser.add_argument('-epo', help='adaptive epoch sizes',
type=int, nargs='*', required=False, default=[100])
parser.add_argument('-batch_size', help='batch size',
type=int, required=False, default=16)
parser.add_argument('-schedule', help='schedule type',
type=str, required=False, default='explicit')
# Trainer arguments
parser = Trainer.add_argparse_args(parser)
args = parser.parse_args()
trainer = prepare_Trainer(args)
if args.mode == "train":
if args.d:
data_path = args.d
dm = SimulationDataModule(data_path, args.batch_size, num_workers=64)
data_format, example_input_array = dm.get_dataFormat()
preprop_params = dm.get_dataParams()
network_kwargs = get_validArgs(CROMnet, args)
net = CROMnet(data_format, preprop_params, example_input_array, **network_kwargs)
else:
exit('Enter data path')
if args.schedule != 'explicit':
net, dm = tune_MinMaxLR(net,dm)
net.epoch_cycle = (trainer.max_epochs//2) // 2 # Divide by Number of cycles you want to do
trainer.fit(net, dm)
elif args.mode == "reconstruct":
if args.m:
weight_path = args.m
net = CROMnet.load_from_checkpoint(weight_path, loaded_from=weight_path)
dm = SimulationDataModule(net.data_format['data_path'], net.batch_size, num_workers=64)
else:
exit('Enter weight path')
trainer.test(net, dm)
if __name__ == "__main__":
main()