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main.py
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import torch
from torch.utils.data import DataLoader
import numpy as np
import os
import time
import pandas as pd
from params import par
from model import DeepVO
from data_helper import get_data_info, SortedRandomBatchSampler, ImageSequenceDataset, get_partition_data_info
# Write all hyperparameters to record_path
# a - append to file; w - write to file; both of them create file if not exists
mode = 'a' if par.resume else 'w'
print ("mode= ",mode)
print("record path: ", par.record_path)
with open(par.record_path, mode) as f:
f.write('\n'+'='*50 + '\n')
f.write('\n'.join("%s: %s" % item for item in vars(par).items()))
f.write('\n'+'='*50 + '\n')
# Prepare Data
if os.path.isfile(par.train_data_info_path) and os.path.isfile(par.valid_data_info_path):
print('Load data info from {}'.format(par.train_data_info_path))
train_df = pd.read_pickle(par.train_data_info_path)
valid_df = pd.read_pickle(par.valid_data_info_path)
else:
print('Create new data info')
if par.partition != None:
partition = par.partition
train_df, valid_df = get_partition_data_info(partition, par.train_video, par.seq_len, overlap=1, sample_times=par.sample_times, shuffle=True, sort=True)
else:
print("going into get_data_info")
train_df = get_data_info(folder_list=par.train_video, seq_len_range=par.seq_len, overlap=1, sample_times=par.sample_times)
valid_df = get_data_info(folder_list=par.valid_video, seq_len_range=par.seq_len, overlap=1, sample_times=par.sample_times)
# save the data info
train_df.to_pickle(par.train_data_info_path)
valid_df.to_pickle(par.valid_data_info_path)
train_sampler = SortedRandomBatchSampler(train_df, par.batch_size, drop_last=True)
train_dataset = ImageSequenceDataset(train_df, par.resize_mode, (par.img_w, par.img_h), par.img_means, par.img_stds, par.minus_point_5)
train_dl = DataLoader(train_dataset, batch_sampler=train_sampler, num_workers=par.n_processors, pin_memory=par.pin_mem)
valid_sampler = SortedRandomBatchSampler(valid_df, par.batch_size, drop_last=True)
valid_dataset = ImageSequenceDataset(valid_df, par.resize_mode, (par.img_w, par.img_h), par.img_means, par.img_stds, par.minus_point_5)
valid_dl = DataLoader(valid_dataset, batch_sampler=valid_sampler, num_workers=par.n_processors, pin_memory=par.pin_mem)
print('Number of samples in training dataset: ', len(train_df.index))
print('Number of samples in validation dataset: ', len(valid_df.index))
print('='*50)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Model
M_deepvo = DeepVO(par.img_h, par.img_w, par.batch_norm)
use_cuda = torch.cuda.is_available()
if use_cuda:
print('CUDA used.')
#M_deepvo = M_deepvo.cuda()
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
# dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs
M_deepvo = nn.DataParallel(M_deepvo)
M_deepvo.to(device)
# Load FlowNet weights pretrained with FlyingChairs
# NOTE: the pretrained model assumes image rgb values in range [-0.5, 0.5]
if par.pretrained_flownet and not par.resume:
if use_cuda:
pretrained_w = torch.load(par.pretrained_flownet)
else:
pretrained_w = torch.load(par.pretrained_flownet_flownet, map_location='cpu')
print('Load FlowNet pretrained model')
# Use only conv-layer-part of FlowNet as CNN for DeepVO
model_dict = M_deepvo.state_dict()
update_dict = {k: v for k, v in pretrained_w['state_dict'].items() if k in model_dict}
model_dict.update(update_dict)
M_deepvo.load_state_dict(model_dict)
# Create optimizer
if par.optim['opt'] == 'Adam':
optimizer = torch.optim.Adam(M_deepvo.parameters(), lr=0.001, betas=(0.9, 0.999))
elif par.optim['opt'] == 'Adagrad':
optimizer = torch.optim.Adagrad(M_deepvo.parameters(), lr=par.optim['lr'])
elif par.optim['opt'] == 'Cosine':
optimizer = torch.optim.SGD(M_deepvo.parameters(), lr=par.optim['lr'])
T_iter = par.optim['T']*len(train_dl)
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_iter, eta_min=0, last_epoch=-1)
# Load trained DeepVO model and optimizer
if par.resume:
M_deepvo.load_state_dict(torch.load(par.load_model_path), strict = False)
optimizer.load_state_dict(torch.load(par.load_optimizer_path))
print('Load model from: ', par.load_model_path)
print('Load optimizer from: ', par.load_optimizer_path)
# Train
print('Record loss in: ', par.record_path)
min_loss_t = 1e10
min_loss_v = 1e10
M_deepvo.train()
for ep in range(par.epochs):
st_t = time.time()
print('='*50)
# Train
# model.train() function doesn't actually train but it just is like an indication to pytorch thatthe following is for training and parameters need to be saved
M_deepvo.train()
loss_mean = 0
t_loss_list = []
#print ("train dl is: ",train_dl)
# train_dl is the entire trg dataset
for _, t_x, t_y in train_dl:
# t_x, t_y are the corresponding input and output pair, the first _ might be the index
if use_cuda:
t_x = t_x.cuda(non_blocking=par.pin_mem)
t_y = t_y.cuda(non_blocking=par.pin_mem)
ls = M_deepvo.module.step(t_x, t_y, optimizer).data.cpu().numpy()
t_loss_list.append(float(ls))
loss_mean += float(ls)
if par.optim == 'Cosine':
lr_scheduler.step()
print('Train take {:.1f} sec'.format(time.time()-st_t))
loss_mean /= len(train_dl)
# Validation
st_t = time.time()
M_deepvo.eval()
loss_mean_valid = 0
v_loss_list = []
for _, v_x, v_y in valid_dl:
if use_cuda:
v_x = v_x.cuda(non_blocking=par.pin_mem)
v_y = v_y.cuda(non_blocking=par.pin_mem)
v_ls = M_deepvo.get_loss(v_x, v_y).data.cpu().numpy()
v_loss_list.append(float(v_ls))
loss_mean_valid += float(v_ls)
print('Valid take {:.1f} sec'.format(time.time()-st_t))
loss_mean_valid /= len(valid_dl)
# till here, we have gone thru entire trg and validn dataset once. thus 1 epoch is complete
f = open(par.record_path, 'a')
f.write('Epoch {}\ntrain loss mean: {}, std: {:.2f}\nvalid loss mean: {}, std: {:.2f}\n'.format(ep+1, loss_mean, np.std(t_loss_list), loss_mean_valid, np.std(v_loss_list)))
print('Epoch {}\ntrain loss mean: {}, std: {:.2f}\nvalid loss mean: {}, std: {:.2f}\n'.format(ep+1, loss_mean, np.std(t_loss_list), loss_mean_valid, np.std(v_loss_list)))
# Save model
# save if the valid loss decrease
check_interval = 1
if loss_mean_valid < min_loss_v and ep % check_interval == 0:
min_loss_v = loss_mean_valid
print('Save model at ep {}, mean of valid loss: {}'.format(ep+1, loss_mean_valid)) # use 4.6 sec
f.write('Save model at ep {}, mean of valid loss: {}'.format(ep+1, loss_mean_valid))
torch.save(M_deepvo.state_dict(), par.save_model_path+'.valid')
torch.save(optimizer.state_dict(), par.save_optimzer_path+'.valid')
# save if the training loss decrease
check_interval = 1
if loss_mean < min_loss_t and ep % check_interval == 0:
min_loss_t = loss_mean
print('Save model at ep {}, mean of train loss: {}'.format(ep+1, loss_mean))
f.write('Save model at ep {}, mean of train loss: {}'.format(ep+1, loss_mean))
torch.save(M_deepvo.state_dict(), par.save_model_path+'.train')
torch.save(optimizer.state_dict(), par.save_optimzer_path+'.train')
f.close()