211 lines
6.7 KiB
Python
Executable File
211 lines
6.7 KiB
Python
Executable File
from __future__ import print_function, division
|
|
import sys
|
|
sys.path.append('core')
|
|
|
|
import argparse
|
|
import os
|
|
import cv2
|
|
import time
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.optim as optim
|
|
import torch.nn.functional as F
|
|
|
|
from torch.utils.data import DataLoader
|
|
from raft import RAFT
|
|
from evaluate import validate_sintel, validate_kitti
|
|
import datasets
|
|
|
|
# exclude extremly large displacements
|
|
MAX_FLOW = 1000
|
|
SUM_FREQ = 1000
|
|
VAL_FREQ = 5000
|
|
|
|
|
|
def count_parameters(model):
|
|
return sum(p.numel() for p in model.parameters() if p.requires_grad)
|
|
|
|
def sequence_loss(flow_preds, flow_gt, valid):
|
|
""" Loss function defined over sequence of flow predictions """
|
|
|
|
n_predictions = len(flow_preds)
|
|
flow_loss = 0.0
|
|
|
|
# exlude invalid pixels and extremely large diplacements
|
|
valid = (valid >= 0.5) & (flow_gt.abs().sum(dim=1) < MAX_FLOW)
|
|
|
|
for i in range(n_predictions):
|
|
i_weight = 0.8**(n_predictions - i - 1)
|
|
i_loss = (flow_preds[i] - flow_gt).abs()
|
|
flow_loss += i_weight * (valid[:, None] * i_loss).mean()
|
|
|
|
epe = torch.sum((flow_preds[-1] - flow_gt)**2, dim=1).sqrt()
|
|
epe = epe.view(-1)[valid.view(-1)]
|
|
|
|
metrics = {
|
|
'epe': epe.mean().item(),
|
|
'1px': (epe < 1).float().mean().item(),
|
|
'3px': (epe < 3).float().mean().item(),
|
|
'5px': (epe < 5).float().mean().item(),
|
|
}
|
|
|
|
return flow_loss, metrics
|
|
|
|
|
|
def fetch_dataloader(args):
|
|
""" Create the data loader for the corresponding trainign set """
|
|
|
|
if args.dataset == 'chairs':
|
|
train_dataset = datasets.FlyingChairs(args, image_size=args.image_size)
|
|
|
|
elif args.dataset == 'things':
|
|
clean_dataset = datasets.SceneFlow(args, image_size=args.image_size, dstype='frames_cleanpass')
|
|
final_dataset = datasets.SceneFlow(args, image_size=args.image_size, dstype='frames_finalpass')
|
|
train_dataset = clean_dataset + final_dataset
|
|
|
|
elif args.dataset == 'sintel':
|
|
clean_dataset = datasets.MpiSintel(args, image_size=args.image_size, dstype='clean')
|
|
final_dataset = datasets.MpiSintel(args, image_size=args.image_size, dstype='final')
|
|
train_dataset = clean_dataset + final_dataset
|
|
|
|
elif args.dataset == 'kitti':
|
|
train_dataset = datasets.KITTI(args, image_size=args.image_size, is_val=False)
|
|
|
|
gpuargs = {'num_workers': 4, 'drop_last' : True}
|
|
train_loader = DataLoader(train_dataset, batch_size=args.batch_size,
|
|
pin_memory=True, shuffle=True, **gpuargs)
|
|
|
|
print('Training with %d image pairs' % len(train_dataset))
|
|
return train_loader
|
|
|
|
def fetch_optimizer(args, model):
|
|
""" Create the optimizer and learning rate scheduler """
|
|
optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.wdecay, eps=args.epsilon)
|
|
|
|
scheduler = optim.lr_scheduler.OneCycleLR(optimizer, args.lr, args.num_steps,
|
|
pct_start=0.2, cycle_momentum=False, anneal_strategy='linear', final_div_factor=0.05)
|
|
|
|
return optimizer, scheduler
|
|
|
|
|
|
class Logger:
|
|
def __init__(self, model, scheduler):
|
|
self.model = model
|
|
self.scheduler = scheduler
|
|
self.total_steps = 0
|
|
self.running_loss = {}
|
|
|
|
def _print_training_status(self):
|
|
metrics_data = [self.running_loss[k]/SUM_FREQ for k in sorted(self.running_loss.keys())]
|
|
training_str = "[{:6d}, {:10.7f}] ".format(self.total_steps+1, self.scheduler.get_lr()[0])
|
|
metrics_str = ("{:10.4f}, "*len(metrics_data)).format(*metrics_data)
|
|
|
|
# print the training status
|
|
print(training_str + metrics_str)
|
|
|
|
for key in self.running_loss:
|
|
self.running_loss[key] = 0.0
|
|
|
|
def push(self, metrics):
|
|
self.total_steps += 1
|
|
|
|
for key in metrics:
|
|
if key not in self.running_loss:
|
|
self.running_loss[key] = 0.0
|
|
|
|
self.running_loss[key] += metrics[key]
|
|
|
|
if self.total_steps % SUM_FREQ == SUM_FREQ-1:
|
|
self._print_training_status()
|
|
self.running_loss = {}
|
|
|
|
|
|
def train(args):
|
|
|
|
model = RAFT(args)
|
|
model = nn.DataParallel(model)
|
|
print("Parameter Count: %d" % count_parameters(model))
|
|
|
|
if args.restore_ckpt is not None:
|
|
model.load_state_dict(torch.load(args.restore_ckpt))
|
|
|
|
model.cuda()
|
|
model.train()
|
|
|
|
if 'chairs' not in args.dataset:
|
|
model.module.freeze_bn()
|
|
|
|
train_loader = fetch_dataloader(args)
|
|
optimizer, scheduler = fetch_optimizer(args, model)
|
|
|
|
total_steps = 0
|
|
logger = Logger(model, scheduler)
|
|
|
|
should_keep_training = True
|
|
while should_keep_training:
|
|
|
|
for i_batch, data_blob in enumerate(train_loader):
|
|
image1, image2, flow, valid = [x.cuda() for x in data_blob]
|
|
|
|
optimizer.zero_grad()
|
|
flow_predictions = model(image1, image2, iters=args.iters)
|
|
|
|
loss, metrics = sequence_loss(flow_predictions, flow, valid)
|
|
loss.backward()
|
|
|
|
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
|
|
optimizer.step()
|
|
scheduler.step()
|
|
total_steps += 1
|
|
|
|
logger.push(metrics)
|
|
|
|
if total_steps % VAL_FREQ == VAL_FREQ-1:
|
|
PATH = 'checkpoints/%d_%s.pth' % (total_steps+1, args.name)
|
|
torch.save(model.state_dict(), PATH)
|
|
|
|
if total_steps == args.num_steps:
|
|
should_keep_training = False
|
|
break
|
|
|
|
|
|
PATH = 'checkpoints/%s.pth' % args.name
|
|
torch.save(model.state_dict(), PATH)
|
|
|
|
return PATH
|
|
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--name', default='bla', help="name your experiment")
|
|
parser.add_argument('--dataset', help="which dataset to use for training")
|
|
parser.add_argument('--restore_ckpt', help="restore checkpoint")
|
|
parser.add_argument('--small', action='store_true', help='use small model')
|
|
|
|
parser.add_argument('--lr', type=float, default=0.00002)
|
|
parser.add_argument('--num_steps', type=int, default=100000)
|
|
parser.add_argument('--batch_size', type=int, default=6)
|
|
parser.add_argument('--image_size', type=int, nargs='+', default=[384, 512])
|
|
|
|
parser.add_argument('--iters', type=int, default=12)
|
|
parser.add_argument('--wdecay', type=float, default=.00005)
|
|
parser.add_argument('--epsilon', type=float, default=1e-8)
|
|
parser.add_argument('--clip', type=float, default=1.0)
|
|
parser.add_argument('--dropout', type=float, default=0.0)
|
|
args = parser.parse_args()
|
|
|
|
torch.manual_seed(1234)
|
|
np.random.seed(1234)
|
|
|
|
if not os.path.isdir('checkpoints'):
|
|
os.mkdir('checkpoints')
|
|
|
|
# scale learning rate and batch size by number of GPUs
|
|
num_gpus = torch.cuda.device_count()
|
|
args.batch_size = args.batch_size * num_gpus
|
|
args.lr = args.lr * num_gpus
|
|
|
|
train(args) |