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# Copyright 2019-present NAVER Corp.
# CC BY-NC-SA 3.0
# Available only for non-commercial use
import os, pdb
import torch
import torch.optim as optim
from tools import common, trainer
from tools.dataloader import *
from nets.patchnet import *
from nets.losses import *
default_net = "Quad_L2Net_ConfCFS()"
toy_db_debug = """SyntheticPairDataset(
ImgFolder('imgs'),
'RandomScale(256,1024,can_upscale=True)',
'RandomTilting(0.5), PixelNoise(25)')"""
db_web_images = """SyntheticPairDataset(
web_images,
'RandomScale(256,1024,can_upscale=True)',
'RandomTilting(0.5), PixelNoise(25)')"""
db_aachen_images = """SyntheticPairDataset(
aachen_db_images,
'RandomScale(256,1024,can_upscale=True)',
'RandomTilting(0.5), PixelNoise(25)')"""
db_aachen_style_transfer = """TransformedPairs(
aachen_style_transfer_pairs,
'RandomScale(256,1024,can_upscale=True), RandomTilting(0.5), PixelNoise(25)')"""
db_aachen_flow = "aachen_flow_pairs"
data_sources = dict(
D = toy_db_debug,
W = db_web_images,
A = db_aachen_images,
F = db_aachen_flow,
S = db_aachen_style_transfer,
)
default_dataloader = """PairLoader(CatPairDataset(`data`),
scale = 'RandomScale(256,1024,can_upscale=True)',
distort = 'ColorJitter(0.2,0.2,0.2,0.1)',
crop = 'RandomCrop(192)')"""
default_sampler = """NghSampler2(ngh=7, subq=-8, subd=1, pos_d=3, neg_d=5, border=16,
subd_neg=-8,maxpool_pos=True)"""
default_loss = """MultiLoss(
1, ReliabilityLoss(`sampler`, base=0.5, nq=20),
1, CosimLoss(N=`N`),
1, PeakyLoss(N=`N`))"""
class MyTrainer(trainer.Trainer):
""" This class implements the network training.
Below is the function I need to overload to explain how to do the backprop.
"""
def forward_backward(self, inputs):
output = self.net(imgs=[inputs.pop('img1'),inputs.pop('img2')])
allvars = dict(inputs, **output)
loss, details = self.loss_func(**allvars)
if torch.is_grad_enabled(): loss.backward()
return loss, details
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser("Train R2D2")
parser.add_argument("--data-loader", type=str, default=default_dataloader)
parser.add_argument("--train-data", type=str, default=list('WASF'), nargs='+',
choices = set(data_sources.keys()))
parser.add_argument("--net", type=str, default=default_net, help='network architecture')
parser.add_argument("--pretrained", type=str, default="", help='pretrained model path')
parser.add_argument("--save-path", type=str, required=True, help='model save_path path')
parser.add_argument("--loss", type=str, default=default_loss, help="loss function")
parser.add_argument("--sampler", type=str, default=default_sampler, help="AP sampler")
parser.add_argument("--N", type=int, default=16, help="patch size for repeatability")
parser.add_argument("--epochs", type=int, default=25, help='number of training epochs')
parser.add_argument("--batch-size", "--bs", type=int, default=8, help="batch size")
parser.add_argument("--learning-rate", "--lr", type=str, default=1e-4)
parser.add_argument("--weight-decay", "--wd", type=float, default=5e-4)
parser.add_argument("--threads", type=int, default=8, help='number of worker threads')
parser.add_argument("--gpu", type=int, nargs='+', default=[0], help='-1 for CPU')
args = parser.parse_args()
iscuda = common.torch_set_gpu(args.gpu)
common.mkdir_for(args.save_path)
# Create data loader
from datasets import *
db = [data_sources[key] for key in args.train_data]
db = eval(args.data_loader.replace('`data`',','.join(db)).replace('\n',''))
print("Training image database =", db)
loader = threaded_loader(db, iscuda, args.threads, args.batch_size, shuffle=True)
# create network
print("\n>> Creating net = " + args.net)
net = eval(args.net)
print(f" ( Model size: {common.model_size(net)/1000:.0f}K parameters )")
# initialization
if args.pretrained:
checkpoint = torch.load(args.pretrained, lambda a,b:a)
net.load_pretrained(checkpoint['state_dict'])
# create losses
loss = args.loss.replace('`sampler`',args.sampler).replace('`N`',str(args.N))
print("\n>> Creating loss = " + loss)
loss = eval(loss.replace('\n',''))
# create optimizer
optimizer = optim.Adam( [p for p in net.parameters() if p.requires_grad],
lr=args.learning_rate, weight_decay=args.weight_decay)
train = MyTrainer(net, loader, loss, optimizer)
if iscuda: train = train.cuda()
# Training loop #
for epoch in range(args.epochs):
print(f"\n>> Starting epoch {epoch}...")
train()
print(f"\n>> Saving model to {args.save_path}")
torch.save({'net': args.net, 'state_dict': net.state_dict()}, args.save_path)
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