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# Copyright 2022-present NAVER Corp.
# CC BY-NC-SA 4.0
# Available only for non-commercial use
import pdb; bb = pdb.set_trace
from tqdm import tqdm
from collections import defaultdict
import torch
import torch.nn as nn
from torch.nn import DataParallel
from .common import todevice
class Trainer (nn.Module):
""" Helper class to train a deep network.
Overload this class `forward_backward` for your actual needs.
Usage:
train = Trainer(net, loss, optimizer)
for epoch in range(n_epochs):
train()
"""
def __init__(self, net, loss, optimizer, epoch=0):
super().__init__()
self.net = net
self.loss = loss
self.optimizer = optimizer
self.epoch = epoch
@property
def device(self):
return next(self.net.parameters()).device
@property
def model(self):
return self.net.module if isinstance(self.net, DataParallel) else self.net
def distribute(self):
self.net = DataParallel(self.net) # DataDistributed not implemented yet
def __call__(self, data_loader):
print(f'>> Training (epoch {self.epoch} --> {self.epoch+1})')
self.net.train()
stats = defaultdict(list)
for batch in tqdm(data_loader):
batch = todevice(batch, self.device)
# compute gradient and do model update
self.optimizer.zero_grad()
details = self.forward_backward(batch)
self.optimizer.step()
for key, val in details.items():
stats[key].append( val )
self.epoch += 1
print(" Summary of losses during this epoch:")
for loss_name, vals in stats.items():
N = 1 + len(vals)//10
print(f" - {loss_name:10}: {avg(vals[:N]):.3f} --> {avg(vals[-N:]):.3f} (avg: {avg(vals):.3f})")
def forward_backward(self, inputs):
raise NotImplementedError()
def save(self, path):
print(f"\n>> Saving model to {path}")
data = {'model': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'loss': self.loss.state_dict(),
'epoch': self.epoch}
torch.save(data, open(path,'wb'))
def load(self, path, resume=True):
print(f">> Loading weights from {path} ...")
checkpoint = torch.load(path, map_location='cpu')
assert isinstance(checkpoint, dict)
self.net.load_state_dict(checkpoint['model'])
if resume:
self.optimizer.load_state_dict(checkpoint['optimizer'])
self.loss.load_state_dict(checkpoint['optimizer'])
self.epoch = checkpoint['epoch']
print(f" Resuming training at Epoch {self.epoch}!")
def get_loss( loss ):
""" returns a tuple (loss, dictionary of loss details)
"""
assert isinstance(loss, dict)
grads = None
k,l = next(iter(loss.items())) # first item is assumed to be the main loss
if isinstance(l, tuple):
l, grads = l
loss[k] = l
return (l, grads), {k:float(v) for k,v in loss.items()}
def backward( loss ):
if isinstance(loss, tuple):
loss, grads = loss
else:
loss, grads = (loss, None)
assert loss == loss, 'loss is NaN'
if grads is None:
loss.backward()
else:
# dictionary of separate subgraphs
for var,grad in grads:
var.backward(grad)
return float(loss)
def avg( lis ):
return sum(lis) / len(lis)
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