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trainer.py
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#!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Duanzhixiang(zhixiangduan@deepmotion.ai)
from __future__ import division
import re
from collections import OrderedDict
import torch
from mmcv.runner import Runner, DistSamplerSeedHook, obj_from_dict
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mono.core import (DistOptimizerHook, DistEvalMonoHook, NonDistEvalHook)
from mono.core import (DistOptimizerHook)
from mono.datasets import build_dataloader
from .env import get_root_logger
def change_input_variable(data):
for k, v in data.items():
if 'kp' not in k:
data[k] = torch.as_tensor(v).float().cuda()
return data
def batch_processor(model, data, train_mode):
data = change_input_variable(data)
model_out, losses = model(data)
log_vars = OrderedDict()
for loss_name, loss_value in losses.items():
if isinstance(loss_value, torch.Tensor):
log_vars[loss_name] = loss_value.mean()
elif isinstance(loss_value, list):
log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value)
else:
raise TypeError(
'{} is not a tensor or list of tensors'.format(loss_name))
loss = sum(_value for _key, _value in log_vars.items())
log_vars['loss'] = loss
# print(log_vars)
new_log_vars=OrderedDict()
for name in log_vars:
new_log_vars[str(name)] = log_vars[name].item()
outputs = dict(loss=loss,
log_vars=new_log_vars,
num_samples=len(data[('color', 0 , 0)].data))
return outputs
def train_mono(model,
dataset_train,
dataset_val,
cfg,
distributed=False,
validate=False,
logger=None):
if logger is None:
logger = get_root_logger(cfg.log_level)
# start training
if distributed:
_dist_train(model, dataset_train, dataset_val, cfg, validate=validate)
else:
_non_dist_train(model, dataset_train, dataset_val, cfg, validate=validate)
def build_optimizer(model, optimizer_cfg):
"""Build optimizer from configs.
Args:
model (:obj:`nn.Module`): The model with parameters to be optimized.
optimizer_cfg (dict): The config dict of the optimizer.
Positional fields are:
- type: class name of the optimizer.
- lr: base learning rate.
Optional fields are:
- any arguments of the corresponding optimizer type, e.g.,
weight_decay, momentum, etc.
- paramwise_options: a dict with 3 accepted fileds
(bias_lr_mult, bias_decay_mult, norm_decay_mult).
`bias_lr_mult` and `bias_decay_mult` will be multiplied to
the lr and weight decay respectively for all bias parameters
(except for the normalization layers), and
`norm_decay_mult` will be multiplied to the weight decay
for all weight and bias parameters of normalization layers.
Returns:
torch.optim.Optimizer: The initialized optimizer.
"""
if hasattr(model, 'module'):
model = model.module
optimizer_cfg = optimizer_cfg.copy()
paramwise_options = optimizer_cfg.pop('paramwise_options', None)
# if no paramwise option is specified, just use the global setting
if paramwise_options is None:
return obj_from_dict(optimizer_cfg, torch.optim,
dict(params=model.parameters()))
else:
assert isinstance(paramwise_options, dict)
# get base lr and weight decay
base_lr = optimizer_cfg['lr']
base_wd = optimizer_cfg.get('weight_decay', None)
# weight_decay must be explicitly specified if mult is specified
if ('bias_decay_mult' in paramwise_options
or 'norm_decay_mult' in paramwise_options):
assert base_wd is not None
# get param-wise options
bias_lr_mult = paramwise_options.get('bias_lr_mult', 1.)
bias_decay_mult = paramwise_options.get('bias_decay_mult', 1.)
norm_decay_mult = paramwise_options.get('norm_decay_mult', 1.)
# set param-wise lr and weight decay
params = []
for name, param in model.named_parameters():
if not param.requires_grad:
continue
param_group = {'params': [param]}
# for norm layers, overwrite the weight decay of weight and bias
# TODO: obtain the norm layer prefixes dynamically
if re.search(r'(bn|gn)(\d+)?.(weight|bias)', name):
if base_wd is not None:
param_group['weight_decay'] = base_wd * norm_decay_mult
# for other layers, overwrite both lr and weight decay of bias
elif name.endswith('.bias'):
param_group['lr'] = base_lr * bias_lr_mult
if base_wd is not None:
param_group['weight_decay'] = base_wd * bias_decay_mult
# otherwise use the global settings
params.append(param_group)
optimizer_cls = getattr(torch.optim, optimizer_cfg.pop('type'))
return optimizer_cls(params, **optimizer_cfg)
def _dist_train(model, dataset_train, dataset_val, cfg, validate=False):
# prepare data loaders
data_loaders = [build_dataloader(dataset_train,
cfg.imgs_per_gpu,
cfg.workers_per_gpu,
dist=True)
]
# put model on gpus
model = MMDistributedDataParallel(model.cuda())
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
print('cfg work dir is ', cfg.work_dir)
runner = Runner(model,
batch_processor,
optimizer,
cfg.work_dir,
cfg.log_level)
# register hooks
optimizer_config = DistOptimizerHook(**cfg.optimizer_config)
runner.register_training_hooks(cfg.lr_config,
optimizer_config,
cfg.checkpoint_config,
cfg.log_config)
runner.register_hook(DistSamplerSeedHook())
# register eval hooks
if validate:
print('validate........................')
interval = cfg.get('validate_interval', 1)
runner.register_hook(DistEvalMonoHook(dataset_val, interval, cfg))
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
def _non_dist_train(model, dataset_train, dataset_val, cfg, validate=False):
# prepare data loaders
data_loaders = [
build_dataloader(dataset_train,
cfg.imgs_per_gpu,
cfg.workers_per_gpu,
cfg.gpus.__len__(),
dist=False)
]
# put model on gpus
model = MMDataParallel(model, device_ids=cfg.gpus).cuda()
# build runner
optimizer = build_optimizer(model,
cfg.optimizer)
runner = Runner(model, batch_processor,
optimizer,
cfg.work_dir,
cfg.log_level)
runner.register_training_hooks(cfg.lr_config,
cfg.optimizer_config,
cfg.checkpoint_config,
cfg.log_config)
if validate:
print('validate........................')
runner.register_hook(NonDistEvalHook(dataset_val, cfg))
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)