train.py

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 mmdet import datasets
from mmdet.core import (DistOptimizerHook, DistEvalmAPHook,
                        CocoDistEvalRecallHook, CocoDistEvalmAPHook)
from mmdet.datasets import build_dataloader
from mmdet.models import RPN
from .env import get_root_logger


def parse_losses(losses):
    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() if 'loss' in _key)

    log_vars['loss'] = loss
    for name in log_vars:
        log_vars[name] = log_vars[name].item()

    return loss, log_vars


def batch_processor(model, data, train_mode):
    losses = model(**data)
    loss, log_vars = parse_losses(losses)

    outputs = dict(
        loss=loss, log_vars=log_vars, num_samples=len(data['img'].data))

    return outputs


def train_detector(model,
                   dataset,
                   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, cfg, validate=validate)
    else:
        _non_dist_train(model, dataset, 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, cfg, validate=False):
    # prepare data loaders
    data_loaders = [
        build_dataloader(
            dataset,
            cfg.data.imgs_per_gpu,
            cfg.data.workers_per_gpu,
            dist=True)
    ]
    # put model on gpus
    model = MMDistributedDataParallel(model.cuda())
    # build runner
    optimizer = build_optimizer(model, cfg.optimizer)
    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:
        val_dataset_cfg = cfg.data.val
        eval_cfg = cfg.get('evaluation', {})
        if isinstance(model.module, RPN):
            # TODO: implement recall hooks for other datasets
            runner.register_hook(
                CocoDistEvalRecallHook(val_dataset_cfg, **eval_cfg))
        else:
            dataset_type = getattr(datasets, val_dataset_cfg.type)
            if issubclass(dataset_type, datasets.CocoDataset):
                runner.register_hook(
                    CocoDistEvalmAPHook(val_dataset_cfg, **eval_cfg))
            else:
                runner.register_hook(
                    DistEvalmAPHook(val_dataset_cfg, **eval_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, cfg, validate=False):
    # prepare data loaders
    data_loaders = [
        build_dataloader(
            dataset,
            cfg.data.imgs_per_gpu,
            cfg.data.workers_per_gpu,
            cfg.gpus,
            dist=False)
    ]
    # put model on gpus
    model = MMDataParallel(model, device_ids=range(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 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)