train.py

from data import *
from utils.augmentations import SSDAugmentation, BaseTransform
from utils.functions import MovingAverage, SavePath
from utils.logger import Log
from utils import timer
from layers.modules import MultiBoxLoss
from yolact import Yolact
import os
import sys
import time
import math, random
from pathlib import Path
import paddle
from paddle import amp
#from torch.autograd import Variable
import paddle.nn as nn
import paddle.optimizer as optim
# import torch.backends.cudnn as cudnn
# import torch.nn.init as init
# import torch.utils.data as data
import numpy as np
import argparse
import datetime

# Oof
import eval as eval_script

def str2bool(v):
    return v.lower() in ("yes", "true", "t", "1")


parser = argparse.ArgumentParser(
    description='Yolact Training Script')

parser.add_argument('--trained_model',
                        default='weights/ssd300_mAP_77.43_v2.pth', type=str,
                        help='Trained state_dict file path to open. If "interrupt", this will open the interrupt file.')
parser.add_argument('--batch_size', default=8, type=int,
                    help='Batch size for training')
parser.add_argument('--resume', default=None, type=str,
                    help='Checkpoint state_dict file to resume training from. If this is "interrupt"'\
                         ', the model will resume training from the interrupt file.')
parser.add_argument('--start_iter', default=-1, type=int,
                    help='Resume training at this iter. If this is -1, the iteration will be'\
                         'determined from the file name.')
parser.add_argument('--num_workers', default=4, type=int,
                    help='Number of workers used in dataloading')
parser.add_argument('--cuda', default=True, type=str2bool,
                    help='Use CUDA to train model')
parser.add_argument('--lr', '--learning_rate', default=None, type=float,
                    help='Initial learning rate. Leave as None to read this from the config.')
parser.add_argument('--momentum', default=None, type=float,
                    help='Momentum for SGD. Leave as None to read this from the config.')
parser.add_argument('--decay', '--weight_decay', default=None, type=float,
                    help='Weight decay for SGD. Leave as None to read this from the config.')
parser.add_argument('--gamma', default=None, type=float,
                    help='For each lr step, what to multiply the lr by. Leave as None to read this from the config.')
parser.add_argument('--save_folder', default='weights/',
                    help='Directory for saving checkpoint models.')
parser.add_argument('--log_folder', default='logs/',
                    help='Directory for saving logs.')
parser.add_argument('--config', default=None,
                    help='The config object to use.')
parser.add_argument('--save_interval', default=10000, type=int,
                    help='The number of iterations between saving the model.')
parser.add_argument('--validation_size', default=350000, type=int,
                    help='The number of images to use for validation.')
parser.add_argument('--validation_epoch', default=2, type=int,
                    help='Output validation information every n iterations. If -1, do no validation.')
parser.add_argument('--keep_latest', dest='keep_latest', action='store_true',
                    help='Only keep the latest checkpoint instead of each one.')
parser.add_argument('--keep_latest_interval', default=100000, type=int,
                    help='When --keep_latest is on, don\'t delete the latest file at these intervals. This should be a multiple of save_interval or 0.')
parser.add_argument('--dataset', default=None, type=str,
                    help='If specified, override the dataset specified in the config with this one (example: coco2017_dataset).')
parser.add_argument('--no_log', dest='log', action='store_false',
                    help='Don\'t log per iteration information into log_folder.')
parser.add_argument('--log_gpu', dest='log_gpu', action='store_true',
                    help='Include GPU information in the logs. Nvidia-smi tends to be slow, so set this with caution.')
parser.add_argument('--no_interrupt', dest='interrupt', action='store_false',
                    help='Don\'t save an interrupt when KeyboardInterrupt is caught.')
parser.add_argument('--batch_alloc', default=None, type=str,
                    help='If using multiple GPUS, you can set this to be a comma separated list detailing which GPUs should get what local batch size (It should add up to your total batch size).')
parser.add_argument('--no_autoscale', dest='autoscale', action='store_false',
                    help='YOLACT will automatically scale the lr and the number of iterations depending on the batch size. Set this if you want to disable that.')

parser.set_defaults(keep_latest=False, log=True, log_gpu=False, interrupt=True, autoscale=True)
args = parser.parse_args()

if args.config is not None:
    set_cfg(args.config)

if args.dataset is not None:
    set_dataset(args.dataset)

if args.autoscale and args.batch_size != 8:
    factor = args.batch_size / 8
    if __name__ == '__main__':
        print('Scaling parameters by %.2f to account for a batch size of %d.' % (factor, args.batch_size))

    cfg.lr *= factor
    cfg.max_iter //= factor
    cfg.lr_steps = [x // factor for x in cfg.lr_steps]

# Update training parameters from the config if necessary
def replace(name):
    if getattr(args, name) == None: setattr(args, name, getattr(cfg, name))
replace('lr')
replace('decay')
replace('gamma')
replace('momentum')

# This is managed by set_lr
cur_lr = args.lr

if 'gpu' not in paddle.get_device():
    print('No GPUs detected. Exiting...')
    exit(-1)

if args.batch_size // paddle.distributed.ParallelEnv().nranks < 6:
    if __name__ == '__main__':
        print('Per-GPU batch size is less than the recommended limit for batch norm. Disabling batch norm.')
    cfg.freeze_bn = True

loss_types = ['B', 'C', 'M', 'P', 'D', 'E', 'S', 'I']

if paddle.is_compiled_with_cuda() :
    paddle.set_device("gpu")
else:
    paddle.set_device("cpu")

class NetLoss(nn.Layer):
    """
    A wrapper for running the network and computing the loss
    This is so we can more efficiently use DataParallel.
    """
    
    def __init__(self, net:Yolact, criterion:MultiBoxLoss):
        super().__init__()

        self.net = net
        self.criterion = criterion
    
    def forward(self, inputs):
        images, targets, masks, num_crowds = inputs
        preds = self.net(images)
        losses = self.criterion(self.net, preds, targets, masks, num_crowds)
        return losses

# class CustomDataParallel(nn.DataParallel):
#     """
#     This is a custom version of DataParallel that works better with our training data.
#     It should also be faster than the general case.
#     """

#     def scatter(self, inputs, kwargs, device_ids):
#         # More like scatter and data prep at the same time. The point is we prep the data in such a way
#         # that no scatter is necessary, and there's no need to shuffle stuff around different GPUs.
#         devices = ['cuda:' + str(x) for x in device_ids]
#         splits = prepare_data(inputs[0], devices, allocation=args.batch_alloc)

#         return [[split[device_idx] for split in splits] for device_idx in range(len(devices))], \
#             [kwargs] * len(devices)

#     def gather(self, outputs, output_device):
#         out = {}

#         for k in outputs[0]:
#             out[k] = torch.stack([output[k].to(output_device) for output in outputs])
        
#         return out


class PiecewiseDecay(object):
    """
    Multi step learning rate decay
    Args:
        gamma (float | list): decay factor
        milestones (list): steps at which to decay learning rate
    """

    def __init__(self,
                 gamma=[0.1, 0.01],
                 milestones=[280000, 360000, 400000],
                 values=None,
                 use_warmup=True):
        super(PiecewiseDecay, self).__init__()
        if type(gamma) is not list:
            self.gamma = []
            for i in range(len(milestones)):
                self.gamma.append(gamma / 10**i)
        else:
            self.gamma = gamma
        self.milestones = milestones
        self.values = values
        self.use_warmup = use_warmup

    def __call__(self,
                 base_lr=None,
                 boundary=None,
                 value=None,
                 step_per_epoch=None):
        if boundary is not None and self.use_warmup:
            boundary.extend([int(step_per_epoch) * i for i in self.milestones])
        else:
            # do not use LinearWarmup
            boundary = [int(step_per_epoch) * i for i in self.milestones]
            value = [base_lr]  # during step[0, boundary[0]] is base_lr

        # self.values is setted directly in config 
        if self.values is not None:
            assert len(self.milestones) + 1 == len(self.values)
            return optim.lr.PiecewiseDecay(boundary, self.values)

        # value is computed by self.gamma
        value = value if value is not None else [base_lr]
        for i in self.gamma:
            value.append(base_lr * i)

        return optim.lr.PiecewiseDecay(boundary, value)

class LinearWarmup(object):
    """
    Warm up learning rate linearly
    Args:
        steps (int): warm up steps
        start_factor (float): initial learning rate factor
    """

    def __init__(self, steps=500, start_factor=1. / 3):
        super(LinearWarmup, self).__init__()
        self.steps = steps
        self.start_factor = start_factor

    def __call__(self, base_lr, step_per_epoch):
        boundary = []
        value = []
        for i in range(self.steps + 1):
            if self.steps > 0:
                alpha = i / self.steps
                lr = (base_lr - self.start_factor) * alpha + self.start_factor
                value.append(lr)
            if i > 0:
                boundary.append(i)
        return boundary, value

class LearningRate(object):
    """
    Learning Rate configuration
    Args:
        base_lr (float): base learning rate
        schedulers (list): learning rate schedulers
    """
    __category__ = 'optim'

    def __init__(self,
                 schedulers=[PiecewiseDecay(gamma=cfg.gamma, milestones=cfg.lr_steps), LinearWarmup(steps=cfg.lr_warmup_until,start_factor=cfg.lr_warmup_init)]):
        super(LearningRate, self).__init__()
        self.schedulers = schedulers

    def __call__(self, step_per_epoch):
        assert len(self.schedulers) >= 1
        # warmup
        boundary, value = self.schedulers[1](cfg.lr, step_per_epoch)
        # decay
        decay_lr = self.schedulers[0](cfg.lr, boundary, value,
                                      step_per_epoch)
        return decay_lr


class BaseDataLoader(object):
    def __init__(self,dataloader,n):
        self.dataloader = dataloader
        self.n = n
        self.loader = iter(self.dataloader)
    
    def __len__(self):
        return self.n 
    
    def __iter__(self):
        return self

    def __next__(self):
        try:
            return next(self.loader)
        except StopIteration:
            self.loader = iter(self.dataloader)
            six.reraise(*sys.exc_info())

    def next(self):
        # python2 compatibility
        return self.__next__()


def train():
    if not os.path.exists(args.save_folder):
        os.mkdir(args.save_folder)
    scaler = amp.GradScaler(
                enable=True, init_loss_scaling=1024)

    dataset = COCODetection(image_path=cfg.dataset.train_images,
                            info_file=cfg.dataset.train_info,
                            transform=SSDAugmentation(MEANS))
    
    if args.validation_epoch > 0:
        setup_eval()
        val_dataset = COCODetection(image_path=cfg.dataset.valid_images,
                                    info_file=cfg.dataset.valid_info,
                                    transform=BaseTransform(MEANS))

    # Parallel wraps the underlying module, but when saving and loading we don't want that
    yolact_net = Yolact()
    net = yolact_net
    net.train()

    if args.log:
        log = Log(cfg.name, args.log_folder, dict(args._get_kwargs()),
            overwrite=(args.resume is None), log_gpu_stats=args.log_gpu)

    # I don't use the timer during training (I use a different timing method).
    # Apparently there's a race condition with multiple GPUs, so disable it just to be safe.
    timer.disable_all()

    # Both of these can set args.resume to None, so do them before the check    
    if args.resume == 'interrupt':
        args.resume = SavePath.get_interrupt(args.save_folder)
    elif args.resume == 'latest':
        args.resume = SavePath.get_latest(args.save_folder, cfg.name)

    if args.resume is not None:
        print('Resuming training, loading {}...'.format(args.resume))
        yolact_net.load_weights(args.resume)
        
        if args.start_iter == -1:
            args.start_iter = SavePath.from_str(args.resume).iteration
    else:
        print('Initializing weights...')
        yolact_net.init_weights(backbone_path=args.save_folder + cfg.backbone.path)

    #net.load_weights(args.trained_model)
    grad_clip = nn.ClipGradByGlobalNorm(clip_norm=35)
    lr_scheduler = LearningRate()(1)
    optimizer = optim.Momentum(parameters=net.parameters(), learning_rate=lr_scheduler, momentum=args.momentum,grad_clip=grad_clip,
                          weight_decay=args.decay)
    
    if args.start_iter != -1:
        optim_state_dict = paddle.load('./weights/' + 'model_%d.pdopt'%args.start_iter)
        optimizer.set_state_dict(optim_state_dict)
    criterion = MultiBoxLoss(num_classes=cfg.num_classes,
                             pos_threshold=cfg.positive_iou_threshold,
                             neg_threshold=cfg.negative_iou_threshold,
                             negpos_ratio=cfg.ohem_negpos_ratio)

    if args.batch_alloc is not None:
        args.batch_alloc = [int(x) for x in args.batch_alloc.split(',')]
        if sum(args.batch_alloc) != args.batch_size:
            print('Error: Batch allocation (%s) does not sum to batch size (%s).' % (args.batch_alloc, args.batch_size))
            exit(-1)
    nranks = paddle.distributed.ParallelEnv().nranks
    local_rank = paddle.distributed.ParallelEnv().local_rank

    if nranks > 1:
        # Initialize parallel environment if not done.
        if not paddle.distributed.parallel.parallel_helper._is_parallel_ctx_initialized(
        ):
            paddle.distributed.init_parallel_env()
    net = paddle.DataParallel(NetLoss(net, criterion))
    #net = CustomDataParallel(NetLoss(net, criterion))  ### 
    # if args.cuda:
    #     net = net.cuda()
    
    # Initialize everything
    if not cfg.freeze_bn: yolact_net.freeze_bn() # Freeze bn so we don't kill our means
    #yolact_net(paddle.zeros((1, 3, cfg.max_size, cfg.max_size)).cuda())
    if not cfg.freeze_bn: yolact_net.freeze_bn(True)

    # loss counters
    loc_loss = 0
    conf_loss = 0
    iteration = max(args.start_iter, 0)
    last_time = time.time()

    epoch_size = len(dataset) // args.batch_size
    num_epochs = math.ceil(cfg.max_iter / epoch_size)
    
    # Which learning rate adjustment step are we on? lr' = lr * gamma ^ step_index
    step_index = 0
    batch_sampler = paddle.io.DistributedBatchSampler(
        dataset, batch_size=args.batch_size, shuffle=True, drop_last=True)
    data_loader = paddle.io.DataLoader(
        dataset,
        batch_sampler=batch_sampler,
        num_workers=0,
        collate_fn=detection_collate,
        return_list=True,
    )
    # data_loader = paddle.io.DataLoader(dataset, args.batch_size,
    #                               num_workers=args.num_workers,
    #                               shuffle=True, collate_fn=detection_collate)#,  ######
    #                               #pin_memory=True)
    
    
    save_path = lambda epoch, iteration: SavePath(cfg.name, epoch, iteration).get_path(root=args.save_folder)
    time_avg = MovingAverage()

    global loss_types # Forms the print order
    loss_avgs  = { k: MovingAverage(100) for k in loss_types }

    print('Begin training!')
    print()
    # try-except so you can use ctrl+c to save early and stop training
    try:
        for epoch in range(num_epochs):
            # Resume from start_iter
            if (epoch+1)*epoch_size < iteration:
                continue
            
            for datum in data_loader:
                #print("datum",datum)
                # Stop if we've reached an epoch if we're resuming from start_iter
                if iteration == (epoch+1)*epoch_size:
                    break

                # Stop at the configured number of iterations even if mid-epoch
                if iteration == cfg.max_iter:
                    break

                # Change a config setting if we've reached the specified iteration
                changed = False
                for change in cfg.delayed_settings:
                    if iteration >= change[0]:
                        changed = True
                        cfg.replace(change[1])

                        # Reset the loss averages because things might have changed
                        for avg in loss_avgs:
                            avg.reset()
                
                # If a config setting was changed, remove it from the list so we don't keep checking
                if changed:
                    cfg.delayed_settings = [x for x in cfg.delayed_settings if x[0] > iteration]

                ############ linear Warm up & PiecewiseDecay 封装在 LearningRate  ############
                
                # Zero the grad to get ready to compute gradients
                optimizer.clear_grad()
                curr_lr = optimizer.get_lr()
                # Forward Pass + Compute loss at the same time (see CustomDataParallel and NetLoss)
                with amp.auto_cast(enable=True):
                    losses = net(datum)
                
                    losses = { k: (v).mean() for k,v in losses.items() } # Mean here because Dataparallel
                    loss = paddle.add_n([losses[k] for k in losses])   # 
                scaled_loss = scaler.scale(loss)
                scaled_loss.backward()
                scaler.minimize(optimizer, scaled_loss)
                lr_scheduler.step()
                # no_inf_mean removes some components from the loss, so make sure to backward through all of it
                # all_loss = sum([v.mean() for v in losses.values()])

                # Backprop
                # loss.backward() # Do this to free up vram even if loss is not finite
                # if paddle.isfinite(loss).item():
                #     optimizer.step()
                #     lr_scheduler.step()
                
                # Add the loss to the moving average for bookkeeping
                for k in losses:
                    loss_avgs[k].add(losses[k].item())

                cur_time  = time.time()
                elapsed   = cur_time - last_time
                last_time = cur_time

                # Exclude graph setup from the timing information
                if iteration != args.start_iter:
                    time_avg.add(elapsed)

                if iteration % 10 == 0:
                    eta_str = str(datetime.timedelta(seconds=(cfg.max_iter-iteration) * time_avg.get_avg())).split('.')[0]
                    
                    total = sum([loss_avgs[k].get_avg() for k in losses])
                    loss_labels = sum([[k, loss_avgs[k].get_avg()] for k in loss_types if k in losses], [])
                    
                    print(('[%3d] %7d ||' + " lr: %.7f |" + (' %s: %.3f |' * len(losses)) + ' T: %.3f || ETA: %s || timer: %.3f')
                            % tuple([epoch, iteration, curr_lr] + loss_labels + [total, eta_str, elapsed]), flush=True)

                if args.log and iteration % 10 == 0:
                    precision = 5
                    loss_info = {k: round(losses[k].item(), precision) for k in losses}
                    loss_info['T'] = round(loss.item(), precision)

                    if args.log_gpu:
                        log.log_gpu_stats = (iteration % 10 == 0) # nvidia-smi is sloooow
                        
                    log.log('train', loss=loss_info, epoch=epoch, iter=iteration,
                        lr=round(curr_lr, 10), elapsed=elapsed)

                    log.log_gpu_stats = args.log_gpu
                
                iteration += 1
                if iteration % args.save_interval == 0 and iteration != args.start_iter:
                    if args.keep_latest:
                        latest = SavePath.get_latest(args.save_folder, cfg.name)

                    print('Saving state, iter:', iteration)
                    yolact_net.save_weights(save_path(epoch, iteration))
                    paddle.save(optimizer.state_dict(),
                            os.path.join("./weights/", 'model_%d.pdopt'%iteration))
                    if iteration >= args.validation_epoch:  # when iter  >= 350000 valid test
						compute_validation_map(epoch, iteration, yolact_net, val_dataset, log if args.log else None)
                    if args.keep_latest and latest is not None:
                        if args.keep_latest_interval <= 0 or iteration % args.keep_latest_interval != args.save_interval:
                            print('Deleting old save...')
                            os.remove(latest)
            
            # This is done per epoch
            # if args.validation_epoch > 0:
            #     if epoch % args.validation_epoch == 0 and epoch > 0:
            #         compute_validation_map(epoch, iteration, yolact_net, val_dataset, log if args.log else None)
        
        # Compute validation mAP after training is finished
        # compute_validation_map(epoch, iteration, yolact_net, val_dataset, log if args.log else None)
    except KeyboardInterrupt:
        if args.interrupt:
            print('Stopping early. Saving network...')
            
            # Delete previous copy of the interrupted network so we don't spam the weights folder
            SavePath.remove_interrupt(args.save_folder)
            paddle.save(optimizer.state_dict(),
                            os.path.join("./weights/", 'model_%d.pdopt'%iteration))
            yolact_net.save_weights(save_path(epoch, repr(iteration) + '_interrupt'))
        exit()

    yolact_net.save_weights(save_path(epoch, iteration))
    paddle.save(optimizer.state_dict(),
                            os.path.join("./weights/", 'model_%d.pdopt'%iteration))


def set_lr(optimizer, new_lr):
    for param_group in optimizer.param_groups:
        param_group['lr'] = new_lr
    
    global cur_lr
    cur_lr = new_lr

def gradinator(x):
    x.stop_gradient = True
    return x

def prepare_data(datum, devices:list=None, allocation:list=None):  ###
    with paddle.no_grad():
        if devices is None:
            devices = ['cuda:0'] if args.cuda else ['cpu']
        if allocation is None:
            allocation = [args.batch_size // len(devices)] * (len(devices) - 1)
            allocation.append(args.batch_size - sum(allocation)) # The rest might need more/less
        
        images, (targets, masks, num_crowds) = datum

        cur_idx = 0
        for device, alloc in zip(devices, allocation):
            for _ in range(alloc):
                images[cur_idx]  = gradinator(images[cur_idx].to(device))
                targets[cur_idx] = gradinator(targets[cur_idx].to(device))
                masks[cur_idx]   = gradinator(masks[cur_idx].to(device))
                cur_idx += 1

        if cfg.preserve_aspect_ratio:
            # Choose a random size from the batch
            _, h, w = images[random.randint(0, len(images)-1)].size()

            for idx, (image, target, mask, num_crowd) in enumerate(zip(images, targets, masks, num_crowds)):
                images[idx], targets[idx], masks[idx], num_crowds[idx] \
                    = enforce_size(image, target, mask, num_crowd, w, h)
        
        cur_idx = 0
        split_images, split_targets, split_masks, split_numcrowds \
            = [[None for alloc in allocation] for _ in range(4)]

        for device_idx, alloc in enumerate(allocation):
            split_images[device_idx]    = paddle.stack(images[cur_idx:cur_idx+alloc], axis=0)
            split_targets[device_idx]   = targets[cur_idx:cur_idx+alloc]
            split_masks[device_idx]     = masks[cur_idx:cur_idx+alloc]
            split_numcrowds[device_idx] = num_crowds[cur_idx:cur_idx+alloc]

            cur_idx += alloc

        return split_images, split_targets, split_masks, split_numcrowds

def no_inf_mean(x:paddle.Tensor):
    """
    Computes the mean of a vector, throwing out all inf values.
    If there are no non-inf values, this will return inf (i.e., just the normal mean).
    """

    no_inf = [a for a in x if paddle.isfinite(a)]

    if len(no_inf) > 0:
        return sum(no_inf) / len(no_inf)
    else:
        return x.mean()

def compute_validation_loss(net, data_loader, criterion):
    global loss_types

    with paddle.no_grad():
        losses = {}
        
        # Don't switch to eval mode because we want to get losses
        iterations = 0
        for datum in data_loader:
            images, targets, masks, num_crowds = prepare_data(datum)
            out = net(images)

            wrapper = ScatterWrapper(targets, masks, num_crowds)  ###
            _losses = criterion(out, wrapper, wrapper.make_mask())
            
            for k, v in _losses.items():
                v = v.mean().item()
                if k in losses:
                    losses[k] += v
                else:
                    losses[k] = v

            iterations += 1
            if args.validation_size <= iterations * args.batch_size:
                break
        
        for k in losses:
            losses[k] /= iterations
            
        
        loss_labels = sum([[k, losses[k]] for k in loss_types if k in losses], [])
        print(('Validation ||' + (' %s: %.3f |' * len(losses)) + ')') % tuple(loss_labels), flush=True)

def compute_validation_map(epoch, iteration, yolact_net, dataset, log:Log=None):
    with paddle.no_grad():
        yolact_net.eval()
        
        start = time.time()
        print()
        print("Computing validation mAP (this may take a while)...", flush=True)
        val_info = eval_script.evaluate(yolact_net, dataset, train_mode=True)
        end = time.time()

        if log is not None:
            log.log('val', val_info, elapsed=(end - start), epoch=epoch, iter=iteration)

        yolact_net.train()

def setup_eval():
    eval_script.parse_args(['--no_bar', '--max_images='+str(args.validation_size)])

if __name__ == '__main__':
    train()