when i train my openpose code from github,i face this error
Traceback (most recent call last):
File "train.py", line 170, in <module>
args.checkpoint_after, args.val_after)
File "train.py", line 80, in train
for batch_data in train_loader:
File "C:\Users\ruin2\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 345, in __next__
data = self._next_data()
File "C:\Users\ruin2\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 841, in _next_data
idx, data = self._get_data()
File "C:\Users\ruin2\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 808, in _get_data
success, data = self._try_get_data()
File "C:\Users\ruin2\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 761, in _try_get_data
data = self._data_queue.get(timeout=timeout)
File "C:\Users\ruin2\Anaconda3\lib\multiprocessing\queues.py", line 113, in get
return _ForkingPickler.loads(res)
File "C:\Users\ruin2\Anaconda3\lib\site-packages\torch\multiprocessing\reductions.py", line 310, in rebuild_storage_filename
storage = cls._new_shared_filename(manager, handle, size)
RuntimeError: Couldn't open shared event: <torch_32244_1540031554_event>, error code: <2>
i changed batch_size but cann’t solve the error,this my code,my dataset is 2017coco,gpu is 2070
import argparse
import cv2
import os
import torch
from torch.nn import DataParallel
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import transforms
from datasets.coco import CocoTrainDataset
from datasets.transformations import ConvertKeypoints, Scale, Rotate, CropPad, Flip
from modules.get_parameters import get_parameters_conv, get_parameters_bn, get_parameters_conv_depthwise
from models.with_mobilenet import PoseEstimationWithMobileNet
from modules.loss import l2_loss
from modules.load_state import load_state, load_from_mobilenet
from val import evaluate
cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False) # To prevent freeze of DataLoader
def train(prepared_train_labels, train_images_folder, num_refinement_stages, base_lr, batch_size, batches_per_iter,
num_workers, checkpoint_path, weights_only, from_mobilenet, checkpoints_folder, log_after,
val_labels, val_images_folder, val_output_name, checkpoint_after, val_after):
net = PoseEstimationWithMobileNet(num_refinement_stages)
batch_size=1
stride = 8
sigma = 7
path_thickness = 1
dataset = CocoTrainDataset(prepared_train_labels, train_images_folder,
stride, sigma, path_thickness,
transform=transforms.Compose([
ConvertKeypoints(),
Scale(),
Rotate(pad=(128, 128, 128)),
CropPad(pad=(128, 128, 128)),
Flip()]))
train_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
optimizer = optim.Adam([
{'params': get_parameters_conv(net.model, 'weight')},
{'params': get_parameters_conv_depthwise(net.model, 'weight'), 'weight_decay': 0},
{'params': get_parameters_bn(net.model, 'weight'), 'weight_decay': 0},
{'params': get_parameters_bn(net.model, 'bias'), 'lr': base_lr * 2, 'weight_decay': 0},
{'params': get_parameters_conv(net.cpm, 'weight'), 'lr': base_lr},
{'params': get_parameters_conv(net.cpm, 'bias'), 'lr': base_lr * 2, 'weight_decay': 0},
{'params': get_parameters_conv_depthwise(net.cpm, 'weight'), 'weight_decay': 0},
{'params': get_parameters_conv(net.initial_stage, 'weight'), 'lr': base_lr},
{'params': get_parameters_conv(net.initial_stage, 'bias'), 'lr': base_lr * 2, 'weight_decay': 0},
{'params': get_parameters_conv(net.refinement_stages, 'weight'), 'lr': base_lr * 4},
{'params': get_parameters_conv(net.refinement_stages, 'bias'), 'lr': base_lr * 8, 'weight_decay': 0},
{'params': get_parameters_bn(net.refinement_stages, 'weight'), 'weight_decay': 0},
{'params': get_parameters_bn(net.refinement_stages, 'bias'), 'lr': base_lr * 2, 'weight_decay': 0},
], lr=base_lr, weight_decay=5e-4)
num_iter = 0
current_epoch = 0
drop_after_epoch = [100, 200, 260]
scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=drop_after_epoch, gamma=0.333)
if checkpoint_path:
checkpoint = torch.load(checkpoint_path,map_location='cpu')
if from_mobilenet:
load_from_mobilenet(net, checkpoint)
else:
load_state(net, checkpoint)
if not weights_only:
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler.load_state_dict(checkpoint['scheduler'])
num_iter = checkpoint['iter']
current_epoch = checkpoint['current_epoch']
net = DataParallel(net).cuda()
net.train()
for epochId in range(current_epoch, 280):
scheduler.step()
total_losses = [0, 0] * (num_refinement_stages + 1) # heatmaps loss, paf loss per stage
batch_per_iter_idx = 0
for batch_data in train_loader:
if batch_per_iter_idx == 0:
optimizer.zero_grad()
images = batch_data['image'].cuda()
keypoint_masks = batch_data['keypoint_mask'].cuda()
paf_masks = batch_data['paf_mask'].cuda()
keypoint_maps = batch_data['keypoint_maps'].cuda()
paf_maps = batch_data['paf_maps'].cuda()
stages_output = net(images)
losses = []
for loss_idx in range(len(total_losses) // 2):
losses.append(l2_loss(stages_output[loss_idx * 2], keypoint_maps, keypoint_masks, images.shape[0]))
losses.append(l2_loss(stages_output[loss_idx * 2 + 1], paf_maps, paf_masks, images.shape[0]))
total_losses[loss_idx * 2] += losses[-2].item() / batches_per_iter
total_losses[loss_idx * 2 + 1] += losses[-1].item() / batches_per_iter
loss = losses[0]
for loss_idx in range(1, len(losses)):
loss += losses[loss_idx]
loss /= batches_per_iter
loss.backward()
batch_per_iter_idx += 1
if batch_per_iter_idx == batches_per_iter:
optimizer.step()
batch_per_iter_idx = 0
num_iter += 1
else:
continue
if num_iter % log_after == 0:
print('Iter: {}'.format(num_iter))
for loss_idx in range(len(total_losses) // 2):
print('\n'.join(['stage{}_pafs_loss: {}', 'stage{}_heatmaps_loss: {}']).format(
loss_idx + 1, total_losses[loss_idx * 2 + 1] / log_after,
loss_idx + 1, total_losses[loss_idx * 2] / log_after))
for loss_idx in range(len(total_losses)):
total_losses[loss_idx] = 0
if num_iter % checkpoint_after == 0:
snapshot_name = '{}/checkpoint_iter_{}.pth'.format(checkpoints_folder, num_iter)
torch.save({'state_dict': net.module.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'iter': num_iter,
'current_epoch': epochId},
snapshot_name)
if num_iter % val_after == 0:
print('Validation...')
evaluate(val_labels, val_output_name, val_images_folder, net)
net.train()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--prepared-train-labels', type=str, required=True,
help='path to the file with prepared annotations')
parser.add_argument('--train-images-folder', type=str, required=True, help='path to COCO train images folder')
parser.add_argument('--num-refinement-stages', type=int, default=1, help='number of refinement stages')
parser.add_argument('--base-lr', type=float, default=4e-5, help='initial learning rate')
parser.add_argument('--batch-size', type=int, default=80, help='batch size')
parser.add_argument('--batches-per-iter', type=int, default=1, help='number of batches to accumulate gradient from')
parser.add_argument('--num-workers', type=int, default=1, help='number of workers')
parser.add_argument('--checkpoint-path', type=str, required=True, help='path to the checkpoint to continue training from')
parser.add_argument('--from-mobilenet', action='store_true',
help='load weights from mobilenet feature extractor')
parser.add_argument('--weights-only', action='store_true',
help='just initialize layers with pre-trained weights and start training from the beginning')
parser.add_argument('--experiment-name', type=str, default='default',
help='experiment name to create folder for checkpoints')
parser.add_argument('--log-after', type=int, default=100, help='number of iterations to print train loss')
parser.add_argument('--val-labels', type=str, required=True, help='path to json with keypoints val labels')
parser.add_argument('--val-images-folder', type=str, required=True, help='path to COCO val images folder')
parser.add_argument('--val-output-name', type=str, default='detections.json',
help='name of output json file with detected keypoints')
parser.add_argument('--checkpoint-after', type=int, default=1000,
help='number of iterations to save checkpoint')
parser.add_argument('--val-after', type=int, default=1000,
help='number of iterations to run validation')
args = parser.parse_args()
checkpoints_folder = '{}_checkpoints'.format(args.experiment_name)
if not os.path.exists(checkpoints_folder):
os.makedirs(checkpoints_folder)
train(args.prepared_train_labels, args.train_images_folder, args.num_refinement_stages, args.base_lr, args.batch_size,
args.batches_per_iter, args.num_workers, args.checkpoint_path, args.weights_only, args.from_mobilenet,
checkpoints_folder, args.log_after, args.val_labels, args.val_images_folder, args.val_output_name,
args.checkpoint_after, args.val_after)
someone who know how to solve this problem ,can teach my,thanks!
