我试图测试经过训练的数据。
!/usr/bin/env python
Copyright (c) 2016 Artsiom Sanakoyeu
from __future__ import division
from chainer import iterators
import cmd_options
import dataset
import os
import time
import regressionnet
import tensorflow as tf
import copy
from tqdm import tqdm
import numpy as np
import math
import pprint
import datetime
from regressionnet import evaluate_pcp, create_sumamry
def evaluate(net, pose_loss_op, test_iterator, summary_writer, tag='test/pose_loss'):
test_it = copy.copy(test_iterator)
total_loss = 0.0
cnt = 0
num_batches = int(math.ceil(len(test_it.dataset) / test_it.batch_size))
print len(test_it.dataset)
for batch in tqdm(test_it, total=num_batches):
feed_dict = regressionnet.fill_joint_feed_dict(net,
regressionnet.batch2feeds(batch)[:3],
conv_lr=0.0,
fc_lr=0.0,
phase='test')
global_step, loss_value = net.sess.run([net.global_iter_counter, pose_loss_op],
feed_dict=feed_dict)
total_loss += loss_value * len(batch)
cnt += len(batch)
avg_loss = total_loss / len(test_it.dataset)
print 'Step {} {} = {:.3f}'.format(global_step, tag, avg_loss)
summary_writer.add_summary(create_sumamry(tag, avg_loss),
global_step=global_step)
assert cnt == 1000, 'cnt = {}'.format(cnt)
def train_loop(net, saver, loss_op, pose_loss_op, train_op, dataset_name, train_iterator, test_iterator,
val_iterator=None,
max_iter=None,
test_step=None,
snapshot_step=None,
log_step=1,
batch_size=None,
conv_lr=None,
fc_lr=None,
fix_conv_iter=None,
output_dir='results',
):
summary_step = 50
with net.graph.as_default():
summary_writer = tf.summary.FileWriter(output_dir, net.sess.graph)
summary_op = tf.summary.merge_all()
fc_train_op = net.graph.get_operation_by_name('fc_train_op')
global_step = None
for step in xrange(max_iter + 1):
# test, snapshot
if step % test_step == 0 or step + 1 == max_iter or step == fix_conv_iter:
global_step = net.sess.run(net.global_iter_counter)
evaluate_pcp(net, pose_loss_op, test_iterator, summary_writer,
dataset_name=dataset_name,
tag_prefix='test')
if val_iterator is not None:
evaluate_pcp(net, pose_loss_op, val_iterator, summary_writer,
dataset_name=dataset_name,
tag_prefix='val')
if step % snapshot_step == 0 and step > 1:
checkpoint_prefix = os.path.join(output_dir, 'checkpoint')
assert global_step is not None
saver.save(net.sess, checkpoint_prefix, global_step=global_step)
if step == max_iter:
break
# training
start_time = time.time()
feed_dict = regressionnet.fill_joint_feed_dict(net,
regressionnet.batch2feeds(train_iterator.next())[:3],
conv_lr=conv_lr,
fc_lr=fc_lr,
phase='train')
if step < fix_conv_iter:
feed_dict['lr/conv_lr:0'] = 0.0
if step < fix_conv_iter:
cur_train_op = fc_train_op
else:
cur_train_op = train_op
if step % summary_step == 0:
global_step, summary_str, _, loss_value = net.sess.run(
[net.global_iter_counter,
summary_op,
cur_train_op,
pose_loss_op],
feed_dict=feed_dict)
summary_writer.add_summary(summary_str, global_step=global_step)
else:
global_step, _, loss_value = net.sess.run(
[net.global_iter_counter, cur_train_op, pose_loss_op],
feed_dict=feed_dict)
duration = time.time() - start_time
if step % log_step == 0 or step + 1 == max_iter:
print('Step %d: train/pose_loss = %.2f (%.3f s, %.2f im/s)'
% (global_step, loss_value, duration,
batch_size // duration))
def main(argv):
"""
Run training of the Deeppose stg-1
"""
args = cmd_options.get_arguments(argv)
if not os.path.exists(args.o_dir):
os.makedirs(args.o_dir)
suffix = datetime.datetime.now().strftime("%y%m%d_%H%M%S")
with open(os.path.join(args.o_dir, 'params.dump_{}.txt'.format(suffix)), 'w') as f:
f.write('{}\n'.format(pprint.pformat(args)))
net, loss_op, pose_loss_op, train_op = regressionnet.create_regression_net(
n_joints=args.n_joints,
init_snapshot_path=args.snapshot,
is_resume=args.resume,
reset_iter_counter=args.reset_iter_counter,
reset_moving_averages=args.reset_moving_averages,
optimizer_type=args.optimizer,
gpu_memory_fraction=0.32, # Set how much GPU memory to reserve for the network
net_type=args.net_type)
with net.graph.as_default():
saver = tf.train.Saver()
print 'args.resume: {}\nargs.snapshot: {}'.format(args.resume, args.snapshot)
bbox_extension_range = (args.bbox_extension_min, args.bbox_extension_max)
if bbox_extension_range[0] is None or bbox_extension_range[1] is None:
bbox_extension_range = None
test_bbox_extension_range = None
else:
test_bbox_extension_range = (bbox_extension_range[1], bbox_extension_range[1])
train_dataset = dataset.PoseDataset(
args.train_csv_fn, args.img_path_prefix, args.im_size,
fliplr=args.fliplr,
rotate=args.rotate,
rotate_range=args.rotate_range,
shift=args.shift,
bbox_extension_range=bbox_extension_range,
min_dim=args.min_dim,
coord_normalize=args.coord_normalize,
gcn=args.gcn,
fname_index=args.fname_index,
joint_index=args.joint_index,
symmetric_joints=args.symmetric_joints,
ignore_label=args.ignore_label,
should_downscale_images=args.should_downscale_images,
downscale_height=args.downscale_height
)
test_dataset = dataset.PoseDataset(
args.test_csv_fn, args.img_path_prefix, args.im_size,
fliplr=False, rotate=False,
shift=None,
bbox_extension_range=test_bbox_extension_range,
coord_normalize=args.coord_normalize,
gcn=args.gcn,
fname_index=args.fname_index,
joint_index=args.joint_index,
symmetric_joints=args.symmetric_joints,
ignore_label=args.ignore_label,
should_return_bbox=True,
should_downscale_images=args.should_downscale_images,
downscale_height=args.downscale_height
)
np.random.seed(args.seed)
train_iterator = iterators.MultiprocessIterator(train_dataset, args.batch_size,
n_processes=args.workers, n_prefetch=3)
test_iterator = iterators.MultiprocessIterator(
test_dataset, args.batch_size,
repeat=False, shuffle=False,
n_processes=1, n_prefetch=1)
val_iterator = None
if args.val_csv_fn is not None and args.val_csv_fn != '':
small_train_dataset = dataset.PoseDataset(
args.val_csv_fn,
args.img_path_prefix, args.im_size,
fliplr=False, rotate=False,
shift=None,
bbox_extension_range=test_bbox_extension_range,
coord_normalize=args.coord_normalize,
gcn=args.gcn,
fname_index=args.fname_index,
joint_index=args.joint_index,
symmetric_joints=args.symmetric_joints,
ignore_label=args.ignore_label,
should_return_bbox=True,
should_downscale_images=args.should_downscale_images,
downscale_height=args.downscale_height
)
val_iterator = iterators.MultiprocessIterator(
small_train_dataset, args.batch_size,
repeat=False, shuffle=False,
n_processes=1, n_prefetch=1)
train_loop(net, saver, loss_op, pose_loss_op, train_op, args.dataset_name,
train_iterator, test_iterator,
val_iterator=val_iterator,
max_iter=args.max_iter,
test_step=args.test_step,
log_step=args.log_step,
snapshot_step=args.snapshot_step,
batch_size=args.batch_size,
conv_lr=args.conv_lr,
fc_lr=args.fc_lr,
fix_conv_iter=args.fix_conv_iter,
output_dir=args.o_dir
)
if __name__ == '__main__':
import sys
main(sys.argv[1:])
这是我使用的代码。 我把它拖了大约370000次迭代，我试着测试训练好的数据。
但是它显示了这条消息
数据丢失：不是sstable（错误的幻数）：也许你的文件采用不同的文件格式，你需要使用不同的恢复操作符？ 回溯（最近一次调用最后一次）：
DataLossError（参见上面的回溯）：无法打开表文件/ lsp_alexnet_imagenet_small / checkpoint-370000.data-00000-of-00001：数据丢失：不是sstable（错误的幻数）：也许你的文件在不同的文件格式，您需要使用不同的还原操作符？
如何解决此问题？