def add_part_indicatormap(data_dir, save_dir, mdim, rate, filter_size, stride):
"""
This function is used for generating part indicator map for old data
data_dir is the directory that you put all batch_datayes
"""
allfile = iu.getfilelist(data_dir, 'data_batch_\d+')
meta_path = iu.fullfile(data_dir, 'batches.meta')
iu.ensure_dir(save_dir)
if iu.exists(meta_path, 'file'):
d_meta = myio.unpickle(meta_path)
if 'savedata_info' not in d_meta:
d_meta['savedata_info'] = dict()
d_meta['savedata_info']['indmap_para'] = dict()
d_meta['savedata_info']['indmap_para']['filter_size'] = filter_size
d_meta['savedata_info']['indmap_para']['stride'] = stride
d_meta['savedata_info']['indmap_para']['rate'] = rate
myio.pickle(iu.fullfile(save_dir, 'batches.meta'), d_meta)
for fn in allfile:
print 'Processing %s' % fn
d = myio.unpickle(iu.fullfile(data_dir, fn))
ndata = d['data'].shape[-1]
nparts = 7
d['indmap'] = np.zeros((nparts, mdim[0], mdim[1], ndata),
dtype=np.bool)
for i in range(ndata):
jts = d['joints8'][..., i]
d['indmap'][..., i] = HMLPE.create_part_indicatormap(
jts, part_idx, mdim, rate, filter_size, stride)
myio.pickle(iu.fullfile(save_dir, fn), d)
def add_part_indicatormap(data_dir, save_dir, mdim, rate, filter_size, stride):
"""
This function is used for generating part indicator map for old data
data_dir is the directory that you put all batch_datayes
"""
allfile = iu.getfilelist(data_dir, 'data_batch_\d+')
meta_path = iu.fullfile(data_dir, 'batches.meta')
iu.ensure_dir(save_dir)
if iu.exists(meta_path, 'file'):
d_meta = myio.unpickle(meta_path)
if 'savedata_info' not in d_meta:
d_meta['savedata_info'] = dict()
d_meta['savedata_info']['indmap_para'] = dict()
d_meta['savedata_info']['indmap_para']['filter_size'] = filter_size
d_meta['savedata_info']['indmap_para']['stride'] = stride
d_meta['savedata_info']['indmap_para']['rate'] = rate
myio.pickle(iu.fullfile(save_dir, 'batches.meta'), d_meta)
for fn in allfile:
print 'Processing %s' % fn
d = myio.unpickle(iu.fullfile(data_dir, fn))
ndata = d['data'].shape[-1]
nparts = 7
d['indmap'] = np.zeros((nparts, mdim[0], mdim[1], ndata), dtype=np.bool)
for i in range(ndata):
jts = d['joints8'][...,i]
d['indmap'][...,i] = HMLPE.create_part_indicatormap(jts, part_idx, mdim, rate, filter_size, stride)
myio.pickle(iu.fullfile(save_dir, fn), d)
def merge_batch_data(data_dir_list,
save_dir,
is_symbolic=True,
batch_start_num=1):
"""
This function will merge all the data_batches in data_dir into one folder
and rename them accordining.
Of cause, meta data will be updated
"""
import os
import shutil
iu.ensure_dir(save_dir)
meta = None
for ddir in data_dir_list:
cur_meta = myio.unpickle(iu.fullfile(ddir, 'batches.meta'))
meta = HMLPE.merge_meta(meta, cur_meta)
myio.pickle(iu.fullfile(save_dir, 'batches.meta'), meta)
cur_id = batch_start_num
for ddir in data_dir_list:
all_file = iu.getfilelist(ddir, 'data_batch_\d+')
print 'I find %d batches in %s' % (len(all_file), ddir)
if is_symbolic:
for fn in all_file:
sn = iu.fullfile(save_dir, 'data_batch_%d' % cur_id)
if iu.exists(sn, 'file'):
os.remove(sn)
os.symlink(iu.fullfile(ddir, fn), sn)
cur_id = cur_id + 1
else:
for fn in all_file:
shutil.copyfile(
iu.fullfile(ddir, fn),
iu.fullfile(save_dir, 'data_batch_%d' % cur_id))
cur_id = cur_id + 1
def test_addmeta(metadir):
metapath = iu.fullfile(metadir, 'batches.meta')
d_meta = myio.unpickle(metapath)
d_meta['ind_dim'] = dict()
d_meta['ind_dim']['part_indmap'] = (8, 8)
d_meta['ind_dim']['joint_indmap'] = (8, 8)
myio.pickle(metapath, d_meta)
def test_addmeta(metadir):
metapath = iu.fullfile(metadir, 'batches.meta')
d_meta = myio.unpickle(metapath)
d_meta['ind_dim'] = dict()
d_meta['ind_dim']['part_indmap'] = (8,8)
d_meta['ind_dim']['joint_indmap'] = (8,8)
myio.pickle(metapath, d_meta)
def merge_batch_data(data_dir_list, save_dir, is_symbolic = True, batch_start_num = 1):
"""
This function will merge all the data_batches in data_dir into one folder
and rename them accordining.
Of cause, meta data will be updated
"""
import os
import shutil
iu.ensure_dir(save_dir)
meta = None
for ddir in data_dir_list:
cur_meta = myio.unpickle(iu.fullfile(ddir, 'batches.meta'))
meta = HMLPE.merge_meta(meta, cur_meta)
myio.pickle(iu.fullfile(save_dir, 'batches.meta'), meta)
cur_id = batch_start_num
for ddir in data_dir_list:
all_file = iu.getfilelist(ddir, 'data_batch_\d+')
print 'I find %d batches in %s' % (len(all_file), ddir)
if is_symbolic:
for fn in all_file:
sn = iu.fullfile(save_dir, 'data_batch_%d' % cur_id)
if iu.exists(sn, 'file'):
os.remove(sn)
os.symlink(iu.fullfile(ddir, fn), sn)
cur_id = cur_id + 1
else:
for fn in all_file:
shutil.copyfile(iu.fullfile(ddir, fn), iu.fullfile(save_dir, 'data_batch_%d' % cur_id))
cur_id = cur_id + 1
def shuffle_data(source_dir, target_dir, max_per_file=4000):
"""
This function will shuflle all the data in source_dir
and save it to target_dir
"""
if source_dir == target_dir:
raise HMLPEError('source dir can not be the same as target dir')
import shutil
import sys
iu.ensure_dir(target_dir)
shutil.copy(iu.fullfile(source_dir, 'batches.meta'), \
iu.fullfile(target_dir, 'batches.meta'))
meta = myio.unpickle(iu.fullfile(source_dir, 'batches.meta'))
ndata = meta['ndata']
nbatch = (ndata - 1) / max_per_file + 1
nparts = meta['nparts']
njoints = meta['njoints']
newdim = meta['savedata_info']['newdim']
filter_size = meta['savedata_info']['indmap_para']['filter_size']
stride = meta['savedata_info']['indmap_para']['stride']
joint_filter_size = meta['savedata_info']['indmap_para'][
'joint_filter_size']
joint_stride = meta['savedata_info']['indmap_para']['joint_stride']
mdim = HMLPE.get_indmapdim(newdim, filter_size, stride)
jtmdim = HMLPE.get_indmapdim(newdim, joint_filter_size, joint_stride)
print('There are %d data in total, I need %d batch to hold it' %
(ndata, nbatch))
print 'Begin creating empty files'
rest = ndata
d = HMLPE.prepare_savebuffer({'data':newdim, 'part_indmap':mdim, \
'joint_indmap': jtmdim}, max_per_file, nparts, njoints)
HMLPE.adjust_savebuffer_shape(d)
for b in range(nbatch):
cur_n = min(max_per_file, rest)
if b != nbatch - 1:
saved = d
else:
saved = HMLPE.prepare_savebuffer({'data':newdim, 'part_indmap':mdim, \
'joint_indmap': jtmdim}, cur_n, nparts, njoints)
HMLPE.adjust_savebuffer_shape(saved)
myio.pickle(iu.fullfile(target_dir, 'data_batch_%d' % (b + 1)), saved)
rest = rest - cur_n
print 'End creating'
allbatchfn = iu.getfilelist(source_dir, 'data_batch_\d+')
np.random.seed(7)
perm = range(ndata)
np.random.shuffle(perm)
buf_cap = 12 # store six batch at most
nround = (nbatch - 1) / buf_cap + 1
for rd in range(nround):
print('Round %d of %d' % (rd, nround))
buf = dict()
offset = 0
for fn in allbatchfn:
print('Processing %s' % fn)
d = myio.unpickle(iu.fullfile(source_dir, fn))
cur_n = d['data'].shape[-1]
for b in range(rd * buf_cap, min(nbatch, (rd + 1) * buf_cap)):
sys.stdout.write('\rpadding %d of %d' % (b + 1, nbatch))
sys.stdout.flush()
sidx = b * max_per_file
eidx = min(ndata, sidx + max_per_file)
cur_idx_list = [
i for i in range(cur_n)
if perm[offset + i] >= sidx and perm[offset + i] < eidx
]
if len(cur_idx_list) == 0:
continue
if not b in buf:
dsave = myio.unpickle(
iu.fullfile(target_dir, 'data_batch_%d' % (b + 1)))
buf[b] = dsave
else:
dsave = buf[b]
save_idx_list = [perm[x + offset] - sidx for x in cur_idx_list]
HMLPE.selective_copydic(d, dsave, cur_idx_list, save_idx_list)
# myio.pickle(iu.fullfile(target_dir, 'data_batch_%d' % (b+1)), dsave)
print 'Finished %s' % fn
offset = offset + cur_n
for b in range(rd * buf_cap, min(nbatch, (rd + 1) * buf_cap)):
myio.pickle(iu.fullfile(target_dir, 'data_batch_%d' % (b + 1)),
buf[b])
def shuffle_data(source_dir, target_dir, max_per_file = 4000):
"""
This function will shuflle all the data in source_dir
and save it to target_dir
"""
if source_dir == target_dir:
raise HMLPEError('source dir can not be the same as target dir')
import shutil
import sys
iu.ensure_dir( target_dir)
shutil.copy(iu.fullfile(source_dir, 'batches.meta'), \
iu.fullfile(target_dir, 'batches.meta'))
meta = myio.unpickle(iu.fullfile(source_dir, 'batches.meta'))
ndata = meta['ndata']
nbatch = (ndata - 1) / max_per_file + 1
nparts = meta['nparts']
njoints = meta['njoints']
newdim = meta['savedata_info']['newdim']
filter_size = meta['savedata_info']['indmap_para']['filter_size']
stride = meta['savedata_info']['indmap_para']['stride']
joint_filter_size = meta['savedata_info']['indmap_para']['joint_filter_size']
joint_stride = meta['savedata_info']['indmap_para']['joint_stride']
mdim = HMLPE.get_indmapdim(newdim, filter_size, stride)
jtmdim = HMLPE.get_indmapdim(newdim, joint_filter_size, joint_stride)
print('There are %d data in total, I need %d batch to hold it' %(ndata, nbatch))
print 'Begin creating empty files'
rest = ndata
d = HMLPE.prepare_savebuffer({'data':newdim, 'part_indmap':mdim, \
'joint_indmap': jtmdim}, max_per_file, nparts, njoints)
HMLPE.adjust_savebuffer_shape(d)
for b in range(nbatch):
cur_n = min(max_per_file, rest)
if b != nbatch - 1:
saved = d
else:
saved = HMLPE.prepare_savebuffer({'data':newdim, 'part_indmap':mdim, \
'joint_indmap': jtmdim}, cur_n, nparts, njoints)
HMLPE.adjust_savebuffer_shape(saved)
myio.pickle(iu.fullfile(target_dir, 'data_batch_%d' % (b + 1)), saved)
rest = rest - cur_n
print 'End creating'
allbatchfn = iu.getfilelist(source_dir, 'data_batch_\d+')
np.random.seed(7)
perm = range(ndata)
np.random.shuffle(perm)
buf_cap = 12 # store six batch at most
nround = (nbatch - 1)/buf_cap + 1
for rd in range(nround):
print ('Round %d of %d' % (rd,nround))
buf = dict()
offset = 0
for fn in allbatchfn:
print( 'Processing %s' % fn )
d = myio.unpickle(iu.fullfile(source_dir, fn))
cur_n = d['data'].shape[-1]
for b in range(rd * buf_cap, min(nbatch, (rd+1)*buf_cap)):
sys.stdout.write('\rpadding %d of %d' % (b + 1, nbatch))
sys.stdout.flush()
sidx = b * max_per_file
eidx = min(ndata, sidx + max_per_file)
cur_idx_list = [i for i in range(cur_n) if perm[offset + i] >= sidx and perm[offset + i] < eidx]
if len(cur_idx_list) == 0:
continue
if not b in buf:
dsave = myio.unpickle(iu.fullfile(target_dir, 'data_batch_%d' % (b+1)))
buf[b] = dsave
else:
dsave = buf[b]
save_idx_list = [perm[ x + offset] - sidx for x in cur_idx_list]
HMLPE.selective_copydic(d, dsave, cur_idx_list, save_idx_list)
# myio.pickle(iu.fullfile(target_dir, 'data_batch_%d' % (b+1)), dsave)
print 'Finished %s' % fn
offset = offset + cur_n
for b in range(rd * buf_cap, min(nbatch, (rd+1)*buf_cap)):
myio.pickle(iu.fullfile(target_dir, 'data_batch_%d' % (b+1)), buf[b])
