def index(request):
"""
Default view
"""
try:
wusers = WaContacts.objects.filter(is_whatsapp_user=1).count()
nwusers = WaContacts.objects.filter(is_whatsapp_user=0).count()
wasize = getsize(DATABASES['default']['NAME'])
wafile = basename(DATABASES['default']['NAME'])
wamd5 = get_md5_file(DATABASES['default']['NAME'])
washa1 = get_sha1_file(DATABASES['default']['NAME'])
waperm = "<b><font color=\"red\">YES (check file permissons)</font></b>" if access(DATABASES['default']['NAME'],W_OK) is True else "<b><font color=\"blue\">No</font></b>"
except:
wusers = Messages.objects.using('msgstore').values('key_remote_jid').distinct().count()
nwusers = 0
wasize = 0
wafile = "Not found!"
wamd5 = "N/A"
washa1 = "N/A"
waperm = 'N/A'
msgperm = "<b><font color=\"red\">YES (check file permissons)</font></b>" if access(DATABASES['msgstore']['NAME'],W_OK) is True else "<b><font color=\"blue\">No</font></b>"
dic = {
'whatsappusers': wusers,
'nonwhatsappusers': nwusers,
'activity': get_activity_data(None),
'fromme': Messages.objects.using('msgstore').filter(key_from_me=1).count(),
'tome': Messages.objects.using('msgstore').filter(key_from_me=0).count(),
'latest': get_latest_peers(),
'toppeers': get_top_peers(),
'msgsize': getsize(DATABASES['msgstore']['NAME']),
'msgfile': basename(DATABASES['msgstore']['NAME']),
'msgperm': msgperm,
'msgmd5': get_md5_file(DATABASES['msgstore']['NAME']),
'msgsha1': get_sha1_file(DATABASES['msgstore']['NAME']),
'wasize': wasize,
'wafile': wafile,
'wamd5': wamd5,
'waperm': waperm,
'washa1': washa1,
}
return render_to_response('whatsapp/index.html', dic, context_instance=RequestContext(request))
def index(request):
try:
wusers = WaContacts.objects.filter(is_whatsapp_user = 1).count()
nwusers = WaContacts.objects.filter(is_whatsapp_user = 0).count()
wasize = getsize(DATABASES['default']['NAME'])
wafile = basename(DATABASES['default']['NAME'])
wamd5 = get_md5_file(DATABASES['default']['NAME'])
washa1 = get_sha1_file(DATABASES['default']['NAME'])
except:
wusers = Messages.objects.using('msgstore').values('key_remote_jid').distinct().count()
nwusers = 0
wasize = 0
wafile = "Not found!"
wamd5 = "N/A"
washa1 = "N/A"
dic = {
'whatsappusers': wusers,
'nonwhatsappusers': nwusers,
'activity': get_activity_data(None),
'fromme': Messages.objects.using('msgstore').filter(key_from_me = 1).count(),
'tome': Messages.objects.using('msgstore').filter(key_from_me = 0).count(),
'latest': get_latest_peers(),
'toppeers': get_top_peers(),
'msgsize': getsize(DATABASES['msgstore']['NAME']),
'msgfile': basename(DATABASES['msgstore']['NAME']),
'msgmd5': get_md5_file(DATABASES['msgstore']['NAME']),
'msgsha1': get_sha1_file(DATABASES['msgstore']['NAME']),
'wasize': wasize,
'wafile': wafile,
'wamd5': wamd5,
'washa1': washa1,
}
return render_to_response('whatsapp/index.html',dic,context_instance=RequestContext(request))
def simplify(save_dir, save_name, nets, total, sup_config):
dataloader_dict = get_nas_bench_loaders(6)
hps, seeds = ['12', '200'], set()
for hp in hps:
sub_save_dir = save_dir / 'raw-data-{:}'.format(hp)
ckps = sorted(list(sub_save_dir.glob('arch-*-seed-*.pth')))
seed2names = defaultdict(list)
for ckp in ckps:
parts = re.split('-|\.', ckp.name)
seed2names[parts[3]].append(ckp.name)
print('DIR : {:}'.format(sub_save_dir))
nums = []
for seed, xlist in seed2names.items():
seeds.add(seed)
nums.append(len(xlist))
print(' [seed={:}] there are {:} checkpoints.'.format(seed, len(xlist)))
assert len(nets) == total == max(nums), 'there are some missed files : {:} vs {:}'.format(max(nums), total)
print('{:} start simplify the checkpoint.'.format(time_string()))
datasets = ('cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120')
# Create the directory to save the processed data
# full_save_dir contains all benchmark files with trained weights.
# simplify_save_dir contains all benchmark files without trained weights.
full_save_dir = save_dir / (save_name + '-FULL')
simple_save_dir = save_dir / (save_name + '-SIMPLIFY')
full_save_dir.mkdir(parents=True, exist_ok=True)
simple_save_dir.mkdir(parents=True, exist_ok=True)
# all data in memory
arch2infos, evaluated_indexes = dict(), set()
end_time, arch_time = time.time(), AverageMeter()
# save the meta information
temp_final_infos = {'meta_archs' : nets,
'total_archs': total,
'arch2infos' : None,
'evaluated_indexes': set()}
pickle_save(temp_final_infos, str(full_save_dir / 'meta.pickle'))
pickle_save(temp_final_infos, str(simple_save_dir / 'meta.pickle'))
for index in tqdm(range(total)):
arch_str = nets[index]
hp2info = OrderedDict()
full_save_path = full_save_dir / '{:06d}.pickle'.format(index)
simple_save_path = simple_save_dir / '{:06d}.pickle'.format(index)
for hp in hps:
sub_save_dir = save_dir / 'raw-data-{:}'.format(hp)
ckps = [sub_save_dir / 'arch-{:06d}-seed-{:}.pth'.format(index, seed) for seed in seeds]
ckps = [x for x in ckps if x.exists()]
if len(ckps) == 0:
raise ValueError('Invalid data : index={:}, hp={:}'.format(index, hp))
arch_info = account_one_arch(index, arch_str, ckps, datasets, dataloader_dict)
hp2info[hp] = arch_info
hp2info = correct_time_related_info(index, hp2info)
evaluated_indexes.add(index)
to_save_data = OrderedDict({'12': hp2info['12'].state_dict(),
'200': hp2info['200'].state_dict()})
pickle_save(to_save_data, str(full_save_path))
for hp in hps: hp2info[hp].clear_params()
to_save_data = OrderedDict({'12': hp2info['12'].state_dict(),
'200': hp2info['200'].state_dict()})
pickle_save(to_save_data, str(simple_save_path))
arch2infos[index] = to_save_data
# measure elapsed time
arch_time.update(time.time() - end_time)
end_time = time.time()
need_time = '{:}'.format(convert_secs2time(arch_time.avg * (total-index-1), True))
# print('{:} {:06d}/{:06d} : still need {:}'.format(time_string(), index, total, need_time))
print('{:} {:} done.'.format(time_string(), save_name))
final_infos = {'meta_archs' : nets,
'total_archs': total,
'arch2infos' : arch2infos,
'evaluated_indexes': evaluated_indexes}
save_file_name = save_dir / '{:}.pickle'.format(save_name)
pickle_save(final_infos, str(save_file_name))
# move the benchmark file to a new path
hd5sum = get_md5_file(str(save_file_name) + '.pbz2')
hd5_file_name = save_dir / '{:}-{:}.pickle.pbz2'.format(NATS_TSS_BASE_NAME, hd5sum)
shutil.move(str(save_file_name) + '.pbz2', hd5_file_name)
print('Save {:} / {:} architecture results into {:} -> {:}.'.format(len(evaluated_indexes), total, save_file_name, hd5_file_name))
# move the directory to a new path
hd5_full_save_dir = save_dir / '{:}-{:}-full'.format(NATS_TSS_BASE_NAME, hd5sum)
hd5_simple_save_dir = save_dir / '{:}-{:}-simple'.format(NATS_TSS_BASE_NAME, hd5sum)
shutil.move(full_save_dir, hd5_full_save_dir)
shutil.move(simple_save_dir, hd5_simple_save_dir)
def simplify(save_dir, save_name, nets, total, sup_config):
hps, seeds = ["12", "200"], set()
for hp in hps:
sub_save_dir = save_dir / "raw-data-{:}".format(hp)
ckps = sorted(list(sub_save_dir.glob("arch-*-seed-*.pth")))
seed2names = defaultdict(list)
for ckp in ckps:
parts = re.split("-|\.", ckp.name)
seed2names[parts[3]].append(ckp.name)
print("DIR : {:}".format(sub_save_dir))
nums = []
for seed, xlist in seed2names.items():
seeds.add(seed)
nums.append(len(xlist))
print(" [seed={:}] there are {:} checkpoints.".format(
seed, len(xlist)))
assert (len(nets) == total ==
max(nums)), "there are some missed files : {:} vs {:}".format(
max(nums), total)
print("{:} start simplify the checkpoint.".format(time_string()))
datasets = ("cifar10-valid", "cifar10", "cifar100", "ImageNet16-120")
# Create the directory to save the processed data
# full_save_dir contains all benchmark files with trained weights.
# simplify_save_dir contains all benchmark files without trained weights.
full_save_dir = save_dir / (save_name + "-FULL")
simple_save_dir = save_dir / (save_name + "-SIMPLIFY")
full_save_dir.mkdir(parents=True, exist_ok=True)
simple_save_dir.mkdir(parents=True, exist_ok=True)
# all data in memory
arch2infos, evaluated_indexes = dict(), set()
end_time, arch_time = time.time(), AverageMeter()
# save the meta information
for index in tqdm(range(total)):
arch_str = nets[index]
hp2info = OrderedDict()
simple_save_path = simple_save_dir / "{:06d}.pickle".format(index)
arch2infos[index] = pickle_load(simple_save_path)
evaluated_indexes.add(index)
# measure elapsed time
arch_time.update(time.time() - end_time)
end_time = time.time()
need_time = "{:}".format(
convert_secs2time(arch_time.avg * (total - index - 1), True))
# print('{:} {:06d}/{:06d} : still need {:}'.format(time_string(), index, total, need_time))
print("{:} {:} done.".format(time_string(), save_name))
final_infos = {
"meta_archs": nets,
"total_archs": total,
"arch2infos": arch2infos,
"evaluated_indexes": evaluated_indexes,
}
save_file_name = save_dir / "{:}.pickle".format(save_name)
pickle_save(final_infos, str(save_file_name))
# move the benchmark file to a new path
hd5sum = get_md5_file(str(save_file_name) + ".pbz2")
hd5_file_name = save_dir / "{:}-{:}.pickle.pbz2".format(
NATS_TSS_BASE_NAME, hd5sum)
shutil.move(str(save_file_name) + ".pbz2", hd5_file_name)
print("Save {:} / {:} architecture results into {:} -> {:}.".format(
len(evaluated_indexes), total, save_file_name, hd5_file_name))
# move the directory to a new path
hd5_full_save_dir = save_dir / "{:}-{:}-full".format(
NATS_TSS_BASE_NAME, hd5sum)
hd5_simple_save_dir = save_dir / "{:}-{:}-simple".format(
NATS_TSS_BASE_NAME, hd5sum)
shutil.move(full_save_dir, hd5_full_save_dir)
shutil.move(simple_save_dir, hd5_simple_save_dir)
