def init_model(config, net, optimizer=None):
"""
load model from checkpoint or pretrained_model
"""
checkpoints = config.get('checkpoints')
if checkpoints and optimizer is not None:
assert os.path.exists(checkpoints + ".pdparams"), \
"Given dir {}.pdparams not exist.".format(checkpoints)
assert os.path.exists(checkpoints + ".pdopt"), \
"Given dir {}.pdopt not exist.".format(checkpoints)
para_dict = paddle.load(checkpoints + ".pdparams")
opti_dict = paddle.load(checkpoints + ".pdopt")
net.set_dict(para_dict)
optimizer.set_state_dict(opti_dict)
logger.info("Finish load checkpoints from {}".format(checkpoints))
return
pretrained_model = config.get('pretrained_model')
load_static_weights = config.get('load_static_weights', False)
use_distillation = config.get('use_distillation', False)
if pretrained_model:
if use_distillation:
load_distillation_model(net, pretrained_model, load_static_weights)
else: # common load
load_dygraph_pretrain(net,
path=pretrained_model,
load_static_weights=load_static_weights)
logger.info(
logger.coloring(
"Finish load pretrained model from {}".format(
pretrained_model), "HEADER"))
def run(dataloader, exe, program, fetchs, epoch=0, mode='train'):
"""
Feed data to the model and fetch the measures and loss
Args:
dataloader(fluid dataloader):
exe():
program():
fetchs(dict): dict of measures and the loss
epoch(int): epoch of training or validation
model(str): log only
Returns:
"""
fetch_list = [f[0] for f in fetchs.values()]
metric_list = [f[1] for f in fetchs.values()]
for m in metric_list:
m.reset()
batch_time = AverageMeter('cost', ':6.3f')
tic = time.time()
trainer_id = int(os.getenv("PADDLE_TRAINER_ID", 0))
for idx, batch in enumerate(dataloader()):
metrics = exe.run(program=program, feed=batch, fetch_list=fetch_list)
batch_time.update(time.time() - tic)
tic = time.time()
for i, m in enumerate(metrics):
metric_list[i].update(m[0], len(batch[0]))
fetchs_str = ''.join([str(m) for m in metric_list] + [str(batch_time)])
if trainer_id == 0:
logger.info("[epoch:%3d][%s][step:%4d]%s" %
(epoch, mode, idx, fetchs_str))
if trainer_id == 0:
logger.info("END [epoch:%3d][%s]%s" % (epoch, mode, fetchs_str))
def run(dataloader, exe, program, fetchs, epoch=0, mode='train'):
"""
Feed data to the model and fetch the measures and loss
Args:
dataloader(fluid dataloader):
exe():
program():
fetchs(dict): dict of measures and the loss
epoch(int): epoch of training or validation
model(str): log only
Returns:
"""
fetch_list = [f[0] for f in fetchs.values()]
metric_list = [f[1] for f in fetchs.values()]
for m in metric_list:
m.reset()
batch_time = AverageMeter('cost', '.3f')
tic = time.time()
for idx, batch in enumerate(dataloader()):
metrics = exe.run(program=program, feed=batch, fetch_list=fetch_list)
batch_time.update(time.time() - tic)
tic = time.time()
for i, m in enumerate(metrics):
metric_list[i].update(m[0], len(batch[0]))
fetchs_str = ''.join([m.value
for m in metric_list] + [batch_time.value])
logger.info("[epoch:{:3d}][{:s}][step:{:4d}]{:s}".format(
epoch, mode, idx, fetchs_str))
end_str = ''.join([m.mean for m in metric_list] + [batch_time.total])
logger.info("END [epoch:{:3d}][{:s}]{:s}".format(epoch, mode, end_str))
def log_info(trainer, batch_size, epoch_id, iter_id):
lr_msg = "lr: {:.5f}".format(trainer.lr_sch.get_lr())
metric_msg = ", ".join([
"{}: {:.5f}".format(key, trainer.output_info[key].avg)
for key in trainer.output_info
])
time_msg = "s, ".join([
"{}: {:.5f}".format(key, trainer.time_info[key].avg)
for key in trainer.time_info
])
ips_msg = "ips: {:.5f} images/sec".format(
batch_size / trainer.time_info["batch_cost"].avg)
eta_sec = ((trainer.config["Global"]["epochs"] - epoch_id + 1
) * len(trainer.train_dataloader) - iter_id
) * trainer.time_info["batch_cost"].avg
eta_msg = "eta: {:s}".format(str(datetime.timedelta(seconds=int(eta_sec))))
logger.info("[Train][Epoch {}/{}][Iter: {}/{}]{}, {}, {}, {}, {}".format(
epoch_id, trainer.config["Global"]["epochs"], iter_id,
len(trainer.train_dataloader), lr_msg, metric_msg, time_msg, ips_msg,
eta_msg))
logger.scaler(
name="lr",
value=trainer.lr_sch.get_lr(),
step=trainer.global_step,
writer=trainer.vdl_writer)
for key in trainer.output_info:
logger.scaler(
name="train_{}".format(key),
value=trainer.output_info[key].avg,
step=trainer.global_step,
writer=trainer.vdl_writer)
def _decompress(fname):
"""
Decompress for zip and tar file
"""
logger.info("Decompressing {}...".format(fname))
# For protecting decompressing interupted,
# decompress to fpath_tmp directory firstly, if decompress
# successed, move decompress files to fpath and delete
# fpath_tmp and remove download compress file.
fpath = os.path.split(fname)[0]
fpath_tmp = os.path.join(fpath, 'tmp')
if os.path.isdir(fpath_tmp):
shutil.rmtree(fpath_tmp)
os.makedirs(fpath_tmp)
if fname.find('tar') >= 0:
with tarfile.open(fname) as tf:
tf.extractall(path=fpath_tmp)
elif fname.find('zip') >= 0:
with zipfile.ZipFile(fname) as zf:
zf.extractall(path=fpath_tmp)
else:
raise TypeError("Unsupport compress file type {}".format(fname))
for f in os.listdir(fpath_tmp):
src_dir = os.path.join(fpath_tmp, f)
dst_dir = os.path.join(fpath, f)
_move_and_merge_tree(src_dir, dst_dir)
shutil.rmtree(fpath_tmp)
os.remove(fname)
def term_mp(sig_num, frame):
""" kill all child processes
"""
pid = os.getpid()
pgid = os.getpgid(os.getpid())
logger.info("main proc {} exit, kill process group "
"{}".format(pid, pgid))
os.killpg(pgid, signal.SIGKILL)
def save_model(program, model_path, epoch_id, prefix='ppcls'):
"""
save model to the target path
"""
model_path = os.path.join(model_path, str(epoch_id))
_mkdir_if_not_exist(model_path)
model_prefix = os.path.join(model_path, prefix)
fluid.save(program, model_prefix)
logger.info("Already save model in {}".format(model_path))
def get(architecture, path, decompress=True):
"""
Get the pretrained model.
"""
_check_pretrained_name(architecture)
url = _get_url(architecture)
fname = _download(url, path)
if decompress: _decompress(fname)
logger.info("download {} finished ".format(fname))
def get(architecture, path, decompress=False, postfix="pdparams"):
"""
Get the pretrained model.
"""
_check_pretrained_name(architecture)
url = _get_url(architecture, postfix=postfix)
fname = _download(url, path)
if postfix == "tar" and decompress:
_decompress(fname)
logger.info("download {} finished ".format(fname))
def _save_student_model(net, model_prefix):
"""
save student model if the net is the network contains student
"""
student_model_prefix = model_prefix + "_student.pdparams"
if hasattr(net, "_layers"):
net = net._layers
if hasattr(net, "student"):
paddle.save(net.student.state_dict(), student_model_prefix)
logger.info(
"Already save student model in {}".format(student_model_prefix))
def save_model(program, model_path, epoch_id, prefix='ppcls'):
"""
save model to the target path
"""
model_path = os.path.join(model_path, str(epoch_id))
_mkdir_if_not_exist(model_path)
model_prefix = os.path.join(model_path, prefix)
paddle.static.save(program, model_prefix)
logger.info(
logger.coloring("Already save model in {}".format(model_path),
"HEADER"))
def _download(url, path, md5sum=None):
"""
Download from url, save to path.
url (str): download url
path (str): download to given path
"""
if not osp.exists(path):
os.makedirs(path)
fname = osp.split(url)[-1]
fullname = osp.join(path, fname)
retry_cnt = 0
while not (osp.exists(fullname) and _md5check(fullname, md5sum)):
if retry_cnt < DOWNLOAD_RETRY_LIMIT:
retry_cnt += 1
else:
raise RuntimeError("Download from {} failed. "
"Retry limit reached".format(url))
logger.info("Downloading {} from {}".format(fname, url))
try:
req = requests.get(url, stream=True)
except Exception as e: # requests.exceptions.ConnectionError
logger.info(
"Downloading {} from {} failed {} times with exception {}".
format(fname, url, retry_cnt + 1, str(e)))
time.sleep(1)
continue
if req.status_code != 200:
raise RuntimeError("Downloading from {} failed with code "
"{}!".format(url, req.status_code))
# For protecting download interupted, download to
# tmp_fullname firstly, move tmp_fullname to fullname
# after download finished
tmp_fullname = fullname + "_tmp"
total_size = req.headers.get('content-length')
with open(tmp_fullname, 'wb') as f:
if total_size:
with tqdm(total=(int(total_size) + 1023) // 1024) as pbar:
for chunk in req.iter_content(chunk_size=1024):
f.write(chunk)
pbar.update(1)
else:
for chunk in req.iter_content(chunk_size=1024):
if chunk:
f.write(chunk)
shutil.move(tmp_fullname, fullname)
return fullname
def save_model(program, model_path, epoch_id, prefix='ppcls'):
"""
save model to the target path
"""
if paddle.distributed.get_rank() != 0:
return
model_path = os.path.join(model_path, str(epoch_id))
_mkdir_if_not_exist(model_path)
model_prefix = os.path.join(model_path, prefix)
paddle.static.save(program, model_prefix)
logger.info("Already save model in {}".format(model_path))
def apply_to_static(config, model):
support_to_static = config['Global'].get('to_static', False)
if support_to_static:
specs = None
if 'image_shape' in config['Global']:
specs = [InputSpec([None] + config['Global']['image_shape'])]
model = to_static(model, input_spec=specs)
logger.info("Successfully to apply @to_static with specs: {}".format(
specs))
return model
def main(args):
benchmark_file_list = args.benchmark_file_list
model_infos = parse_model_infos(benchmark_file_list)
right_models = []
wrong_models = []
for model_info in model_infos:
try:
pretrained_url = model_info["pretrain_path"]
fname = _download(pretrained_url, args.pretrained_dir)
pretrained_path = os.path.splitext(fname)[0]
if pretrained_url.endswith("tar"):
path = _decompress(fname)
pretrained_path = os.path.join(
os.path.dirname(pretrained_path), path)
args.config = model_info["config_path"]
args.override = [
"pretrained_model={}".format(pretrained_path),
"VALID.batch_size=256",
"VALID.num_workers=16",
"load_static_weights=True",
"print_interval=100",
]
manager = Manager()
return_dict = manager.dict()
# A hack method to avoid name conflict.
# Multi-process maybe a better method here.
# More details can be seen in branch 2.0-beta.
# TODO: fluid needs to be removed in the future.
with paddle.utils.unique_name.guard():
eval.main(args, return_dict)
top1_acc = return_dict.get("top1_acc", 0.0)
except Exception as e:
logger.error(e)
top1_acc = 0.0
diff = abs(top1_acc - model_info["top1_acc"])
if diff > 0.001:
err_info = "[{}]Top-1 acc diff should be <= 0.001 but got diff {}, gt acc: {}, eval acc: {}".format(
model_info["model_name"], diff, model_info["top1_acc"],
top1_acc)
logger.warning(err_info)
wrong_models.append(model_info["model_name"])
else:
right_models.append(model_info["model_name"])
logger.info("[number of right models: {}, they are: {}".format(
len(right_models), right_models))
logger.info("[number of wrong models: {}, they are: {}".format(
len(wrong_models), wrong_models))
def _get_unique_endpoints(trainer_endpoints):
# Sorting is to avoid different environmental variables for each card
trainer_endpoints.sort()
ips = set()
unique_endpoints = set()
for endpoint in trainer_endpoints:
ip = endpoint.split(":")[0]
if ip in ips:
continue
ips.add(ip)
unique_endpoints.add(endpoint)
logger.info("unique_endpoints {}".format(unique_endpoints))
return unique_endpoints
def quantize_model(config, model):
if config.get("Slim", False) and config["Slim"].get("quant", False):
from paddleslim.dygraph.quant import QAT
assert config["Slim"]["quant"]["name"].lower(
) == 'pact', 'Only PACT quantization method is supported now'
QUANT_CONFIG["activation_preprocess_type"] = "PACT"
model.quanter = QAT(config=QUANT_CONFIG)
model.quanter.quantize(model)
logger.info("QAT model summary:")
paddle.summary(model, (1, 3, 224, 224))
else:
model.quanter = None
return
def init_model(config, program, exe):
"""
load model from checkpoint or pretrained_model
"""
checkpoints = config.get('checkpoints')
if checkpoints:
fluid.load(program, checkpoints, exe)
logger.info("Finish initing model from {}".format(checkpoints))
return
pretrained_model = config.get('pretrained_model')
if pretrained_model:
load_params(exe, program, pretrained_model)
logger.info("Finish initing model from {}".format(pretrained_model))
def get_gpu_count():
"""get avaliable gpu count
Returns:
gpu_count: int
"""
gpu_count = 0
env_cuda_devices = os.environ.get('CUDA_VISIBLE_DEVICES', None)
if env_cuda_devices is not None:
assert isinstance(env_cuda_devices, str)
try:
if not env_cuda_devices:
return 0
gpu_count = len(
[x for x in env_cuda_devices.split(',') if int(x) >= 0])
logger.info(
'CUDA_VISIBLE_DEVICES found gpu count: {}'.format(gpu_count))
except:
logger.info('Cannot find available GPU devices, using CPU now.')
gpu_count = 0
else:
try:
gpu_count = str(subprocess.check_output(["nvidia-smi",
"-L"])).count('UUID')
logger.info('nvidia-smi -L found gpu count: {}'.format(gpu_count))
except:
logger.info('Cannot find available GPU devices, using CPU now.')
gpu_count = 0
return gpu_count
def run(dataloader, exe, program, fetchs, epoch=0, mode='train', vdl_writer=None):
"""
Feed data to the model and fetch the measures and loss
Args:
dataloader(fluid dataloader):
exe():
program():
fetchs(dict): dict of measures and the loss
epoch(int): epoch of training or validation
model(str): log only
Returns:
"""
fetch_list = [f[0] for f in fetchs.values()]
metric_list = [f[1] for f in fetchs.values()]
for m in metric_list:
m.reset()
batch_time = AverageMeter('elapse', '.3f')
tic = time.time()
for idx, batch in enumerate(dataloader()):
metrics = exe.run(program=program, feed=batch, fetch_list=fetch_list)
batch_time.update(time.time() - tic)
tic = time.time()
for i, m in enumerate(metrics):
metric_list[i].update(m[0], len(batch[0]))
fetchs_str = ''.join([str(m.value) + ' '
for m in metric_list] + [batch_time.value]) + 's'
if vdl_writer:
global total_step
logger.scaler('loss', metrics[0][0], total_step, vdl_writer)
total_step += 1
if mode == 'eval':
logger.info("{:s} step:{:<4d} {:s}s".format(mode, idx, fetchs_str))
else:
epoch_str = "epoch:{:<3d}".format(epoch)
step_str = "{:s} step:{:<4d}".format(mode, idx)
logger.info("{:s} {:s} {:s}".format(
logger.coloring(epoch_str, "HEADER")
if idx == 0 else epoch_str,
logger.coloring(step_str, "PURPLE"),
logger.coloring(fetchs_str, 'OKGREEN')))
end_str = ''.join([str(m.mean) + ' '
for m in metric_list] + [batch_time.total]) + 's'
if mode == 'eval':
logger.info("END {:s} {:s}s".format(mode, end_str))
else:
end_epoch_str = "END epoch:{:<3d}".format(epoch)
logger.info("{:s} {:s} {:s}".format(
logger.coloring(end_epoch_str, "RED"),
logger.coloring(mode, "PURPLE"),
logger.coloring(end_str, "OKGREEN")))
# return top1_acc in order to save the best model
if mode == 'valid':
return fetchs["top1"][1].avg
def load_params(exe, prog, path, ignore_params=None):
"""
Load model from the given path.
Args:
exe (fluid.Executor): The fluid.Executor object.
prog (fluid.Program): load weight to which Program object.
path (string): URL string or loca model path.
ignore_params (list): ignore variable to load when finetuning.
It can be specified by finetune_exclude_pretrained_params
and the usage can refer to the document
docs/advanced_tutorials/TRANSFER_LEARNING.md
"""
if not (os.path.isdir(path) or os.path.exists(path + '.pdparams')):
raise ValueError("Model pretrain path {} does not "
"exists.".format(path))
logger.info(
logger.coloring('Loading parameters from {}...'.format(path),
'HEADER'))
ignore_set = set()
state = _load_state(path)
# ignore the parameter which mismatch the shape
# between the model and pretrain weight.
all_var_shape = {}
for block in prog.blocks:
for param in block.all_parameters():
all_var_shape[param.name] = param.shape
ignore_set.update([
name for name, shape in all_var_shape.items()
if name in state and shape != state[name].shape
])
if ignore_params:
all_var_names = [var.name for var in prog.list_vars()]
ignore_list = filter(
lambda var: any([re.match(name, var) for name in ignore_params]),
all_var_names)
ignore_set.update(list(ignore_list))
if len(ignore_set) > 0:
for k in ignore_set:
if k in state:
logger.warning(
'variable {} is already excluded automatically'.format(k))
del state[k]
paddle.static.set_program_state(prog, state)
def save_model(net, optimizer, model_path, epoch_id, prefix='ppcls'):
"""
save model to the target path
"""
if paddle.distributed.get_rank() != 0:
return
model_path = os.path.join(model_path, str(epoch_id))
_mkdir_if_not_exist(model_path)
model_prefix = os.path.join(model_path, prefix)
_save_student_model(net, model_prefix)
paddle.save(net.state_dict(), model_prefix + ".pdparams")
paddle.save(optimizer.state_dict(), model_prefix + ".pdopt")
logger.info("Already save model in {}".format(model_path))
def _md5check(fullname, md5sum=None):
if md5sum is None:
return True
logger.info("File {} md5 checking...".format(fullname))
md5 = hashlib.md5()
with open(fullname, 'rb') as f:
for chunk in iter(lambda: f.read(4096), b""):
md5.update(chunk)
calc_md5sum = md5.hexdigest()
if calc_md5sum != md5sum:
logger.info("File {} md5 check failed, {}(calc) != "
"{}(base)".format(fullname, calc_md5sum, md5sum))
return False
return True
def init_model(config, program, exe):
"""
load model from checkpoint or pretrained_model
"""
checkpoints = config.get('checkpoints')
if checkpoints:
paddle.static.load(program, checkpoints, exe)
logger.info("Finish initing model from {}".format(checkpoints))
return
pretrained_model = config.get('pretrained_model')
if pretrained_model:
if not isinstance(pretrained_model, list):
pretrained_model = [pretrained_model]
for pretrain in pretrained_model:
load_params(exe, program, pretrain)
logger.info("Finish initing model from {}".format(pretrained_model))
def parse_model_infos(benchmark_file_list):
model_infos = []
with open(benchmark_file_list, "r") as fin:
lines = fin.readlines()
for idx, line in enumerate(lines):
strs = line.strip("\n").strip("\r").split(" ")
if len(strs) != 4:
logger.info(
"line {0}(info: {1}) format wrong, it should be splited into 4 parts, but got {2}"
.format(idx, line, len(strs)))
model_infos.append({
"top1_acc": float(strs[0]),
"model_name": strs[1],
"config_path": strs[2],
"pretrain_path": strs[3],
})
return model_infos
def get_path_from_url(url,
root_dir,
md5sum=None,
check_exist=True,
decompress=True):
""" Download from given url to root_dir.
if file or directory specified by url is exists under
root_dir, return the path directly, otherwise download
from url and decompress it, return the path.
Args:
url (str): download url
root_dir (str): root dir for downloading, it should be
WEIGHTS_HOME or DATASET_HOME
md5sum (str): md5 sum of download package
Returns:
str: a local path to save downloaded models & weights & datasets.
"""
from paddle.fluid.dygraph.parallel import ParallelEnv
assert is_url(url), "downloading from {} not a url".format(url)
# parse path after download to decompress under root_dir
fullpath = _map_path(url, root_dir)
# Mainly used to solve the problem of downloading data from different
# machines in the case of multiple machines. Different ips will download
# data, and the same ip will only download data once.
unique_endpoints = _get_unique_endpoints(
ParallelEnv().trainer_endpoints[:])
if osp.exists(fullpath) and check_exist and _md5check(fullpath, md5sum):
logger.info("Found {}".format(fullpath))
else:
if ParallelEnv().current_endpoint in unique_endpoints:
fullpath = _download(url, root_dir, md5sum)
else:
while not os.path.exists(fullpath):
time.sleep(1)
if ParallelEnv().current_endpoint in unique_endpoints:
if decompress and (tarfile.is_tarfile(fullpath)
or zipfile.is_zipfile(fullpath)):
fullpath = _decompress(fullpath)
return fullpath
def __iter__(self):
while self.iter_counter < self.length:
batch = []
for i, iter_i in enumerate(self.iter_list):
batch_i = next(iter_i, None)
if batch_i is None:
iter_i = iter(self.sampler_list[i])
self.iter_list[i] = iter_i
batch_i = next(iter_i, None)
assert batch_i is not None, "dataset {} return None".format(
i)
batch += [idx + self.start_list[i] for idx in batch_i]
if len(batch) == self.batch_size:
self.iter_counter += 1
yield batch
else:
logger.info("Some dataset reaches end")
self.iter_counter = 0
def _download(url, path):
"""
Download from url, save to path.
url (str): download url
path (str): download to given path
"""
if not os.path.exists(path):
os.makedirs(path)
fname = os.path.split(url)[-1]
fullname = os.path.join(path, fname)
retry_cnt = 0
while not os.path.exists(fullname):
if retry_cnt < DOWNLOAD_RETRY_LIMIT:
retry_cnt += 1
else:
raise RetryError(url, DOWNLOAD_RETRY_LIMIT)
logger.info("Downloading {} from {}".format(fname, url))
req = requests.get(url, stream=True)
if req.status_code != 200:
raise UrlError(url, req.status_code)
# For protecting download interupted, download to
# tmp_fullname firstly, move tmp_fullname to fullname
# after download finished
tmp_fullname = fullname + "_tmp"
total_size = req.headers.get('content-length')
with open(tmp_fullname, 'wb') as f:
if total_size:
for chunk in tqdm.tqdm(
req.iter_content(chunk_size=1024),
total=(int(total_size) + 1023) // 1024,
unit='KB'):
f.write(chunk)
else:
for chunk in req.iter_content(chunk_size=1024):
if chunk:
f.write(chunk)
shutil.move(tmp_fullname, fullname)
return fullname
def create_strategy(config):
"""
Create build strategy and exec strategy.
Args:
config(dict): config
Returns:
build_strategy: build strategy
exec_strategy: exec strategy
"""
build_strategy = paddle.static.BuildStrategy()
exec_strategy = paddle.static.ExecutionStrategy()
exec_strategy.num_threads = 1
exec_strategy.num_iteration_per_drop_scope = (
10000
if 'AMP' in config and config.AMP.get("use_pure_fp16", False) else 10)
fuse_op = True if 'AMP' in config else False
fuse_bn_act_ops = config.get('fuse_bn_act_ops', fuse_op)
fuse_elewise_add_act_ops = config.get('fuse_elewise_add_act_ops', fuse_op)
fuse_bn_add_act_ops = config.get('fuse_bn_add_act_ops', fuse_op)
enable_addto = config.get('enable_addto', fuse_op)
try:
build_strategy.fuse_bn_act_ops = fuse_bn_act_ops
except Exception as e:
logger.info(
"PaddlePaddle version 1.7.0 or higher is "
"required when you want to fuse batch_norm and activation_op.")
try:
build_strategy.fuse_elewise_add_act_ops = fuse_elewise_add_act_ops
except Exception as e:
logger.info(
"PaddlePaddle version 1.7.0 or higher is "
"required when you want to fuse elewise_add_act and activation_op."
)
try:
build_strategy.fuse_bn_add_act_ops = fuse_bn_add_act_ops
except Exception as e:
logger.info(
"PaddlePaddle 2.0-rc or higher is "
"required when you want to enable fuse_bn_add_act_ops strategy.")
try:
build_strategy.enable_addto = enable_addto
except Exception as e:
logger.info("PaddlePaddle 2.0-rc or higher is "
"required when you want to enable addto strategy.")
return build_strategy, exec_strategy
def __init__(self,
dataset,
batch_size,
sample_per_id,
shuffle=True,
drop_last=True,
sample_method="sample_avg_prob"):
super().__init__(dataset,
batch_size,
shuffle=shuffle,
drop_last=drop_last)
assert batch_size % sample_per_id == 0, \
"PKSampler configs error, Sample_per_id must be a divisor of batch_size."
assert hasattr(self.dataset,
"labels"), "Dataset must have labels attribute."
self.sample_per_label = sample_per_id
self.label_dict = defaultdict(list)
self.sample_method = sample_method
for idx, label in enumerate(self.dataset.labels):
self.label_dict[label].append(idx)
self.label_list = list(self.label_dict)
assert len(self.label_list) * self.sample_per_label > self.batch_size, \
"batch size should be smaller than "
if self.sample_method == "id_avg_prob":
self.prob_list = np.array([1 / len(self.label_list)] *
len(self.label_list))
elif self.sample_method == "sample_avg_prob":
counter = []
for label_i in self.label_list:
counter.append(len(self.label_dict[label_i]))
self.prob_list = np.array(counter) / sum(counter)
else:
logger.error(
"PKSampler only support id_avg_prob and sample_avg_prob sample method, "
"but receive {}.".format(self.sample_method))
diff = np.abs(sum(self.prob_list) - 1)
if diff > 0.00000001:
self.prob_list[-1] = 1 - sum(self.prob_list[:-1])
if self.prob_list[-1] > 1 or self.prob_list[-1] < 0:
logger.error("PKSampler prob list error")
else:
logger.info(
"PKSampler: sum of prob list not equal to 1, diff is {}, change the last prob"
.format(diff))
