SubOpr,
TanHOpr,
TransposeOpr,
TrueDivOpr,
TypeCvtOpr,
VolatileSharedDeviceTensorOpr,
)
from ...converter_ir.ir_tensor import IRTensor
from ...frontend.mge_to_ir.mge_utils import get_symvar_value
if "USE_CAFFE_PROTO" not in os.environ:
from .caffe_pb import caffe_pb2 as cp
else:
from caffe.proto import caffe_pb2 as cp
logger = get_logger(__name__)
MGE2CAFFE = {}
class BackEnd(IntEnum):
CAFFE = 1
SNPE = 2
TRT = 3
NNIE = 4
def isconst(x):
return x.np_data is not None
def _register_op(*oprs):
import os
import time
import numpy as np
import megengine as mge
import megengine.distributed as dist
import megengine.functional as F
from megengine.autodiff import GradManager
from megengine.data import DataLoader, Infinite, RandomSampler, dataset
from megengine.data import transform as T
from megengine.optimizer import SGD
from official.vision.segmentation.tools.utils import AverageMeter, get_config_info, import_from_file
logger = mge.get_logger(__name__)
logger.setLevel("INFO")
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-f",
"--file",
default="net.py",
type=str,
help="net description file")
parser.add_argument("-n",
"--devices",
type=int,
default=8,
help="batch size for training")
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#
# This file has been modified by Megvii ("Megvii Modifications").
# All Megvii Modifications are Copyright (C) 2014-2021 Megvii Inc. All rights reserved.
# ----------------------------------------------------------------------
"""Tokenization classes."""
from __future__ import absolute_import, division, print_function, unicode_literals
import collections
import os
import unicodedata
from io import open
import megengine
logger = megengine.get_logger(__name__)
VOCAB_NAME = "vocab.txt"
def load_vocab(vocab_file):
"""Loads a vocabulary file into a dictionary."""
vocab = collections.OrderedDict()
index = 0
with open(vocab_file, "r", encoding="utf-8") as reader:
while True:
token = reader.readline()
if not token:
break
token = token.strip()
vocab[token] = index