def __init__(self, app_name, cfg):
super(InferenceServer, self).__init__(app_name)
self.inference_req = InferenceReq()
self.classes = aisdk.common.other.make_synset(
cfg['model_files']['labels.csv'])
label_file = cfg['model_files']['labels.csv']
self.labels = parse_label_file(label_file)
yaml_file = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'resnet.yaml')
update_config(yaml_file)
def create_net(configs):
CTX.logger.info("load configs: %s", configs)
# configs = json.loads(configs)
tar_files_name='model_files'
# load tar files
if tar_files_name not in configs:
return None, 400, 'no field tar_files'
tar_files = configs[tar_files_name]
conf, err = net.parse_infer_config(tar_files)
if err:
return None, 400, err
params_file, sym_file, label_file = (conf.weight, conf.deploy_sym, conf.labels)
use_device_name = 'use_device'
if use_device_name not in configs:
return None, 400, 'no field use_devices'
use_device = configs[use_device_name]
# threshold = 1e-3
thresholds = []
if 'custom_params' in configs:
custom_values = configs['custom_params']
# if 'threshold' in custom_values:
# threshold = custom_values['threshold']
if 'thresholds' in custom_values:
thresholds = custom_values['thresholds']
else:
thresholds = [1.0, 0.8, 0.8, 0.8, 0.7, 0.7, 1.0, 1.0, 1.0, 1.0, 1.0]
ctx = [mx.gpu()] if use_device == 'GPU' else [mx.cpu()]
classes = _make_synset(label_file)
os.rename(sym_file, sym_file+'-symbol.json')
os.rename(params_file, sym_file+'-0000.params')
yaml_file = "/workspace/serving/python/evals/resnet.yaml"
if os.path.isfile(yaml_file):
update_config(yaml_file)
else:
return None, 400, 'No yaml files'
predictor=get_net(config, ctx, sym_file, config.TEST.test_epoch, True)
if predictor==None:
return None, 400, 'fail to create predictor'
return dict(
error='',
predictor=predictor,
classes=classes,
thresholds=thresholds), 0, ''
def net_init():
# configs = json.loads(config_str)
# tar_files_name = 'tar_files'
# # load tar files
# if tar_files_name not in configs:
# return dict(error='no field "tar_files"')
# tar_files = configs[tar_files_name]
# conf, err = net.parse_infer_config(tar_files)
# if err:
# return dict(error=err)
# params_file, sym_file, label_file = (conf.weight, conf.deploy_sym,
# conf.labels)
# use_device_name = 'use_device'
# if use_device_name not in configs:
# return dict(error='no field "use_device"')
# use_device = configs[use_device_name]
sym_file = "/tmp/eval/init/old-1222/deploy.symbol.json"
params_file = "/tmp/eval/init/old-1222/weight.params"
label_file = "/tmp/eval/init/old-1222/labels.csv"
use_device = "GPU"
threshold = 1e-3
# thresholds = [0, 0.8, 0.8, 0.8, 0.7, 0.7, 1, 1, 1, 1, 1]
thresholds = [0, 0.9, 0.8, 0.8, 1, 0.9, 0.9]
# if 'custom_values' in configs:
# custom_values = configs['custom_values']
# if 'threshold' in custom_values:
# threshold = custom_values["threshold"]
# if 'thresholds' in custom_values:
# thresholds = custom_values['thresholds']
ctx = [mx.gpu()] if use_device == 'GPU' else [mx.cpu()
] # TODO set the gpu/cpu
classes = _make_synset(label_file)
os.rename(sym_file, sym_file + '-symbol.json')
os.rename(params_file, sym_file + '-0000.params')
update_config("/tmp/eval/init/old-1222/resnet.yaml")
return dict(error='',
predictor=get_net(config, ctx, sym_file,
config.TEST.test_epoch, True),
classes=classes,
threshold=threshold,
thresholds=thresholds)
def parse_args():
parser = argparse.ArgumentParser(description='Train R-FCN network')
# general
parser.add_argument(
'--cfg',
help='experiment configure file name',
default=
"../experiments/rfcn/cfgs/resnet_v1_101_imagenet_vid_rfcn_end2end_ohem.yaml",
type=str)
args, rest = parser.parse_known_args()
# update config
update_config(args.cfg)
# training
parser.add_argument('--frequent',
help='frequency of logging',
default=config.default.frequent,
type=int)
args = parser.parse_args()
return args
def create_net(configs):
CTX.logger.info("load configs: %s", configs)
# configs = json.loads(configs)
tar_files_name='model_files'
# load tar files
if tar_files_name not in configs:
return None, 400, 'no field tar_files'
tar_files = configs[tar_files_name]
conf, err = net.parse_infer_config(tar_files)
if err:
return None, 400, err
params_file, sym_file, label_file = (conf.weight, conf.deploy_sym, conf.labels)
use_device_name = 'use_device'
if use_device_name not in configs:
return None, 400, 'no field use_devices'
use_device = configs[use_device_name]
ctx = [mx.gpu()] if use_device == 'GPU' else [mx.cpu()]
labels = parse_label_file(label_file)
os.rename(sym_file, sym_file+'-symbol.json')
os.rename(params_file, sym_file+'-0000.params')
yaml_file = "/workspace/serving/python/evals/resnet.yaml"
if os.path.isfile(yaml_file):
update_config(yaml_file)
else:
return None, 400, 'No yaml files'
predictor=get_net(config, ctx, sym_file, config.TEST.test_epoch, True)
if predictor==None:
return None, 400, 'fail to create predictor'
return dict(
error='',
predictor=predictor,
labels=labels), 0, ''
def __init__(self, app_name, batch_size, lock, cfg):
super(ForwardServer, self).__init__(app_name, batch_size)
self.lock = lock
use_device = cfg['use_device']
ctx = [mx.gpu()] if use_device == 'GPU' else [mx.cpu()]
self.classes = aisdk.common.other.make_synset(
cfg['model_files']['labels.csv'])
sym_file = cfg['model_files']['deploy.symbol.json']
params_file = cfg['model_files']['weight.params']
_, arg_params, aux_params = aisdk.common.mxnet_utils.load_checkpoint(
sym_file, params_file)
arg_params, aux_params = load_param(arg_params,
aux_params,
process=True)
yaml_file = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'resnet.yaml')
update_config(yaml_file)
self.predictor = get_net(config, ctx, arg_params, aux_params, True)
os.chdir('/home/tharun/Deformable-ConvNets/rfcn')
print os.getcwd()
sys.path.append(
os.path.dirname(os.path.expanduser('~/Deformable-ConvNets/rfcn/symbols')))
sys.path.append(
os.path.dirname(os.path.expanduser('~/Deformable-ConvNets/lib/utils')))
sys.path.append(
os.path.dirname(os.path.expanduser('~/Deformable-ConvNets/rfcn')))
from rfcn.symbols import *
from rfcn.config.config import config, update_config
import cv2
import numpy as np
import pprint
update_config('transfer_config.yaml')
def get_iterators(batch_size, data_shape=(3, 224, 224)):
train = mx.io.ImageRecordIter(path_imgrec='''TODO''',
data_name='data',
label_name='softmax_label',
batch_size=batch_size,
data_shape=data_shape,
shuffle=True,
rand_crop=True,
rand_mirror=True)
val = mx.io.ImageRecordIter(path_imgrec='''TODO''',
data_name='data',
label_name='softmax_label',
batch_size=batch_size,
def __init__(self,
scales=(800, 1200),
batch_size=1,
dcn=True,
softNMS=True,
pretrained=True,
model_dir=None,
force_reload=False,
gpu=0):
os.environ['PYTHONUNBUFFERED'] = '1'
os.environ['MXNET_CUDNN_AUTOTUNE_DEFAULT'] = '0'
os.environ['MXNET_ENABLE_GPU_P2P'] = '0'
import mxnet as mx
from dcn.nms import gpu_nms_wrapper, cpu_softnms_wrapper, soft_nms
from dcn.utils.load_model import load_param
from rfcn.config.config import config, update_config
from rfcn.core.tester import Predictor
from rfcn.symbols import resnet_v1_101_rfcn
from rfcn.symbols import resnet_v1_101_rfcn_dcn
from rfcn.symbols import resnet_v1_101_rfcn_dcn_rpn
from rfcn import CFGS
cfg = CFGS / f"rfcn_coco_demo{softNMS and '_softNMS' or ''}.yaml"
update_config(cfg)
config.TEST.BATCH_IMAGES = batch_size
config.symbol = 'resnet_v1_101_rfcn_dcn_rpn' if dcn else 'resnet_v1_101_rfcn'
config.SCALES[0] = scales
params, arg_params, aux_params = None, None, None
if pretrained:
epoch = softNMS and 8 or 0
params = dcn and 'rfcn_dcn_coco' or 'rfcn_coco'
path = attempt_download(params,
epoch=epoch,
model_dir=model_dir,
force=force_reload)
arg_params, aux_params = load_param(
f"{Path(path).parent / params}", epoch, process=True)
instance = eval(config.symbol + '.' + config.symbol)()
sym = instance.get_symbol(config, is_train=False)
# Build the model predictor
# BATCH_IMAGES Per GPU context
self.config = config
gpus = [gpu] if type(gpu) == int else gpu
data_names = ['data', 'im_info']
label_names = []
data = self.preprocess()
data = [[mx.nd.array(data[i][name]) for name in data_names]
for i in range(len(data))] # [[data, im_info], ...]
max_data_shape = [[('data', (1, 3, max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES])))]
] # [[('data', (1, 3, 800, 1200))]]
provide_data = [
[(k, v.shape) for k, v in zip(data_names, data[i])]
for i in range(len(data))
] # [[('data', (1, 3, h, w)), ('im_info', (h, w, scale))], ...]]
provide_label = [None for i in range(len(data))] # [None, ...]
self.predictor = Predictor(sym,
data_names,
label_names,
context=[mx.gpu(gpu) for gpu in gpus],
max_data_shapes=max_data_shape,
provide_data=provide_data,
provide_label=provide_label,
arg_params=arg_params,
aux_params=aux_params)
self.data_names = data_names
self.nms = gpu_nms_wrapper(config.TEST.NMS,
0) # 0.2 for SNMS and 0.3 otherwise
if softNMS:
self.snms = cpu_softnms_wrapper()
