def build_engine_from_onnx(onnx_path, engine_name, batch_size, TRT_LOGGER):
model = ModelProto()
with open(onnx_path, "rb") as f:
model.ParseFromString(f.read())
d0 = model.graph.input[0].type.tensor_type.shape.dim[1].dim_value
d1 = model.graph.input[0].type.tensor_type.shape.dim[2].dim_value
d2 = model.graph.input[0].type.tensor_type.shape.dim[3].dim_value
shape = [batch_size, d0, d1, d2]
engine = eng.build_engine(TRT_LOGGER, onnx_path, shape=shape)
eng.save_engine(engine, engine_name)
return engine
def main(args):
engine_name = args.plan_file
onnx_path = args.onnx_file
batch_size = 1
model = ModelProto()
with open(onnx_path, "rb") as f:
model.ParseFromString(f.read())
d0 = model.graph.input[0].type.tensor_type.shape.dim[1].dim_value
d1 = model.graph.input[0].type.tensor_type.shape.dim[2].dim_value
d2 = model.graph.input[0].type.tensor_type.shape.dim[3].dim_value
shape = [batch_size, d0, d1, d2]
engine = eng.build_engine(onnx_path, shape=shape)
eng.save_engine(engine, engine_name)
def create_engine(onnx_path, engine_output_path):
batch_size = 1
model = ModelProto()
with open(onnx_path, "rb") as f:
model.ParseFromString(f.read())
print('ONNX model laoded...')
print('Creating engine from this onnx file, ', onnx_path)
d0 = model.graph.input[0].type.tensor_type.shape.dim[1].dim_value
d1 = model.graph.input[0].type.tensor_type.shape.dim[2].dim_value
d2 = model.graph.input[0].type.tensor_type.shape.dim[3].dim_value
shape = [batch_size , d0, d1 ,d2]
engine = eng.build_engine(onnx_path, shape= shape)
eng.save_engine(engine, engine_output_path)
print('TRT engine created and saved at, ', engine_output_path)
def main(args):
engine_name = args.plan_file
onnx_path = args.onnx_file
batch_size = config.batch_size
model = ModelProto()
with open(onnx_path, "rb") as f:
print("parsing")
model.ParseFromString(f.read())
d0 = model.graph.input[0].type.tensor_type.shape.dim[1].dim_value
d1 = model.graph.input[0].type.tensor_type.shape.dim[2].dim_value
d2 = model.graph.input[0].type.tensor_type.shape.dim[3].dim_value
d3 = model.graph.input[0].type.tensor_type.shape.dim[4].dim_value
d4 = model.graph.input[0].type.tensor_type.shape.dim[5].dim_value
shape = [batch_size , d0, d1 , d2, d3, d4] # for CNN-LSTM model
#shape = [1, d0, d1 , d2, d3]
print("data shape required",shape)
engine = eng.build_engine(onnx_path, shape= shape)
eng.save_engine(engine, engine_name)
import engine as eng
import argparse
from onnx import ModelProto
import tensorrt as trt
engine_name = "yolo_pedestrian.plan"
onnx_path = "model.onnx"
batch_size = 1
model = ModelProto()
with open(onnx_path, "rb") as f:
model.ParseFromString(f.read())
d0 = model.graph.input[0].type.tensor_type.shape.dim[1].dim_value
d1 = model.graph.input[0].type.tensor_type.shape.dim[2].dim_value
d2 = model.graph.input[0].type.tensor_type.shape.dim[3].dim_value
shape = [batch_size, d0, d1, d2]
engine = eng.build_engine(onnx_path, shape=shape)
eng.save_engine(engine, engine_name)
def latest(): conn = engine.build_engine().connect() results = conn.execute(select([" * FROM messages"])).fetchall() return [build_message(r) for r in results]
def build_message(result):
return Message({
"id": int(result[0]),
"recipient": result[1],
"sender": result[2],
'who_from': result[3],
'subject': result[4],
'body_plain': result[5],
'stripped_text': result[6],
'timestamp': result[7],
'signature': result[8],
'message_headers': result[9]
})
if __name__ == "__main__":
e = engine.build_engine()
metadata = MetaData(bind=e)
messages_table = Table('messages', metadata,
Column('id', Integer, primary_key=True),
Column('recipient', String(255)),
Column('sender', String(255)),
Column('who_from', String(255)),
Column('subject', String(255)),
Column('body_plain', Text),
Column('stripped_text', Text),
Column('timestamp', Integer),
Column('signature', String(255)),
Column('message_headers', Text),
)
import engine as eng
import argparse
from onnx import ModelProto
import tensorrt as trt
args = argparse.ArgumentParser()
args.add_argument('-engine-name')
args.add_argument('-onnx-path')
args = args.parse_args()
batch_size = 1
model = ModelProto()
with open(args.onnx_path, "rb") as f:
model.ParseFromString(f.read()) # load the model to get the input shape
dims = model.graph.input[0].type.tensor_type.shape.dim[1:4]
shape = [batch_size] + [d.dim_value for d in dims]
print(shape)
# build and save the engine
engine = eng.build_engine(args.onnx_path, shape)
eng.save_engine(engine, args.engine_name)
import engine as eng
import argparse
import os
from onnx import ModelProto
import tensorrt as trt
batch_size = 1
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--onnx', help='File path to .onnx model', required=True)
parser.add_argument('--precision', help='FP32 or FP16', required=True)
args = parser.parse_args()
engine_name = os.path.dirname(args.onnx)+os.path.basename(args.onnx).split('.')[0]+'.plan'
model = ModelProto()
with open(args.onnx, "rb") as f:
model.ParseFromString(f.read())
d0 = model.graph.input[0].type.tensor_type.shape.dim[1].dim_value
d1 = model.graph.input[0].type.tensor_type.shape.dim[2].dim_value
d2 = model.graph.input[0].type.tensor_type.shape.dim[3].dim_value
shape = [batch_size , d0, d1 ,d2]
engine = eng.build_engine(args.onnx, shape = shape, precision=args.precision)
eng.save_engine(engine, engine_name)
