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#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import argparse
import os
import onnx
import torch
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import build_detection_test_loader
from detectron2.evaluation import COCOEvaluator, inference_on_dataset, print_csv_format
from detectron2.export import Caffe2Tracer, add_export_config
from detectron2.modeling import build_model
from detectron2.utils.logger import setup_logger
def setup_cfg(args):
cfg = get_cfg()
# cuda context is initialized before creating dataloader, so we don't fork anymore
cfg.DATALOADER.NUM_WORKERS = 0
cfg = add_export_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
if cfg.MODEL.DEVICE != "cpu":
TORCH_VERSION = tuple(int(x) for x in torch.__version__.split(".")[:2])
assert TORCH_VERSION >= (1, 5), "PyTorch>=1.5 required for GPU conversion!"
return cfg
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Convert a model using caffe2 tracing.")
parser.add_argument(
"--format",
choices=["caffe2", "onnx", "torchscript"],
help="output format",
default="caffe2",
)
parser.add_argument("--config-file", default="", metavar="FILE", help="path to config file")
parser.add_argument("--run-eval", action="store_true")
parser.add_argument("--output", help="output directory for the converted model")
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
logger = setup_logger()
logger.info("Command line arguments: " + str(args))
os.makedirs(args.output, exist_ok=True)
cfg = setup_cfg(args)
# create a torch model
torch_model = build_model(cfg)
DetectionCheckpointer(torch_model).resume_or_load(cfg.MODEL.WEIGHTS)
# get a sample data
data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST[0])
first_batch = next(iter(data_loader))
# convert and save caffe2 model
tracer = Caffe2Tracer(cfg, torch_model, first_batch)
if args.format == "caffe2":
caffe2_model = tracer.export_caffe2()
caffe2_model.save_protobuf(args.output)
# draw the caffe2 graph
caffe2_model.save_graph(os.path.join(args.output, "model.svg"), inputs=first_batch)
elif args.format == "onnx":
onnx_model = tracer.export_onnx()
onnx.save(onnx_model, os.path.join(args.output, "model.onnx"))
elif args.format == "torchscript":
script_model = tracer.export_torchscript()
script_model.save(os.path.join(args.output, "model.ts"))
# Recursively print IR of all modules
with open(os.path.join(args.output, "model_ts_IR.txt"), "w") as f:
try:
f.write(script_model._actual_script_module._c.dump_to_str(True, False, False))
except AttributeError:
pass
# Print IR of the entire graph (all submodules inlined)
with open(os.path.join(args.output, "model_ts_IR_inlined.txt"), "w") as f:
f.write(str(script_model.inlined_graph))
# Print the model structure in pytorch style
with open(os.path.join(args.output, "model.txt"), "w") as f:
f.write(str(script_model))
# run evaluation with the converted model
if args.run_eval:
assert args.format == "caffe2", "Python inference in other format is not yet supported."
dataset = cfg.DATASETS.TEST[0]
data_loader = build_detection_test_loader(cfg, dataset)
# NOTE: hard-coded evaluator. change to the evaluator for your dataset
evaluator = COCOEvaluator(dataset, cfg, True, args.output)
metrics = inference_on_dataset(caffe2_model, data_loader, evaluator)
print_csv_format(metrics)
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