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# Copyright (c) Facebook, Inc. and its affiliates.
import io
import unittest
import warnings
import torch
from torch.hub import _check_module_exists
from detectron2 import model_zoo
from detectron2.config import get_cfg
from detectron2.export import STABLE_ONNX_OPSET_VERSION
from detectron2.export.flatten import TracingAdapter
from detectron2.modeling import build_model
from detectron2.utils.testing import (
_pytorch1111_symbolic_opset9_repeat_interleave,
_pytorch1111_symbolic_opset9_to,
get_sample_coco_image,
register_custom_op_onnx_export,
skipIfOnCPUCI,
skipIfUnsupportedMinOpsetVersion,
skipIfUnsupportedMinTorchVersion,
unregister_custom_op_onnx_export,
)
@unittest.skipIf(not _check_module_exists("onnx"), "ONNX not installed.")
@skipIfUnsupportedMinTorchVersion("1.10")
class TestONNXTracingExport(unittest.TestCase):
def testMaskRCNNFPN(self):
def inference_func(model, images):
with warnings.catch_warnings(record=True):
inputs = [{"image": image} for image in images]
inst = model.inference(inputs, do_postprocess=False)[0]
return [{"instances": inst}]
self._test_model_zoo_from_config_path(
"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml", inference_func
)
@skipIfOnCPUCI
def testMaskRCNNC4(self):
def inference_func(model, image):
inputs = [{"image": image}]
return model.inference(inputs, do_postprocess=False)[0]
self._test_model_zoo_from_config_path(
"COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml", inference_func
)
@skipIfOnCPUCI
def testCascadeRCNN(self):
def inference_func(model, image):
inputs = [{"image": image}]
return model.inference(inputs, do_postprocess=False)[0]
self._test_model_zoo_from_config_path(
"Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml", inference_func
)
def testRetinaNet(self):
def inference_func(model, image):
return model.forward([{"image": image}])[0]["instances"]
self._test_model_zoo_from_config_path(
"COCO-Detection/retinanet_R_50_FPN_3x.yaml", inference_func
)
@skipIfOnCPUCI
def testMaskRCNNFPN_batched(self):
def inference_func(model, image1, image2):
inputs = [{"image": image1}, {"image": image2}]
return model.inference(inputs, do_postprocess=False)
self._test_model_zoo_from_config_path(
"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml", inference_func, batch=2
)
@skipIfUnsupportedMinOpsetVersion(16, STABLE_ONNX_OPSET_VERSION)
@skipIfUnsupportedMinTorchVersion("1.11.1")
def testMaskRCNNFPN_with_postproc(self):
def inference_func(model, image):
inputs = [{"image": image, "height": image.shape[1], "width": image.shape[2]}]
return model.inference(inputs, do_postprocess=True)[0]["instances"]
self._test_model_zoo_from_config_path(
"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml",
inference_func,
opset_version=STABLE_ONNX_OPSET_VERSION,
)
################################################################################
# Testcase internals - DO NOT add tests below this point
################################################################################
def setUp(self):
register_custom_op_onnx_export("::to", _pytorch1111_symbolic_opset9_to, 9, "1.11.1")
register_custom_op_onnx_export(
"::repeat_interleave",
_pytorch1111_symbolic_opset9_repeat_interleave,
9,
"1.11.1",
)
def tearDown(self):
unregister_custom_op_onnx_export("::to", 9, "1.11.1")
unregister_custom_op_onnx_export("::repeat_interleave", 9, "1.11.1")
def _test_model(
self,
model,
inputs,
inference_func=None,
opset_version=STABLE_ONNX_OPSET_VERSION,
save_onnx_graph_path=None,
**export_kwargs,
):
import onnx # isort:skip
f = io.BytesIO()
adapter_model = TracingAdapter(model, inputs, inference_func)
adapter_model.eval()
with torch.no_grad():
try:
torch.onnx.enable_log()
except AttributeError:
# Older ONNX versions does not have this API
pass
torch.onnx.export(
adapter_model,
adapter_model.flattened_inputs,
f,
training=torch.onnx.TrainingMode.EVAL,
opset_version=opset_version,
verbose=True,
**export_kwargs,
)
onnx_model = onnx.load_from_string(f.getvalue())
assert onnx_model is not None
if save_onnx_graph_path:
onnx.save(onnx_model, save_onnx_graph_path)
def _test_model_zoo_from_config_path(
self,
config_path,
inference_func,
batch=1,
opset_version=STABLE_ONNX_OPSET_VERSION,
save_onnx_graph_path=None,
**export_kwargs,
):
model = model_zoo.get(config_path, trained=True)
image = get_sample_coco_image()
inputs = tuple(image.clone() for _ in range(batch))
return self._test_model(
model, inputs, inference_func, opset_version, save_onnx_graph_path, **export_kwargs
)
def _test_model_from_config_path(
self,
config_path,
inference_func,
batch=1,
opset_version=STABLE_ONNX_OPSET_VERSION,
save_onnx_graph_path=None,
**export_kwargs,
):
from projects.PointRend import point_rend # isort:skip
cfg = get_cfg()
cfg.DATALOADER.NUM_WORKERS = 0
point_rend.add_pointrend_config(cfg)
cfg.merge_from_file(config_path)
cfg.freeze()
model = build_model(cfg)
image = get_sample_coco_image()
inputs = tuple(image.clone() for _ in range(batch))
return self._test_model(
model, inputs, inference_func, opset_version, save_onnx_graph_path, **export_kwargs
)
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