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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2021/03/10 05:23:23 UTC
[GitHub] [incubator-mxnet] Zha0q1 opened a new pull request #20001: [WIP] Onnx Dynamic Shapes Support
Zha0q1 opened a new pull request #20001:
URL: https://github.com/apache/incubator-mxnet/pull/20001
RFC https://github.com/apache/incubator-mxnet/issues/20000
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[GitHub] [incubator-mxnet] Zha0q1 commented on a change in pull request #20001: Onnx Dynamic Shapes Support
Posted by GitBox <gi...@apache.org>.
Zha0q1 commented on a change in pull request #20001:
URL: https://github.com/apache/incubator-mxnet/pull/20001#discussion_r592887159
##########
File path: python/mxnet/contrib/onnx/mx2onnx/export_model.py
##########
@@ -62,42 +74,57 @@ def export_model(sym, params, input_shape, input_type=np.float32,
"""
try:
- from onnx import helper, mapping
+ from onnx import helper, mapping, shape_inference
from onnx.defs import onnx_opset_version
except ImportError:
raise ImportError("Onnx and protobuf need to be installed. "
+ "Instructions to install - https://github.com/onnx/onnx")
+ if input_type is not None:
+ in_types = input_type
+
+ if input_shape is not None:
+ in_shapes = input_shape
+
converter = MXNetGraph()
if opset_version is None:
# default is to use latest opset version the onnx package supports
opset_version = onnx_opset_version()
- if not isinstance(input_type, list):
- input_type = [input_type for _ in range(len(input_shape))]
- input_dtype = [mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(inp_type)] for inp_type in input_type]
+ if not isinstance(in_types, list):
+ in_types = [in_types for _ in range(len(in_shapes))]
+ in_types_t = [mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(i_t)] for i_t in in_types]
# if input parameters are strings(file paths), load files and create symbol parameter objects
if isinstance(sym, string_types) and isinstance(params, string_types):
logging.info("Converting json and weight file to sym and params")
sym_obj, params_obj = load_module(sym, params)
- onnx_graph = converter.create_onnx_graph_proto(sym_obj, params_obj, input_shape,
- input_dtype,
- verbose=verbose, opset_version=opset_version)
+ onnx_graph = converter.create_onnx_graph_proto(sym_obj, params_obj, in_shapes,
+ in_types_t,
+ verbose=verbose, opset_version=opset_version,
+ dynamic=dynamic, dynamic_input_shapes=dynamic_input_shapes)
elif isinstance(sym, symbol.Symbol) and isinstance(params, dict):
- onnx_graph = converter.create_onnx_graph_proto(sym, params, input_shape,
- input_dtype,
- verbose=verbose, opset_version=opset_version)
+ onnx_graph = converter.create_onnx_graph_proto(sym, params, in_shapes,
+ in_types_t,
+ verbose=verbose, opset_version=opset_version,
+ dynamic=dynamic, dynamic_input_shapes=dynamic_input_shapes)
else:
raise ValueError("Input sym and params should either be files or objects")
# Create the model (ModelProto)
onnx_model = helper.make_model(onnx_graph)
+ # Run shape inference on the model. Due to ONNX bug/incompatibility this may or may not crash
Review comment:
shape inference is default to off
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[GitHub] [incubator-mxnet] waytrue17 commented on a change in pull request #20001: Onnx Dynamic Shapes Support
Posted by GitBox <gi...@apache.org>.
waytrue17 commented on a change in pull request #20001:
URL: https://github.com/apache/incubator-mxnet/pull/20001#discussion_r592886498
##########
File path: python/mxnet/contrib/onnx/mx2onnx/export_model.py
##########
@@ -62,42 +74,57 @@ def export_model(sym, params, input_shape, input_type=np.float32,
"""
try:
- from onnx import helper, mapping
+ from onnx import helper, mapping, shape_inference
from onnx.defs import onnx_opset_version
except ImportError:
raise ImportError("Onnx and protobuf need to be installed. "
+ "Instructions to install - https://github.com/onnx/onnx")
+ if input_type is not None:
+ in_types = input_type
+
+ if input_shape is not None:
+ in_shapes = input_shape
+
converter = MXNetGraph()
if opset_version is None:
# default is to use latest opset version the onnx package supports
opset_version = onnx_opset_version()
- if not isinstance(input_type, list):
- input_type = [input_type for _ in range(len(input_shape))]
- input_dtype = [mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(inp_type)] for inp_type in input_type]
+ if not isinstance(in_types, list):
+ in_types = [in_types for _ in range(len(in_shapes))]
+ in_types_t = [mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(i_t)] for i_t in in_types]
# if input parameters are strings(file paths), load files and create symbol parameter objects
if isinstance(sym, string_types) and isinstance(params, string_types):
logging.info("Converting json and weight file to sym and params")
sym_obj, params_obj = load_module(sym, params)
- onnx_graph = converter.create_onnx_graph_proto(sym_obj, params_obj, input_shape,
- input_dtype,
- verbose=verbose, opset_version=opset_version)
+ onnx_graph = converter.create_onnx_graph_proto(sym_obj, params_obj, in_shapes,
+ in_types_t,
+ verbose=verbose, opset_version=opset_version,
+ dynamic=dynamic, dynamic_input_shapes=dynamic_input_shapes)
elif isinstance(sym, symbol.Symbol) and isinstance(params, dict):
- onnx_graph = converter.create_onnx_graph_proto(sym, params, input_shape,
- input_dtype,
- verbose=verbose, opset_version=opset_version)
+ onnx_graph = converter.create_onnx_graph_proto(sym, params, in_shapes,
+ in_types_t,
+ verbose=verbose, opset_version=opset_version,
+ dynamic=dynamic, dynamic_input_shapes=dynamic_input_shapes)
else:
raise ValueError("Input sym and params should either be files or objects")
# Create the model (ModelProto)
onnx_model = helper.make_model(onnx_graph)
+ # Run shape inference on the model. Due to ONNX bug/incompatibility this may or may not crash
Review comment:
Why do we run shape inference here? Seems previously we don't have it
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[GitHub] [incubator-mxnet] mxnet-bot commented on pull request #20001: Onnx Dynamic Shapes Support
Posted by GitBox <gi...@apache.org>.
mxnet-bot commented on pull request #20001:
URL: https://github.com/apache/incubator-mxnet/pull/20001#issuecomment-796445922
Jenkins CI successfully triggered : [centos-gpu, centos-cpu, website, clang, unix-gpu, miscellaneous, unix-cpu, windows-cpu, edge, windows-gpu, sanity]
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[GitHub] [incubator-mxnet] Zha0q1 commented on a change in pull request #20001: Onnx Dynamic Shapes Support
Posted by GitBox <gi...@apache.org>.
Zha0q1 commented on a change in pull request #20001:
URL: https://github.com/apache/incubator-mxnet/pull/20001#discussion_r592887059
##########
File path: python/mxnet/contrib/onnx/mx2onnx/export_model.py
##########
@@ -62,42 +74,57 @@ def export_model(sym, params, input_shape, input_type=np.float32,
"""
try:
- from onnx import helper, mapping
+ from onnx import helper, mapping, shape_inference
from onnx.defs import onnx_opset_version
except ImportError:
raise ImportError("Onnx and protobuf need to be installed. "
+ "Instructions to install - https://github.com/onnx/onnx")
+ if input_type is not None:
+ in_types = input_type
+
+ if input_shape is not None:
+ in_shapes = input_shape
+
converter = MXNetGraph()
if opset_version is None:
# default is to use latest opset version the onnx package supports
opset_version = onnx_opset_version()
- if not isinstance(input_type, list):
- input_type = [input_type for _ in range(len(input_shape))]
- input_dtype = [mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(inp_type)] for inp_type in input_type]
+ if not isinstance(in_types, list):
+ in_types = [in_types for _ in range(len(in_shapes))]
+ in_types_t = [mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(i_t)] for i_t in in_types]
# if input parameters are strings(file paths), load files and create symbol parameter objects
if isinstance(sym, string_types) and isinstance(params, string_types):
logging.info("Converting json and weight file to sym and params")
sym_obj, params_obj = load_module(sym, params)
- onnx_graph = converter.create_onnx_graph_proto(sym_obj, params_obj, input_shape,
- input_dtype,
- verbose=verbose, opset_version=opset_version)
+ onnx_graph = converter.create_onnx_graph_proto(sym_obj, params_obj, in_shapes,
+ in_types_t,
+ verbose=verbose, opset_version=opset_version,
+ dynamic=dynamic, dynamic_input_shapes=dynamic_input_shapes)
elif isinstance(sym, symbol.Symbol) and isinstance(params, dict):
- onnx_graph = converter.create_onnx_graph_proto(sym, params, input_shape,
- input_dtype,
- verbose=verbose, opset_version=opset_version)
+ onnx_graph = converter.create_onnx_graph_proto(sym, params, in_shapes,
+ in_types_t,
+ verbose=verbose, opset_version=opset_version,
+ dynamic=dynamic, dynamic_input_shapes=dynamic_input_shapes)
else:
raise ValueError("Input sym and params should either be files or objects")
# Create the model (ModelProto)
onnx_model = helper.make_model(onnx_graph)
+ # Run shape inference on the model. Due to ONNX bug/incompatibility this may or may not crash
Review comment:
This is an optional step. Doing shape inference may help with some runtime optimization and we can visualize the graph better, since the noes will have input and output shapes labeled
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[GitHub] [incubator-mxnet] waytrue17 commented on a change in pull request #20001: Onnx Dynamic Shapes Support
Posted by GitBox <gi...@apache.org>.
waytrue17 commented on a change in pull request #20001:
URL: https://github.com/apache/incubator-mxnet/pull/20001#discussion_r592893626
##########
File path: python/mxnet/contrib/onnx/mx2onnx/export_model.py
##########
@@ -62,42 +74,57 @@ def export_model(sym, params, input_shape, input_type=np.float32,
"""
try:
- from onnx import helper, mapping
+ from onnx import helper, mapping, shape_inference
from onnx.defs import onnx_opset_version
except ImportError:
raise ImportError("Onnx and protobuf need to be installed. "
+ "Instructions to install - https://github.com/onnx/onnx")
+ if input_type is not None:
+ in_types = input_type
+
+ if input_shape is not None:
+ in_shapes = input_shape
+
converter = MXNetGraph()
if opset_version is None:
# default is to use latest opset version the onnx package supports
opset_version = onnx_opset_version()
- if not isinstance(input_type, list):
- input_type = [input_type for _ in range(len(input_shape))]
- input_dtype = [mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(inp_type)] for inp_type in input_type]
+ if not isinstance(in_types, list):
+ in_types = [in_types for _ in range(len(in_shapes))]
+ in_types_t = [mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(i_t)] for i_t in in_types]
# if input parameters are strings(file paths), load files and create symbol parameter objects
if isinstance(sym, string_types) and isinstance(params, string_types):
logging.info("Converting json and weight file to sym and params")
sym_obj, params_obj = load_module(sym, params)
- onnx_graph = converter.create_onnx_graph_proto(sym_obj, params_obj, input_shape,
- input_dtype,
- verbose=verbose, opset_version=opset_version)
+ onnx_graph = converter.create_onnx_graph_proto(sym_obj, params_obj, in_shapes,
+ in_types_t,
+ verbose=verbose, opset_version=opset_version,
+ dynamic=dynamic, dynamic_input_shapes=dynamic_input_shapes)
elif isinstance(sym, symbol.Symbol) and isinstance(params, dict):
- onnx_graph = converter.create_onnx_graph_proto(sym, params, input_shape,
- input_dtype,
- verbose=verbose, opset_version=opset_version)
+ onnx_graph = converter.create_onnx_graph_proto(sym, params, in_shapes,
+ in_types_t,
+ verbose=verbose, opset_version=opset_version,
+ dynamic=dynamic, dynamic_input_shapes=dynamic_input_shapes)
else:
raise ValueError("Input sym and params should either be files or objects")
# Create the model (ModelProto)
onnx_model = helper.make_model(onnx_graph)
+ # Run shape inference on the model. Due to ONNX bug/incompatibility this may or may not crash
Review comment:
Makes sense. We can keep it here, and turn it off if it crashes on some cases later on.
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[GitHub] [incubator-mxnet] Zha0q1 merged pull request #20001: Onnx Dynamic Shapes Support
Posted by GitBox <gi...@apache.org>.
Zha0q1 merged pull request #20001:
URL: https://github.com/apache/incubator-mxnet/pull/20001
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[GitHub] [incubator-mxnet] mxnet-bot commented on pull request #20001: [WIP] Onnx Dynamic Shapes Support
Posted by GitBox <gi...@apache.org>.
mxnet-bot commented on pull request #20001:
URL: https://github.com/apache/incubator-mxnet/pull/20001#issuecomment-794901775
Hey @Zha0q1 , Thanks for submitting the PR
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[GitHub] [incubator-mxnet] Zha0q1 commented on pull request #20001: Onnx Dynamic Shapes Support
Posted by GitBox <gi...@apache.org>.
Zha0q1 commented on pull request #20001:
URL: https://github.com/apache/incubator-mxnet/pull/20001#issuecomment-796445845
@mxnet-bot run ci [all]
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