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mindinsight
提交 2f2f6a3f 编写于 作者: M mindspore-ci-bot 提交者： Gitee
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!245 use json to save op mapping in mindconverter 

Merge pull request !245 from quyongxiu1/br_0601_config_optim
上级 3abcb4a2 6adc58f0
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Showing with 660 addition and 312 deletion
mindinsight/mindconverter/config.py
浏览文件 @ 2f2f6a3f
...@@ -15,17 +15,18 @@ ...@@ -15,17 +15,18 @@
"""API config""" """API config"""
import ast import ast
from collections import OrderedDict from collections import OrderedDict
from functools import partial from importlib import import_module
import json import json
import os import os
import pasta import pasta
from mindinsight.mindconverter.enums import RequriedType
from mindinsight.mindconverter.common.log import logger from mindinsight.mindconverter.common.log import logger
REQUIRED = RequriedType.REQUIRED.name
UNREQUIRED = RequriedType.UNREQUIRED.name REQUIRED = 'REQUIRED'
UNREQUIRED = 'UNREQUIRED'
FUNC_MODULE = 'mindinsight.mindconverter.funcs'
class APIPt: class APIPt:
...@@ -250,88 +251,65 @@ class MappingHelper: ...@@ -250,88 +251,65 @@ class MappingHelper:
return expr_ms return expr_ms
def gen_explicit_map_nn_sequential(_, args_pt): def get_ms_api(ms_api_info):
""" """
Generate explicit_map for nn.Sequential. Get APIMs instance from ms_api_info.
Args: Args:
args_pt (dict): Args for APIPt. ms_api_info (list): info for create an APIMs instance, the first value in list is name for APIMs, the second(if
provided) is params for APIMs, the third(if provided) is p_attrs for APIMs.
Returns: Returns:
dict, map between frames. APIMs, instance of APIMs parsed from given info.
""" """
args = args_pt['*args'] ms_name = ms_api_info[0]
return {"*args": "[{}]".format(args)} ms_params = ms_api_info[1] if len(ms_api_info) >= 2 else None
ms_p_attrs = set(ms_api_info[2]) if len(ms_api_info) >= 3 else None
ms_api = APIMs(name=ms_name, params=ms_params, p_attrs=ms_p_attrs)
return ms_api
def gen_explicit_map_nn_maxpool2d(params_pt, args_pt): def get_pt_api(pt_api_info):
""" """
Generate explicit_map for nn.MaxPool2d. Get APIPt instance from pt_api_info.
Args: Args:
params_pt (dict): Params for APIPt. pt_api_info (list): info for create an APIMs instance, the first value in list is name for APIPt, the second(if
args_pt (dict): Args for APIPt. provided) is params for APIPt.
Returns: Returns:
dict, map between frames. APIMs, instance of APIMs parsed from given info.
"""
if 'padding' in args_pt:
padding = args_pt['padding']
else:
padding = params_pt['padding']
if padding.strip() in ("0", "(0,0)", "(0, 0)"):
pad_mode = "'valid'"
else:
pad_mode = "'same'"
return {"pad_mode": pad_mode}
def gen_explicit_map_f_max_pool2d(params_pt, args_pt):
""" """
Generate explicit_map for F.MaxPool2d. pt_name = pt_api_info[0]
pt_params = pt_api_info[1] if len(pt_api_info) >= 2 else None
pt_api = APIPt(name=pt_name, params=pt_params)
return pt_api
Args:
params_pt (dict): Params for APIPt.
args_pt (dict): Args for APIPt.
Returns: def get_mapping_from_file(path):
dict, map between frames.
""" """
if 'padding' in args_pt: Parse mapping info from given file.
padding = args_pt['padding']
else:
padding = params_pt['padding']
if padding.strip() in ("0", "(0,0)", "(0, 0)"):
padding = "'valid'"
else:
padding = "'same'"
return {"padding": padding}
def gen_explicit_map_one_delta(params_pt, args_pt, k_ms, k_pt):
"""
Generate explicit_map for which include mapping relationship is `1 - k_ms = k_pt`.
Args: Args:
params_pt (dict): Params for APIPt. path (str): The file path.
args_pt (dict): Args for APIPt.
Returns: Returns:
dict, map between frames. dict, key is op name, value is a relevant instance of MappingHelper.
""" """
value = args_pt[k_pt] if k_pt in args_pt else params_pt[k_pt] mapping_info_d = load_json_file(path)
value = value.strip() parse_mapping_dict = {}
for key, value in mapping_info_d.items():
def is_number(string): ms_api_info = value.pop('ms_api')
try: ms_api = get_ms_api(ms_api_info)
float(string) pt_api_info = value.pop('pt_api')
return True pt_api = get_pt_api(pt_api_info)
except ValueError: gen_explicit_map = value.get('gen_explicit_map')
return False if gen_explicit_map:
module_name = import_module(FUNC_MODULE)
if is_number(value): value['gen_explicit_map'] = getattr(module_name, gen_explicit_map)
return {k_ms: str(1 - float(value))}
return {k_ms: "1.0 - " + value} parse_mapping_dict.update({key: MappingHelper(**dict(ms_api=ms_api, pt_api=pt_api), **value)})
return parse_mapping_dict
def load_json_file(file_path): def load_json_file(file_path):
...@@ -350,244 +328,38 @@ def load_json_file(file_path): ...@@ -350,244 +328,38 @@ def load_json_file(file_path):
# ---------------------------- mappings ---------------------------- # ---------------------------- mappings ----------------------------
NN_MAPPING = { NN_MAPPING_PATH = os.path.realpath(os.path.join(os.path.dirname(__file__), 'mappings/nn_mappings.json'))
'nn.Sequential': MappingHelper(**{"ms_api": APIMs('nn.SequentialCell', OrderedDict([('*args', REQUIRED)])), NN_MAPPING = get_mapping_from_file(NN_MAPPING_PATH)
"pt_api": APIPt('nn.Sequential', OrderedDict([('*args', REQUIRED)])), # update to add key with full api_name, which starts with 'torch.nn.'
"gen_explicit_map": gen_explicit_map_nn_sequential,
"export_key": False
}),
'nn.Conv2d': MappingHelper(**{"ms_api": APIMs('nn.Conv2d', OrderedDict(in_channels=REQUIRED,
out_channels=REQUIRED,
kernel_size=REQUIRED,
stride=1,
pad_mode='same',
padding=0,
dilation=1,
group=1,
has_bias=False,
weight_init='normal',
bias_init='zeros')),
"pt_api": APIPt('nn.Conv2d', OrderedDict(in_channels=REQUIRED,
out_channels=REQUIRED,
kernel_size=REQUIRED,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
padding_mode='zeros')),
"ms2pt_mapping": {'in_channels': 'in_channels',
'out_channels': 'out_channels',
'kernel_size': 'kernel_size',
'stride': 'stride',
'padding': 'padding',
'dilation': 'dilation',
'group': 'groups',
'has_bias': 'bias'},
"gen_explicit_map": (lambda params_pt, args_pt: {"pad_mode": "'pad'"})
}),
'nn.BatchNorm2d': MappingHelper(**{"ms_api": APIMs('nn.BatchNorm2d', OrderedDict(num_features=REQUIRED,
eps=1e-5,
momentum=0.9,
affine=True,
gamma_init='ones',
beta_init='zeros',
moving_mean_init='zeros',
moving_var_init='ones',
use_batch_statistics=True)),
"pt_api": APIPt('nn.BatchNorm2d', OrderedDict(num_features=REQUIRED,
eps=1e-5,
momentum=0.1,
affine=True,
track_running_stats=True)),
"ms2pt_mapping": {"num_features": "num_features",
"eps": "eps",
"affine": "affine",
"use_batch_statistics": "track_running_stats"},
"gen_explicit_map": partial(gen_explicit_map_one_delta,
k_ms="momentum", k_pt="momentum")
}),
'nn.ReLU': MappingHelper(**{"ms_api": APIMs('nn.ReLU', OrderedDict()),
"pt_api": APIPt('nn.ReLU', OrderedDict(inplace=False)),
"ms2pt_mapping": {}}),
'nn.ReLU6': MappingHelper(**{"ms_api": APIMs('nn.ReLU6', OrderedDict()),
"pt_api": APIPt('nn.ReLU6', OrderedDict(inplace=False)),
"ms2pt_mapping": {}}),
'nn.Linear': MappingHelper(**{"ms_api": APIMs('nn.Dense', OrderedDict(in_channels=REQUIRED,
out_channels=REQUIRED,
weight_init='normal',
bias_init='zeros',
has_bias=True,
activation=None)),
"pt_api": APIPt('nn.Linear', OrderedDict(in_features=REQUIRED,
out_features=REQUIRED,
bias=True)),
"ms2pt_mapping": {"in_channels": "in_features",
"out_channels": "out_features",
"has_bias": "bias"}
}),
'nn.MaxPool2d': MappingHelper(**{"ms_api": APIMs('nn.MaxPool2d', OrderedDict(kernel_size=1,
stride=1,
pad_mode="valid")),
"pt_api": APIPt('nn.MaxPool2d', OrderedDict(kernel_size=REQUIRED,
stride=None,
padding=0,
dilation=1,
return_indices=False,
ceil_mode="False")),
"ms2pt_mapping": {"kernel_size": "kernel_size",
"stride": "stride"},
"gen_explicit_map": gen_explicit_map_nn_maxpool2d
}),
'nn.AvgPool2d': MappingHelper(**{"ms_api": APIMs('nn.AvgPool2d', OrderedDict(kernel_size=1,
stride=1,
pad_mode="valid")),
"pt_api": APIPt('nn.AvgPool2d', OrderedDict(kernel_size=REQUIRED,
stride=None,
padding=0,
dilation=1,
return_indices=False,
ceil_mode="False")),
"ms2pt_mapping": {"kernel_size": "kernel_size",
"stride": "stride"},
"gen_explicit_map": gen_explicit_map_nn_maxpool2d
}),
'nn.Dropout': MappingHelper(**{"ms_api": APIMs('nn.Dropout', OrderedDict(keep_prob=0.5,
seed0=0,
seed1=0,
dtype="mstype.float32")),
"pt_api": APIPt('nn.Dropout', OrderedDict(p=0.5,
inplace=False)),
"ms2pt_mapping": {"keep_prob": "p"},
"gen_explicit_map": partial(gen_explicit_map_one_delta,
k_ms="keep_prob", k_pt="p")
})
}
NN_MAPPING.update({"torch." + k: v for k, v in NN_MAPPING.items()}) NN_MAPPING.update({"torch." + k: v for k, v in NN_MAPPING.items()})
F_MAPPING_PATH = os.path.realpath(os.path.join(os.path.dirname(__file__), 'mappings/f_mappings.json'))
F_MAPPING = get_mapping_from_file(F_MAPPING_PATH)
# update to add key starts with 'nn.functional.'
NN_FUNCTIONAL_D = {"nn.functional." + k[len('F.'):]: v for k, v in F_MAPPING.items()}
# update to add key starts with 'torch.nn.functiona.l'
TORCH_NN_FUNCTIONAL_D = {"torch.nn.functional." + k[len('F.'):]: v for k, v in F_MAPPING.items()}
F_MAPPING.update(NN_FUNCTIONAL_D)
F_MAPPING.update(TORCH_NN_FUNCTIONAL_D)
F_MAPPING = { TORCH_DOT_MAPPING_PATH = os.path.realpath(os.path.join(os.path.dirname(__file__), 'mappings/torch_dot_mappings.json'))
'F.relu': MappingHelper(**{"ms_api": APIMs('P.ReLU', OrderedDict(input=REQUIRED)), TORCH_DOT_MAPPING = get_mapping_from_file(TORCH_DOT_MAPPING_PATH)
"pt_api": APIPt('F.relu', OrderedDict(input=REQUIRED, inplace=False)),
"ms2pt_mapping": {"input": "input"},
}),
'F.relu6': MappingHelper(**{"ms_api": APIMs('P.ReLU6', OrderedDict(input=REQUIRED)),
"pt_api": APIPt('F.relu6', OrderedDict(input=REQUIRED, inplace=False)),
"ms2pt_mapping": {"input": "input"},
}),
'F.max_pool2d': MappingHelper(**{"ms_api": APIMs('P.MaxPool', OrderedDict(ksize=1,
strides=1,
padding="valid",
input=REQUIRED),
p_attrs={"ksize", "strides", "padding"}),
"pt_api": APIPt('F.max_pool2d', OrderedDict(input=REQUIRED,
kernel_size=REQUIRED,
stride=None,
padding=0,
dilation=1,
ceil_mode=False,
return_indices=False)),
"ms2pt_mapping": {"ksize": "kernel_size",
"strides": "stride",
"input": "input",
},
"gen_explicit_map": gen_explicit_map_f_max_pool2d
}),
'F.avg_pool2d': MappingHelper(**{"ms_api": APIMs('P.AvgPool', OrderedDict(ksize=1,
strides=1,
padding="valid",
input=REQUIRED),
p_attrs={"ksize", "strides", "padding"}),
"pt_api": APIPt('F.avg_pool2d', OrderedDict(input=REQUIRED,
kernel_size=REQUIRED,
stride=None,
padding=0,
dilation=1,
ceil_mode=False,
return_indices=False)),
"ms2pt_mapping": {"ksize": "kernel_size",
"strides": "stride",
"input": "input",
},
"gen_explicit_map": gen_explicit_map_f_max_pool2d
}),
}
nn_functional_d = {"nn.functional." + k[2:]: v for k, v in F_MAPPING.items()}
torch_nn_functional_d = {"torch.nn.functional." + k[2:]: v for k, v in F_MAPPING.items()}
F_MAPPING.update(nn_functional_d)
F_MAPPING.update(torch_nn_functional_d)
TORCH_DOT_MAPPING = {
'torch.flatten': MappingHelper(**{"ms_api": APIMs('P.Flatten', OrderedDict(input=REQUIRED)),
"pt_api": APIPt('torch.flatten', OrderedDict(input=REQUIRED,
start_dim=0,
end_dim=-1)),
"ms2pt_mapping": {"input": "input"}
}),
'torch.cat': MappingHelper(**{"ms_api": APIMs('P.Concat',
OrderedDict(axis=0, input=REQUIRED),
p_attrs={"axis"}),
"pt_api": APIPt('torch.flatten', OrderedDict(tensors=REQUIRED, dim=0, out=None)),
"ms2pt_mapping": {"input": "tensors",
"axis": "dim"}
}),
}
TENSOR_DOT_MAPPING = {
'.view': MappingHelper(**{"ms_api": APIMs('P.Reshape', OrderedDict(x=REQUIRED, shape=REQUIRED)),
"pt_api": APIPt('.view', OrderedDict([('*shape', REQUIRED)])),
"ms2pt_mapping": {"x": "call_name"},
"gen_explicit_map": (lambda params_pt, args_pt: {"shape": "(" + args_pt["*shape"] + ",)"})
}),
'.size': MappingHelper(**{"ms_api": APIMs('P.Shape', OrderedDict(x=REQUIRED)),
"pt_api": APIPt('.size', OrderedDict([('idx', REQUIRED)])),
"ms2pt_mapping": {"x": "call_name"}
}),
'.flatten': MappingHelper(**{"ms_api": APIMs('P.Flatten', OrderedDict(input=REQUIRED)),
"pt_api": APIPt('.flatten', OrderedDict(start_dim=0,
end_dim=-1)),
"ms2pt_mapping": {"input": "call_name"}
}),
'.reshape': MappingHelper(**{"ms_api": APIMs('P.Reshape', OrderedDict(x=REQUIRED, shape=REQUIRED)),
"pt_api": APIPt('.reshape', OrderedDict([('*shape', REQUIRED)])),
"ms2pt_mapping": {"x": "call_name"},
"gen_explicit_map": (
lambda params_pt, args_pt: {"shape": "(" + args_pt["*shape"] + ",)"})
}),
'.mean': MappingHelper(**{"ms_api": APIMs('P.ReduceMean', OrderedDict(keep_dims=False,
input=REQUIRED,
axis=()),
p_attrs={"keep_dims"}),
"pt_api": APIPt('.mean', OrderedDict(dim=None,
keepdim=False)),
"ms2pt_mapping": {"keep_dims": "keepdim",
"axis": "dim",
"input": "call_name"},
}),
'.squeeze': MappingHelper(**{"ms_api": APIMs('P.ReduceMean', OrderedDict(input=REQUIRED,
axis=()),
p_attrs={"axis"}),
"pt_api": APIPt('.squeeze', OrderedDict(dim=None)),
"ms2pt_mapping": {"axis": "dim",
"input": "call_name"},
}),
}
TENSOR_DOT_MAPPING_PATH = os.path.realpath(os.path.join(os.path.dirname(__file__), 'mappings/tensor_dot_mappings.json'))
TENSOR_DOT_MAPPING = get_mapping_from_file(TENSOR_DOT_MAPPING_PATH)
ALL_MAPPING = {**NN_MAPPING, **F_MAPPING, **TORCH_DOT_MAPPING, **TENSOR_DOT_MAPPING} ALL_MAPPING = {**NN_MAPPING, **F_MAPPING, **TORCH_DOT_MAPPING, **TENSOR_DOT_MAPPING}
# ---------------------------- api list support or not support ---------------------------- # ---------------------------- api list support or not support ----------------------------
NN_LIST_PATH = os.path.realpath(os.path.join(os.path.dirname(__file__), 'nn_list.json')) NN_LIST_PATH = os.path.realpath(os.path.join(os.path.dirname(__file__), 'ops', 'nn_list.json'))
NN_LIST = load_json_file(NN_LIST_PATH) NN_LIST = load_json_file(NN_LIST_PATH)
NN_LIST += ["torch." + name for name in NN_LIST] NN_LIST += ["torch." + name for name in NN_LIST]
NN_SUPPORTED = [x for x in NN_LIST if x in ALL_MAPPING] NN_SUPPORTED = [x for x in NN_LIST if x in ALL_MAPPING]
NN_UNSUPPORTED = [x for x in NN_LIST if x not in ALL_MAPPING] NN_UNSUPPORTED = [x for x in NN_LIST if x not in ALL_MAPPING]
F_LIST_PATH = os.path.realpath(os.path.join(os.path.dirname(__file__), 'f_list.json')) F_LIST_PATH = os.path.realpath(os.path.join(os.path.dirname(__file__), 'ops', 'f_list.json'))
F_LIST = load_json_file(F_LIST_PATH) F_LIST = load_json_file(F_LIST_PATH)
F_LIST += ["F." + name[len("torch.nn.functional."):] for name in F_LIST] + \ F_LIST += ["F." + name[len("torch.nn.functional."):] for name in F_LIST] + \
[name[len("torch."):] for name in F_LIST] [name[len("torch."):] for name in F_LIST]
...@@ -595,7 +367,7 @@ F_SUPPORTED = [x for x in F_LIST if x in ALL_MAPPING] ...@@ -595,7 +367,7 @@ F_SUPPORTED = [x for x in F_LIST if x in ALL_MAPPING]
F_UNSUPPORTED = [x for x in F_LIST if x not in ALL_MAPPING] F_UNSUPPORTED = [x for x in F_LIST if x not in ALL_MAPPING]
TORCH_DOT_LIST_PATH = os.path.realpath(os.path.join(os.path.dirname(__file__), 'torch_dot_list.json')) TORCH_DOT_LIST_PATH = os.path.realpath(os.path.join(os.path.dirname(__file__), 'ops', 'torch_dot_list.json'))
TORCH_DOT_LIST = load_json_file(TORCH_DOT_LIST_PATH) TORCH_DOT_LIST = load_json_file(TORCH_DOT_LIST_PATH)
...@@ -603,7 +375,7 @@ TORCH_DOT_SUPPORTED = [x for x in TORCH_DOT_LIST if x in ALL_MAPPING] ...@@ -603,7 +375,7 @@ TORCH_DOT_SUPPORTED = [x for x in TORCH_DOT_LIST if x in ALL_MAPPING]
TORCH_DOT_UNSUPPORTED = [x for x in TORCH_DOT_LIST if x not in ALL_MAPPING] TORCH_DOT_UNSUPPORTED = [x for x in TORCH_DOT_LIST if x not in ALL_MAPPING]
TENSOR_DOT_LIST_PATH = os.path.realpath(os.path.join(os.path.dirname(__file__), 'tensor_dot_list.json')) TENSOR_DOT_LIST_PATH = os.path.realpath(os.path.join(os.path.dirname(__file__), 'ops', 'tensor_dot_list.json'))
TENSOR_DOT_LIST = load_json_file(TENSOR_DOT_LIST_PATH) TENSOR_DOT_LIST = load_json_file(TENSOR_DOT_LIST_PATH)
...@@ -620,5 +392,5 @@ ALL_UNSUPPORTED = NN_UNSUPPORTED + F_UNSUPPORTED + TORCH_DOT_UNSUPPORTED + TENSO ...@@ -620,5 +392,5 @@ ALL_UNSUPPORTED = NN_UNSUPPORTED + F_UNSUPPORTED + TORCH_DOT_UNSUPPORTED + TENSO
UNSUPPORTED_WARN_INFOS = { UNSUPPORTED_WARN_INFOS = {
"nn.AdaptiveAvgPool2d": "maybe could convert to P.ReduceMean", "nn.AdaptiveAvgPool2d": "maybe could convert to P.ReduceMean",
"F.adaptive_avg_pool2d": "maybe could convert to P.ReduceMean", "F.adaptive_avg_pool2d": "maybe could convert to P.ReduceMean",
"F.dropout": "please use nn.Dropout in __init__()", "F.dropout": "please use nn.Dropout in __init__()"
} }
mindinsight/mindconverter/enums.py 已删除 100644 → 0
浏览文件 @ 3abcb4a2
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Enums."""
from enum import Enum
class RequriedType(Enum):
"""If param is required"""
REQUIRED = 1
UNREQUIRED = 2
mindinsight/mindconverter/funcs.py 0 → 100644
浏览文件 @ 2f2f6a3f
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless REQUIRED by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""funcs for gen_explicit_map"""
from functools import partial
def gen_explicit_map_f_max_pool2d(params_pt, args_pt):
"""
Generate explicit_map for F.MaxPool2d.
Args:
params_pt (dict): Params for APIPt.
args_pt (dict): Args for APIPt.
Returns:
dict, map between frames.
"""
if 'padding' in args_pt:
padding = args_pt['padding']
else:
padding = params_pt['padding']
if padding.strip() in ("0", "(0,0)", "(0, 0)"):
padding = "'valid'"
else:
padding = "'same'"
return {"padding": padding}
def gen_explicit_map_nn_sequential(_, args_pt):
"""
Generate explicit_map for nn.Sequential.
Args:
args_pt (dict): Args for APIPt.
Returns:
dict, map between frames.
"""
args = args_pt['*args']
return {"*args": "[{}]".format(args)}
def gen_explicit_map_one_delta(params_pt, args_pt, k_ms, k_pt):
"""
Generate explicit_map for which include mapping relationship is `1 - k_ms = k_pt`.
Args:
params_pt (dict): Params for APIPt.
args_pt (dict): Args for APIPt.
Returns:
dict, map between frames.
"""
value = args_pt[k_pt] if k_pt in args_pt else params_pt[k_pt]
value = value.strip()
def is_number(string):
try:
float(string)
return True
except ValueError:
return False
if is_number(value):
return {k_ms: str(1 - float(value))}
return {k_ms: "1.0 - " + value}
def gen_explicit_map_nn_maxpool2d(params_pt, args_pt):
"""
Generate explicit_map for nn.MaxPool2d.
Args:
params_pt (dict): Params for APIPt.
args_pt (dict): Args for APIPt.
Returns:
dict, map between frames.
"""
if 'padding' in args_pt:
padding = args_pt['padding']
else:
padding = params_pt['padding']
if padding.strip() in ("0", "(0,0)", "(0, 0)"):
pad_mode = "'valid'"
else:
pad_mode = "'same'"
return {"pad_mode": pad_mode}
tensor_dot_view_gen_explicit_map = lambda params_pt, args_pt: {"shape": "(" + args_pt["*shape"] + ",)"}
tensor_dot_reshape_gen_explicit_map = lambda params_pt, args_pt: {"shape": "(" + args_pt["*shape"] + ",)"}
nn_conv2d_gen_explicit_map = lambda params_pt, args_pt: {"pad_mode": "'pad'"}
nn_batchnorm2d_gen_explicit_map = partial(gen_explicit_map_one_delta, k_ms="momentum", k_pt="momentum")
nn_dropout_gen_explicit_map = partial(gen_explicit_map_one_delta, k_ms="keep_prob", k_pt="p")
mindinsight/mindconverter/mappings/f_mappings.json 0 → 100644
浏览文件 @ 2f2f6a3f
{
"F.avg_pool2d": {
"ms_api": [
"P.AvgPool",
{
"ksize": 1,
"strides": 1,
"padding": "valid",
"input": "REQUIRED"
},
[
"ksize",
"strides",
"padding"
]
],
"pt_api": [
"F.avg_pool2d",
{
"input": "REQUIRED",
"kernel_size": "REQUIRED",
"stride": null,
"padding": 0,
"dilation": 1,
"ceil_mode": false,
"return_indices": false
}
],
"ms2pt_mapping": {
"ksize": "kernel_size",
"strides": "stride",
"input": "input"
},
"gen_explicit_map": "gen_explicit_map_f_max_pool2d"
},
"F.max_pool2d": {
"ms_api": [
"P.MaxPool",
{
"ksize": 1,
"strides": 1,
"padding": "valid",
"input": "REQUIRED"
},
[
"ksize",
"strides",
"padding"
]
],
"pt_api": [
"F.max_pool2d",
{
"input": "REQUIRED",
"kernel_size": "REQUIRED",
"stride": null,
"padding": 0,
"dilation": 1,
"ceil_mode": false,
"return_indices": false
}
],
"ms2pt_mapping": {
"ksize": "kernel_size",
"strides": "stride",
"input": "input"
},
"gen_explicit_map": "gen_explicit_map_f_max_pool2d"
},
"F.relu": {
"ms_api": [
"P.ReLU",
{
"input": "REQUIRED"
}
],
"pt_api": [
"F.relu",
{
"input": "REQUIRED",
"inplace": false
}
],
"ms2pt_mapping": {
"input": "input"
},
"gen_explicit_map": null
},
"F.relu6": {
"ms_api": [
"P.ReLU6",
{
"input": "REQUIRED"
}
],
"pt_api": [
"F.relu6",
{
"input": "REQUIRED",
"inplace": false
}
],
"ms2pt_mapping": {
"input": "input"
},
"gen_explicit_map": null
}
}
\ No newline at end of file
mindinsight/mindconverter/mappings/nn_mappings.json 0 → 100644
浏览文件 @ 2f2f6a3f
{
"nn.Dropout": {
"ms_api": [
"nn.Dropout",
{
"keep_prob": 0.5,
"seed0": 0,
"seed1": 0,
"dtype": "mstype.float32"
}
],
"pt_api": [
"nn.Dropout",
{
"p": 0.5,
"inplace": false
}
],
"ms2pt_mapping": {
"keep_prob": "p"
},
"gen_explicit_map": "nn_dropout_gen_explicit_map"
},
"nn.AvgPool2d": {
"ms_api": [
"nn.AvgPool2d",
{
"kernel_size": 1,
"stride": 1,
"pad_mode": "valid"
}
],
"pt_api": [
"nn.AvgPool2d",
{
"kernel_size": "REQUIRED",
"stride": null,
"padding": 0,
"dilation": 1,
"return_indices": false,
"ceil_mode": "False"
}
],
"ms2pt_mapping": {
"kernel_size": "kernel_size",
"stride": "stride"
},
"gen_explicit_map": "gen_explicit_map_nn_maxpool2d"
},
"nn.MaxPool2d": {
"ms_api": [
"nn.MaxPool2d",
{
"kernel_size": 1,
"stride": 1,
"pad_mode": "valid"
}
],
"pt_api": [
"nn.MaxPool2d",
{
"kernel_size": "REQUIRED",
"stride": null,
"padding": 0,
"dilation": 1,
"return_indices": false,
"ceil_mode": "False"
}
],
"ms2pt_mapping": {
"kernel_size": "kernel_size",
"stride": "stride"
},
"gen_explicit_map": "gen_explicit_map_nn_maxpool2d"
},
"nn.Linear": {
"ms_api": [
"nn.Dense",
{
"in_channels": "REQUIRED",
"out_channels": "REQUIRED",
"weight_init": "normal",
"bias_init": "zeros",
"has_bias": true,
"activation": null
}
],
"pt_api": [
"nn.Linear",
{
"in_features": "REQUIRED",
"out_features": "REQUIRED",
"bias": true
}
],
"ms2pt_mapping": {
"in_channels": "in_features",
"out_channels": "out_features",
"has_bias": "bias"
}
},
"nn.ReLU6": {
"ms_api": [
"nn.ReLU6",
{}
],
"pt_api": [
"nn.ReLU6",
{
"inplace": false
}
],
"ms2pt_mapping": {}
},
"nn.ReLU": {
"ms_api": [
"nn.ReLU",
{}
],
"pt_api": [
"F.relu",
{
"inplace": false
}
],
"ms2pt_mapping": {}
},
"nn.BatchNorm2d": {
"ms_api": [
"nn.BatchNorm2d",
{
"num_features": "REQUIRED",
"eps": 1e-05,
"momentum": 0.9,
"affine": true,
"gamma_init": "ones",
"beta_init": "zeros",
"moving_mean_init": "zeros",
"moving_var_init": "ones",
"use_batch_statistics": true
}
],
"pt_api": [
"nn.BatchNorm2d",
{
"num_features": "REQUIRED",
"eps": 1e-05,
"momentum": 0.1,
"affine": true,
"track_running_stats": true
}
],
"ms2pt_mapping": {
"num_features": "num_features",
"eps": "eps",
"affine": "affine",
"use_batch_statistics": "track_running_stats"
},
"gen_explicit_map": "nn_batchnorm2d_gen_explicit_map"
},
"nn.Conv2d": {
"ms_api": [
"nn.Conv2d",
{
"in_channels": "REQUIRED",
"out_channels": "REQUIRED",
"kernel_size": "REQUIRED",
"stride": 1,
"pad_mode": "same",
"padding": 0,
"dilation": 1,
"group": 1,
"has_bias": false,
"weight_init": "normal",
"bias_init": "zeros"
}
],
"pt_api": [
"nn.Conv2d",
{
"in_channels": "REQUIRED",
"out_channels": "REQUIRED",
"kernel_size": "REQUIRED",
"stride": 1,
"padding": 0,
"dilation": 1,
"groups": 1,
"bias": true,
"padding_mode": "zeros"
}
],
"ms2pt_mapping": {
"in_channels": "in_channels",
"out_channels": "out_channels",
"kernel_size": "kernel_size",
"stride": "stride",
"padding": "padding",
"dilation": "dilation",
"group": "groups",
"has_bias": "bias"
},
"gen_explicit_map": "nn_conv2d_gen_explicit_map"
},
"nn.Sequential": {
"ms_api": [
"nn.SequentialCell",
{
"*args": " REQUIRED"
}
],
"pt_api": [
"nn.Sequential",
{
"*args": " REQUIRED"
}
],
"export_key": false,
"gen_explicit_map": "gen_explicit_map_nn_sequential"
}
}
\ No newline at end of file
mindinsight/mindconverter/mappings/tensor_dot_mappings.json 0 → 100644
浏览文件 @ 2f2f6a3f
{
".view": {
"ms_api": [
"P.Reshape",
{
"x": "REQUIRED",
"shape": "REQUIRED"
}
],
"pt_api": [
".view",
{
"*shape": "REQUIRED"
}
],
"ms2pt_mapping": {
"x": "call_name"
},
"gen_explicit_map": "tensor_dot_view_gen_explicit_map"
},
".size": {
"ms_api": [
"P.Shape",
{
"x": "REQUIRED"
}
],
"pt_api": [
".size",
{
"idx": "REQUIRED"
}
],
"ms2pt_mapping": {
"x": "call_name"
}
},
".flatten": {
"ms_api": [
"P.Flatten",
{
"input": "REQUIRED"
}
],
"pt_api": [
".flatten",
{
"start_dim": 0,
"end_dim": -1
}
],
"ms2pt_mapping": {
"input": "call_name"
}
},
".reshape": {
"ms_api": [
"P.Reshape",
{
"x": "REQUIRED",
"shape": "REQUIRED"
}
],
"pt_api": [
".reshape",
{
"*shape": "REQUIRED"
}
],
"ms2pt_mapping": {
"x": "call_name"
},
"gen_explicit_map": "tensor_dot_reshape_gen_explicit_map"
},
".mean": {
"ms_api": [
"P.ReduceMean",
{
"keep_dims": false,
"input": "REQUIRED",
"axis": []
}
],
"pt_api": [
".mean",
{
"dim": null,
"keepdim": false
}
],
"ms2pt_mapping": {
"keep_dims": "keepdim",
"axis": "dim",
"input": "call_name"
}
},
".squeeze": {
"ms_api": [
"P.ReduceMean",
{
"input": "REQUIRED",
"axis": []
},
[
"axis"
]
],
"pt_api": [
".squeeze",
{
"dim": null
}
],
"ms2pt_mapping": {
"axis": "dim",
"input": "call_name"
}
}
}
\ No newline at end of file
mindinsight/mindconverter/mappings/torch_dot_mappings.json 0 → 100644
浏览文件 @ 2f2f6a3f
{
"torch.flatten": {
"ms_api": [
"P.Flatten",
{
"input": "REQUIRED"
}
],
"pt_api": [
"torch.flatten",
{
"input": "REQUIRED",
"start_dim": 0,
"end_dim": -1
}
],
"ms2pt_mapping": {
"input": "input"
}
},
"torch.cat": {
"ms_api": [
"P.Concat",
{
"axis": 0,
"input": "REQUIRED"
},
[
"axis"
]
],
"pt_api": [
"torch.cat",
{
"tensors": "REQUIRED",
"dim": 0,
"out": null
}
],
"ms2pt_mapping": {
"input": "tensors",
"axis": "dim"
}
}
}
\ No newline at end of file
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