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提交 fcbdcb82 编写于 作者: M Macrobull
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add ops and update readme

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onnx2fluid/README.md
浏览文件 @ fcbdcb82
......@@ -54,7 +54,7 @@ onnx2fluid sample_1.onnx -t sample_1.npz
onnx2fluid:
```shell
onnx2fluid [-dexy] [-o /path/to/export_dir/] [-z archive.zip] [-t test_data.npz] /path/to/onnx/model.onnx
onnx2fluid [-dexy] [-o /path/to/export_dir/] [-z archive.zip] [-t test_data.npz] [-i [input_name1,input_name2]] /path/to/onnx/model.onnx
optional arguments:
--debug, -d 启用调试
......@@ -65,6 +65,8 @@ optional arguments:
--output_dir, -o 指定输出目录
--archive [ARCHIVE], -z [ARCHIVE]
如果验证通过，打包到指定的ZIP文件
--infer_inputs, -i [input_name1,input_name2]
调用PaddlePaddle fluid类形推导完善模型
```
转换工具onnx2fluid.conversion:
......@@ -76,7 +78,7 @@ onnx2fluid.conversion [-dexy] [-o /path/to/export_dir/] /path/to/onnx/model.onnx
验证工具onnx2fluid.validate:
```shell
onnx2fluid.validate [-d] [-t test_data.npz] [-p 1e-3] /path/to/onnx/model.onnx
onnx2fluid.validate [-d] [-t test_data.npz] [-i [input_name1,input_name2]] [-p 1e-3] /path/to/onnx/model.onnx
```
## 参考
......
onnx2fluid/README_en.md
浏览文件 @ fcbdcb82
......@@ -19,8 +19,8 @@ PyTorch to Paddlepaddle model conversion can be easily achieved with PyTorch ONN
## Environment and dependency
* python 3.5+ (python 2 not fully supported yet)
* onnx == 1.4.0
* paddlepaddle == 1.3.0 (optional for validation)
* onnx >= 1.4
* paddlepaddle >= 1.3.0 (optional for validation)
## Get started
......@@ -47,10 +47,12 @@ onnx2fluid sample_unet.onnx -t sample_unet.npz
## Usage
**ONNX opset 9+** is mainly supported, corresponded to PyTorch **1.0/1.1(stable opset)**，for more information: [ONNX doc](https://github.com/onnx/onnx/blob/master/docs/Operators.md)
onnx2fluid (all in one):
```shell
onnx2fluid [-dexy] [-o /path/to/export_dir/] [-z archive.zip] [-t test_data.npz] /path/to/onnx/model.onnx
onnx2fluid [-dexy] [-o /path/to/export_dir/] [-z archive.zip] [-t test_data.npz] [-i [input_name1,input_name2]] /path/to/onnx/model.onnx
optional arguments:
--debug, -d enable debug logging and checking
......@@ -61,6 +63,8 @@ optional arguments:
--output_dir, -o output directory
--archive [ARCHIVE], -z [ARCHIVE]
compress outputs to ZIP file if conversion successed
--infer_inputs, -i [input_name1,input_name2]
invoke PaddlePaddle fluid type-shape inference
```
onnx2fluid.conversion:
......@@ -72,10 +76,10 @@ onnx2fluid.conversion [-dexy] [-o /path/to/export_dir/] /path/to/onnx/model.onnx
onnx2fluid.validate:
```shell
onnx2fluid.validate [-d] [-t test_data.npz] [-p 1e-3] /path/to/onnx/model.onnx
onnx2fluid.validate [-d] [-t test_data.npz] [-i [input_name1,input_name2]] [-p 1e-3] /path/to/onnx/model.onnx
```
## Reference
* [PaddlePaddle fluid operators](http://www.paddlepaddle.org/documentation/docs/en/1.4/api/layers.html)
* load converted model via [load_inference_model](http://www.paddlepaddle.org/documentation/docs/en/1.4/api/io.html#permalink-1-load_inference_model)
* [PaddlePaddle fluid operators](http://www.paddlepaddle.org/documentation/docs/en/1.5/api/layers.html)
* load converted model via [load_inference_model](http://www.paddlepaddle.org/documentation/docs/en/1.5/api/io.html#permalink-1-load_inference_model)
onnx2fluid/examples/convert_data_npz_0.py 已删除 100644 → 0
浏览文件 @ 2a82fdeb
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 27 11:50:03 2019
@author: Macrobull
"""
import sys
import numpy as np
from collections import OrderedDict as Dict
def _make_var_name(name):
"""
make a valid variable name in Python code
"""
if name == '':
return '_'
if name[0].isdigit():
return 'var_' + name
for s in ' *?\\/-:':
name = name.replace(s, '_')
if name.startswith('_'):
name = 'var' + name
return name
fn = sys.argv[1]
input_names = sys.argv[2].split(':')
output_name = sys.argv[3].split(':')
squeeze_data = len(sys.argv) > 4
data = np.load(fn, encoding='bytes')
input_data = data['inputs']
output_data = data['outputs']
while squeeze_data and input_data.ndim > 4 and input_data.shape[0] == 1:
input_data = input_data.squeeze(0)
while squeeze_data and output_data.ndim > 2 and output_data.shape[0] == 1:
output_data = output_data.squeeze(0)
inputs = Dict(zip(map(_make_var_name, input_names), [input_data]))
outputs = Dict(zip(map(_make_var_name, output_name), [output_data]))
np.savez(fn, inputs=inputs, outputs=outputs) # overwrite
onnx2fluid/examples/convert_data_pb_0.py 已删除 100644 → 0
浏览文件 @ 2a82fdeb
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 27 11:50:03 2019
@author: Macrobull
"""
import os, sys
import numpy as np
import onnx
import onnx.numpy_helper as numpy_helper
from collections import OrderedDict as Dict
from glob import glob
def _make_var_name(name):
"""
make a valid variable name in Python code
"""
if name == '':
return '_'
if name[0].isdigit():
return 'var_' + name
for s in ' *?\\/-:':
name = name.replace(s, '_')
if name.startswith('_'):
name = 'var' + name
return name
data_dir = os.path.dirname(sys.argv[1])
input_names = sys.argv[2].split(':')
output_name = sys.argv[3].split(':')
squeeze_data = len(sys.argv) > 4
# Load inputs
inputs = []
for fn in glob(os.path.join(data_dir, 'input_*.pb')):
tensor = onnx.TensorProto()
with open(fn, 'rb') as f:
tensor.ParseFromString(f.read())
tensor = numpy_helper.to_array(tensor)
while squeeze_data and tensor.ndim > 4 and tensor.shape[0] == 1:
tensor = tensor.squeeze(0)
inputs.append(tensor)
# Load outputs
outputs = []
for fn in glob(os.path.join(data_dir, 'output_*.pb')):
tensor = onnx.TensorProto()
with open(fn, 'rb') as f:
tensor.ParseFromString(f.read())
tensor = numpy_helper.to_array(tensor)
while squeeze_data and tensor.ndim > 2 and tensor.shape[0] == 1:
tensor = tensor.squeeze(0)
outputs.append(tensor)
inputs = Dict(zip(map(_make_var_name, input_names), inputs))
outputs = Dict(zip(map(_make_var_name, output_name), outputs))
np.savez(data_dir, inputs=inputs, outputs=outputs)
onnx2fluid/examples/gen_some_samples.py
浏览文件 @ fcbdcb82
......@@ -20,34 +20,74 @@ from onnx2fluid.torch_export_helper import export_onnx_with_validation
prefix = 'sample_'
idx = 0
######## example: RNN cell ########
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.gru = nn.GRUCell(6, 5)
self.lstm = nn.LSTMCell(5, 4)
def forward(self, x, h1, h2, c2):
h = self.gru(x, h1)
h, c = self.lstm(h, (h2, c2))
return h, c
model = Model()
model.eval()
xb = torch.rand((7, 6))
h1 = torch.zeros((7, 5))
h2 = torch.zeros((7, 4))
c2 = torch.zeros((7, 4))
yp = model(xb, h1, h2, c2)
idx += 1
print('index: ', idx)
export_onnx_with_validation(model, [xb, h1, h2, c2],
prefix + str(idx), ['x', 'h1', 'h2', 'c2'],
['h', 'c'],
verbose=True,
training=False)
######## example: RNN ########
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.gru = nn.GRU(4, 5, 3)
self.lstm = nn.LSTM(5, 6, 2)
self.gru = nn.GRU(6, 5, 3)
self.lstm = nn.LSTM(5, 4, 2)
def forward(self, x):
y = x
y, h = self.gru(y)
y, h = self.lstm(y)
def forward(self, x, h1, h2, c2):
y, h1 = self.gru(x, h1)
y, (h2, c2) = self.lstm(y, (h2, c2))
return y
model = Model()
model.eval()
xb = torch.rand((2, 3, 4))
yp = model(xb)
xb = torch.rand((8, 1, 6))
h1 = torch.zeros((3, 1, 5))
h2 = torch.zeros((2, 1, 4))
c2 = torch.zeros((2, 1, 4))
yp = model(xb, h1, h2, c2)
idx += 1
print('index: ', idx)
export_onnx_with_validation(model, [xb],
prefix + str(idx), ['x'], ['y'],
export_onnx_with_validation(model, [xb, h1, h2, c2],
prefix + str(idx), ['x', 'h1', 'h2', 'c2'], ['y'],
verbose=True,
training=False)
######## example: random ########
"""
symbolic registration:
def rand(g, *shapes):
shapes_list = list(shapes)
shape = _maybe_get_const(shapes_list[0], "is")
return g.op('RandomUniform', shape_i=shape)
"""
class Model(nn.Module):
......@@ -55,8 +95,9 @@ class Model(nn.Module):
super(Model, self).__init__()
def forward(self, x):
y = torch.rand((2, 3)) # + torch.rand_like(xb)
y = y + torch.randn((2, 3)) # + torch.randn_like(xb)
y = torch.rand((2, 3)) # + torch.rand_like(x)
y = y + torch.randn((2, 3)) # + torch.randn_like(x)
y = y + x
return y
......@@ -124,6 +165,13 @@ export_onnx_with_validation(model, [xb0, xb1],
training=False)
######## example: affine_grid ########
"""
symbolic registration:
@parse_args('v', 'is')
def affine_grid_generator(g, theta, size):
return g.op('AffineGrid', theta, size_i=size)
"""
class Model(nn.Module):
......
onnx2fluid/onnx2fluid/cmdline.py
浏览文件 @ fcbdcb82
......@@ -61,7 +61,7 @@ def main(**kwargs):
passed = True
golden_data_filename = kwargs.pop('test_data', '')
infer_inputs = kwargs.pop('infer_inputs', None)
if golden_data_filename or infer_inputs:
if golden_data_filename or infer_inputs is not None:
from .validation import validate
save_inference_model = infer_inputs is not None
......
onnx2fluid/onnx2fluid/conversion.py
浏览文件 @ fcbdcb82
......@@ -91,7 +91,7 @@ def convert(onnx_model_filename,
# onnx model optimization
logger.info('model has %d ops', len(onnx_model.graph.node))
logger.info('optimizing model ...')
onnx_model = polish_model(onnx_model)
onnx_model = polish_model(onnx_model, checking=onnx_opset_pedantic)
# prepare filesystem
shutil.rmtree(save_dir, ignore_errors=True)
......@@ -123,6 +123,7 @@ def convert(onnx_model_filename,
for name, weight in graph_weights(onnx_graph):
var_name = make_var_name(name)
value_info = value_infos[var_name]
value_info['lod'] = [0]
value_info['embedded_as'] = []
value_info['get_weight'] = (lambda w: lambda: w.tolist())(
weight) # lazy getter
......@@ -134,8 +135,8 @@ def convert(onnx_model_filename,
for name, domain, op_type, inputs, outputs, attrs in graph_ops(onnx_graph,
topo=topo):
op_name = make_var_name(name)
inputs = [make_var_name(val) for val in inputs]
outputs = [make_var_name(val) for val in outputs]
inputs = list(map(make_var_name, inputs))
outputs = list(map(make_var_name, outputs))
logger.debug('translating op %s(%s) %s::%s ...', name, op_name, domain,
op_type)
if domain == DEFAULT_OP_DOMAIN:
......@@ -192,13 +193,16 @@ def convert(onnx_model_filename,
weight.dtype, weight.size, weight.nbytes,
embedded_names)
for embedded_name in embedded_names: # multiple references
fluid_writer.write_weight(
weight, shutil.os.path.join(save_dir, embedded_name))
fluid_writer.write_weight(weight,
shutil.os.path.join(
save_dir, embedded_name),
lod=value_info['lod'])
else:
logger.debug('saving weight %s(%s[%d], %dB) to %s ...', name,
weight.dtype, weight.size, weight.nbytes, var_name)
fluid_writer.write_weight(weight,
shutil.os.path.join(save_dir, var_name))
shutil.os.path.join(save_dir, var_name),
lod=value_info['lod'])
fluid_writer.emit_param(fluid_program, var_name, value_info)
param_codes = fluid_program.codes
fluid_program.codes = []
......
onnx2fluid/onnx2fluid/onnx_utils.py
浏览文件 @ fcbdcb82
......@@ -319,15 +319,18 @@ def skip_node_backward(nodes, src_input_name, dst_output_name, output_refs):
return processed
def polish_model(model, extras=True):
def polish_model(model, internals=True, extras=True, checking=True):
"""
polish_model enhanced for inference
"""
if checking:
check_model(model)
strip_doc_string(model)
if internals:
passes = optimizer.get_available_passes()
passes = list(filter(lambda name: not name.startswith('split_'), passes)) #
passes = list(filter(lambda name: not name.startswith('split_'),
passes)) #
logger.debug('builtin optimizations to perform in ONNX:\n\t%s', passes)
model = optimizer.optimize(model, passes=passes)
if extras:
......@@ -339,6 +342,7 @@ def polish_model(model, extras=True):
):
model = optimize(model)
model = infer_shapes(model)
if checking:
check_model(model)
return model
......
onnx2fluid/onnx2fluid/symbolic.py
浏览文件 @ fcbdcb82
......@@ -44,10 +44,10 @@ DEFAULT_OP_MAPPING = {
## nil ops ##
'RandomUniform':
['uniform_random', [], ['Out'], dict(high='max', low='min'),
dict(), None, None, False],
dict(), None, None, False], # TODO: add dtype support
'RandomNormal':
['gaussian_random', [], ['Out'], dict(scale='std'),
dict(), None, None, False],
dict(), None, None, False], # TODO: add dtype support
## unary ops ##
'Abs': ['abs', ['X'], ['Out']],
'Acos': ['acos', ['X'], ['Out']],
......@@ -63,21 +63,27 @@ DEFAULT_OP_MAPPING = {
'Flatten': ['flatten', ['X'], ['Out']], # attrs bypassed, FIXME: emit flatten2
'Floor': ['floor', ['X'], ['Out']],
'Gather': ['gather', ['X'], ['Out'], dict(axis='')],
'HardSigmoid': ['hard_sigmoid', ['X'], ['Out'], dict(alpha='slope', beta='offset')],
'Identity': ['assign', ['X'], ['Out']],
'LeakyRelu': ['leaky_relu', ['X'], ['Out']],
'Log': ['log', ['X'], ['Out']],
'LRN': ['lrn', ['X'], ['Out', 'MidOut'], dict(size='n', bias='k')], #
'Reciprocal': ['reciprocal', ['X'], ['Out']],
'Relu': ['relu', ['X'], ['Out']],
'Round': ['round', ['X'], ['Out']],
'Selu': ['selu', ['X'], ['Out'], dict(gamma='scale')],
'Shape': ['shape', ['X'], ['Out']], # FIXME: out is int64 vs int32
'Shrink': ['softshrink', ['X'], ['Out'], dict(bias='', labmd='')],
'Sigmoid': ['sigmoid', ['X'], ['Out']],
'Sign': ['sign', ['X'], ['Out']],
'Sin': ['sin', ['X'], ['Out']],
'Squeeze': ['squeeze', ['X'], ['Out']], # attrs bypassed, FIXME: emit squeeze2
'Softplus': ['softplus', ['X'], ['Out']],
# FIXME: default axis = -1, reshape required before and after
'Softmax': ['softmax', ['X'], ['Out'], dict(axis='')],
'Softplus': ['softplus', ['X'], ['Out']],
'Softsign': ['softsign', ['X'], ['Out']],
'SpaceToDepth': ['space_to_depth', ['X'], ['Out']],
'Sqrt': ['sqrt', ['X'], ['Out']],
'Tanh': ['tanh', ['X'], ['Out']],
'ThresholdedRelu': ['thresholded_relu', ['X'], ['Out'], dict(alpha='threshold')],
......@@ -94,6 +100,7 @@ DEFAULT_OP_MAPPING = {
'MatMul': ['matmul', ['X', 'Y'], ['Out']], # defaults excluded for transpose_x vs transpose_X
'Max': ['elementwise_max', ['X', 'Y'], ['Out'], dict(), dict(axis=-1)],
'Min': ['elementwise_min', ['X', 'Y'], ['Out'], dict(), dict(axis=-1)],
'Mod': ['elementwise_mod', ['X', 'Y'], ['Out'], dict(), dict(axis=-1)],
'Mul': ['elementwise_mul', ['X', 'Y'], ['Out'], dict(), dict(axis=-1)],
'Not': ['logical_not', ['X', 'Y'], ['Out']],
'OneHot': # assuming values=[0, 1], axis=-1 and drop them
......@@ -117,32 +124,30 @@ DEFAULT_OP_MAPPING = {
DEFAULT_IOA_CONSTRAINTS = {
'ArgMax': [
(lambda i, o, a: a.get('keepdims', 1) == 1,
'only keepdims = 0 is supported'),
'only keepdims = 0 supported'),
],
'ArgMin': [
(lambda i, o, a: a.get('keepdims', 1) == 1,
'only keepdims = 0 is supported'),
'only keepdims = 0 supported'),
],
'Gather': [
(lambda i, o, a: a.get('axis', 0) == 0, 'only axis = 0 is supported'),
(lambda i, o, a: a.get('axis', 0) == 0, 'only axis = 0 supported'),
],
'Shrink': [
(lambda i, o, a: a.get('bias', 0) == a.get('lambd', 0.5),
'only SoftShrink with bias = lambd is supported'),
'only SoftShrink with bias = lambd supported'),
],
# 'Softmax':
# [(lambda i, o, a: a.get('axis', 1) == -2, 'Paddle fluid Softmax works on dim -2 only'),
# ],
'OneHot': [
(lambda i, o, a: a.get('axis', -1) == -1,
'only axis = -1 is supported'),
(lambda i, o, a: a.get('axis', -1) == -1, 'only axis = -1 supported'),
],
'Scatter': [
(lambda i, o, a: a.get('axis', 0) == 0, 'only axis = 0 is supported'),
(lambda i, o, a: a.get('axis', 0) == 0, 'only axis = 0 supported'),
],
'TopK': [
(lambda i, o, a: a.get('axis', -1) == -1,
'only axis = -1 is supported'),
(lambda i, o, a: a.get('axis', -1) == -1, 'only axis = -1 supported'),
],
}
......@@ -226,10 +231,10 @@ def _default(prog, op_type, inputs, outputs, attrs, *args, name='', **kwargs):
fluid_attrs = default_attrs.copy()
fluid_attrs.update(mapped_attrs) # as new attrs
var_inps = inputs if input_perm is None else list(
map(inputs.__getitem__, input_perm))
var_outs = outputs if output_perm is None else list(
map(outputs.__getitem__, output_perm))
var_inps = list(map(inputs.__getitem__,
input_perm)) if input_perm else inputs
var_outs = list(map(outputs.__getitem__,
output_perm)) if output_perm else outputs
arg_name = ', name={}'.format(
repr(name)) if fill_name_field and name else ''
arg_attrs = [
......@@ -240,7 +245,7 @@ def _default(prog, op_type, inputs, outputs, attrs, *args, name='', **kwargs):
', '.join(var_outs),
fluid_op,
', '.join(var_inps),
''.join(arg_attrs),
''.join(arg_attrs)[(0 if var_inps else 2):],
arg_name,
))
......@@ -255,8 +260,8 @@ def _default(prog, op_type, inputs, outputs, attrs, *args, name='', **kwargs):
for var_out in var_outs:
prog.VarDesc(var_out)
prog.OpDesc(fluid_op, (var_inps, *fluid_input_args),
(var_outs, *fluid_output_args), fluid_attrs)
prog.OpDesc(fluid_op, (fluid_input_args, var_inps),
(fluid_output_args, var_outs), fluid_attrs)
def _assign(prog, mapping):
......@@ -272,8 +277,8 @@ def _assign(prog, mapping):
prog.VarDesc(var_dst)
prog.OpDesc(
fluid_op,
([var_src], 'X'),
([var_dst], 'Out'),
(['X'], [var_src]),
(['Out'], [var_dst]),
dict(),
)
......@@ -289,7 +294,7 @@ def _zeros_like(prog, var_ref, var_out, value_infos):
)
def _pad_if_asymmetric(prog, pads, var_name, value_infos): # pads: SSEE
def _pad_if_asymmetric(prog, pads, var_input, value_infos): # pads: SSEE
assert len(pads) & 1 == 0
ndims = len(pads) // 2
symmetric = True
......@@ -298,13 +303,13 @@ def _pad_if_asymmetric(prog, pads, var_name, value_infos): # pads: SSEE
symmetric = False
break
if symmetric:
return pads[:ndims], var_name
return pads[:ndims], var_input
var_padded = var_name + '_padded' # explicit variable
var_padded = var_input + '_padded' # explicit variable
prog.Op(
'',
'Pad',
[var_name],
[var_input],
[var_padded],
{
'mode': 'constant',
......@@ -312,7 +317,7 @@ def _pad_if_asymmetric(prog, pads, var_name, value_infos): # pads: SSEE
'pads': pads,
},
value_infos=value_infos,
name=var_padded,
name=(var_input + '_pad'),
)
return [0] * ndims, var_padded
......@@ -320,12 +325,12 @@ def _pad_if_asymmetric(prog, pads, var_name, value_infos): # pads: SSEE
def _adaptive_pool(prog, pool_type, inputs, outputs, attrs, name=''):
# I/O
var_x, = inputs
var_y, var_indices = (outputs + [None] * 1)[:2]
var_y, var_indices, = (outputs + [None] * 1)[:2]
# interpretation
pool_size = attrs['output_size'] # required
poolnd = len(pool_size)
assert 2 <= poolnd <= 3, 'only pool2d and pool3d is supported'
assert 2 <= poolnd <= 3, 'only pool2d and pool3d supported'
fluid_op = 'adaptive_pool{}d'.format(poolnd)
name_attr = ', name={}'.format(repr(name)) if name else ''
......@@ -352,15 +357,16 @@ def _adaptive_pool(prog, pool_type, inputs, outputs, attrs, name=''):
prog.VarDesc(var_indices)
prog.OpDesc(
fluid_op,
([var_x], 'X'),
([var_y] + ([var_indices] if var_indices else []), 'Out', 'Indices'),
(['X'], [var_x]),
(['Out', 'Indices'], [var_y] + ([var_indices] if var_indices else [])),
{
'global_pooling': False,
'adaptive': True,
'exclusive': True,
'require_index': bool(var_indices),
'pooling_type': pool_type,
'ksize': pool_size,
# unused
# 'exclusive': True,
},
)
......@@ -378,7 +384,7 @@ def _global_pool(prog, pool_type, inputs, outputs, attrs, value_infos, name=''):
poolnd = len(input_shape) - 2 # NC...
elif output_shape is not None:
poolnd = len(output_shape) - 2 # NC...
assert 2 <= poolnd <= 3, 'only pool2d and pool3d is supported'
assert 2 <= poolnd <= 3, 'only pool2d and pool3d supported'
fluid_op = 'pool{}d'.format(poolnd)
name_attr = ', name={}'.format(repr(name)) if name else ''
......@@ -397,13 +403,17 @@ def _global_pool(prog, pool_type, inputs, outputs, attrs, value_infos, name=''):
prog.VarDesc(var_y)
prog.OpDesc(
fluid_op,
([var_x], 'X'),
([var_y], 'Out'),
(['X'], [var_x]),
(['Out'], [var_y]),
{
'global_pooling': True,
'adaptive': False,
'pooling_type': pool_type,
'ksize': [-1, -1],
# unused
'strides': [-1, -1],
'paddings': [0, 0],
'ceil_mode': False,
},
)
......@@ -411,15 +421,17 @@ def _global_pool(prog, pool_type, inputs, outputs, attrs, value_infos, name=''):
def _pool(prog, pool_type, inputs, outputs, attrs, value_infos, name=''):
# I/O
var_x, = inputs
var_y, var_indices = (outputs + [None] * 1)[:2]
var_y, var_indices, = (outputs + [None] * 1)[:2]
# interpretation
assert attrs.get(
'auto_pad',
'NOTSET') == 'NOTSET', 'only auto_pad = NOTSET is supported' # optional
'NOTSET') == 'NOTSET', 'only auto_pad = NOTSET supported' # optional
assert attrs.get('count_include_pad',
0) == 0, 'only count_include_pad = 0 supported' # optional
pool_size = attrs['kernel_shape'] # required
poolnd = len(pool_size)
assert 2 <= poolnd <= 3, 'only pool2d and pool3d is supported'
assert 2 <= poolnd <= 3, 'only pool2d and pool3d supported'
fluid_op = 'pool{}d'.format(poolnd)
strides = attrs.get('strides', [1] * poolnd) # optional
......@@ -452,25 +464,26 @@ def _pool(prog, pool_type, inputs, outputs, attrs, value_infos, name=''):
prog.VarDesc(var_indices)
prog.OpDesc(
fluid_op,
([var_x], 'X'),
([var_y] + ([var_indices] if var_indices else []), 'Out', 'Indices'),
(['X'], [var_x]),
(['Out', 'Indices'], [var_y] + ([var_indices] if var_indices else [])),
{
'global_pooling': False,
'adaptive': False,
'exclusive': True,
'require_index': bool(var_indices),
'pooling_type': pool_type,
'ksize': pool_size,
'strides': strides,
'paddings': paddings,
'ceil_mode': ceil_mode,
# unused
'exclusive': True,
},
)
def _roi_pool(prog, fluid_op, inputs, outputs, attrs, value_infos, name):
# I/O
var_x, var_rois = inputs
var_x, var_rois, = inputs
var_y, = outputs
# interpretation
......@@ -514,15 +527,15 @@ def _roi_pool(prog, fluid_op, inputs, outputs, attrs, value_infos, name):
prog.VarDesc(var_argmax)
prog.OpDesc(
fluid_op,
([var_x, var_rois], 'X', 'Rois'),
([var_y] + ([var_argmax] if is_max_pool else []), 'Out', 'Argmax'),
(['X', 'Rois'], [var_x, var_rois]),
(['Out', 'Argmax'], [var_y] + ([var_argmax] if is_max_pool else [])),
od_attrs,
)
def _interpolate(prog, inputs, outputs, attrs, value_infos, name=''):
# I/O
var_x, var_scales = inputs
var_x, var_scales, = inputs
var_y, = outputs
# interpretation
......@@ -542,7 +555,7 @@ def _interpolate(prog, inputs, outputs, attrs, value_infos, name=''):
scale = scales and scales[2]
# try input shape
if scale is None:
assert out_shape_, 'neither scales nor output shape is available'
assert out_shape_, 'neither scales nor output shape available'
out_shape = out_shape_
else:
out_shape = None
......@@ -572,8 +585,8 @@ def _interpolate(prog, inputs, outputs, attrs, value_infos, name=''):
prog.VarDesc(var_y)
prog.OpDesc(
fluid_op,
([var_x], 'X'),
([var_y], 'Out'),
(['X'], [var_x]),
(['Out'], [var_y]),
{
'interp_method': mode,
'out_h ': out_shape_[0],
......@@ -626,8 +639,8 @@ def AffineGrid(prog, inputs, outputs, attrs, *args, name='', **kwargs):
prog.VarDesc(var_grid)
prog.OpDesc(
fluid_op,
([var_theta], 'Theta'),
([var_grid], 'Output'),
(['Theta'], [var_theta]),
(['Output'], [var_grid]),
{'output_shape': size}, # f**k you API
)
......@@ -661,10 +674,14 @@ def BatchNormalization(prog,
"""
# I/O
var_x, var_scale, var_b, var_mean, var_var = inputs
var_y, = outputs
var_saved_mean = name + '.saved_mean' # dummy output
var_saved_variance = name + '.saved_variance' # dummy output
var_x, var_scale, var_b, var_mean, var_var, = inputs
var_y, var_mean_, var_var_, var_saved_mean, var_saved_variance, = (
outputs + [None] * 4)[:5]
assert var_saved_mean or (name != '')
assert var_saved_variance or (name != '')
var_saved_mean = var_saved_mean or (name + '.saved_mean') # dummy output
var_saved_variance = var_saved_variance or (name + '.saved_variance'
) # dummy output
# interpretation
fluid_op = 'batch_norm'
......@@ -700,7 +717,7 @@ def BatchNormalization(prog,
repr(var_var))
# generation
prog.Code('{} = layers.{}({}, is_test=True, data_layout="NCHW"'
prog.Code('{} = layers.{}({}, is_test=True'
', momentum={}'
', epsilon={}'
'{}{})'.format(
......@@ -718,16 +735,15 @@ def BatchNormalization(prog,
prog.VarDesc(var_saved_variance)
prog.OpDesc(
fluid_op,
([var_x, var_scale, var_b, var_mean, var_var
], 'X', 'Scale', 'Bias', 'Mean', 'Variance'),
([var_y, var_mean, var_saved_mean, var_saved_variance, var_var
], 'Y', 'MeanOut', 'SavedMean', 'SavedVariance', 'VarianceOut'),
(['X', 'Scale', 'Bias', 'Mean', 'Variance'
], [var_x, var_scale, var_b, var_mean, var_var]),
(['Y', 'MeanOut', 'SavedMean', 'SavedVariance', 'VarianceOut'
], [var_y, var_mean, var_saved_mean, var_saved_variance, var_var]),
{
'is_test': 1,
'data_layout': 'NCHW',
'use_global_stats': False,
'momentum': momentum,
'epsilon': epsilon,
'is_test': 1,
# unused
},
)
......@@ -745,9 +761,11 @@ def Cast(prog, inputs, outputs, attrs, value_infos, *args, **kwargs):
dtype = attrs['to'] # required
if not isinstance(dtype, _np.dtype): # additional: possible np.dtype
dtype = TENSOR_TYPE_TO_NP_TYPE[dtype]
output_dtype = _dtype_or_none(value_infos, var_output)
if output_dtype is not None:
assert dtype == output_dtype, 'dtype of to unmatches output'
# output_dtype = _dtype_or_none(value_infos, var_output)
# if output_dtype is not None:
# assert dtype == output_dtype, 'dtype of to unmatches output'
fluid_op = 'cast'
......@@ -764,8 +782,8 @@ def Cast(prog, inputs, outputs, attrs, value_infos, *args, **kwargs):
prog.VarDesc(var_output)
prog.OpDesc(
fluid_op,
([var_input], 'X'),
([var_output], 'Out'),
(['X'], [var_input]),
(['Out'], [var_output]),
{
'in_dtype': prog.Dtype(_dtype(value_infos,
var_input)), # holy, required
......@@ -801,8 +819,8 @@ def Concat(prog, inputs, outputs, attrs, *args, name='', **kwargs):
prog.VarDesc(var_ret)
prog.OpDesc(
fluid_op,
(inputs, *(['X'] * len(inputs))),
([var_ret], 'Out'),
(['X'] * len(inputs), inputs),
(['Out'], [var_ret]),
{'axis': axis},
)
......@@ -819,13 +837,11 @@ def Constant(prog, inputs, outputs, attrs, value_infos, *args, **kwargs):
# interpretation
value = attrs['value'] # required
dtype = _np.dtype(value.dtype)
output_dtype = _dtype_or_none(value_infos, var_output)
if output_dtype is not None:
assert dtype == output_dtype, 'tensor dtype unmatches storage dtype'
# dtype = _np.dtype('float32') # HINT: force to float32
shape = attrs.get('shape', None) #
# output_dtype = _dtype_or_none(value_infos, var_output)
# if output_dtype is not None:
# assert dtype == output_dtype, 'tensor dtype unmatches storage dtype'
# dtype = _np.dtype('float32') # HINT: force to float32
shape = attrs.get('shape', None) # additional
if shape is None:
shape = _shape_or_none(value_infos, var_output)
if shape is None:
......@@ -836,10 +852,9 @@ def Constant(prog, inputs, outputs, attrs, value_infos, *args, **kwargs):
'using value as 1-D tensor may lead to fails', outputs, var_output)
# generation
value = value.tolist()
if len(value) == 1: # scalar
if len(shape) == 0 or value.size == 1: # scalar or 1-size
shape = [1] # WORKAROUND: bad scalar support
value = value[0]
value = value.tolist()[0]
fluid_op = 'fill_constant'
prog.Code('{} = layers.{}(shape={}, dtype={}, value={})'.format(
var_output,
......@@ -852,8 +867,8 @@ def Constant(prog, inputs, outputs, attrs, value_infos, *args, **kwargs):
prog.VarDesc(var_output)
prog.OpDesc(
fluid_op,
([], ),
([var_output], 'Out'),
([], []),
(['Out'], [var_output]),
{
'shape': shape,
'dtype': prog.Dtype(dtype),
......@@ -862,7 +877,7 @@ def Constant(prog, inputs, outputs, attrs, value_infos, *args, **kwargs):
)
else: # list parameter -> const_value
prog.Code('# {} = {} # passed directly as literal'.format(
var_output, value))
var_output, value.tolist()))
value_infos[var_output]['const_value'] = value
......@@ -882,16 +897,16 @@ def ConstantOfShape(prog, inputs, outputs, attrs, value_infos, *args, **kwargs):
assert shape is not None, (
'given shape is neither const value nor deductible from output, '
'this is not supported')
dtype = attrs['value'].dtype
attrs = attrs.copy()
attrs.update({'shape': shape, 'dtype': dtype}) # pass const
attrs.setdefault('value', np.array(0, dtype=np.float32))
attrs.update({'shape': shape}) # pass const
prog.Code('# shape:{}={} # const as literal'.format(var_shape, shape))
prog.Op(
'',
'Constant',
[],
outputs, # val
outputs,
attrs,
value_infos,
)
......@@ -902,7 +917,7 @@ def Conv(prog,
outputs,
attrs,
value_infos,
name='',
name,
embed_params=False,
*args,
**kwargs):
......@@ -911,18 +926,18 @@ def Conv(prog,
"""
# I/O
var_x, var_w = inputs[:2]
var_y, var_b = (outputs + [None] * 1)[:2]
var_x, var_w, var_b, = (inputs + [None] * 1)[:3]
var_y, = outputs
# interpretation
assert attrs.get(
'auto_pad', 'NOTSET'
) == 'NOTSET', 'only auto_pad == NOTSET is supported' # optional
kernel_shape = _shape(value_infos, var_w)[2:] # OI...
assert kernel_shape == attrs[
'kernel_shape'], 'kernel_shape in attr unmatches value_info' # HW
'auto_pad',
'NOTSET') == 'NOTSET', 'only auto_pad = NOTSET supported' # optional
kernel_shape = attrs.get('kernel_shape',
_shape(value_infos, var_w)[2:]) # optional, HW
assert kernel_shape, 'kernel_shape not inferred'
convnd = len(kernel_shape)
assert 2 <= convnd <= 3, 'only conv2d and conv3d is supported'
assert 2 <= convnd <= 3, 'only conv2d and conv3d supported'
num_out_channels = _shape(value_infos, var_w)[0] # OI...
fluid_op = 'conv{}d'.format(convnd)
......@@ -931,16 +946,15 @@ def Conv(prog,
dilations = attrs.get('dilations', [1] * convnd) # optional
pads = attrs.get('pads', [0] * (convnd * 2)) # optional
paddings, var_x = _pad_if_asymmetric(prog, pads, var_x, value_infos)
name_attr = ', name={}'.format(repr(name)) if name else ''
name_attr = ', name={}'.format(repr(name))
if embed_params:
embed_params = (_check_embeddable(value_infos, var_w) and not var_b
or _check_embeddable(value_infos, var_b))
if not embed_params and name:
embed_params = _check_embeddable(
value_infos, *([var_w] + ([var_b] if var_b else [])))
if not embed_params:
_logger.warning('for op %s(%s -> Conv -> %s)', name, inputs,
outputs)
_logger.warning('broken Python code will be generated')
if embed_params:
assert name != ''
embedded_w = name + '.w_0'
value_infos[var_w]['embedded_as'].append(embedded_w)
var_w = embedded_w
......@@ -978,11 +992,11 @@ def Conv(prog,
param_attr,
name_attr,
))
var_conv = name + '.conv' # hidden variable
var_conv = (name + '.conv') if var_b else var_y # hidden variable
prog.OpDesc(
fluid_op,
([var_x, var_w], 'Input', 'Filter'), # , 'Bias', 'ResidualData'
([var_conv if var_b else var_y], 'Output'),
(['Input', 'Filter'], [var_x, var_w]), # , 'Bias', 'ResidualData'
(['Output'], [var_conv]),
{
'strides': strides,
'paddings': paddings,
......@@ -1010,7 +1024,7 @@ def ConvTranspose(prog,
outputs,
attrs,
value_infos,
name='',
name,
embed_params=False,
*args,
**kwargs):
......@@ -1019,39 +1033,39 @@ def ConvTranspose(prog,
"""
# I/O
var_x, var_w = inputs[:2]
var_y, var_b = (outputs + [None] * 1)[:2]
var_x, var_w, var_b, = (inputs + [None] * 1)[:3]
var_y, = outputs
# interpretation
assert attrs.get(
'auto_pad', 'NOTSET'
) == 'NOTSET', 'only auto_pad == NOTSET is supported' # optional
assert sum(attrs.get(
'output_padding',
[])) == 0, 'only zero output_padding is supported' # optional ?
kernel_shape = _shape(value_infos, var_w)[2:] # IO...
assert kernel_shape == attrs[
'kernel_shape'], 'kernel_shape in attr unmatches value_info' # HW
'auto_pad',
'NOTSET') == 'NOTSET', 'only auto_pad = NOTSET supported' # optional
assert sum(
attrs.get('output_padding',
[])) == 0, 'only zero output_padding supported' # optional ?
kernel_shape = attrs.get('kernel_shape',
_shape(value_infos, var_w)[2:]) # optional, HW
assert kernel_shape, 'kernel_shape not inferred'
convnd = len(kernel_shape)
assert 2 <= convnd <= 3, 'only conv2d_transpose and conv3d_transpose is supported'
assert 2 <= convnd <= 3, 'only conv2d_transpose and conv3d_transpose supported'
num_out_channels = _shape(value_infos, var_w)[1] # IO...
fluid_op = 'conv{}d_transpose'.format(convnd)
num_groups = attrs.get('group', 1) # optional
strides = attrs.get('strides', [1] * convnd) # optional
dilations = attrs.get('dilations', [1] * convnd) # optional
output_size = attrs.get('output_shape', []) # optional
pads = attrs.get('pads', [0] * (convnd * 2)) # optional
paddings, var_x = _pad_if_asymmetric(prog, pads, var_x, value_infos)
name_attr = ', name={}'.format(repr(name)) if name else ''
name_attr = ', name={}'.format(repr(name))
if embed_params:
embed_params = (_check_embeddable(value_infos, var_w) and not var_b
or _check_embeddable(value_infos, var_b))
if not embed_params and name:
embed_params = _check_embeddable(
value_infos, *([var_w] + ([var_b] if var_b else [])))
if not embed_params:
_logger.warning('for op %s(%s -> ConvTranspose -> %s)', name,
inputs, outputs)
_logger.warning('broken Python code will be generated')
if embed_params:
assert name != ''
embedded_w = name + '.w_0'
value_infos[var_w]['embedded_as'].append(embedded_w)
var_w = embedded_w
......@@ -1070,7 +1084,7 @@ def ConvTranspose(prog,
# generation
prog.Code('{} = layers.{}({}'
', num_filters={}'
# ', output_size={}'
', output_size={}'
', filter_size={}'
', padding={}'
', stride={}'
......@@ -1082,6 +1096,7 @@ def ConvTranspose(prog,
var_x,
# attrs
num_out_channels,
output_size or None,
kernel_shape,
paddings,
strides,
......@@ -1090,17 +1105,18 @@ def ConvTranspose(prog,
param_attr,
name_attr,
))
var_conv = name + '.conv' # hidden variable
var_conv = (name + '.conv') if var_b else var_y # hidden variable
prog.OpDesc(
fluid_op,
([var_x, var_w], 'Input', 'Filter'), # , 'Bias', 'ResidualData'
([var_conv if var_b else var_y], 'Output'),
(['Input', 'Filter'], [var_x, var_w]), # , 'Bias', 'ResidualData'
(['Output'], [var_conv]),
{
'strides': strides,
'paddings': paddings,
'dilations': dilations,
# 'output_size': output_size,
'groups': num_groups,
# unused
'output_size': output_size,
},
)
if var_b:
......@@ -1124,7 +1140,7 @@ def Gemm(prog, inputs, outputs, attrs, value_infos, name, *args, **kwargs):
"""
# due to fluid fc don't support transposed weight, we use matmul + ew_add
var_a, var_b, var_c = inputs
var_a, var_b, var_c, = inputs
var_y, = outputs
alpha = attrs.get('alpha', 1.) # optional
......@@ -1132,19 +1148,19 @@ def Gemm(prog, inputs, outputs, attrs, value_infos, name, *args, **kwargs):
trans_a = bool(attrs.get('transA', 0)) # optional
trans_b = bool(attrs.get('transB', 0)) # optional
var_mm = name + '_mm' # explicit variable
var_mm = var_y if beta == 0 else (name + '_mmed') # explicit variable
prog.Op(
'',
'MatMul',
[var_a, var_b],
[var_mm], # val
[var_mm],
{
'transpose_x': trans_a,
'transpose_y': trans_b,
'alpha': alpha,
},
value_infos=value_infos,
name=var_mm,
name=(name + '_mm'),
)
prog.op_descs[-1].attrs.extend(
prog.OpDescAttrs({
......@@ -1157,10 +1173,10 @@ def Gemm(prog, inputs, outputs, attrs, value_infos, name, *args, **kwargs):
'',
'Add',
[var_mm, var_c],
[var_y], # val
[var_y],
{'axis': 1},
value_infos=value_infos,
name=(name + '_beta'),
name=(name + '_bias'),
)
else:
var_beta = name + '_beta' # explicit variable
......@@ -1179,7 +1195,7 @@ def Gemm(prog, inputs, outputs, attrs, value_infos, name, *args, **kwargs):
'',
'Constant',
[],
[var_beta], # val
[var_beta],
{'value': beta},
value_infos=value_infos,
name=var_beta,
......@@ -1188,17 +1204,17 @@ def Gemm(prog, inputs, outputs, attrs, value_infos, name, *args, **kwargs):
'',
'Mul',
[var_c, var_beta],
[var_vm], # val
[var_vm],
dict(),
value_infos=value_infos,
name=(name + '_scale'),
name=(var_beta + '_scale'),
)
prog.Op(
'',
'Add',
[var_mm, var_vm],
[var_y], # val
{'axis': 1},
[var_y],
{'axis': 1}, #
name=(name + '_bias'),
)
......@@ -1250,57 +1266,441 @@ def GRU(prog, inputs, outputs, attrs, value_infos, *args, **kwargs):
onnx::GRU-7:
"""
var_x, var_w, var_r, var_b, var_len, var_xh = (inputs + [None] * 3)[:6]
var_y, var_yh = (outputs + [None] * 2)[:2]
var_x, var_w, var_r, var_b, var_len, var_xh, = (inputs + [None] * 3)[:6]
var_y, var_yh, = (outputs + [None] * 2)[:2]
var_gate = var_y + '.gate' # dummy output
var_reset = var_y + '.reset' # dummy output
var_hidden = var_y + '.hidden' # dummy output, # var_yh
# interpretation
fluid_op = 'gru_unit'
param_attr = ''
x_shape = _shape_or_none(value_infos, var_x)
assert x_shape is not None, 'shape of X required to be known'
assert x_shape[1] == 1, 'only X with batch_size = 1 supported'
assert 'clip' not in attrs, 'clipping not supported'
hidden_size = attrs.get('hidden_size', None) # optional
if not hidden_size:
r_shape = _shape_or_none(value_infos, var_r)
if r_shape:
hidden_size = r_shape[-1]
if not hidden_size:
w_shape = _shape_or_none(value_infos, var_w)
if w_shape:
hidden_size = w_shape[-2] // 3
if not hidden_size and var_b:
b_shape = _shape_or_none(value_infos, var_b)
if b_shape:
hidden_size = b_shape[-1] // 6
if not hidden_size and var_xh:
xh_shape = _shape_or_none(value_infos, var_xh)
if xh_shape:
hidden_size = xh_shape[-1]
assert hidden_size, 'hidden_size not inferred'
assert attrs.get(
'linear_before_reset',
0) == 0, 'only linear_before_reset = 0 supported' # optional
direction = attrs.get('direction', 'forward') # optional
assert direction != 'bidirectional', 'direction = bidirectional not supported'
activations = attrs.get('activations', ['Sigmoid', 'Tanh']) # optional
assert len(activations) == 2, 'bidirectional operation not supported'
activations = [s.lower() for s in activations] # TODO: check support
gate_activation, candidate_activation = activations
is_reverse = direction == 'reverse'
fluid_op = 'dynamic_gru'
# generation
prog.Code('{}, _, {} = layers.{}({}, {}, {}'
'{})'.format(
var_yh,
var_y,
var_x0 = var_x + '_0' # explicit variable
prog.Op(
'',
'Squeeze',
[var_x],
[var_x0],
{'axes': [1]}, # index on n
name=(var_x + '_index'),
)
var_w0 = var_w + '_0' # explicit variable
prog.Op(
'',
'Squeeze',
[var_w],
[var_w0],
{'axes': [0]}, # index on d
name=(var_w + '_index'),
)
var_fc = var_x0 + '_fc'
var_mm = (var_x0 + '_mmed') if var_b else var_fc
prog.Op(
'',
'MatMul',
[var_x0, var_w0],
[var_mm],
{
'transpose_x': 0,
'transpose_y': 1,
},
value_infos=value_infos,
name=(var_x0 + '_mm'),
)
prog.op_descs[-1].attrs.extend(
prog.OpDescAttrs({
'transpose_X': 0,
'transpose_Y': 1,
})) # f**k you API
var_r0 = var_r + '_0' # explicit variable
prog.Op(
'',
'Squeeze',
[var_r],
[var_r0],
{'axes': [0]}, # index on d
name=(var_r + '_index'),
)
var_r0t = var_r0 + '_t' # explicit variable
prog.Op(
'',
'Transpose',
[var_r0],
[var_r0t],
{'perm': [1, 0]}, # transpose OI->IO
name=(var_r0 + '_transpose'),
)
if var_b:
var_bi = var_b + '_i' # explicit variable
var_bh = var_b + '_h' # explicit variable
prog.Op(
'',
'Split',
[var_b],
[var_bi, var_bh],
{
'axis': 1, # split on x
'split': [hidden_size * 3, hidden_size * 3],
},
name=(var_b + '_split'),
)
# squeeze bi so Gemm Add can be performed on axis=1 exaclty
var_bi0 = var_bi + '_0' # explicit variable
prog.Op(
'',
'Squeeze',
[var_bi],
[var_bi0],
{'axes': [0]}, # slice on d
name=(var_bi + '_index'),
)
prog.Op(
'',
'Add',
[var_mm, var_bi0],
[var_fc],
{'axis': 1}, #
name=(var_x0 + '_bias'),
)
if var_xh:
var_xh0 = var_xh + '_0' # explicit variable
prog.Op(
'',
'Squeeze',
[var_xh],
[var_xh0],
{'axes': [1]}, # index on n
name=(var_xh + '_index'),
)
var_y00 = var_y + '_00' # explicit variable
prog.Code('{} = layers.{}({}, {}, origin_mode=True'
', h_0={}'
', is_reverse={}'
', gate_activation={}'
', candidate_activation={}'
', param_attr={}, bias_attr={})'.format(
var_y00,
fluid_op,
var_x,
var_xh,
0,
param_attr,
var_fc,
hidden_size,
var_xh0 if var_xh else None,
is_reverse,
repr(gate_activation),
repr(candidate_activation),
repr(var_r0t),
repr(var_bh) if var_b else False,
))
# raise NotImplementedError()
fluid_op = 'gru'
prog.VarDesc(var_y00)
prog.VarDesc(var_gate)
prog.VarDesc(var_reset)
prog.VarDesc(var_hidden)
prog.OpDesc(
fluid_op,
(['Input', 'Weight', 'Bias', 'H0'], [var_fc, var_r0t] +
([var_bh] if var_b else []) + ([var_xh0] if var_xh else [])),
(['Hidden', 'BatchGate', 'BatchResetHiddenPrev', 'BatchHidden'
], [var_y00, var_gate, var_reset, var_hidden]),
{
'is_reverse': is_reverse,
'gate_activation': gate_activation,
'activation': candidate_activation,
'origin_mode': True,
},
)
prog.Op(
'',
'Unsqueeze',
[var_y00],
[var_y],
{'axes': [1, 1]}, # extrude on dn
name=(var_y + '_reshape'),
)
def LSTM(prog, inputs, outputs, attrs, value_infos, name='', *args, **kwargs):
def LSTM(prog, inputs, outputs, attrs, value_infos, name, *args, **kwargs):
"""
onnx::LSTM-7:
"""
var_x, var_w, var_r, var_b, var_len, var_xh, var_xc, var_p = (
var_x, var_w, var_r, var_b, var_len, var_xh, var_xc, var_p, = (
inputs + [None] * 5)[:8]
var_y, var_yh, var_yc = (outputs + [None] * 3)[:3]
var_y, var_yh, var_yc, = (outputs + [None] * 3)[:3]
var_gate = name + '.gate'
var_pre = name + '.pre'
# interpretation
fluid_op = 'lstm_unit'
param_attr = ''
x_shape = _shape_or_none(value_infos, var_x)
assert x_shape is not None, 'shape of X required to be known'
assert x_shape[1] == 1, 'only X with batch_size = 1 supported'
assert 'clip' not in attrs, 'clipping not supported'
hidden_size = attrs.get('hidden_size', None) # optional
if not hidden_size:
r_shape = _shape_or_none(value_infos, var_r)
if r_shape:
hidden_size = r_shape[-1]
if not hidden_size:
w_shape = _shape_or_none(value_infos, var_w)
if w_shape:
hidden_size = w_shape[-2] // 4
if not hidden_size and var_b:
b_shape = _shape_or_none(value_infos, var_b)
if b_shape:
hidden_size = b_shape[-1] // 8
if not hidden_size and var_xh:
xh_shape = _shape_or_none(value_infos, var_xh)
if xh_shape:
hidden_size = xh_shape[-1]
if not hidden_size and var_xc:
xc_shape = _shape_or_none(value_infos, var_xc)
if xc_shape:
hidden_size = xc_shape[-1]
if not hidden_size and var_p:
p_shape = _shape_or_none(value_infos, var_p)
if p_shape:
hidden_size = p_shape[-1] // 3
assert hidden_size, 'hidden_size not inferred'
assert attrs.get(
'linear_before_reset',
0) == 0, 'only linear_before_reset = 0 supported' # optional
assert attrs.get('input_forget',
0) == 0, 'only input_forget = 0 supported' # optional
direction = attrs.get('direction', 'forward') # optional
assert direction != 'bidirectional', 'direction = bidirectional not supported'
activations = attrs.get('activations',
['Sigmoid', 'Tanh', 'Tanh']) # optional
assert len(activations) == 3, 'bidirectional operation not supported'
activations = [s.lower() for s in activations] # TODO: check support
gate_activation, cell_activation, candidate_activation = activations
is_reverse = direction == 'reverse'
fluid_op = 'dynamic_lstm'
name_attr = ', name={}'.format(repr(name))
# generation
prog.Code('{}, {}, {} = layers.{}({}, {}, {}'
var_x0 = var_x + '_0' # explicit variable
prog.Op(
'',
'Squeeze',
[var_x],
[var_x0],
{'axes': [1]}, # index on n
name=(var_x + '_index'),
)
var_w0 = var_w + '_0' # explicit variable
prog.Op(
'',
'Squeeze',
[var_w],
[var_w0],
{'axes': [0]}, # index on d
name=(var_w + '_index'),
)
var_fc = var_x0 + '_fc'
var_mm = (var_x0 + '_mmed') if var_b else var_fc
prog.Op(
'',
'MatMul',
[var_x0, var_w0],
[var_mm],
{
'transpose_x': 0,
'transpose_y': 1,
},
value_infos=value_infos,
name=(name + '_mm'),
)
prog.op_descs[-1].attrs.extend(
prog.OpDescAttrs({
'transpose_X': 0,
'transpose_Y': 1,
})) # f**k you API
var_r0 = var_r + '_0' # explicit variable
prog.Op(
'',
'Squeeze',
[var_r],
[var_r0],
{'axes': [0]}, # index on d
name=(var_r + '_index'),
)
var_r0t = var_r0 + '_t' # explicit variable
prog.Op(
'',
'Transpose',
[var_r0],
[var_r0t],
{'perm': [1, 0]}, # transpose OI->IO
name=(var_r0 + '_transpose'),
)
if var_b:
var_bi = var_b + '_i' # explicit variable
var_bh = var_b + '_h' # explicit variable
prog.Op(
'',
'Split',
[var_b],
[var_bi, var_bh],
{
'axis': 1, # split on x
'split': [hidden_size * 4, hidden_size * 4],
},
name=(var_b + '_split'),
)
# squeeze bi so Gemm Add can be performed on axis=1 exaclty
var_bi0 = var_bi + '_0' # explicit variable
prog.Op(
'',
'Squeeze',
[var_bi],
[var_bi0],
{'axes': [0]}, # slice on d
name=(var_bi + '_index'),
)
prog.Op(
'',
'Add',
[var_mm, var_bi0],
[var_fc],
{'axis': 1}, #
name=(name + '_bias'),
)
if var_xh:
var_xh0 = var_xh + '_0' # explicit variable
prog.Op(
'',
'Squeeze',
[var_xh],
[var_xh0],
{'axes': [1]}, # index on n
name=(var_xh + '_index'),
)
if var_xc:
var_xc0 = var_xc + '_0' # explicit variable
prog.Op(
'',
'Squeeze',
[var_xc],
[var_xc0],
{'axes': [1]}, # index on n
name=(var_xc + '_index'),
)
var_bhp = var_p
if var_b:
if var_p:
var_bhp = var_bh + '_p' # explicit variable
prog.Op(
'',
'Concat',
[var_bh, var_p],
[var_bhp],
{'axes': [1]}, # cat on x
name=(name + '_concat'),
)
else:
var_bhp = var_bh
var_yh0 = var_yh + '_0' # explicit variable
var_yc0 = var_yc + '_0' # explicit variable
prog.Code('{}, {} = layers.{}({}, {}'
', h_0={}'
', c_0={}'
', use_peepholes={}'
', is_reverse={}'
', gate_activation={}'
', cell_activation={}'
', candidate_activation={}'
', param_attr={}, bias_attr={}'
'{})'.format(
var_y,
var_yh,
var_yc,
var_yh0,
var_yc0,
fluid_op,
var_x,
var_xh,
var_xc,
param_attr,
var_fc,
hidden_size * 4,
var_xh0 if var_xh else None,
var_xc0 if var_xc else None,
bool(var_p),
is_reverse,
repr(gate_activation),
repr(cell_activation),
repr(candidate_activation),
repr(var_r0t),
repr(var_bhp) if var_bhp else False,
name_attr,
))
# raise NotImplementedError()
fluid_op = 'lstm'
prog.VarDesc(var_yh0)
prog.VarDesc(var_yc0)
prog.VarDesc(var_gate)
prog.VarDesc(var_pre)
prog.OpDesc(
fluid_op,
(['Input', 'Weight', 'Bias', 'H0', 'C0'], [var_fc, var_r0t] +
([var_bhp] if var_bhp else []) + ([var_xh0] if var_xh else []) +
([var_xc0] if var_xc else [])),
(['Hidden', 'Cell', 'BatchGate', 'BatchCellPreAct'
], [var_yh0, var_yc0, var_gate, var_pre]),
{
'use_peepholes': bool(var_p),
'is_reverse': is_reverse,
'gate_activation': gate_activation,
'cell_activation': cell_activation,
'candidate_activation': candidate_activation,
},
)
# if var_yh:
prog.Op(
'',
'Unsqueeze',
[var_yh0],
[var_y], # var_yh
{'axes': [1, 1]}, # extrude on dn
name=(var_y + '_reshape'),
)
if var_yc:
prog.Op(
'',
'Unsqueeze',
[var_yc0],
[var_yc],
{'axes': [1, 1]}, # extrude on dn
name=(var_yc + '_reshape'),
)
def MaxPool(prog, inputs, outputs, attrs, value_infos, name='', *args,
......@@ -1350,7 +1750,7 @@ def Pad(prog, inputs, outputs, attrs, value_infos, name='', *args, **kwargs):
od_attrs['mode'] = mode
od_attrs['data_format'] = "NCHW"
else:
assert mode == 'constant', 'mode {} is supported only in pad2d'.format(
assert mode == 'constant', 'mode {} supported only in pad2d'.format(
mode)
fluid_op = 'pad'
pad2d_attr = ''
......@@ -1376,8 +1776,8 @@ def Pad(prog, inputs, outputs, attrs, value_infos, name='', *args, **kwargs):
prog.VarDesc(var_output)
prog.OpDesc(
fluid_op,
([var_data], 'X'),
([var_output], 'Out'),
(['X'], [var_data]),
(['Out'], [var_output]),
od_attrs,
)
......@@ -1396,7 +1796,7 @@ def PRelu(prog,
"""
# I/O
var_x, var_slope = inputs
var_x, var_slope, = inputs
var_y, = outputs
# interpretation
......@@ -1441,8 +1841,8 @@ def PRelu(prog,
prog.VarDesc(var_y)
prog.OpDesc(
fluid_op,
([var_x, var_slope], 'X', 'Alpha'),
([var_y], 'Out'),
(['X', 'Alpha'], [var_x, var_slope]),
(['Out'], [var_y]),
{'mode': mode},
)
......@@ -1461,7 +1861,7 @@ def Reshape(prog, inputs, outputs, attrs, value_infos, name, *args, **kwargs):
"""
# I/O
var_data, var_shape = inputs
var_data, var_shape, = inputs
var_reshaped, = outputs
# interpretation
......@@ -1481,7 +1881,7 @@ def Reshape(prog, inputs, outputs, attrs, value_infos, name, *args, **kwargs):
'the behavior of Paddle fluid maybe undefined', name, inputs,
outputs)
fluid_op = 'reshape'
name_attr = ', name={}'.format(repr(name)) if name else ''
name_attr = ', name={}'.format(repr(name))
# generation
var_shape_int32 = var_shape + '_int32' # explicit variable
......@@ -1502,7 +1902,7 @@ def Reshape(prog, inputs, outputs, attrs, value_infos, name, *args, **kwargs):
'',
'Cast',
[var_shape],
[var_shape_int32], # var
[var_shape_int32],
{'to': _np.dtype('int32')}, # use np.dtype
value_infos=value_infos,
name=(name + '_cast'),
......@@ -1525,8 +1925,8 @@ def Reshape(prog, inputs, outputs, attrs, value_infos, name, *args, **kwargs):
prog.VarDesc(var_xshape)
prog.OpDesc(
fluid_op,
([var_data, var_shape_int32], 'X', 'Shape'),
([var_reshaped, var_xshape], 'Out', 'XShape'),
(['X', 'Shape'], [var_data, var_shape_int32]),
(['Out', 'XShape'], [var_reshaped, var_xshape]),
{'shape': shape},
)
......@@ -1626,8 +2026,8 @@ def Slice(prog, inputs, outputs, attrs, value_infos, *args, **kwargs):
prog.VarDesc(var_output)
prog.OpDesc(
fluid_op,
([var_data], 'Input'),
([var_output], 'Out'),
(['Input'], [var_data]),
(['Out'], [var_output]),
{
'axes': axes,
'starts': starts,
......@@ -1666,11 +2066,13 @@ def Split(prog, inputs, outputs, attrs, *args, name='', **kwargs):
prog.VarDesc(var_out)
prog.OpDesc(
fluid_op,
(var_input, 'X'),
([outputs], *(['Out'] * len(outputs))),
(['X'], [var_input]),
(['Out'] * len(outputs), outputs),
{
'axis': axis,
'sections': split,
# unused
'num': 0,
},
)
......@@ -1697,8 +2099,8 @@ def Sum(prog, inputs, outputs, *args, **kwargs):
prog.VarDesc(var_sum)
prog.OpDesc(
fluid_op,
(inputs, *(['X'] * len(inputs))),
([var_sum], 'Out'),
(['X'] * len(inputs), inputs),
(['Out'], [var_sum]),
dict(),
)
......@@ -1709,12 +2111,12 @@ def Tile(prog, inputs, outputs, attrs, value_infos, name='', *args, **kwargs):
"""
# I/O
var_input, var_repeats = inputs
var_input, var_repeats, = inputs
var_output, = outputs
# interpretation
repeats = _const_weight_or_none(value_infos, var_repeats)
assert repeats is not None, 'only const repeats is supported'
assert repeats is not None, 'only const repeats supported'
fluid_op = 'expand'
name_attr = ', name={}'.format(repr(name)) if name else ''
......@@ -1733,8 +2135,8 @@ def Tile(prog, inputs, outputs, attrs, value_infos, name='', *args, **kwargs):
prog.VarDesc(var_output)
prog.OpDesc(
fluid_op,
([var_input], 'X'),
([var_output], 'Out'),
(['X'], [var_input]),
(['Out'], [var_output]),
{'expand_times': repeats},
)
......@@ -1770,8 +2172,8 @@ def Transpose(prog, inputs, outputs, attrs, *args, name='', **kwargs):
prog.VarDesc(var_transposed)
prog.OpDesc(
fluid_op,
([var_data], 'X'),
([var_transposed, var_xshape], 'Out', 'XShape'),
(['X'], [var_data]),
(['Out', 'XShape'], [var_transposed, var_xshape]),
{'axis': perm}, # f**k you API
)
......
onnx2fluid/onnx2fluid/validation.py
浏览文件 @ fcbdcb82
......@@ -159,7 +159,7 @@ def validate(fluid_model_filename,
# output_names = output_data.keys()
logger.info('with %d inputs and %d outputs', len(input_data),
len(output_data))
else:
elif save_inference_model:
assert inference_input_names, 'input names required for type-shape inference'
input_names = inference_input_names
......
onnx2fluid/onnx2fluid/writer.py
浏览文件 @ fcbdcb82
......@@ -96,7 +96,7 @@ class Program(object):
return Program.DTYPE_TO_FRAMEWORK_DTYPE[dtype]
@staticmethod
def OpDescVars(vals, *keys):
def OpDescVars(keys, vals):
"""
make (OpDesc.Var)s
"""
......@@ -150,13 +150,11 @@ class Program(object):
else:
raise ValueError('unsupported attribute {} = {}'.format(
key, value))
else: # WORKAROUND: shape of scalars is []
raise ValueError('unsupported attribute {} = {}'.format(
key, value))
# od_attr.type = framework_pb2.INTS
# logger.warning('using attribute %s = %s as INTS', key, value)
else: # WORKAROUND: [] not inferred
# raise ValueError('unsupported attribute {} = {}'.format(key, value))
od_attr.type = framework_pb2.INTS
logger.warning('using attribute %s = %s as INTS', key,
value)
else:
raise ValueError('unsupported attribute {} = {}'.format(
key, value))
......@@ -187,8 +185,8 @@ class Program(object):
def OpDesc(self,
op_type,
input_val_keys=None,
output_val_keys=None,
input_key_vals=None,
output_key_vals=None,
attrs=None):
"""
add OpDesc
......@@ -196,10 +194,10 @@ class Program(object):
desc = framework_pb2.OpDesc()
desc.type = op_type
if input_val_keys:
desc.inputs.extend(self.OpDescVars(*input_val_keys))
if output_val_keys:
desc.outputs.extend(self.OpDescVars(*output_val_keys))
if input_key_vals:
desc.inputs.extend(self.OpDescVars(*input_key_vals))
if output_key_vals:
desc.outputs.extend(self.OpDescVars(*output_key_vals))
if attrs:
desc.attrs.extend(self.OpDescAttrs(attrs))
self.op_descs.append(desc)
......@@ -388,8 +386,8 @@ class Writer(object):
))
prog.OpDesc(
'feed',
(['feed'], 'X'),
([name], 'Out'),
(['X'], ['feed']),
(['Out'], [name]),
{'col': idx},
)
prog.VarDesc(name, value_info=value_info, remove_batch=remove_batch)
......@@ -406,8 +404,8 @@ class Writer(object):
prog.OpDesc(
'fetch',
([name], 'X'),
(['fetch'], 'Out'),
(['X'], [name]),
(['Out'], ['fetch']),
{'col': idx},
)
# var is emitted over ops
......@@ -424,12 +422,16 @@ class Writer(object):
return codes
@staticmethod
def write_weight(weight, filename):
def write_weight(weight, filename, lod=None):
"""
write single weight in fluid desc
"""
assert isinstance(weight, np.ndarray), 'weight is not an ndarray'
assert lod is None or isinstance(lod,
list), 'lod should be None or list'
lod = lod or [0]
tensor_desc = framework_pb2.VarType.TensorDesc()
tensor_desc.data_type = Program.Dtype(weight.dtype)
......@@ -437,7 +439,7 @@ class Writer(object):
fp = open(filename, 'wb')
np.array([0], dtype=np.int32).tofile(fp) # version
np.array([0], dtype=np.int64).tofile(fp) # LOD level
np.array(lod, dtype=np.int64).tofile(fp) # LOD level
np.array([0], dtype=np.int32).tofile(fp) # tensor version
np.array([tensor_desc.ByteSize()], dtype=np.int32).tofile(fp)
fp.write(tensor_desc.SerializeToString())
......
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