From d6e4a4bab467e0c0735acab5a401895cb86dcd84 Mon Sep 17 00:00:00 2001 From: Macrobull Date: Thu, 18 Jul 2019 20:44:29 +0800 Subject: [PATCH] add naive option --- onnx2fluid/examples/convert_data_npz.py | 6 ++-- onnx2fluid/examples/convert_data_pb.py | 6 ++-- onnx2fluid/onnx2fluid/__main__.py | 9 ++++- onnx2fluid/onnx2fluid/cmdline.py | 6 +++- onnx2fluid/onnx2fluid/conversion.py | 34 ++++++++++++------ onnx2fluid/onnx2fluid/onnx_utils.py | 6 ++-- onnx2fluid/onnx2fluid/symbolic.py | 38 ++++++++++---------- onnx2fluid/onnx2fluid/torch_export_helper.py | 8 ++--- onnx2fluid/onnx2fluid/validation.py | 9 ++--- onnx2fluid/onnx2fluid/writer.py | 22 ++++++++---- 10 files changed, 90 insertions(+), 54 deletions(-) diff --git a/onnx2fluid/examples/convert_data_npz.py b/onnx2fluid/examples/convert_data_npz.py index 0bf613d..d7c8d3f 100644 --- a/onnx2fluid/examples/convert_data_npz.py +++ b/onnx2fluid/examples/convert_data_npz.py @@ -19,12 +19,12 @@ def make_var_name(name): assert name - if name[0].isdigit(): - return 'var_' + name - for s in ' \\|/:-': # + for s in ' \\|/:.-': name = name.replace(s, '_') if name.startswith('_'): name = 'var' + name + elif name[0].isdigit(): + name = 'var_' + name return name diff --git a/onnx2fluid/examples/convert_data_pb.py b/onnx2fluid/examples/convert_data_pb.py index f48f72e..413b4aa 100644 --- a/onnx2fluid/examples/convert_data_pb.py +++ b/onnx2fluid/examples/convert_data_pb.py @@ -22,12 +22,12 @@ def make_var_name(name): assert name - if name[0].isdigit(): - return 'var_' + name - for s in ' \\|/:-': # + for s in ' \\|/:.-': name = name.replace(s, '_') if name.startswith('_'): name = 'var' + name + elif name[0].isdigit(): + name = 'var_' + name return name diff --git a/onnx2fluid/onnx2fluid/__main__.py b/onnx2fluid/onnx2fluid/__main__.py index f09f63e..97e3e7f 100644 --- a/onnx2fluid/onnx2fluid/__main__.py +++ b/onnx2fluid/onnx2fluid/__main__.py @@ -64,7 +64,14 @@ parser.add_argument( '-x', action='store_false', dest='pedantic', - help='process non-standard ONNX ops, this may lead to fails', + help='process non-standard ONNX ops, this may lead to failures', +) +parser.add_argument( + '--naive', + '-n', + action='store_true', + default=False, + help='bypass ONNX op optimizations, especially for training purpose', ) parser.add_argument( '--skip-version-conversion', diff --git a/onnx2fluid/onnx2fluid/cmdline.py b/onnx2fluid/onnx2fluid/cmdline.py index 4f6a674..e435d8c 100644 --- a/onnx2fluid/onnx2fluid/cmdline.py +++ b/onnx2fluid/onnx2fluid/cmdline.py @@ -18,6 +18,8 @@ from __future__ import unicode_literals import logging, shutil, zipfile +logger = logging.getLogger(__name__) + __all__ = [ 'main', ] @@ -45,6 +47,7 @@ def main(**kwargs): model_basename = DEFAULT_MODEL_MODULE + '.py' model_func_name = DEFAULT_MODEL_FUNC onnx_opset_pedantic = kwargs.pop('pedantic', True) + onnx_skip_optimization = kwargs.pop('naive', False) skip_version_conversion = kwargs.pop('skip_version_conversion', False) onnx_opset_version = None if skip_version_conversion else DEFAULT_ONNX_OPSET_VERSION @@ -55,6 +58,7 @@ def main(**kwargs): model_func_name=model_func_name, onnx_opset_version=onnx_opset_version, onnx_opset_pedantic=onnx_opset_pedantic, + onnx_skip_optimization=onnx_skip_optimization, **kwargs) # validate @@ -65,7 +69,7 @@ def main(**kwargs): if golden_data_filename or save_inference_model: from .validation import validate - if save_inference_model: + if infer_inputs: inference_input_names = infer_inputs.split(',') else: inference_input_names = None diff --git a/onnx2fluid/onnx2fluid/conversion.py b/onnx2fluid/onnx2fluid/conversion.py index 4113b06..8c604de 100644 --- a/onnx2fluid/onnx2fluid/conversion.py +++ b/onnx2fluid/onnx2fluid/conversion.py @@ -24,12 +24,12 @@ def make_var_name(name): if name == '': return '' - if name[0].isdigit(): - return 'var_' + name for s in ' \\|/:.-': name = name.replace(s, '_') if name.startswith('_'): name = 'var' + name + elif name[0].isdigit(): + name = 'var_' + name return name @@ -40,6 +40,7 @@ def convert(onnx_model_filename, embed_params=False, onnx_opset_version=None, onnx_opset_pedantic=True, + onnx_skip_optimization=False, debug=False, **kwargs): """ @@ -61,10 +62,10 @@ def convert(onnx_model_filename, from .onnx_utils import DEFAULT_OP_DOMAIN from .onnx_utils import graph_ops, graph_weights from .onnx_utils import inferred_model_value_info - from .onnx_utils import polish_model + from .onnx_utils import polish_model, optimize_model_strip_initializer from .writer import Program, Writer - logger = logging.getLogger('convert') + logger = logging.getLogger('onnx2fluid') # prepare onnx model logger.info('loading model: %s ...', onnx_model_filename) @@ -90,8 +91,12 @@ 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, checking=onnx_opset_pedantic) + if onnx_skip_optimization: + logger.info('stripping model ...') + onnx_model = optimize_model_strip_initializer(onnx_model) + else: + logger.info('optimizing model ...') + onnx_model = polish_model(onnx_model, checking=onnx_opset_pedantic) # prepare filesystem shutil.rmtree(save_dir, ignore_errors=True) @@ -123,7 +128,7 @@ 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['lod'] = [] value_info['embedded_as'] = [] value_info['get_weight'] = (lambda w: lambda: w.tolist())( weight) # lazy getter @@ -306,7 +311,14 @@ def main(): '-x', action='store_false', dest='pedantic', - help='process non-standard ONNX ops, this may lead to fails', + help='process non-standard ONNX ops, this may lead to failures', + ) + parser.add_argument( + '--naive', + '-n', + action='store_true', + default=False, + help='bypass ONNX op optimizations, especially for training purpose', ) parser.add_argument( '--skip-version-conversion', @@ -329,13 +341,15 @@ def main(): if save_dir else basepath) + shutil.os.sep embed_params = args.embed_params pedantic = args.pedantic - skip_version_conversion = args.skip_version_conversion + skip_optimization = args.naive + onnx_opset_version = None if args.skip_version_conversion else DEFAULT_ONNX_OPSET_VERSION convert(model_filename, save_dir, embed_params=embed_params, + onnx_opset_version=onnx_opset_version, onnx_opset_pedantic=pedantic, - onnx_skip_version_conversion=skip_version_conversion, + onnx_skip_optimization=skip_optimization, debug=debug) diff --git a/onnx2fluid/onnx2fluid/onnx_utils.py b/onnx2fluid/onnx2fluid/onnx_utils.py index c0c3c66..759c9bd 100644 --- a/onnx2fluid/onnx2fluid/onnx_utils.py +++ b/onnx2fluid/onnx2fluid/onnx_utils.py @@ -356,16 +356,16 @@ def polish_model(model, internals=True, extras=True, checking=True): def polish_and_save(model_filename, + save_filename='', suffix='.polished', - save_filename=None, *args, **kwargs): """ run polish_model and save """ - if save_filename is None: - save_filename = model_filename.replace('.onnx', suffix + '.onnx') + save_filename = save_filename or model_filename.replace( + '.onnx', suffix + '.onnx') model = onnx.load(model_filename) model = polish_model(model, *args, **kwargs) diff --git a/onnx2fluid/onnx2fluid/symbolic.py b/onnx2fluid/onnx2fluid/symbolic.py index e781d7d..1279e41 100644 --- a/onnx2fluid/onnx2fluid/symbolic.py +++ b/onnx2fluid/onnx2fluid/symbolic.py @@ -18,7 +18,8 @@ import numpy as _np from collections import OrderedDict as _dict from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE -_logger = _logging.getLogger(__name__) +# _logger = _logging.getLogger(__name__) +_logger = _logging.getLogger('onnx2fluid') ONNX_INT_MAX = 2**63 - 1 FLUID_INT_MAX = 2**31 - 1 # @@ -58,8 +59,8 @@ DEFAULT_OP_MAPPING = { 'Ceil': ['ceil', ['X'], ['Out']], 'Clip': ['clip', ['X'], ['Out'], dict(), dict( - min=(_np.array([255, 255, 127, 255], dtype=_np.uint8).view(_np.float32)), - max=(_np.array([255, 255, 127, 127], dtype=_np.uint8).view(_np.float32)), + min=(_np.asarray([255, 255, 127, 255], dtype=_np.uint8).view(_np.float32)), + max=(_np.asarray([255, 255, 127, 127], dtype=_np.uint8).view(_np.float32)), )], 'Cos': ['cos', ['X'], ['Out']], 'Elu': ['elu', ['X'], ['Out'], dict(), dict(alpha=1.)], @@ -449,7 +450,7 @@ def _pool(prog, pool_type, inputs, outputs, attrs, value_infos, name): # I/O var_x, = inputs var_y, var_indices, = (outputs + [''] * 1)[:2] - assert name and var_x and var_y + assert name and all(inputs) and var_y # interpretation assert attrs.get( @@ -512,7 +513,7 @@ def _roi_pool(prog, fluid_op, inputs, outputs, attrs, name): # I/O var_x, var_rois, = inputs var_y, = outputs - assert name and var_x and var_rois and var_y + assert name and all(inputs) and all(outputs) # interpretation spatial_scale = attrs['spatial_scale'] # required @@ -565,7 +566,7 @@ def _interpolate(prog, inputs, outputs, attrs, value_infos, name=''): # I/O var_x, var_scales, = inputs var_y, = outputs - assert var_x and var_scales and var_y + assert all(inputs) and all(outputs) # interpretation # output shape @@ -701,7 +702,7 @@ def BatchNormalization(prog, var_x, var_scale, var_b, var_mean, var_var, = inputs var_y, var_mean_, var_var_, var_saved_mean, var_saved_variance, = ( outputs + [''] * 4)[:5] - assert var_x and var_scale and var_b and var_mean and var_var and var_y + assert all(inputs) and var_y assert var_saved_mean or name assert var_saved_variance or name var_saved_mean = var_saved_mean or (name + '.saved_mean') # dummy output @@ -879,7 +880,8 @@ def Constant(prog, inputs, outputs, attrs, value_infos, *args, **kwargs): _logger.warning( 'in op (Constant -> %s): ' 'attribute "shape" of %s not inferred, ' - 'using value as 1-D tensor may lead to fails', outputs, var_output) + 'using value as 1-D tensor may lead to failures', outputs, + var_output) # generation if not shape or value.size == 1: # scalar or 1-size @@ -929,7 +931,7 @@ def ConstantOfShape(prog, inputs, outputs, attrs, value_infos, *args, **kwargs): 'given shape is neither const value nor deductible from output, ' 'this is not supported') attrs = attrs.copy() - attrs.setdefault('value', _np.array(0, dtype=_np.float32)) + attrs.setdefault('value', _np.asarray(0, dtype=_np.float32)) attrs.update({'shape': shape}) # pass const prog.Code('# shape: {} = {} # const as literal'.format(var_shape, shape)) @@ -959,7 +961,7 @@ def Conv(prog, # I/O var_x, var_w, var_b, = (inputs + [''] * 1)[:3] var_y, = outputs - assert name and var_x and var_w and var_y + assert name and var_x and var_w and all(outputs) # interpretation assert attrs.get( @@ -1066,7 +1068,7 @@ def ConvTranspose(prog, # I/O var_x, var_w, var_b, = (inputs + [''] * 1)[:3] var_y, = outputs - assert name and var_x and var_w and var_y + assert name and var_x and var_w and all(outputs) # interpretation assert attrs.get( @@ -1174,7 +1176,7 @@ 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_y, = outputs - assert name and var_a and var_b and var_c and var_y + assert name and all(inputs) and all(outputs) alpha = attrs.get('alpha', 1.) # optional beta = attrs.get('beta', 1.) # optional @@ -1794,7 +1796,7 @@ def Pad(prog, inputs, outputs, attrs, value_infos, name='', *args, **kwargs): mode) fluid_op = 'pad' pad2d_attr = '' - paddings = _np.array(pads).reshape( + paddings = _np.asarray(pads).reshape( (-1, 2)).transpose().flatten().tolist() # SSEE -> SESE od_attrs['paddings'] = paddings name_attr = ', name={}'.format(repr(name)) if name else '' @@ -1838,7 +1840,7 @@ def PRelu(prog, # I/O var_x, var_slope, = inputs var_y, = outputs - assert name and var_x and var_slope and var_y + assert name and all(inputs) and all(outputs) # interpretation mode = 'channel' @@ -1904,7 +1906,7 @@ def Reshape(prog, inputs, outputs, attrs_, value_infos, name, *args, **kwargs): # I/O var_data, var_shape, = inputs var_reshaped, = outputs - assert name and var_data and var_shape and var_reshaped + assert name and all(inputs) and all(outputs) # interpretation shape = _const_weight_or_none(value_infos, var_shape) @@ -2015,7 +2017,7 @@ def Shape(prog, inputs, outputs, attrs_, name, **kwargs): # I/O var_data, = inputs var_shape, = outputs - assert name and var_data and var_shape + assert name and all(inputs) and all(outputs) # interpretation fluid_op = 'shape' @@ -2189,7 +2191,7 @@ def Tile(prog, inputs, outputs, attrs_, value_infos, name='', *args, **kwargs): # I/O var_input, var_repeats, = inputs var_output, = outputs - assert var_input and var_repeats and var_output + assert all(inputs) and all(outputs) # interpretation repeats = _const_weight_or_none(value_infos, var_repeats) @@ -2227,7 +2229,7 @@ def Transpose(prog, inputs, outputs, attrs, name, *args, **kwargs): # I/O var_data, = inputs var_transposed, = outputs - assert name and var_data and var_transposed + assert name and all(inputs) and all(outputs) # interpretation fluid_op = 'transpose' diff --git a/onnx2fluid/onnx2fluid/torch_export_helper.py b/onnx2fluid/onnx2fluid/torch_export_helper.py index ef13cea..8c49d4c 100644 --- a/onnx2fluid/onnx2fluid/torch_export_helper.py +++ b/onnx2fluid/onnx2fluid/torch_export_helper.py @@ -138,10 +138,10 @@ def export_onnx_with_validation( outputs = torch.onnx.export(model, torch_inputs, export_basepath + '.onnx', - input_names=(None if input_names is None else - flatten_list(input_names)), - output_names=(None if output_names is None else - flatten_list(output_names)), + input_names=(input_names + and flatten_list(input_names)), + output_names=(output_names + and flatten_list(output_names)), *args, **kwargs) if outputs is None: # WORKAROUND: for torch.onnx diff --git a/onnx2fluid/onnx2fluid/validation.py b/onnx2fluid/onnx2fluid/validation.py index efd5609..6d656ba 100644 --- a/onnx2fluid/onnx2fluid/validation.py +++ b/onnx2fluid/onnx2fluid/validation.py @@ -90,7 +90,7 @@ def validate(fluid_model_filename, import numpy as np import paddle.fluid as fluid - logger = logging.getLogger('validate') + logger = logging.getLogger('onnx2fluid') place = fluid.CPUPlace() exe = fluid.Executor(place) @@ -126,6 +126,7 @@ def validate(fluid_model_filename, logger.info('import passed') prog = fluid.default_main_program() + prog = prog.clone(for_test=True) # force inference mode fluid.io.load_persistables(executor=exe, dirname=fluid_model_dir, main_program=prog) @@ -160,8 +161,7 @@ def validate(fluid_model_filename, logger.info('with %d inputs and %d outputs', len(input_data), len(output_data)) elif save_inference_model: - assert inference_input_names is not None, ( - 'input names required for type-shape inference') + assert inference_input_names, 'input names required for type-shape inference' input_names = inference_input_names logger.info('using input names: %s', ', '.join(input_names)) @@ -185,6 +185,7 @@ def validate(fluid_model_filename, # execute outputs = exe.run(prog, feed=input_data, fetch_list=out_names) # out_names can be vars + exe.close() logger.info('execution passed') # validate @@ -264,7 +265,7 @@ def main(): atol, rtol = args.atol, args.rtol save_inference_model = args.infer_inputs is not None inference_input_names = args.infer_inputs.split( - ',') if args.infer_inputs else None + ',') if save_inference_model else None validate(fluid_model_filename, golden_data_filename=golden_data_filename, diff --git a/onnx2fluid/onnx2fluid/writer.py b/onnx2fluid/onnx2fluid/writer.py index 32b91b5..7f958d8 100644 --- a/onnx2fluid/onnx2fluid/writer.py +++ b/onnx2fluid/onnx2fluid/writer.py @@ -372,7 +372,7 @@ class Writer(object): prog.Code('# input {}'.format(name)) prog.Code(( '{} = layers.data(name={}, shape={}, dtype={}, ' - 'append_batch_size={})' # , stop_gradient=True + 'append_batch_size={}, lod_level=1)' # , stop_gradient=True ).format( name, repr(name), @@ -427,20 +427,28 @@ class Writer(object): assert lod is None or isinstance(lod, list), 'lod should be None or list' - if lod is None: - lod = [0] + lod = lod or [] tensor_desc = framework_pb2.VarType.TensorDesc() tensor_desc.data_type = Program.Dtype(weight.dtype) tensor_desc.dims.extend(weight.shape) fp = open(filename, 'wb') - np.array([0], dtype=np.int32).tofile(fp) # version - 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) + + # lod_tensor.cc: SerializeToStream + np.asarray([0], dtype=np.uint32).tofile(fp) # version + np.asarray([len(lod)], dtype=np.int64).tofile(fp) # LOD levels + for level in lod: + np.asarray([len(level)], dtype=np.int64).tofile(fp) # level size + np.asarray(level, dtype=np.uint64).tofile(fp) # LOD: size_t + + # tensor_util.cc: TensorToStream + np.asarray([0], dtype=np.uint32).tofile(fp) # tensor version + np.asarray([tensor_desc.ByteSize()], dtype=np.int32).tofile(fp) fp.write(tensor_desc.SerializeToString()) + weight.tofile(fp) + fp.close() @staticmethod -- GitLab