diff --git a/onnx2fluid/onnx2fluid/onnx_utils.py b/onnx2fluid/onnx2fluid/onnx_utils.py index 5bfdff119dcf08ec3bbafc6dc8df978b73b6babc..05f20e7127e6e727138311d8e440c110777808aa 100644 --- a/onnx2fluid/onnx2fluid/onnx_utils.py +++ b/onnx2fluid/onnx2fluid/onnx_utils.py @@ -80,7 +80,7 @@ def build_value_refs(nodes): def get_attribute_value2(attr): """ - get_attribute_value with tensor conversion + get_attribute_value enhanced """ if attr.type == onnx.AttributeProto.TENSOR: @@ -88,6 +88,9 @@ def get_attribute_value2(attr): data = attr.t.raw_data value = np.frombuffer( data, dtype=dtype, count=(len(data) // dtype.itemsize)) + elif attr.type == onnx.AttributeProto.STRING: + value = attr.s + value = value.decode() if isinstance(value, bytes) else value else: value = get_attribute_value(attr) return value diff --git a/onnx2fluid/onnx2fluid/symbolic.py b/onnx2fluid/onnx2fluid/symbolic.py index 30322c3fd74e04cad54e2efe1c29be85b7e8c3f6..2b10b52c05f9fc597fabb8892e30514ca1f20bcc 100644 --- a/onnx2fluid/onnx2fluid/symbolic.py +++ b/onnx2fluid/onnx2fluid/symbolic.py @@ -3,6 +3,8 @@ """ ONNX to Paddle fluid symbolic translation +TODO: move non-ONNX ops out to symbolic_aten.py, symbolic_caffe2.py ... + Created on Mon Feb 25 09:33:43 2019 @author: Macrobull @@ -288,6 +290,17 @@ def _assign(prog, attrs): ) +def _zeros_like(prog, val_ref, val_out, value_infos): + prog.Op( + '', + 'Sub', + [val_ref, val_ref], + [val_out], # val + dict(axis=0), + value_infos, + ) + + def _pad_if_asymmetric(prog, pads, val_name, value_infos): # pads: SSEE assert len(pads) & 1 == 0 ndims = len(pads) // 2 @@ -331,7 +344,7 @@ def _adaptive_pool(prog, pool_type, inputs, outputs, attrs, name=''): # interpretation pool_size = attrs['output_size'] # required poolnd = len(pool_size) - assert 2 <= poolnd <= 3, 'only pool2d and pool3d supported' + assert 2 <= poolnd <= 3, 'only pool2d and pool3d is supported' fluid_op = 'adaptive_pool{}d'.format(poolnd) name_attr = ', name={}'.format(repr(name)) if name else '' @@ -386,7 +399,7 @@ def _global_pool(prog, pool_type, inputs, outputs, attrs, value_infos, name=''): poolnd = len(input_shape) - 2 # NC... elif output_shape: poolnd = len(output_shape) - 2 # NC... - assert 2 <= poolnd <= 3, 'only pool2d and pool3d supported' + assert 2 <= poolnd <= 3, 'only pool2d and pool3d is supported' fluid_op = 'pool{}d'.format(poolnd) name_attr = ', name={}'.format(repr(name)) if name else '' @@ -430,10 +443,10 @@ def _pool(prog, pool_type, inputs, outputs, attrs, value_infos, name=''): # interpretation assert attrs.get( 'auto_pad', - 'NOTSET') == 'NOTSET', 'only auto_pad = NOTSET supported' # optional + 'NOTSET') == 'NOTSET', 'only auto_pad = NOTSET is supported' # optional pool_size = attrs['kernel_shape'] # required poolnd = len(pool_size) - assert 2 <= poolnd <= 3, 'only pool2d and pool3d supported' + assert 2 <= poolnd <= 3, 'only pool2d and pool3d is supported' fluid_op = 'pool{}d'.format(poolnd) strides = attrs.get('strides', [1] * poolnd) # optional @@ -538,14 +551,68 @@ def _roi_pool(prog, fluid_op, inputs, outputs, attrs, value_infos, name): ) -def _zeros_like(prog, val_ref, val_out, value_infos): - prog.Op( - '', - 'Sub', - [val_ref, val_ref], - [val_out], # val - dict(axis=0), - value_infos, +def _interpolate(prog, inputs, outputs, attrs, value_infos, name=''): + + # I/O + val_x, val_scales = inputs + val_y, = outputs + var_x = _make_var_name(val_x) + var_y = _make_var_name(val_y) + + # interpretation + # output shape + out_shape_ = _shape_or_none(value_infos, val_y) + if out_shape_ is not None: + assert len(out_shape_) == 4, 'only 4-D Tensor as X and Y supported' + out_shape_ = out_shape_[2:] + # try scales + scales = _const_weight_or_none(value_infos, val_scales) + if scales is not None: + assert len(scales) == 4, 'only 4-D Tensor as X and Y supported' + assert scales[0] == 1 and scales[ + 1] == 1, 'only scale on (NC)HW supported' + assert scales[2] == scales[ + 3], 'only aspect-ratio-invariant scale supported' + scale = scales[2] if scales else None + # try input shape + if scale is None: + assert out_shape_, 'neither scales nor output shape is available' + out_shape = out_shape_ + else: + out_shape = None + if out_shape_ is None: + in_shape = _shape_or_none(value_infos, val_x) + assert in_shape is not None, 'out_shape required but not inferrable' + assert len(in_shape) == 4, 'only 4-D Tensor as X and Y supported' + out_shape_ = [in_shape[2] * scale, in_shape[3] * scale] + mode = attrs.get('mode', 'nearest') + fluid_op = 'resize_{}'.format(mode) # not sure bilinear will be linear? + name_attr = ', name={}'.format(repr(name)) if name else '' + + # generation + prog.Code('{} = layers.{}({}' + ', scale={}' + ', out_shape={}' + '{})'.format( + var_y, + fluid_op, + var_x, + # attrs + scale, + out_shape, + name_attr, + )) + fluid_op = '{}_interp'.format(mode) + prog.VarDesc(var_y) + prog.OpDesc( + fluid_op, + ([var_x], 'X'), + ([var_y], 'Out'), + dict( + interp_method=mode, + out_h=out_shape_[0], + out_w=out_shape_[1], + ), ) @@ -565,21 +632,6 @@ def AdaptiveMaxPool(prog, inputs, outputs, attrs, *args, name='', **kwargs): return _adaptive_pool(prog, 'max', inputs, outputs, attrs, name=name) -def AveragePool(prog, - inputs, - outputs, - attrs, - value_infos, - name='', - *args, - **kwargs): - """ - onnx::AveragePool-10: - """ - - return _pool(prog, 'avg', inputs, outputs, attrs, value_infos, name=name) - - def AffineGrid(prog, inputs, outputs, attrs, *args, name='', **kwargs): """ aten::affine_grid @@ -616,6 +668,21 @@ def AffineGrid(prog, inputs, outputs, attrs, *args, name='', **kwargs): ) +def AveragePool(prog, + inputs, + outputs, + attrs, + value_infos, + name='', + *args, + **kwargs): + """ + onnx::AveragePool-10: + """ + + return _pool(prog, 'avg', inputs, outputs, attrs, value_infos, name=name) + + def BatchNormalization(prog, inputs, outputs, @@ -805,6 +872,7 @@ def Constant(prog, inputs, outputs, attrs, value_infos, *args, **kwargs): # generation value = value.tolist() if len(value) == 1: # scalar + shape = [1] # WORKAROUND: bad scalar support value = value[0] fluid_op = 'fill_constant' prog.Code('{} = layers.{}(shape={}, dtype={}, value={})'.format( @@ -874,7 +942,7 @@ def Conv(prog, *args, **kwargs): """ - onnx::ConstantOfShape-1: + onnx::Conv-1: """ # I/O @@ -888,13 +956,13 @@ def Conv(prog, # interpretation assert attrs.get( - 'auto_pad', - 'NOTSET') == 'NOTSET', 'only auto_pad == NOTSET supported' # optional + 'auto_pad', 'NOTSET' + ) == 'NOTSET', 'only auto_pad == NOTSET is supported' # optional kernel_shape = _shape(value_infos, val_w)[2:] # OI... assert kernel_shape == attrs[ 'kernel_shape'], 'kernel_shape in attr unmatches value_info' # HW convnd = len(kernel_shape) - assert 2 <= convnd <= 3, 'only conv2d and conv3d supported' + assert 2 <= convnd <= 3, 'only conv2d and conv3d is supported' num_out_channels = _shape(value_infos, val_w)[0] # OI... fluid_op = 'conv{}d'.format(convnd) @@ -994,16 +1062,16 @@ def ConvTranspose(prog, # interpretation assert attrs.get( - 'auto_pad', - 'NOTSET') == 'NOTSET', 'only auto_pad == NOTSET supported' # optional - assert sum( - attrs.get('output_padding', - [])) == 0, 'only zero output_padding supported' # optional ? + '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, val_w)[2:] # IO... assert kernel_shape == attrs[ 'kernel_shape'], 'kernel_shape in attr unmatches value_info' # HW convnd = len(kernel_shape) - assert 2 <= convnd <= 3, 'only conv2d_transpose and conv3d_transpose supported' + assert 2 <= convnd <= 3, 'only conv2d_transpose and conv3d_transpose is supported' num_out_channels = _shape(value_infos, val_w)[1] # IO... fluid_op = 'conv{}d_transpose'.format(convnd) @@ -1285,14 +1353,6 @@ def MaxRoiPool(prog, inputs, outputs, attrs, value_infos, name, *args, _roi_pool(prog, 'roi_pool', inputs, outputs, attrs, value_infos, name) -def RoiAlign(prog, inputs, outputs, attrs, value_infos, name, *args, **kwargs): - """ - caffe2::RoiAlign - """ - - _roi_pool(prog, 'roi_align', inputs, outputs, attrs, value_infos, name) - - def Pad(prog, inputs, outputs, attrs, value_infos, name='', *args, **kwargs): """ onnx::Pad-2: @@ -1502,6 +1562,54 @@ def Reshape(prog, inputs, outputs, attrs, value_infos, name, *args, **kwargs): ) +def Resize(prog, inputs, outputs, attrs, value_infos, name='', *args, **kwargs): + """ + onnx::Resize-10: + """ + + return _interpolate(prog, inputs, outputs, attrs, value_infos, name=name) + + +def RoiAlign(prog, inputs, outputs, attrs, value_infos, name, *args, **kwargs): + """ + caffe2::RoiAlign + """ + + _roi_pool(prog, 'roi_align', inputs, outputs, attrs, value_infos, name) + + +#def Shape( +# prog, inputs, outputs, attrs, value_infos, +# *args, **kwargs): +# """ +# onnx::ConstantOfShape-1: +# """ +# +# # I/O +# val_data, = inputs +# val_shape, = outputs +# var_data = _make_var_name(val_data) +# var_shape = _make_var_name(val_shape) +# +# # interpretation +# fluid_op = 'shape' +## value_infos[val_shape]['remove_batch'] = False +# +# # generation +# prog.Code('{} = layers.{}({})' +# .format(var_shape, +# fluid_op, +# var_data, +# # attrs +# )) +# prog.VarDesc(var_shape) # , _value_info_or_none(value_infos, val_shape)) +# prog.OpDesc(fluid_op, +# ([var_data], 'X'), +# ([var_shape], 'Out'), +# dict(), +# ) + + def Slice(prog, inputs, outputs, attrs, value_infos, *args, **kwargs): """ onnx::Slice-1:9 @@ -1523,15 +1631,15 @@ def Slice(prog, inputs, outputs, attrs, value_infos, *args, **kwargs): # ndims = len(shape) # for idx, value in enumerate(axes): # if value > ONNX_INT_MAX // 2: - # axes[idx] = ndims + value - ONNX_INT_MAX - 1 + # axes[idx] = ndims + value - ONNX_INT_MAX # FIXME: Paddle 1.3 Doc: '对于未知大小维度的末尾进行切片，则建议传入 INT_MAX' not works ? for idx, value in enumerate(starts): if value > ONNX_INT_MAX // 2: - value = value - ONNX_INT_MAX - 1 + value = value - ONNX_INT_MAX starts[idx] = shape[axes[idx]] + value for idx, value in enumerate(ends): if value > ONNX_INT_MAX // 2: - value = value - ONNX_INT_MAX - 1 + value = value - ONNX_INT_MAX ends[idx] = shape[axes[idx]] + value # generation @@ -1561,6 +1669,47 @@ def Slice(prog, inputs, outputs, attrs, value_infos, *args, **kwargs): ) +def Split(prog, inputs, outputs, attrs, *args, name='', **kwargs): + """ + onnx::Split-2: + """ + + # I/O + val_input, = inputs + var_outs = [_make_var_name(val) for val in outputs] + var_input = _make_var_name(val_input) + + # interpretation + fluid_op = 'split' + split = attrs['split'] # required + axis = attrs.get('axis', 0) # optional + name_attr = ', name={}'.format(repr(name)) if name else '' + + # generation + prog.Code('{} = layers.{}({}, {}' + ', dim={}' + '{})'.format( + ', '.join(var_outs), + fluid_op, + var_input, + split, + # attrs + axis, + name_attr, + )) + for var_out in var_outs: + prog.VarDesc(var_out) + prog.OpDesc( + fluid_op, + (var_input, 'X'), + ([var_outs], *(['Out'] * len(var_outs))), + dict( + axis=axis, + sections=split, + ), + ) + + def Sum(prog, inputs, outputs, *args, **kwargs): """ onnx::Sum-8: @@ -1593,7 +1742,7 @@ def Sum(prog, inputs, outputs, *args, **kwargs): def Tile(prog, inputs, outputs, attrs, value_infos, name='', *args, **kwargs): """ - onnx::ConstantOfShape-6: + onnx::Tile-1: """ # I/O @@ -1630,77 +1779,19 @@ def Tile(prog, inputs, outputs, attrs, value_infos, name='', *args, **kwargs): ) -#def Shape( -# prog, inputs, outputs, attrs, value_infos, -# *args, **kwargs): -# """ -# onnx::ConstantOfShape-1: -# """ -# -# # I/O -# val_data, = inputs -# val_shape, = outputs -# var_data = _make_var_name(val_data) -# var_shape = _make_var_name(val_shape) -# -# # interpretation -# fluid_op = 'shape' -## value_infos[val_shape]['remove_batch'] = False -# -# # generation -# prog.Code('{} = layers.{}({})' -# .format(var_shape, -# fluid_op, -# var_data, -# # attrs -# )) -# prog.VarDesc(var_shape) # , _value_info_or_none(value_infos, val_shape)) -# prog.OpDesc(fluid_op, -# ([var_data], 'X'), -# ([var_shape], 'Out'), -# dict(), -# ) - - -def Split(prog, inputs, outputs, attrs, *args, name='', **kwargs): +def Upsample(prog, + inputs, + outputs, + attrs, + value_infos, + name='', + *args, + **kwargs): """ - onnx::Split-2: + onnx::Upsample-9:9 """ - # I/O - val_input, = inputs - var_outs = [_make_var_name(val) for val in outputs] - var_input = _make_var_name(val_input) - - # interpretation - fluid_op = 'split' - split = attrs['split'] # required - axis = attrs.get('axis', 0) # optional - name_attr = ', name={}'.format(repr(name)) if name else '' - - # generation - prog.Code('{} = layers.{}({}, {}' - ', dim={}' - '{})'.format( - ', '.join(var_outs), - fluid_op, - var_input, - split, - # attrs - axis, - name_attr, - )) - for var_out in var_outs: - prog.VarDesc(var_out) - prog.OpDesc( - fluid_op, - (var_input, 'X'), - ([var_outs], *(['Out'] * len(var_outs))), - dict( - axis=axis, - sections=split, - ), - ) + return _interpolate(prog, inputs, outputs, attrs, value_infos, name=name) if __name__ == '__main__': diff --git a/onnx2fluid/onnx2fluid/writer.py b/onnx2fluid/onnx2fluid/writer.py index c5196d96ab93d0e97248c78348516ae14171665b..3ca66f5f0bc1bf2c34d7fa532555c36ccc9c2fcf 100644 --- a/onnx2fluid/onnx2fluid/writer.py +++ b/onnx2fluid/onnx2fluid/writer.py @@ -158,9 +158,12 @@ class Program(object): raise ValueError('unsupported attribute {} = {}'.format( key, value)) else: # WORKAROUND: shape of scalars is [] - od_attr.type = framework_pb2.INTS - logger.warning('using attribute %s = %s as INTS', key, - value) + 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))