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X2Paddle
提交 73830eb2 编写于 作者: J jiangjiajun
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modify code format

上级 0edcc783
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Showing with 9494 addition and 9323 deletion
tools/merge_params.py
浏览文件 @ 73830eb2
......@@ -5,12 +5,14 @@ model_dir = sys.argv[1]
new_model_dir = sys.argv[2]
exe = fluid.Executor(fluid.CPUPlace())
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(dirname=model_dir, executor=exe)
fetch_targets] = fluid.io.load_inference_model(
dirname=model_dir, executor=exe)
print(feed_target_names)
fluid.io.save_inference_model(dirname=new_model_dir,
feeded_var_names=feed_target_names,
target_vars=fetch_targets,
executor=exe,
main_program=inference_program,
params_filename="__params__")
fluid.io.save_inference_model(
dirname=new_model_dir,
feeded_var_names=feed_target_names,
target_vars=fetch_targets,
executor=exe,
main_program=inference_program,
params_filename="__params__")
x2paddle/core/fluid_code.py
浏览文件 @ 73830eb2
......@@ -46,8 +46,8 @@ class Layer(object):
for input in self.inputs:
if isinstance(input, GraphNode):
if hasattr(input, "index"):
in_list += (input.layer_name +
"[{}]".format(input.index) + ", ")
in_list += (input.layer_name + "[{}]".format(
input.index) + ", ")
else:
in_list += (input.layer_name + ", ")
elif isinstance(input, six.string_types):
......@@ -71,8 +71,8 @@ class Layer(object):
layer_code = layer_code + key + "={}, ".format(input)
elif isinstance(self.inputs, GraphNode):
if hasattr(self.inputs, "index"):
layer_code += (self.inputs.layer_name +
"[{}]".format(self.inputs.index))
layer_code += (
self.inputs.layer_name + "[{}]".format(self.inputs.index))
else:
layer_code += (self.inputs.layer_name)
if self.op != "=":
......
x2paddle/core/op_mapper.py
浏览文件 @ 73830eb2
......@@ -64,10 +64,8 @@ def run_net(param_dir="./"):
b = os.path.exists(os.path.join(param_dir, var.name))
return b
fluid.io.load_vars(exe,
param_dir,
fluid.default_main_program(),
predicate=if_exist)
fluid.io.load_vars(
exe, param_dir, fluid.default_main_program(), predicate=if_exist)
class OpMapper(object):
......@@ -98,8 +96,8 @@ class OpMapper(object):
def add_codes(self, codes, indent=0):
if isinstance(codes, list):
for code in codes:
self.paddle_codes += (self.tab * indent + code.strip('\n') +
'\n')
self.paddle_codes += (
self.tab * indent + code.strip('\n') + '\n')
elif isinstance(codes, str):
self.paddle_codes += (self.tab * indent + codes.strip('\n') + '\n')
else:
......@@ -135,24 +133,25 @@ class OpMapper(object):
os.path.join(os.path.join(py_code_dir, var.name)))
return b
fluid.io.load_vars(exe,
py_code_dir,
fluid.default_main_program(),
predicate=if_exist)
fluid.io.load_vars(
exe,
py_code_dir,
fluid.default_main_program(),
predicate=if_exist)
if params_merge:
fluid.io.save_inference_model(dirname=os.path.join(
save_dir, "inference_model"),
feeded_var_names=input_names,
target_vars=outputs,
executor=exe,
params_filename="__params__")
fluid.io.save_inference_model(
dirname=os.path.join(save_dir, "inference_model"),
feeded_var_names=input_names,
target_vars=outputs,
executor=exe,
params_filename="__params__")
else:
fluid.io.save_inference_model(dirname=os.path.join(
save_dir, "inference_model"),
feeded_var_names=input_names,
target_vars=outputs,
executor=exe,
params_filename=None)
fluid.io.save_inference_model(
dirname=os.path.join(save_dir, "inference_model"),
feeded_var_names=input_names,
target_vars=outputs,
executor=exe,
params_filename=None)
except:
raise Exception(
"Paddle code was saved in {}/model.py, but seems there's wrong exist, please check model.py manually."
......
x2paddle/decoder/caffe_decoder.py
浏览文件 @ 73830eb2
......@@ -34,8 +34,8 @@ class CaffeResolver(object):
if not os.path.isfile(self.caffe_proto):
raise Exception(
"The .py file compiled by caffe.proto is not exist.")
(filepath,
tempfilename) = os.path.split(os.path.abspath(self.caffe_proto))
(filepath, tempfilename) = os.path.split(
os.path.abspath(self.caffe_proto))
(filename, extension) = os.path.splitext(tempfilename)
sys.path.append(filepath)
out = __import__(filename)
......@@ -49,13 +49,13 @@ class CaffeResolver(object):
class CaffeGraphNode(GraphNode):
def __init__(self, layer, type_str, layer_name=None):
if layer_name is None:
super(CaffeGraphNode,
self).__init__(layer,
layer.name.replace('/', '_').replace('-', '_'))
super(CaffeGraphNode, self).__init__(
layer,
layer.name.replace('/', '_').replace('-', '_'))
else:
super(CaffeGraphNode,
self).__init__(layer,
layer_name.replace('/', '_').replace('-', '_'))
super(CaffeGraphNode, self).__init__(
layer,
layer_name.replace('/', '_').replace('-', '_'))
self.layer_type = type_str
self.fluid_code = FluidCode()
self.data = None
......@@ -268,8 +268,8 @@ class CaffeDecoder(object):
c_i = blob.channels
h = blob.height
w = blob.width
data = np.asarray(list(blob.data),
dtype=np.float32).reshape(c_o, c_i, h, w)
data = np.asarray(
list(blob.data), dtype=np.float32).reshape(c_o, c_i, h, w)
transformed.append(data)
return transformed
x2paddle/decoder/caffe_pb2.py
浏览文件 @ 73830eb2
因为 它太大了无法显示 source diff 。你可以改为 查看blob。
x2paddle/decoder/onnx_decoder.py
浏览文件 @ 73830eb2
......@@ -71,9 +71,8 @@ class ONNXGraphNode(GraphNode):
if attr.type == onnx.AttributeProto.TENSOR:
dtype = np.dtype(TENSOR_TYPE_TO_NP_TYPE[attr.t.data_type])
data = attr.t.raw_data
value = np.frombuffer(data,
dtype=dtype,
count=(len(data) // dtype.itemsize))
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
......@@ -205,9 +204,8 @@ class ONNXGraph(Graph):
self.node_map[name].weight = weight
self.node_map[name].embeded_as = []
else:
self.node_map[name] = ONNXGraphDataNode(initializer,
layer_name=name,
is_global_input=False)
self.node_map[name] = ONNXGraphDataNode(
initializer, layer_name=name, is_global_input=False)
self.node_map[name].weight = weight
self.node_map[name].embeded_as = []
......
x2paddle/decoder/tf_decoder.py
浏览文件 @ 73830eb2
......@@ -120,13 +120,13 @@ class TFGraph(Graph):
def build(self):
for layer in self.model.node:
self.node_map[layer.name.replace('/', '_').replace(
'-', '_')] = TFGraphNode(layer, data_format=self.tf_data_format)
'-', '_')] = TFGraphNode(
layer, data_format=self.tf_data_format)
for layer_name, node in self.node_map.items():
for in_node in node.layer.input:
in_node = in_node.replace('/',
'_').replace('-',
'_').replace('^', '')
in_node = in_node.replace('/', '_').replace('-', '_').replace(
'^', '')
if in_node not in self.node_map:
if in_node.strip().split(':')[0] in self.node_map:
self.connect(in_node.strip().split(':')[0], layer_name)
......@@ -390,10 +390,10 @@ class TFDecoder(object):
shape=shape,
name="x2paddle_{}".format(layer.name))
except:
x2paddle_input = tf.placeholder(dtype=dtype,
shape=shape,
name="x2paddle_{}".format(
layer.name))
x2paddle_input = tf.placeholder(
dtype=dtype,
shape=shape,
name="x2paddle_{}".format(layer.name))
input_map["{}:0".format(layer.name)] = x2paddle_input
if shape.count(None) > 0:
......
x2paddle/op_mapper/caffe_custom_layer/convolutiondepthwise.py
浏览文件 @ 73830eb2
......@@ -120,18 +120,19 @@ def convolutiondepthwise_layer(inputs,
dila_len)
c_in = input_shape[0][1]
c_out = num_output if num_output is not None else input_shape[0][1]
group = int(c_in / (c_in / c_out)) if c_in > c_out else int(c_in /
(c_out / c_in))
out = fluid.layers.conv2d(input,
dilation=[dila_h, dila_w],
filter_size=[k_h, k_w],
stride=[s_h, s_w],
padding=[p_h, p_w],
groups=group,
num_filters=c_out,
param_attr=name + '_weights',
bias_attr=name + '_bias',
name=name)
group = int(c_in / (c_in / c_out)) if c_in > c_out else int(
c_in / (c_out / c_in))
out = fluid.layers.conv2d(
input,
dilation=[dila_h, dila_w],
filter_size=[k_h, k_w],
stride=[s_h, s_w],
padding=[p_h, p_w],
groups=group,
num_filters=c_out,
param_attr=name + '_weights',
bias_attr=name + '_bias',
name=name)
return out
......@@ -142,7 +143,8 @@ def convolutiondepthwise_weights(name, data=None):
return weights_name
register(kind='ConvolutionDepthwise',
shape=convolutiondepthwise_shape,
layer=convolutiondepthwise_layer,
weights=convolutiondepthwise_weights)
register(
kind='ConvolutionDepthwise',
shape=convolutiondepthwise_shape,
layer=convolutiondepthwise_layer,
weights=convolutiondepthwise_weights)
x2paddle/op_mapper/caffe_custom_layer/detectionoutput.py
浏览文件 @ 73830eb2
......@@ -37,8 +37,8 @@ def detectionoutput_layer(inputs,
pbv = fluid.layers.reshape(x=pbv, shape=[-1, 4])
mbox_loc = inputs[0]
mbox_loc = fluid.layers.reshape(x=mbox_loc, shape=[-1, pb.shape[0], 4])
mbox_conf_flatten = fluid.layers.reshape(x=mbox_conf_flatten,
shape=[0, pb.shape[0], -1])
mbox_conf_flatten = fluid.layers.reshape(
x=mbox_conf_flatten, shape=[0, pb.shape[0], -1])
default = {"nms_threshold": 0.3, "top_k": 10, "eta": 1.0}
fields = ['eta', 'top_k', 'nms_threshold']
......@@ -64,7 +64,8 @@ def detectionoutput_weights(name, data=None):
return weights_name
register(kind='DetectionOutput',
shape=detectionoutput_shape,
layer=detectionoutput_layer,
weights=detectionoutput_weights)
register(
kind='DetectionOutput',
shape=detectionoutput_shape,
layer=detectionoutput_layer,
weights=detectionoutput_weights)
x2paddle/op_mapper/caffe_custom_layer/normalize.py
浏览文件 @ 73830eb2
......@@ -20,9 +20,8 @@ def normalize_layer(inputs,
attr=name + '_scale')
scale_param = fluid.layers.reshape(x=scale_param, \
shape=[1] if channel_shared else [input_shape[0][1]])
out = fluid.layers.elementwise_mul(x=l2_norm,
y=scale_param,
axis=-1 if channel_shared else 1)
out = fluid.layers.elementwise_mul(
x=l2_norm, y=scale_param, axis=-1 if channel_shared else 1)
return out
......@@ -31,7 +30,8 @@ def normalize_weights(name, data=None):
return weights_name
register(kind='Normalize',
shape=normalize_shape,
layer=normalize_layer,
weights=normalize_weights)
register(
kind='Normalize',
shape=normalize_shape,
layer=normalize_layer,
weights=normalize_weights)
x2paddle/op_mapper/caffe_custom_layer/permute.py
浏览文件 @ 73830eb2
......@@ -23,7 +23,8 @@ def permute_weights(name, data=None):
return weights_name
register(kind='Permute',
shape=permute_shape,
layer=permute_layer,
weights=permute_weights)
register(
kind='Permute',
shape=permute_shape,
layer=permute_layer,
weights=permute_weights)
x2paddle/op_mapper/caffe_custom_layer/priorbox.py
浏览文件 @ 73830eb2
......@@ -30,18 +30,19 @@ def priorbox_layer(inputs,
steps = tuple(step) if type(step) is list or type(step) is tuple else (step,
step)
box, variance_ = fluid.layers.prior_box(input,
image,
min_sizes=min_size,
max_sizes=max_size,
aspect_ratios=aspect_ratio,
variance=variance,
flip=flip,
clip=clip,
steps=steps,
offset=offset,
name=name,
min_max_aspect_ratios_order=True)
box, variance_ = fluid.layers.prior_box(
input,
image,
min_sizes=min_size,
max_sizes=max_size,
aspect_ratios=aspect_ratio,
variance=variance,
flip=flip,
clip=clip,
steps=steps,
offset=offset,
name=name,
min_max_aspect_ratios_order=True)
box = fluid.layers.reshape(box, [1, 1, -1])
variance_ = fluid.layers.reshape(variance_, [1, 1, -1])
out = fluid.layers.concat([box, variance_], axis=1)
......@@ -53,7 +54,8 @@ def priorbox_weights(name, data=None):
return weights_name
register(kind='PriorBox',
shape=priorbox_shape,
layer=priorbox_layer,
weights=priorbox_weights)
register(
kind='PriorBox',
shape=priorbox_shape,
layer=priorbox_layer,
weights=priorbox_weights)
x2paddle/op_mapper/caffe_custom_layer/roipooling.py
浏览文件 @ 73830eb2
......@@ -21,11 +21,12 @@ def roipooling_layer(inputs,
input = inputs[0]
roi = inputs[1]
roi = fluid.layers.slice(roi, axes=[1], starts=[1], ends=[5])
out = fluid.layers.roi_pool(input,
roi,
pooled_height=pooled_h,
pooled_width=pooled_w,
spatial_scale=spatial_scale)
out = fluid.layers.roi_pool(
input,
roi,
pooled_height=pooled_h,
pooled_width=pooled_w,
spatial_scale=spatial_scale)
return out
......@@ -34,7 +35,8 @@ def roipooling_weights(name, data=None):
return weights_name
register(kind='ROIPooling',
shape=roipooling_shape,
layer=roipooling_layer,
weights=roipooling_weights)
register(
kind='ROIPooling',
shape=roipooling_shape,
layer=roipooling_layer,
weights=roipooling_weights)
x2paddle/op_mapper/caffe_custom_layer/select.py
浏览文件 @ 73830eb2
......@@ -30,11 +30,12 @@ def select_layer(inputs,
out = []
for i in range(len(slice_point)):
out.append(
fluid.layers.slice(input,
axes=[axis],
starts=[slice_point[i]],
ends=[slice_point[i + 1]],
name=name + '_' + str(i)))
fluid.layers.slice(
input,
axes=[axis],
starts=[slice_point[i]],
ends=[slice_point[i + 1]],
name=name + '_' + str(i)))
if i == len(slice_point) - 2:
break
return out
......@@ -45,7 +46,8 @@ def select_weights(name, data=None):
return weights_name
register(kind='Select',
shape=select_shape,
layer=select_layer,
weights=select_weights)
register(
kind='Select',
shape=select_shape,
layer=select_layer,
weights=select_weights)
x2paddle/op_mapper/caffe_custom_layer/shufflechannel.py
浏览文件 @ 73830eb2
......@@ -17,7 +17,8 @@ def shufflechannel_weights(name, data=None):
return weights_name
register(kind='ShuffleChannel',
shape=shufflechannel_shape,
layer=shufflechannel_layer,
weights=shufflechannel_weights)
register(
kind='ShuffleChannel',
shape=shufflechannel_shape,
layer=shufflechannel_layer,
weights=shufflechannel_weights)
x2paddle/op_mapper/caffe_op_mapper.py
浏览文件 @ 73830eb2
......@@ -195,10 +195,8 @@ class CaffeOpMapper(OpMapper):
'shape': shape,
'name': string(node.layer_name)
}
node.fluid_code.add_layer("data",
inputs=None,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"data", inputs=None, output=node, param_attr=attr)
def MemoryData(self, node):
# TODO(syf): Paddlepaddle can't fully support
......@@ -209,10 +207,8 @@ class CaffeOpMapper(OpMapper):
'shape': shape,
'name': string(node.layer_name)
}
node.fluid_code.add_layer("data",
inputs=None,
output=node.layer_name + '0',
param_attr=attr)
node.fluid_code.add_layer(
"data", inputs=None, output=node.layer_name + '0', param_attr=attr)
node.fluid_code.add_note('{} = [{}]'.format(node.layer_name,
node.layer_name + '0'))
......@@ -263,10 +259,8 @@ class CaffeOpMapper(OpMapper):
'bias_attr':
False if len(data) == 1 else string(node.layer_name + '_bias'),
}
node.fluid_code.add_layer("conv2d",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"conv2d", inputs=input, output=node, param_attr=attr)
def Deconvolution(self, node):
data = node.data
......@@ -316,10 +310,8 @@ class CaffeOpMapper(OpMapper):
'bias_attr':
False if len(data) == 1 else string(node.layer_name + '_bias')
}
node.fluid_code.add_layer("conv2d_transpose",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"conv2d_transpose", inputs=input, output=node, param_attr=attr)
def Pooling(self, node):
params = node.layer.pooling_param
......@@ -345,10 +337,8 @@ class CaffeOpMapper(OpMapper):
'global_pooling': global_pool,
'name': string(node.layer_name)
}
node.fluid_code.add_layer("pool2d",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"pool2d", inputs=input, output=node, param_attr=attr)
def LRN(self, node):
assert len(node.inputs) == 1, 'The count of LRN node\'s input is not 1.'
......@@ -368,10 +358,8 @@ class CaffeOpMapper(OpMapper):
'beta': params.beta,
'name': string(node.layer_name)
}
node.fluid_code.add_layer("lrn",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"lrn", inputs=input, output=node, param_attr=attr)
def InnerProduct(self, node):
data = node.data
......@@ -420,10 +408,8 @@ class CaffeOpMapper(OpMapper):
'bias_attr':
False if len(data) == 1 else string(node.layer_name + '_bias')
}
node.fluid_code.add_layer("fc",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"fc", inputs=input, output=node, param_attr=attr)
def Softmax(self, node):
assert len(
......@@ -435,10 +421,8 @@ class CaffeOpMapper(OpMapper):
dims = len(shape)
axis = axis + dims if axis < 0 else axis
attr = {'axis': axis, 'name': string(node.layer_name + '_softmax')}
node.fluid_code.add_layer("softmax",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"softmax", inputs=input, output=node, param_attr=attr)
def Slice(self, node):
assert len(
......@@ -459,10 +443,8 @@ class CaffeOpMapper(OpMapper):
'dim': axis,
'name': string(node.layer_name)
}
node.fluid_code.add_layer("split",
inputs=input,
output=node.layer_name,
param_attr=attr)
node.fluid_code.add_layer(
"split", inputs=input, output=node.layer_name, param_attr=attr)
def Concat(self, node):
assert len(
......@@ -475,10 +457,8 @@ class CaffeOpMapper(OpMapper):
params = node.layer.concat_param
axis = params.axis
attr = {'axis': axis, 'name': string(node.layer_name)}
node.fluid_code.add_layer("concat",
inputs=inputs,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"concat", inputs=inputs, output=node, param_attr=attr)
def PReLU(self, node):
assert len(
......@@ -499,10 +479,8 @@ class CaffeOpMapper(OpMapper):
'param_attr': string(node.layer_name + '_weights'),
'name': string(node.layer_name)
}
node.fluid_code.add_layer("prelu",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"prelu", inputs=input, output=node, param_attr=attr)
def Accuracy(self, node):
assert len(
......@@ -526,10 +504,8 @@ class CaffeOpMapper(OpMapper):
assert axis == 1, 'PaddlePaddle can not support the situation when the axis is not 1.'
assert not ignore_label >= 0, 'PaddlePaddle can not support the situation when the model has ignore label.'
attr = {'k': top_k}
node.fluid_code.add_layer("accuracy",
inputs=inputs,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"accuracy", inputs=inputs, output=node, param_attr=attr)
def Eltwise(self, node):
assert len(
......@@ -546,10 +522,11 @@ class CaffeOpMapper(OpMapper):
inputs_dict['x'] = inputs[0]
inputs_dict['y'] = inputs[1]
attr = {'act': None, 'name': string(node.layer_name)}
node.fluid_code.add_layer("elementwise_mul",
inputs=inputs_dict,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"elementwise_mul",
inputs=inputs_dict,
output=node,
param_attr=attr)
elif mode == 1:
if hasattr(params, 'coeff') and len(params.coeff) == 2:
coeff = params.coeff
......@@ -559,57 +536,62 @@ class CaffeOpMapper(OpMapper):
'value': coeff[0],
'dtype': '{}.dtype'.format(input1_name)
}
node.fluid_code.add_layer("fill_constant",
inputs=None,
output=node.layer_name + '_const1',
param_attr=attr)
node.fluid_code.add_layer(
"fill_constant",
inputs=None,
output=node.layer_name + '_const1',
param_attr=attr)
attr = {'act': None, 'name': string(node.layer_name + '_mul1')}
node.fluid_code.add_layer("elementwise_mul",
inputs=input1_name + ', ' +
node.layer_name + '_const1',
output=node.layer_name + '_mul1',
param_attr=attr)
node.fluid_code.add_layer(
"elementwise_mul",
inputs=input1_name + ', ' + node.layer_name + '_const1',
output=node.layer_name + '_mul1',
param_attr=attr)
input2_name = self.get_input_name(inputs[1])
attr = {
'shape': [1],
'value': coeff[1],
'dtype': '{}.dtype'.format(input2_name)
}
node.fluid_code.add_layer("fill_constant",
inputs=None,
output=node.layer_name + '_const2',
param_attr=attr)
node.fluid_code.add_layer(
"fill_constant",
inputs=None,
output=node.layer_name + '_const2',
param_attr=attr)
attr = {'act': None, 'name': string(node.layer_name + '_mul2')}
node.fluid_code.add_layer("elementwise_mul",
inputs=input2_name + ', ' +
node.layer_name + '_const2',
output=node.layer_name + '_mul2',
param_attr=attr)
node.fluid_code.add_layer(
"elementwise_mul",
inputs=input2_name + ', ' + node.layer_name + '_const2',
output=node.layer_name + '_mul2',
param_attr=attr)
attr = {'act': None, 'name': string(node.layer_name)}
node.fluid_code.add_layer("elementwise_add",
inputs='{}_mul1, {}_mul2'.format(
node.layer_name, node.layer_name),
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"elementwise_add",
inputs='{}_mul1, {}_mul2'.format(node.layer_name,
node.layer_name),
output=node,
param_attr=attr)
else:
inputs_dict = {}
inputs_dict['x'] = inputs[0]
inputs_dict['y'] = inputs[1]
attr = {'act': None, 'name': string(node.layer_name)}
node.fluid_code.add_layer("elementwise_add",
inputs=inputs_dict,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"elementwise_add",
inputs=inputs_dict,
output=node,
param_attr=attr)
else:
inputs_dict = {}
inputs_dict['x'] = inputs[0]
inputs_dict['y'] = inputs[1]
attr = {'act': None, 'name': string(node.layer_name)}
node.fluid_code.add_layer("elementwise_max",
inputs=inputs_dict,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"elementwise_max",
inputs=inputs_dict,
output=node,
param_attr=attr)
def BatchNorm(self, node):
assert len(
......@@ -651,10 +633,8 @@ class CaffeOpMapper(OpMapper):
'epsilon': eps,
'name': string(node.layer_name)
}
node.fluid_code.add_layer("batch_norm",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"batch_norm", inputs=input, output=node, param_attr=attr)
def Scale(self, node):
if node.data is None:
......@@ -687,10 +667,11 @@ class CaffeOpMapper(OpMapper):
inputs_dict['x'] = input0
inputs_dict['y'] = input1
attr = {'axis': axis, 'name': string(node.layer_name + '_mul')}
node.fluid_code.add_layer("elementwise_mul",
inputs=inputs_dict,
output=node.layer_name + '_mul',
param_attr=attr)
node.fluid_code.add_layer(
"elementwise_mul",
inputs=inputs_dict,
output=node.layer_name + '_mul',
param_attr=attr)
else:
bias_shape = node.input_shape[0][axis:axis + num_axes]
input0 = self.graph.get_bottom_node(node, idx=0, copy=True)
......@@ -703,18 +684,17 @@ class CaffeOpMapper(OpMapper):
'is_bias': True,
'default_initializer': 'Constant(value=1.0)'
}
node.fluid_code.add_layer("create_parameter",
inputs=None,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"create_parameter", inputs=None, output=node, param_attr=attr)
inputs_dict = {}
inputs_dict['x'] = input0
inputs_dict['y'] = node
attr = {'axis': axis, 'name': string(node.layer_name + '_mul')}
node.fluid_code.add_layer("elementwise_mul",
inputs=inputs_dict,
output=node.layer_name + '_mul',
param_attr=attr)
node.fluid_code.add_layer(
"elementwise_mul",
inputs=inputs_dict,
output=node.layer_name + '_mul',
param_attr=attr)
scale_shape = bias_shape
input0_name = self.get_input_name(input0)
attr = {
......@@ -725,16 +705,18 @@ class CaffeOpMapper(OpMapper):
'is_bias': True,
'default_initializer': 'Constant(value=1.0)'
}
node.fluid_code.add_layer("create_parameter",
inputs=None,
output=node.layer_name + '_offset_param',
param_attr=attr)
node.fluid_code.add_layer(
"create_parameter",
inputs=None,
output=node.layer_name + '_offset_param',
param_attr=attr)
attr = {'axis': axis, 'name': string(node.layer_name + '_add')}
node.fluid_code.add_layer("elementwise_add",
inputs='{}_mul, {}_offset_param'.format(
node.layer_name, node.layer_name),
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"elementwise_add",
inputs='{}_mul, {}_offset_param'.format(node.layer_name,
node.layer_name),
output=node,
param_attr=attr)
def Reshape(self, node):
input = self.graph.get_bottom_node(node, idx=0, copy=True)
......@@ -747,10 +729,8 @@ class CaffeOpMapper(OpMapper):
'act': None,
'name': string(node.layer_name)
}
node.fluid_code.add_layer("reshape",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"reshape", inputs=input, output=node, param_attr=attr)
def ArgMax(self, node):
assert len(node.inputs) == 1 and len(
......@@ -767,11 +747,12 @@ class CaffeOpMapper(OpMapper):
axis += len(input_shape)
if out_max_val is True:
attr = {'k': top_k, 'name': string(node.layer_name + '_topk')}
node.fluid_code.add_layer("topk",
inputs=input,
output='{}_topk_var, {}_index_var'.format(
node.layer_name, node.layer_name),
param_attr=attr)
node.fluid_code.add_layer(
"topk",
inputs=input,
output='{}_topk_var, {}_index_var'.format(
node.layer_name, node.layer_name),
param_attr=attr)
attr = {'dtype': '{}_topk_var.dtype'.format(node.layer_name)}
node.fluid_code.add_layer(
"cast",
......@@ -779,17 +760,19 @@ class CaffeOpMapper(OpMapper):
output='{}_index_var'.format(node.layer_name),
param_attr=attr)
attr = {'axis': axis, 'name': string(node.layer_name)}
node.fluid_code.add_layer("concat",
inputs='{}_topk_var, {}_index_var'.format(
node.layer_name, node.layer_name),
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"concat",
inputs='{}_topk_var, {}_index_var'.format(
node.layer_name, node.layer_name),
output=node,
param_attr=attr)
else:
attr = {'k': top_k, 'name': string(node.layer_name)}
node.fluid_code.add_layer("topk",
inputs=input,
output='_, {}'.format(node.layer_name),
param_attr=attr)
node.fluid_code.add_layer(
"topk",
inputs=input,
output='_, {}'.format(node.layer_name),
param_attr=attr)
def Crop(self, node):
assert len(
......@@ -808,13 +791,14 @@ class CaffeOpMapper(OpMapper):
offset), "invalid offset[%s] in crop layer" % (str(offset))
offset_real = [0] * axis + offset
attr = {'offsets': list(offset_real), 'name': string(node.layer_name)}
node.fluid_code.add_layer("crop",
inputs={
'x': input,
'shape': node.input_shape[1]
},
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"crop",
inputs={
'x': input,
'shape': node.input_shape[1]
},
output=node,
param_attr=attr)
def Flatten(self, node):
assert len(
......@@ -823,10 +807,8 @@ class CaffeOpMapper(OpMapper):
input = self.graph.get_bottom_node(node, idx=0, copy=True)
shape = node.output_shape[0]
attr = {'shape': shape, 'name': string(node.layer_name)}
node.fluid_code.add_layer("reshape",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"reshape", inputs=input, output=node, param_attr=attr)
def Power(self, node):
assert len(
......@@ -842,15 +824,11 @@ class CaffeOpMapper(OpMapper):
'bias_after_scale': True,
'name': string(node.layer_name + '_scale')
}
node.fluid_code.add_layer("scale",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"scale", inputs=input, output=node, param_attr=attr)
attr = {'factor': power, 'name': string(node.layer_name)}
node.fluid_code.add_layer("pow",
inputs=node,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"pow", inputs=node, output=node, param_attr=attr)
def Reduction(self, node):
assert len(
......@@ -872,55 +850,41 @@ class CaffeOpMapper(OpMapper):
'keep_dim': False,
'name': string(node.layer_name)
}
node.fluid_code.add_layer("reduce_sum",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"reduce_sum", inputs=input, output=node, param_attr=attr)
elif operation == 2: ## operation = ASUM
attr = {'name': string(node.layer_name + '_abs')}
node.fluid_code.add_layer("abs",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"abs", inputs=input, output=node, param_attr=attr)
attr = {
'dim': dim[axis:],
'keep_dim': False,
'name': string(node.layer_name)
}
node.fluid_code.add_layer("reduce_sum",
inputs=node,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"reduce_sum", inputs=node, output=node, param_attr=attr)
elif operation == 3: ## operation = SUMSQ
attr = {'factor': 2.0, 'name': string(node.layer_name + '_pow')}
node.fluid_code.add_layer("pow",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"pow", inputs=input, output=node, param_attr=attr)
attr = {
'dim': dim[axis:],
'keep_dim': False,
'name': string(node.layer_name)
}
node.fluid_code.add_layer("reduce_sum",
inputs=node,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"reduce_sum", inputs=node, output=node, param_attr=attr)
else: ## operation = MEAN
attr = {
'dim': dim[axis:],
'keep_dim': False,
'name': string(node.layer_name)
}
node.fluid_code.add_layer("reduce_mean",
inputs=node,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"reduce_mean", inputs=node, output=node, param_attr=attr)
attr = {'scale': coeff}
node.fluid_code.add_layer("scale",
inputs=node,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"scale", inputs=node, output=node, param_attr=attr)
def deal_custom_layer(self, node):
op = node.layer_type
......@@ -947,11 +911,12 @@ class CaffeOpMapper(OpMapper):
assert input is not None, 'This kind of DetectionOutput is not supported!'
input = self.graph.get_bottom_node(input, idx=0, copy=True)
inputs_node.append(input)
node.fluid_code.add_layer(func.__code__.co_name,
inputs=inputs_node,
output=node,
param_attr=kwargs,
is_custom_layer=True)
node.fluid_code.add_layer(
func.__code__.co_name,
inputs=inputs_node,
output=node,
param_attr=kwargs,
is_custom_layer=True)
if op not in self.used_custom_layers:
self.used_custom_layers[op] = custom_code
......@@ -960,7 +925,5 @@ class CaffeOpMapper(OpMapper):
op_info = self.directly_map_ops[node.layer_type]
input = self.graph.get_bottom_node(node, idx=0, copy=True)
attr = {'name': string(node.layer_name)}
node.fluid_code.add_layer(op_info,
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
op_info, inputs=input, output=node, param_attr=attr)
x2paddle/op_mapper/caffe_shape.py
浏览文件 @ 73830eb2
......@@ -67,10 +67,10 @@ def get_strided_kernel_output_shape(params, input_shape, round_func):
i_w = input_shape[3]
dila_h, dila_w, pad_h, pad_w, kernel_h, kernel_w, stride_h, stride_w = get_kernel_parameters(
params)
o_h = (i_h + 2 * pad_h - (dila_h *
(kernel_h - 1) + 1)) / float(stride_h) + 1
o_w = (i_w + 2 * pad_w - (dila_w *
(kernel_w - 1) + 1)) / float(stride_w) + 1
o_h = (i_h + 2 * pad_h -
(dila_h * (kernel_h - 1) + 1)) / float(stride_h) + 1
o_w = (i_w + 2 * pad_w -
(dila_w * (kernel_w - 1) + 1)) / float(stride_w) + 1
o_h = int(round_func(o_h))
o_w = int(round_func(o_w))
has_c_o = hasattr(params, 'num_output')
......
x2paddle/op_mapper/onnx_custom_layer/InstanceNormalization.py
浏览文件 @ 73830eb2
......@@ -24,21 +24,18 @@ def InstanceNormalization_layer(inputs, name=None):
epsilon = 1e-5
input_ = inputs[0]
mean = fluid.layers.reduce_mean(input_, dim=[2, 3], keep_dim=True)
var = fluid.layers.reduce_mean(fluid.layers.square(input_ - mean),
dim=[2, 3],
keep_dim=True)
var = fluid.layers.reduce_mean(
fluid.layers.square(input_ - mean), dim=[2, 3], keep_dim=True)
if name is not None:
scale_name = name + "_scale"
offset_name = name + "_offset"
scale_param = inputs[1]
offset_param = inputs[2]
scale = fluid.layers.create_parameter(name=scale_param.name,
shape=input_.shape[1:2],
dtype="float32")
offset = fluid.layers.create_parameter(name=offset_param.name,
shape=input_.shape[1:2],
dtype="float32")
scale = fluid.layers.create_parameter(
name=scale_param.name, shape=input_.shape[1:2], dtype="float32")
offset = fluid.layers.create_parameter(
name=offset_param.name, shape=input_.shape[1:2], dtype="float32")
tmp = fluid.layers.elementwise_mul(x=(input_ - mean), y=scale, axis=1)
tmp = tmp / fluid.layers.sqrt(var + epsilon)
......@@ -51,8 +48,9 @@ def InstanceNormalization_weights(name, data=None):
return weights_name
register(kind='InstanceNormalization',
shape=InstanceNormalization_shape,
layer=InstanceNormalization_layer,
child_func=None,
weights=InstanceNormalization_weights)
register(
kind='InstanceNormalization',
shape=InstanceNormalization_shape,
layer=InstanceNormalization_layer,
child_func=None,
weights=InstanceNormalization_weights)
x2paddle/op_mapper/tf_op_mapper.py
浏览文件 @ 73830eb2
......@@ -141,10 +141,8 @@ class TFOpMapper(OpMapper):
pd_param_name = list(param.values())[0]
tf_param = node.get_attr(tf_param_name)
attr[pd_param_name] = tf_param
node.fluid_code.add_layer(op_info[0],
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
op_info[0], inputs=input, output=node, param_attr=attr)
def elementwise_map(self, node):
assert node.layer_type in self.elementwise_ops
......@@ -179,21 +177,21 @@ class TFOpMapper(OpMapper):
0] == y_shape[-1] and y_shape.count(-1) < 1:
shape = [1, x_shape[0], 1, 1]
attr = {"shape": shape}
node.fluid_code.add_layer("reshape",
inputs=x_input,
output="reshape_x",
param_attr=attr)
node.fluid_code.add_layer(
"reshape",
inputs=x_input,
output="reshape_x",
param_attr=attr)
if y_shape[0] != 1:
attr = {"expand_times": [y_shape[0], 1, 1, 1]}
node.fluid_code.add_layer("expand",
inputs="reshape_x",
output="reshape_x",
param_attr=attr)
node.fluid_code.add_layer(
"expand",
inputs="reshape_x",
output="reshape_x",
param_attr=attr)
inputs = {"x": "reshape_x", "y": y_input}
node.fluid_code.add_layer(op_type,
inputs=inputs,
output=node,
param_attr=None)
node.fluid_code.add_layer(
op_type, inputs=inputs, output=node, param_attr=None)
return
else:
raise Exception("Unexpected situation happend")
......@@ -205,10 +203,8 @@ class TFOpMapper(OpMapper):
axis = -1
attr = {"axis": axis}
inputs = {"x": x_input, "y": y_input}
node.fluid_code.add_layer(op_type,
inputs=inputs,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
op_type, inputs=inputs, output=node, param_attr=attr)
return
is_sub_seq = True
......@@ -242,10 +238,8 @@ class TFOpMapper(OpMapper):
if len(x_expand_times) == 4 and x.tf_data_format == "NHWC":
x_expand_times = [x_expand_times[i] for i in [0, 3, 1, 2]]
attr = {"expand_times": x_expand_times}
node.fluid_code.add_layer("expand",
inputs=x_input,
output="x_tmp",
param_attr=attr)
node.fluid_code.add_layer(
"expand", inputs=x_input, output="x_tmp", param_attr=attr)
x_input = "x_tmp"
if y_need_expand:
if len(y_expand_times) == 3 and y.tf_data_format == "NHWC":
......@@ -253,16 +247,12 @@ class TFOpMapper(OpMapper):
if len(y_expand_times) == 4 and y.tf_data_format == "NHWC":
y_expand_times = [y_expand_times[i] for i in [0, 3, 1, 2]]
attr = {"expand_times": y_expand_times}
node.fluid_code.add_layer("expand",
inputs=y_input,
output="y_tmp",
param_attr=attr)
node.fluid_code.add_layer(
"expand", inputs=y_input, output="y_tmp", param_attr=attr)
y_input = "y_tmp"
inputs = {"x": x_input, "y": y_input}
node.fluid_code.add_layer(op_type,
inputs=inputs,
output=node,
param_attr=None)
node.fluid_code.add_layer(
op_type, inputs=inputs, output=node, param_attr=None)
def Placeholder(self, node):
shape = node.out_shapes[0]
......@@ -283,10 +273,8 @@ class TFOpMapper(OpMapper):
if shape[0] < 0:
self.batch_node = node
node.fluid_code.add_layer("data",
inputs=None,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"data", inputs=None, output=node, param_attr=attr)
def OneShotIterator(self, node):
return self.Placeholder(node)
......@@ -320,10 +308,8 @@ class TFOpMapper(OpMapper):
'name': string(node.layer_name),
'default_initializer': initializer
}
node.fluid_code.add_layer("create_parameter",
inputs=None,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"create_parameter", inputs=None, output=node, param_attr=attr)
def Transpose(self, node):
input = self.graph.get_node(node.layer.input[0], copy=True)
......@@ -362,16 +348,12 @@ class TFOpMapper(OpMapper):
node.tf_data_format = [tf_data_format[i] for i in perm]
node.pd_data_format = [pd_data_format[i] for i in perm]
attr = {'perm': new_perm}
node.fluid_code.add_layer("transpose",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"transpose", inputs=input, output=node, param_attr=attr)
elif len(node.out_shapes[0]) != 4:
attr = {'perm': perm}
node.fluid_code.add_layer("transpose",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"transpose", inputs=input, output=node, param_attr=attr)
else:
raise Exception("Unexpected situation happend in Transpose OP")
......@@ -401,10 +383,8 @@ class TFOpMapper(OpMapper):
"pool_padding": string(pad_mode),
"pool_stride": strides[2:4]
}
node.fluid_code.add_layer("pool2d",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"pool2d", inputs=input, output=node, param_attr=attr)
def Conv2D(self, node):
input = self.graph.get_node(node.layer.input[0], copy=True)
......@@ -444,10 +424,8 @@ class TFOpMapper(OpMapper):
"dilation": dilations[2:4],
"padding": string(pad_mode)
}
node.fluid_code.add_layer("conv2d",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"conv2d", inputs=input, output=node, param_attr=attr)
def BiasAdd(self, node):
input = self.graph.get_node(node.layer.input[0], copy=True)
......@@ -457,10 +435,8 @@ class TFOpMapper(OpMapper):
axis = 1
inputs = {"x": input, "y": bias}
attr = {"axis": axis}
node.fluid_code.add_layer("elementwise_add",
inputs=inputs,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"elementwise_add", inputs=inputs, output=node, param_attr=attr)
def FusedBatchNorm(self, node):
input = self.graph.get_node(node.layer.input[0], copy=True)
......@@ -491,10 +467,8 @@ class TFOpMapper(OpMapper):
"is_test": True
}
node.fluid_code.add_layer("batch_norm",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"batch_norm", inputs=input, output=node, param_attr=attr)
def FusedBatchNormV3(self, node):
return self.FusedBatchNorm(node)
......@@ -539,10 +513,8 @@ class TFOpMapper(OpMapper):
"use_cudnn": False,
"padding": string(pad_mode)
}
node.fluid_code.add_layer("conv2d",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"conv2d", inputs=input, output=node, param_attr=attr)
def Reshape(self, node):
input = self.graph.get_node(node.layer.input[0], copy=True)
......@@ -565,15 +537,14 @@ class TFOpMapper(OpMapper):
assert len(param.out_shapes[0]
) == 1, "Unexpected situation of shape parameter"
attr = {"shape": [-1]}
node.fluid_code.add_layer("reshape",
inputs=param,
output="shape_param",
param_attr=attr)
node.fluid_code.add_layer(
"reshape",
inputs=param,
output="shape_param",
param_attr=attr)
attr = {"num_or_sections": param.out_shapes[0][0], "dim": 0}
node.fluid_code.add_layer("split",
inputs="shape_param",
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"split", inputs="shape_param", output=node, param_attr=attr)
new_param = "["
for i in range(param.out_shapes[0][0]):
new_param += (node.layer_name + "[{}]".format(i) + ", ")
......@@ -601,14 +572,10 @@ class TFOpMapper(OpMapper):
if len(input.out_shapes[0]) == 4 and node.tf_data_format == "NHWC":
if len(attr["shape"]) < 3:
perm = {"perm": [0, 2, 3, 1]}
node.fluid_code.add_layer("transpose",
inputs=input,
output=node,
param_attr=perm)
node.fluid_code.add_layer("reshape",
inputs=node,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"transpose", inputs=input, output=node, param_attr=perm)
node.fluid_code.add_layer(
"reshape", inputs=node, output=node, param_attr=attr)
return
if len(attr["shape"]) == 4 and node.tf_data_format == "NHWC":
......@@ -617,27 +584,19 @@ class TFOpMapper(OpMapper):
attr["shape"] = [attr["shape"][i] for i in [0, 3, 1, 2]]
else:
perm = {"perm": [0, 2, 3, 1]}
node.fluid_code.add_layer("transpose",
inputs=input,
output=node,
param_attr=perm)
node.fluid_code.add_layer("reshape",
inputs=node,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"transpose", inputs=input, output=node, param_attr=perm)
node.fluid_code.add_layer(
"reshape", inputs=node, output=node, param_attr=attr)
perm = {"perm": [0, 3, 1, 2]}
node.fluid_code.add_layer("transpose",
inputs=node,
output=node,
param_attr=perm)
node.fluid_code.add_layer(
"transpose", inputs=node, output=node, param_attr=perm)
return
if len(attr["shape"]) == 5:
attr["shape"] = [attr["shape"][i] for i in [0, 1, 4, 2, 3]]
node.fluid_code.add_layer("reshape",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"reshape", inputs=input, output=node, param_attr=attr)
def AvgPool(self, node):
input = self.graph.get_node(node.layer.input[0], copy=True)
......@@ -665,10 +624,8 @@ class TFOpMapper(OpMapper):
"pool_stride": strides[2:4],
"pool_padding": string(pad_mode)
}
node.fluid_code.add_layer("pool2d",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"pool2d", inputs=input, output=node, param_attr=attr)
def SplitV(self, node):
input = self.graph.get_node(node.layer.input[0], copy=True)
......@@ -685,10 +642,8 @@ class TFOpMapper(OpMapper):
"num_or_sections": num_sections.value.tolist(),
"dim": dim.value
}
node.fluid_code.add_layer("split",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"split", inputs=input, output=node, param_attr=attr)
def ConcatV2(self, node):
inputs = [
......@@ -703,10 +658,8 @@ class TFOpMapper(OpMapper):
inputs[0].out_shapes[0]) == 4:
axis = nhwc_dim_to_nchw(inputs[0], axis)
attr = {"axis": axis}
node.fluid_code.add_layer("concat",
inputs=inputs,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"concat", inputs=inputs, output=node, param_attr=attr)
def Tile(self, node):
input = self.graph.get_node(node.layer.input[0], copy=True)
......@@ -726,10 +679,8 @@ class TFOpMapper(OpMapper):
expand_times[i] = 1
attr = {"expand_times": expand_times}
node.fluid_code.add_layer("expand",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"expand", inputs=input, output=node, param_attr=attr)
def Pack(self, node):
inputs = [
......@@ -747,10 +698,8 @@ class TFOpMapper(OpMapper):
node.pd_data_format = "".join(pd_data_format)
attr = {"axis": axis}
node.fluid_code.add_layer("stack",
inputs=inputs,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"stack", inputs=inputs, output=node, param_attr=attr)
def Pad(self, node):
input = self.graph.get_node(node.layer.input[0], copy=True)
......@@ -767,10 +716,8 @@ class TFOpMapper(OpMapper):
paddings = paddings[4:]
pad_op = "pad2d"
attr = {"paddings": paddings}
node.fluid_code.add_layer(pad_op,
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
pad_op, inputs=input, output=node, param_attr=attr)
def MirrorPad(self, node):
input = self.graph.get_node(node.layer.input[0], copy=True)
......@@ -789,10 +736,8 @@ class TFOpMapper(OpMapper):
paddings = paddings[4:]
pad_op = "pad2d"
attr = {"paddings": paddings, "mode": string("reflect")}
node.fluid_code.add_layer(pad_op,
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
pad_op, inputs=input, output=node, param_attr=attr)
def Range(self, node):
start = self.graph.get_node(node.layer.input[0], copy=True)
......@@ -816,10 +761,8 @@ class TFOpMapper(OpMapper):
inputs = {"start": start, "end": limit, "step": delta}
attr = {"dtype": string(node.dtype)}
node.fluid_code.add_layer("range",
inputs=inputs,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"range", inputs=inputs, output=node, param_attr=attr)
def Mean(self, node):
input = self.graph.get_node(node.layer.input[0], copy=True)
......@@ -833,10 +776,8 @@ class TFOpMapper(OpMapper):
dims[i] = nhwc_dim_to_nchw(input, dims[i])
attr = {"dim": dims, "keep_dim": keep_dims}
node.fluid_code.add_layer("reduce_mean",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"reduce_mean", inputs=input, output=node, param_attr=attr)
def MatMul(self, node):
x = self.graph.get_node(node.layer.input[0], copy=True)
......@@ -850,15 +791,11 @@ class TFOpMapper(OpMapper):
shape = x.out_shapes[0]
shape[-1] = y.out_shapes[0][0]
attr = {"shape": shape}
node.fluid_code.add_layer("reshape",
inputs=x,
output=x,
param_attr=attr)
node.fluid_code.add_layer(
"reshape", inputs=x, output=x, param_attr=attr)
attr = {"transpose_x": transpose_a, "transpose_y": transpose_b}
node.fluid_code.add_layer("matmul",
inputs=inputs,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"matmul", inputs=inputs, output=node, param_attr=attr)
def ArgMax(self, node):
input = self.graph.get_node(node.layer.input[0], copy=True)
......@@ -869,10 +806,8 @@ class TFOpMapper(OpMapper):
if input.tf_data_format == "NHWC" and len(input.out_shapes[0]) == 4:
axis = nhwc_dim_to_nchw(input, axis)
attr = {"axis": axis}
node.fluid_code.add_layer("argmax",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"argmax", inputs=input, output=node, param_attr=attr)
def StridedSlice(self, node):
input = self.graph.get_node(node.layer.input[0], copy=True)
......@@ -910,16 +845,12 @@ class TFOpMapper(OpMapper):
x = shrink_axis_mask >> i & 1
if x == 1:
squeeze_dims.append(i)
node.fluid_code.add_layer("slice",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"slice", inputs=input, output=node, param_attr=attr)
if shrink_axis_mask > 0 and len(input.out_shapes[0]) == 5:
attr = {"axes": squeeze_dims}
node.fluid_code.add_layer("squeeze",
inputs=node,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"squeeze", inputs=node, output=node, param_attr=attr)
def Slice(self, node):
input = self.graph.get_node(node.layer.input[0], copy=True)
......@@ -951,10 +882,8 @@ class TFOpMapper(OpMapper):
"starts": begin,
"ends": size
}
node.fluid_code.add_layer("slice",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"slice", inputs=input, output=node, param_attr=attr)
def Conv2DBackpropInput(self, node):
out_shape = self.graph.get_node(node.layer.input[0], copy=True)
......@@ -1004,10 +933,8 @@ class TFOpMapper(OpMapper):
"padding": string(pad_mode),
"output_size": out_shape[1:3]
}
node.fluid_code.add_layer("conv2d_transpose",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"conv2d_transpose", inputs=input, output=node, param_attr=attr)
def Max(self, node):
input = self.graph.get_node(node.layer.input[0], copy=True)
......@@ -1019,10 +946,8 @@ class TFOpMapper(OpMapper):
dim = nhwc_dim_to_nchw(input, dim)
attr = {"dim": dim, "keep_dim": keep_dims}
node.fluid_code.add_layer("reduce_max",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"reduce_max", inputs=input, output=node, param_attr=attr)
def Sum(self, node):
input = self.graph.get_node(node.layer.input[0], copy=True)
......@@ -1034,19 +959,15 @@ class TFOpMapper(OpMapper):
dim = nhwc_dim_to_nchw(input, dim)
attr = {"dim": dim, "keep_dim": keep_dims}
node.fluid_code.add_layer("reduce_sum",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"reduce_sum", inputs=input, output=node, param_attr=attr)
def Cast(self, node):
input = self.graph.get_node(node.layer.input[0], copy=True)
dtype = node.dtype_map[node.get_attr('DstT')]
attr = {"dtype": string(dtype)}
node.fluid_code.add_layer("cast",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"cast", inputs=input, output=node, param_attr=attr)
def Split(self, node):
dim = self.graph.get_node(node.layer.input[0], copy=True)
......@@ -1058,10 +979,8 @@ class TFOpMapper(OpMapper):
dim = nhwc_dim_to_nchw(input, dim)
attr = {"num_or_sections": num_split, "dim": dim}
node.fluid_code.add_layer("split",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"split", inputs=input, output=node, param_attr=attr)
def Squeeze(self, node):
input = self.graph.get_node(node.layer.input[0], copy=True)
......@@ -1070,10 +989,8 @@ class TFOpMapper(OpMapper):
for i in range(len(squeeze_dims)):
squeeze_dims[i] = nhwc_dim_to_nchw(input, squeeze_dims[i])
attr = {"axes": squeeze_dims}
node.fluid_code.add_layer("squeeze",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"squeeze", inputs=input, output=node, param_attr=attr)
def Softmax(self, node):
input = self.graph.get_node(node.layer.input[0], copy=True)
......@@ -1083,10 +1000,8 @@ class TFOpMapper(OpMapper):
if input.tf_data_format == "NHWC" and len(input.out_shapes[0]) == 4:
axis = nhwc_dim_to_nchw(input, axis)
attr = {"axis": axis}
node.fluid_code.add_layer("softmax",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"softmax", inputs=input, output=node, param_attr=attr)
def ResizeNearestNeighbor(self, node):
input = self.graph.get_node(node.layer.input[0], copy=True)
......@@ -1099,10 +1014,8 @@ class TFOpMapper(OpMapper):
resize_shape, node.out_shapes[0])
align_corners = node.get_attr("align_corners")
attr = {"align_corners": align_corners, "out_shape": resize_shape}
node.fluid_code.add_layer("resize_nearest",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"resize_nearest", inputs=input, output=node, param_attr=attr)
def ResizeBilinear(self, node):
input = self.graph.get_node(node.layer.input[0], copy=True)
......@@ -1119,19 +1032,15 @@ class TFOpMapper(OpMapper):
"out_shape": resize_shape,
"align_mode": 1
}
node.fluid_code.add_layer("resize_bilinear",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"resize_bilinear", inputs=input, output=node, param_attr=attr)
def GreaterEqual(self, node):
x = self.graph.get_node(node.layer.input[0], copy=True)
y = self.graph.get_node(node.layer.input[1], copy=True)
inputs = {"x": x, "y": y}
node.fluid_code.add_layer("greater_equal",
inputs=inputs,
output=node,
param_attr=None)
node.fluid_code.add_layer(
"greater_equal", inputs=inputs, output=node, param_attr=None)
def RandomUniform(self, node):
shape = self.graph.get_node(node.layer.input[0], copy=True)
......@@ -1145,26 +1054,21 @@ class TFOpMapper(OpMapper):
attr = {"shape": shape, "min": 0.0, "max": 0.9999}
if shape[0] < 0:
input = self.batch_node
node.fluid_code.add_layer("uniform_random_batch_size_like",
inputs=input,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"uniform_random_batch_size_like",
inputs=input,
output=node,
param_attr=attr)
else:
node.fluid_code.add_layer("uniform_random",
inputs=None,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"uniform_random", inputs=None, output=node, param_attr=attr)
def SquaredDifference(self, node):
x = self.graph.get_node(node.layer.input[0], copy=True)
y = self.graph.get_node(node.layer.input[1], copy=True)
inputs = {"x": x, "y": y}
node.fluid_code.add_layer("elementwise_sub",
inputs=inputs,
output=node,
param_attr=None)
node.fluid_code.add_layer(
"elementwise_sub", inputs=inputs, output=node, param_attr=None)
inputs = {"x": node, "y": node}
node.fluid_code.add_layer("elementwise_mul",
inputs=inputs,
output=node,
param_attr=None)
node.fluid_code.add_layer(
"elementwise_mul", inputs=inputs, output=node, param_attr=None)
x2paddle/optimizer/caffe_optimizer.py
浏览文件 @ 73830eb2
......@@ -41,10 +41,11 @@ class CaffeOptimizer(object):
if is_delete_node:
parent_node.fluid_code.clear()
node.fluid_code.clear()
node.fluid_code.add_layer("batch_norm",
inputs=input,
output=node,
param_attr=parent_param_attr)
node.fluid_code.add_layer(
"batch_norm",
inputs=input,
output=node,
param_attr=parent_param_attr)
def merge_op_activation(self):
for node_name in self.graph.topo_sort:
......@@ -62,7 +63,8 @@ class CaffeOptimizer(object):
if is_delete_node:
parent_node.fluid_code.clear()
node.fluid_code.clear()
node.fluid_code.add_layer(op,
inputs=input,
output=node,
param_attr=parent_param_attr)
node.fluid_code.add_layer(
op,
inputs=input,
output=node,
param_attr=parent_param_attr)
x2paddle/optimizer/tf_optimizer.py
浏览文件 @ 73830eb2
......@@ -554,10 +554,11 @@ class TFOptimizer(object):
node.fluid_code.layers[0].param_attr["shape"] = shape
node.fluid_code.layers[0].output = "nhwc_" + name
attr = {"perm": [0, 2, 3, 1]}
node.fluid_code.add_layer("transpose",
inputs="nhwc_" + name,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"transpose",
inputs="nhwc_" + name,
output=node,
param_attr=attr)
self.graph.input_nodes[i] = "nhwc_" + name
for i, name in enumerate(self.graph.output_nodes):
node = self.graph.get_node(name)
......@@ -972,10 +973,8 @@ class TFOptimizer(object):
"bias_after_scale": True,
"act": act
}
node.fluid_code.add_layer("scale",
inputs=in_node,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"scale", inputs=in_node, output=node, param_attr=attr)
del self.graph.node_map[in_nodes0[0].layer_name]
del self.graph.node_map[in_nodes0[1].layer_name]
......@@ -1055,29 +1054,32 @@ class TFOptimizer(object):
"shape": [channel],
"name": string(node.layer_name + "_scale")
}
node.fluid_code.add_layer("create_parameter",
inputs=None,
output=node.layer_name + "_scale",
param_attr=attr)
node.fluid_code.add_layer(
"create_parameter",
inputs=None,
output=node.layer_name + "_scale",
param_attr=attr)
attr = {
"dtype": string(scale.dtype),
"shape": [channel],
"name": string(node.layer_name + "_bias")
}
node.fluid_code.add_layer("create_parameter",
inputs=None,
output=node.layer_name + "_bias",
param_attr=attr)
node.fluid_code.add_layer(
"create_parameter",
inputs=None,
output=node.layer_name + "_bias",
param_attr=attr)
inputs = {
"x": in_node,
"scale": node.layer_name + "_scale",
"bias": node.layer_name + "_bias"
}
attr = {"act": act}
node.fluid_code.add_layer("affine_channel",
inputs=inputs,
output=node,
param_attr=attr)
node.fluid_code.add_layer(
"affine_channel",
inputs=inputs,
output=node,
param_attr=attr)
del self.graph.node_map[in_nodes0[0].layer_name]
del self.graph.node_map[in_nodes0[1].layer_name]
......
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