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未验证 提交 bdc67376 编写于 作者: J Jason 提交者： GitHub
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Merge pull request #163 from PaddlePaddle/develop 

pull
上级 73986da4 c206ce0f
隐藏空白更改
内联 并排
Showing with 323 addition and 145 deletion
x2paddle/convert.py
浏览文件 @ bdc67376
......@@ -129,9 +129,12 @@ def caffe2paddle(proto, weight, save_dir, caffe_proto):
from x2paddle.op_mapper.caffe_op_mapper import CaffeOpMapper
from x2paddle.optimizer.caffe_optimizer import CaffeOptimizer
import google.protobuf as gpb
ver_str = gpb.__version__.replace('.', '')
ver_int = int(ver_str[0:2])
assert ver_int >= 36, 'The version of protobuf must be larger than 3.6.0!'
ver_part = gpb.__version__.split('.')
version_satisfy = False
if (int(ver_part[0]) == 3 and int(ver_part[1]) >= 6) \
or (int(ver_part[0]) > 3):
version_satisfy = True
assert version_satisfy, 'google.protobuf >= 3.6.0 is required'
print("Now translating model from caffe to paddle.")
model = CaffeDecoder(proto, weight, caffe_proto)
mapper = CaffeOpMapper(model)
......
x2paddle/op_mapper/caffe_custom_layer/shufflechannel.py
浏览文件 @ bdc67376
......@@ -8,13 +8,7 @@ def shufflechannel_shape(input_shape):
def shufflechannel_layer(inputs, group=None, input_shape=None, name=None):
input = inputs[0]
c_fm = fluid.layers.split(input, num_or_sections=input_shape[0][1], dim=1)
size = int(input_shape[0][1] / group)
new_c_fm = []
for i in range(size):
for j in range(group):
new_c_fm.append(c_fm[j * size + i])
out = fluid.layers.concat(new_c_fm, axis=1)
out = fluid.layers.shuffle_channel(x=input, group=group)
return out
......
x2paddle/op_mapper/caffe_op_mapper.py
浏览文件 @ bdc67376
......@@ -363,7 +363,7 @@ class CaffeOpMapper(OpMapper):
input = self.graph.get_bottom_node(node, idx=0, copy=True)
attr = {
'n': params.local_size,
'k': 1.0,
'k': params.k,
'alpha': alpha,
'beta': params.beta,
'name': string(node.layer_name)
......@@ -450,35 +450,19 @@ class CaffeOpMapper(OpMapper):
slice_dim = params.slice_dim
if slice_dim != 1 and axis == 1:
axis = slice_dim
points = list(params.slice_point)
if len(points) == 0:
dims = node.input_shape[0][axis]
assert dims % top_len == 0, "the parameter of Slice is wrong"
part = dims / top_len
t = part
while t < dims:
points.append(int(t))
t += part
maxint32 = 2147483647
points = [0] + points
points.append(maxint32)
i = 0
node.fluid_code.add_note('{} = []'.format(node.layer_name))
for i in range(len(points)):
attr = {
'axes': [axis],
'starts': [points[i]],
'ends': [points[i + 1]]
}
node.fluid_code.add_layer("slice",
inputs=input,
output=node.layer_name + '_' + str(i),
param_attr=attr)
node.fluid_code.add_note('{}.append({})'.format(
node.layer_name, node.layer_name + '_' + str(i)))
if i == len(points) - 2:
break
output_shape = node.output_shape
sections_list = []
for s in output_shape:
sections_list.append(s[axis])
attr = {
'num_or_sections': sections_list,
'dim': axis,
'name': string(node.layer_name)
}
node.fluid_code.add_layer("split",
inputs=input,
output=node.layer_name,
param_attr=attr)
def Concat(self, node):
assert len(
......@@ -649,7 +633,8 @@ class CaffeOpMapper(OpMapper):
]).astype('float32')
scale = 0
else:
node.data = [np.squeeze(i) for i in node.data]
node.data = [np.squeeze(i).astype('float32') for i in node.data]
mean, variance, scale = node.data
# Prescale the stats
scaling_factor = 1.0 / scale if scale != 0 else 0
......@@ -684,8 +669,10 @@ class CaffeOpMapper(OpMapper):
input_c,
]).astype('float32')
else:
self.weights[node.layer_name + '_scale'] = np.squeeze(node.data[0])
self.weights[node.layer_name + '_offset'] = np.squeeze(node.data[1])
self.weights[node.layer_name + '_scale'] = np.squeeze(
node.data[0]).astype('float32')
self.weights[node.layer_name + '_offset'] = np.squeeze(
node.data[1]).astype('float32')
params = node.layer.scale_param
axis = params.axis
num_axes = params.num_axes
......
x2paddle/op_mapper/tf_op_mapper.py
浏览文件 @ bdc67376
......@@ -168,7 +168,11 @@ class TFOpMapper(OpMapper):
x_input = y
y_input = x
x_shape = y.out_shapes[0]
if len(x_shape) == 0:
x_shape = [1]
y_shape = x.out_shapes[0]
if len(y_shape) == 0:
y_shape = [1]
else:
if len(x_shape) == 1 and len(y_shape) == 4 and x_shape[
0] == y_shape[-1] and y_shape.count(-1) < 1:
......
x2paddle/op_mapper/tf_op_mapper_nhwc.py
浏览文件 @ bdc67376
......@@ -121,10 +121,29 @@ class TFOpMapperNHWC(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)
if len(input.out_shapes[0]) == 4 and op_info[0] != 'shape':
attr1 = {"perm": [0, 3, 1, 2]}
node.fluid_code.add_layer('transpose',
inputs=input,
output=node,
param_attr=attr1)
input = node
node.fluid_code.add_layer(op_info[0],
inputs=input,
output=node,
param_attr=attr)
input = node
attr2 = {"perm": [0, 2, 3, 1]}
node.fluid_code.add_layer('transpose',
inputs=input,
output=node,
param_attr=attr2)
else:
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
......@@ -149,7 +168,11 @@ class TFOpMapperNHWC(OpMapper):
x_input = y
y_input = x
x_shape = y.out_shapes[0]
if len(x_shape) == 0:
x_shape = [1]
y_shape = x.out_shapes[0]
if len(y_shape) == 0:
y_shape = [1]
else:
raise Exception("Unexpected situation happend")
......@@ -193,11 +216,30 @@ class TFOpMapperNHWC(OpMapper):
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)
if len(x_shape) == 4 and len(y_shape) == 4:
node.fluid_code.add_layer("transpose",
inputs=x_input,
output=x_input,
param_attr={'perm': [0, 3, 1, 2]})
node.fluid_code.add_layer("transpose",
inputs=y_input,
output=y_input,
param_attr={'perm': [0, 3, 1, 2]})
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("transpose",
inputs=node,
output=node,
param_attr={'perm': [0, 2, 3, 1]})
else:
inputs = {"x": x_input, "y": y_input}
node.fluid_code.add_layer(op_type,
inputs=inputs,
output=node,
param_attr=None)
def Placeholder(self, node):
shape = node.out_shapes[0]
......@@ -978,9 +1020,7 @@ class TFOpMapperNHWC(OpMapper):
if pad_mode == "SAME":
if node.tf_data_format == "NHWC":
print(out_shape)
out_shape = [out_shape[i] for i in [0, 3, 1, 2]]
print(out_shape)
for i in range(4):
if out_shape[i] < 0:
out_shape[i] = 999999
......
x2paddle/optimizer/tf_optimizer.py
浏览文件 @ bdc67376
......@@ -232,84 +232,35 @@ class TFOptimizer(object):
'act']
node.fluid_code.clear()
self.graph.remove_node(node.layer_name)
self.graph.identity_map[node.layer_name] = input.layer_name
def remove_transpose(self):
graph_copy = cp.deepcopy(self.graph)
nhwc_insensitive_ops = [
'Relu', 'Relu6', 'Abs', 'Sigmoid', 'Exp', 'Rsqrt', 'swish_f32',
'LeakyRelu', 'Cast'
'LeakyRelu', 'Cast', 'Tanh'
]
elementwise_ops = [
'Sub', 'Add', 'RealDiv', 'Maximum', 'Mul', 'FloorDiv',
'GreaterEqual'
]
for node_name in self.graph.topo_sort:
node = graph_copy.get_node(node_name)
if node is None:
continue
if node.layer_type in nhwc_insensitive_ops:
graph_copy.remove_node(node_name)
optimize_ops = [
'Conv2D', 'MaxPool', 'FusedBatchNorm', 'DepthwiseConv2dNative',
'AvgPool', 'Pad', 'Conv2DBackpropInput', 'ResizeNearestNeighbor',
'ResizeBilinear', "Placeholder"
]
can_be_optimized_ops = [
'Conv2D', 'MaxPool', 'FusedBatchNorm', 'DepthwiseConv2dNative',
'AvgPool', 'Pad', 'Conv2DBackpropInput', 'ResizeNearestNeighbor',
'ResizeBilinear', "Placeholder", 'Relu', 'Relu6', 'Abs', 'Sigmoid',
'Exp', 'Rsqrt', 'swish_f32', 'LeakyRelu', 'Cast', 'Tanh'
]
for node_name in self.graph.topo_sort:
node = graph_copy.get_node(node_name)
if node is None:
continue
if node.layer_type in elementwise_ops:
is_nhwc = True
for in_name in node.inputs:
in_node = graph_copy.get_node(in_name)
if hasattr(in_node, "is_nhwc"):
if not in_node.is_nhwc:
is_nhwc = False
else:
if len(in_node.fluid_code.layers) < 2:
is_nhwc = False
continue
if in_node.fluid_code.layers[
-1].op != "transpose" or in_node.fluid_code.layers[
-1].param_attr["perm"] != [0, 2, 3, 1]:
is_nhwc = False
continue
node.is_nhwc = is_nhwc
for i in range(len(self.graph.topo_sort)):
node_name = self.graph.topo_sort[-1 * i - 1]
node = graph_copy.get_node(node_name)
if node is None:
continue
if node.layer_type in elementwise_ops:
can_be_removed = True
if len(node.fluid_code.layers) > 1:
can_be_removed = False
if not node.is_nhwc:
can_be_removed = False
for out_name in node.outputs:
out_node = graph_copy.get_node(out_name)
if hasattr(out_node, "is_nhwc"):
if not out_node.is_nhwc:
can_be_removed = False
else:
if len(out_node.fluid_code.layers) < 2:
can_be_removed = False
break
if out_node.fluid_code.layers[
0].op != "transpose" or out_node.fluid_code.layers[
0].param_attr["perm"] != [0, 3, 1, 2]:
can_be_removed = False
break
node.can_be_removed = can_be_removed
for node_name in self.graph.topo_sort:
node = graph_copy.get_node(node_name)
if node is None:
continue
if node.layer_type in optimize_ops:
if node.layer_type in can_be_optimized_ops:
if node.fluid_code.layers[
-1].op != "transpose" or node.fluid_code.layers[
-1].param_attr["perm"] != [0, 2, 3, 1]:
......@@ -327,6 +278,9 @@ class TFOptimizer(object):
0].param_attr["perm"] != [0, 3, 1, 2]:
can_be_removed = False
break
elif out_node.layer_type in elementwise_ops:
can_be_removed = False
break
if can_be_removed and len(node.fluid_code.layers) > 1:
true_node = self.graph.get_node(node_name)
if true_node.layer_type == "Placeholder":
......@@ -346,8 +300,6 @@ class TFOptimizer(object):
del true_node.fluid_code.layers[-1]
for out_name in output_names:
out_node = self.graph.get_node(out_name)
if out_node.layer_type in elementwise_ops:
continue
out_node.fluid_code.layers[
1].inputs = out_node.fluid_code.layers[0].inputs
del out_node.fluid_code.layers[0]
......@@ -357,43 +309,241 @@ class TFOptimizer(object):
if node is None:
continue
if node.layer_type in elementwise_ops:
if not node.can_be_removed:
can_be_removed = True
if node.fluid_code.layers[
-1].op != "transpose" or node.fluid_code.layers[
-1].param_attr["perm"] != [0, 2, 3, 1]:
continue
can_be_removed = True
output_names = node.outputs
for out_name in output_names:
out_node = graph_copy.get_node(out_name)
if len(out_node.fluid_code.layers) < 3:
can_be_removed = False
break
if hasattr(out_node, "can_be_removed"):
if not out_node.can_be_removed:
can_be_removed = False
break
if out_node.layer_type in can_be_optimized_ops:
if out_node.fluid_code.layers[
0].op != "transpose" or out_node.fluid_code.layers[
0].param_attr["perm"] != [0, 3, 1, 2]:
can_be_removed = False
break
elif out_node.layer_type in elementwise_ops:
if out_node.fluid_code.layers[
0].op != "transpose" and out_node.fluid_code.layers[
1].op != "transpose":
can_be_removed = False
break
if out_node.fluid_code.layers[0].op == "transpose":
if out_node.fluid_code.layers[0].param_attr[
"perm"] != [0, 3, 1, 2]:
can_be_removed = False
break
if out_node.fluid_code.layers[1].op == "transpose":
if out_node.fluid_code.layers[1].param_attr[
"perm"] != [0, 3, 1, 2]:
can_be_removed = False
break
if can_be_removed and len(node.fluid_code.layers) > 1:
true_node = self.graph.get_node(node_name)
for i, in_name in enumerate(node.inputs):
in_node = graph_copy.get_node(in_name)
if hasattr(in_node, "is_nhwc") and in_node.is_nhwc:
if i == 0:
l = Layer()
l.op = "transpose"
l.inputs = true_node.fluid_code.layers[
0].inputs["x"]
l.param_attr = {"perm": [0, 2, 3, 1]}
l.output = "nhwc_" + l.inputs.layer_name
true_node.fluid_code.layers[0].inputs[
"x"] = l.output
true_node.fluid_code.layers.insert(0, l)
elif i == 1:
l = Layer()
l.op = "transpose"
l.inputs = true_node.fluid_code.layers[
0].inputs["y"]
l.param_attr = {"perm": [0, 2, 3, 1]}
l.output = "nhwc_" + l.inputs.layer_name
true_node.fluid_code.layers[0].inputs[
"y"] = l.output
true_node.fluid_code.layers.insert(0, l)
else:
raise Exception("Unexpected situation happend")
true_node.fluid_code.layers[
-2].output = true_node.fluid_code.layers[-1].output
del true_node.fluid_code.layers[-1]
for out_name in output_names:
out_node = self.graph.get_node(out_name)
if out_node.layer_type in can_be_optimized_ops:
out_node.fluid_code.layers[
1].inputs = out_node.fluid_code.layers[0].inputs
del out_node.fluid_code.layers[0]
elif out_node.layer_type in elementwise_ops:
if out_node.inputs[0] in node.layer_name:
if out_node.fluid_code.layers[
1].op == 'transpose':
out_node.fluid_code.layers[2].inputs[
'x'] = out_node.fluid_code.layers[
0].inputs
del out_node.fluid_code.layers[0]
else:
out_node.fluid_code.layers[1].inputs[
'x'] = out_node.fluid_code.layers[
0].inputs
del out_node.fluid_code.layers[0]
elif out_node.inputs[1] in node.layer_name:
if out_node.fluid_code.layers[
1].op == 'transpose':
out_node.fluid_code.layers[2].inputs[
'y'] = out_node.fluid_code.layers[
1].inputs
del out_node.fluid_code.layers[1]
else:
out_node.fluid_code.layers[1].inputs[
'y'] = out_node.fluid_code.layers[
0].inputs
del out_node.fluid_code.layers[0]
graph_copy = cp.deepcopy(self.graph)
for node_name in self.graph.topo_sort:
node = graph_copy.get_node(node_name)
if node is None or len(node.fluid_code.layers) < 2:
continue
if node.layer_type in can_be_optimized_ops and node.layer_type != "Placeholder":
if node.fluid_code.layers[
-1].op != "transpose" or node.fluid_code.layers[
-1].param_attr["perm"] != [0, 2, 3, 1]:
continue
else:
for out_name in node.outputs:
can_be_removed = True
output_names = node.outputs
for out_name in output_names:
out_node = graph_copy.get_node(out_name)
if hasattr(out_node, "can_be_removed"):
if not out_node.can_be_removed:
can_be_removed = False
break
if len(out_node.fluid_code.layers) < 2:
can_be_removed = False
break
if out_node.layer_type in can_be_optimized_ops:
if out_node.fluid_code.layers[
0].op != "transpose" or out_node.fluid_code.layers[
0].param_attr["perm"] != [0, 3, 1, 2]:
can_be_removed = False
break
elif out_node.layer_type in elementwise_ops:
if out_node.fluid_code.layers[
0].op != "transpose" and out_node.fluid_code.layers[
1].op != "transpose":
can_be_removed = False
break
if out_node.fluid_code.layers[
0].op == "expand" or out_node.fluid_code.layers[
1].op == "expand":
can_be_removed = False
break
if out_node.fluid_code.layers[0].op == "transpose":
if out_node.fluid_code.layers[0].param_attr[
"perm"] != [0, 3, 1, 2]:
can_be_removed = False
break
if out_node.fluid_code.layers[1].op == "transpose":
if out_node.fluid_code.layers[1].param_attr[
"perm"] != [0, 3, 1, 2]:
can_be_removed = False
break
elif out_node.layer_type not in elementwise_ops and out_node.layer_type not in can_be_optimized_ops:
can_be_removed = False
break
if can_be_removed:
true_node = self.graph.get_node(node_name)
if len(true_node.fluid_code.layers) < 2:
continue
true_node.fluid_code.layers[
-2].output = true_node.fluid_code.layers[-1].output
del true_node.fluid_code.layers[-1]
for out_name in output_names:
out_node = self.graph.get_node(out_name)
if out_node.layer_type not in elementwise_ops:
assert out_node.fluid_code.layers[
0].op == "transpose", "unexpected situation happend"
if out_node.layer_type in can_be_optimized_ops:
out_node.fluid_code.layers[
1].inputs = out_node.fluid_code.layers[0].inputs
del out_node.fluid_code.layers[0]
elif out_node.layer_type in elementwise_ops:
if out_node.inputs[0] in node.layer_name:
if out_node.fluid_code.layers[
1].op == 'transpose':
if out_node.fluid_code.layers[
2].op == 'transpose':
out_node.fluid_code.layers[3].inputs[
'x'] = out_node.fluid_code.layers[
0].inputs
else:
out_node.fluid_code.layers[2].inputs[
'x'] = out_node.fluid_code.layers[
0].inputs
del out_node.fluid_code.layers[0]
else:
out_node.fluid_code.layers[1].inputs[
'x'] = out_node.fluid_code.layers[
0].inputs
del out_node.fluid_code.layers[0]
elif out_node.inputs[1] in node.layer_name:
if out_node.fluid_code.layers[
1].op == 'transpose':
out_node.fluid_code.layers[2].inputs[
'y'] = out_node.fluid_code.layers[
1].inputs
del out_node.fluid_code.layers[1]
else:
out_node.fluid_code.layers[1].inputs[
'y'] = out_node.fluid_code.layers[
0].inputs
del out_node.fluid_code.layers[0]
graph_copy = cp.deepcopy(self.graph)
for node_name in self.graph.topo_sort:
node = graph_copy.get_node(node_name)
if node is None:
continue
if node.layer_type in elementwise_ops:
can_be_removed = True
if len(node.fluid_code.layers) < 3:
continue
numTranspose = 0
numNotTranspose = 0
for i in range(len(node.fluid_code.layers)):
if node.fluid_code.layers[i].op == 'transpose':
numTranspose += 1
elif node.fluid_code.layers[i].op != 'expand':
numNotTranspose += 1
if numTranspose > numNotTranspose:
if node.fluid_code.layers[0].op == 'expand':
if node.fluid_code.layers[
1].op != 'transpose' or node.fluid_code.layers[
2].op != 'transpose':
continue
else:
true_node = self.graph.get_node(node_name)
true_node.fluid_code.layers[3].inputs[
'x'] = true_node.fluid_code.layers[1].inputs
true_node.fluid_code.layers[3].inputs[
'y'] = true_node.fluid_code.layers[2].inputs
l = Layer()
l.op = 'transpose'
l.inputs = true_node.fluid_code.layers[3].output
l.param_attr = {'perm': [0, 3, 1, 2]}
if type(l.inputs) == str:
l.output = l.inputs
else:
l.output = l.inputs.layer_name
true_node.fluid_code.layers.append(l)
del true_node.fluid_code.layers[1]
del true_node.fluid_code.layers[1]
else:
if node.fluid_code.layers[
0].op != 'transpose' or node.fluid_code.layers[
1].op != 'transpose':
continue
else:
true_node = self.graph.get_node(node_name)
true_node.fluid_code.layers[2].inputs[
'x'] = true_node.fluid_code.layers[0].inputs
true_node.fluid_code.layers[2].inputs[
'y'] = true_node.fluid_code.layers[1].inputs
l = Layer()
l.op = 'transpose'
l.inputs = true_node.fluid_code.layers[2].output
l.param_attr = {'perm': [0, 3, 1, 2]}
l.output = l.inputs.layer_name
true_node.fluid_code.layers.append(l)
del true_node.fluid_code.layers[0]
del true_node.fluid_code.layers[0]
def make_nchw_input_output(self):
for i, name in enumerate(self.graph.input_nodes):
......
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