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tf_decoder.py
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tf_decoder.py
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from x2paddle.core.graph import GraphNode, Graph
from x2paddle.core.fluid_code import FluidCode
from tensorflow.python.framework import tensor_util
from tensorflow.core.framework import attr_value_pb2
import tensorflow as tf
import copy as cp
import numpy
import sys
class TFGraphNode(GraphNode):
def __init__(self, layer, layer_name=None, data_format="NHWC"):
if layer_name is None:
super(TFGraphNode, self).__init__(
layer,
layer.name.replace('/', '_').replace('-', '_').replace('^', ''))
else:
super(TFGraphNode, self).__init__(
layer,
layer_name.replace('/', '_').replace('-', '_').replace('^', ''))
self.layer_type = layer.op
self.tf_data_format = data_format
self.pd_data_format = "NCHW"
self.fluid_code = FluidCode()
self.dtype_map = {
1: "float32",
3: "int32",
4: "uint8",
9: "int64",
10: "bool"
}
@property
def out_shapes(self):
values = self.layer.attr["_output_shapes"].list.shape
out_shapes = list()
for value in values:
shape = [dim.size for dim in value.dim]
out_shapes.append(shape)
return out_shapes
@property
def dtype(self):
keys = ['dtype', 'Tidx', 'T', 'DstT']
for k in keys:
dtype = self.layer.attr[k].type
if dtype > 0:
break
if dtype not in self.dtype_map:
raise Exception("Dtype[{}] not in dtype_map".format(dtype))
return self.dtype_map[dtype]
@property
def raw_dtype(self):
keys = ['dtype', 'Tidx', 'T', 'DstT']
for k in keys:
dtype = self.layer.attr[k].type
if dtype > 0:
break
return dtype
@property
def value(self):
assert self.layer_type == "Const", "Only Const node has value."
attr = self.layer.attr['value']
field = getattr(attr, attr.WhichOneof('value'))
return tensor_util.MakeNdarray(field)
def get_attr(self, name):
if name not in self.layer.attr:
return None
attr = self.layer.attr[name]
field = attr.WhichOneof('value')
value = getattr(attr, field) if field else None
if isinstance(value, attr_value_pb2.AttrValue.ListValue):
result = list(value.ListFields()[0][1])
for i in range(len(result)):
if isinstance(result[i], int):
result[i] = int(result[i])
try:
if isinstance(result[i], long):
result[i] = int(result[i])
except:
pass
return result
else:
return value
class TFGraph(Graph):
def __init__(self, model, data_format="NHWC"):
super(TFGraph, self).__init__(model)
self.identity_map = dict()
self.multi_out_ops = ['Split', 'SplitV']
self.tf_data_format = data_format
def build(self):
for layer in self.model.node:
self.node_map[layer.name.replace('/', '_').replace(
'-', '_')] = 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('^', '')
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)
else:
raise Exception(
'input[{}] of node[{}] does not exist in node_map'.
format(in_node, layer_name))
else:
self.connect(in_node, layer_name)
super(TFGraph, self).build()
# tensorflow graph optimize
self._remove_isolated_node()
self._remove_identity_node()
self._remove_cast_node()
def get_node(self, node_name, copy=False):
items = node_name.strip().split(':')
items[0] = items[0].replace('/', '_').replace('-', '_')
if items[0] in self.identity_map:
items[0] = self.identity_map[items[0]]
new_node_name = ":".join(items)
node = super(TFGraph, self).get_node(new_node_name, copy)
if node is None:
return None
if node.layer_type == "Switch":
if hasattr(node, 'index'):
del node.index
if len(items) == 1 and node.layer_type in self.multi_out_ops:
node.index = 0
return node
def remove_node(self, node_name):
if node_name not in self.node_map:
raise Exception("Node[{}] not in graph".format(node_name))
inputs = self.node_map[node_name].inputs
outputs = self.node_map[node_name].outputs
# assert len(inputs) == 1
input_node = self.node_map[inputs[0]]
idx = input_node.outputs.index(node_name)
del input_node.outputs[idx]
for output in outputs:
node = self.node_map[output]
idx = node.inputs.index(node_name)
node.inputs[idx] = inputs[0]
input_node.outputs.append(output)
del self.node_map[node_name]
idx = self.topo_sort.index(node_name)
del self.topo_sort[idx]
def _remove_isolated_node(self):
# delete isolated nodes
isolated_nodes = list()
for node_name in self.node_map.keys():
if len(self.get_node(node_name).inputs) == 0 and len(
self.get_node(node_name).outputs) == 0:
isolated_nodes.append(node_name)
for node_name in isolated_nodes:
del self.node_map[node_name]
if node_name in self.input_nodes:
idx = self.input_nodes.index(node_name)
del self.input_nodes[idx]
if node_name in self.output_nodes:
idx = self.output_nodes.index(node_name)
del self.output_nodes[idx]
idx = self.topo_sort.index(node_name)
del self.topo_sort[idx]
def _remove_identity_node(self):
identity_ops = [
'Identity', 'StopGradient', 'Switch', 'Merge',
'PlaceholderWithDefault'
]
identity_node = list()
for node_name, node in self.node_map.items():
if node.layer_type in identity_ops:
identity_node.append(node_name)
for node_name in identity_node:
node = self.get_node(node_name)
input_node = self.get_node(node.inputs[0])
self.remove_node(node_name)
self.identity_map[node_name] = input_node.layer_name
if node_name in self.output_nodes:
idx = self.output_nodes.index(node_name)
self.output_nodes[idx] = input_node.layer_name
def _remove_cast_node(self):
cast_node = list()
for node_name, node in self.node_map.items():
if node.layer_type == "Cast":
input = self.get_node(node.inputs[0])
if input.layer_type != "Placeholder" or len(input.outputs) != 1:
continue
cast_node.append(node_name)
for node_name in cast_node:
node = self.get_node(node_name)
input_node = self.get_node(node.inputs[0])
input_node.layer.attr["dtype"].type = node.raw_dtype
self.remove_node(node_name)
self.identity_map[node_name] = input_node.layer_name
if node_name in self.output_nodes:
idx = self.output_nodes.index(node_name)
self.output_nodes[idx] = input_node.layer_name
def data_format_propagation(self, node):
current_node = self.node_map[node.layer_name]
current_node = node.tf_data_format
outputs = current_node.outputs
if len(outputs) == 0:
return
for out in outputs:
next_node = self.node_map[out]
next_node.tf_data_format = node.tf_data_format
self.data_format_propagation(next_node)
class TFDecoder(object):
def __init__(self, pb_model, data_format="NHWC", define_input_shape=False):
try:
self.sess = tf.compat.v1.Session()
except:
self.sess = tf.Session()
self.input_info = dict()
self.define_input_shape = define_input_shape
with open(pb_model, 'rb') as f:
try:
graph_def = tf.compat.v1.GraphDef()
except:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
input_map = self._check_input_shape(graph_def)
self._fix_output_shape(graph_def)
self.sess.graph.as_default()
tf.import_graph_def(graph_def, name='', input_map=input_map)
try:
initializer = tf.compat.v1.global_variables_initializer()
except:
initializer = tf.global_variables_initializer()
self.sess.run(initializer)
self.tf_graph = TFGraph(
self.sess.graph._as_graph_def(add_shapes=True)[0], data_format)
self.tf_graph.build()
def _fix_output_shape(self, graph):
for i in range(len(graph.node)):
node = graph.node[i]
if node.op == "swish_f32":
graph.node[i].attr['_disable_call_shape_inference'].b = False
def _check_input_shape(self, graph_def):
numpy.random.seed(13)
graph_def = cp.deepcopy(graph_def)
input_map = dict()
for layer in graph_def.node:
if layer.op != "Placeholder":
continue
graph_node = TFGraphNode(layer)
dtype = graph_node.layer.attr['dtype'].type
need_define_shape = 0
if self.define_input_shape:
need_define_shape = 3
elif graph_node.layer.attr[
'shape'].shape.unknown_rank or not graph_node.get_attr(
"shape"):
need_define_shape = 1
else:
value = graph_node.layer.attr["shape"].shape
shape = [dim.size for dim in value.dim]
if shape.count(-1) > 1:
need_define_shape = 2
if need_define_shape > 0:
shape = None
if graph_node.get_attr("shape"):
value = value = graph_node.layer.attr["shape"].shape
shape = [dim.size for dim in value.dim]
if need_define_shape == 1:
print("Unknown shape for input tensor[tensor name: \"{}\"]".
format(layer.name))
elif need_define_shape == 2:
print(
"\nShape[now is {}] for input tensor[tensor name: \"{}\"] not support yet"
.format(shape, layer.name))
else:
print(
"Define shape[now is {}] for input tensor[tensor name: \"{}\']"
.format(shape, layer.name))
print(
"Use your keyboard type the shape of input tensor below :)")
right_shape_been_input = False
while not right_shape_been_input:
try:
shape = raw_input(
"Shape of Input(e.g. None,224,224,3): ")
except:
shape = input("Shape of Input(e.g. None,224,224,3): ")
if shape.count("None") > 1:
print("Only 1 dimension can be None, type again:)")
else:
right_shape_been_input = True
shape = [
None if dim == "None" else int(dim)
for dim in shape.strip().split(',')
]
assert shape.count(None) <= 1, "Only one dimension can be None"
try:
x2paddle_input = tf.compat.v1.placeholder(
dtype=dtype,
shape=shape,
name="x2paddle_{}".format(layer.name))
except:
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:
shape[shape.index(None)] = -1
self.input_info["x2paddle_{}".format(layer.name)] = (shape,
dtype)
else:
value = graph_node.layer.attr["shape"].shape
shape = [dim.size for dim in value.dim]
self.input_info[graph_node.layer_name] = (shape, dtype)
return input_map
# trick method
# should be removed after PaddlePaddle V1.6 been released
def infer_tensor(self, graph_node):
if hasattr(graph_node, "index"):
tensor_name = graph_node.layer.name + ":{}".format(graph_node.index)
else:
tensor_name = graph_node.layer.name + ":0"
feed = dict()
for input_name, info in self.input_info.items():
(shape, dtype) = cp.deepcopy(info)
input_tensor = self.sess.graph.get_tensor_by_name(input_name + ":0")
if shape.count(-1) > 0:
shape[shape.index(-1)] = 2
feed[input_tensor] = numpy.random.random_sample(shape)
output_tensor = self.sess.graph.get_tensor_by_name(tensor_name)
return self.sess.run([output_tensor], feed)[0]
def infer_shape_tensor(self, graph_node, out_shape=None):
if hasattr(graph_node, "index"):
tensor_name = graph_node.layer.name + ":{}".format(graph_node.index)
else:
tensor_name = graph_node.layer.name + ":0"
feed = dict()
batch_size = [2, 3, 5]
results = list()
for b in batch_size:
for input_name, info in self.input_info.items():
(shape, dtype) = cp.deepcopy(info)
input_tensor = self.sess.graph.get_tensor_by_name(input_name +
":0")
if shape.count(-1) > 0:
shape[shape.index(-1)] = b
feed[input_tensor] = numpy.random.random_sample(shape)
output_tensor = self.sess.graph.get_tensor_by_name(tensor_name)
results.append(self.sess.run([output_tensor], feed)[0].flatten())
compare01 = (results[0] == results[1])
compare12 = (results[1] == results[2])
if compare01.all() and compare12.all():
return results[0].tolist()
if (compare01 == compare12).all():
index = numpy.argwhere(compare01 == False).flatten()
if index.shape[0] != 1:
raise Exception("There's not only one unstable dimension")
results[0][index[0]] = -1
index = numpy.argwhere(results[0] < 0).flatten()
if index.shape[0] > 2:
print("Warning: More than two dimension less than zero")
if index.shape[0] == 2 and out_shape is not None:
if out_shape[index[1]] > 0:
results[0][index[1]] = out_shape[index[1]]
else:
results[0][index[0]] = out_shape[index[0]]
return results[0].tolist()
else:
raise Exception("Couldn't infer a stable shape shape tensor value")
def infer_tensor_shape(self, graph_node):
if hasattr(graph_node, "index"):
tensor_name = graph_node.layer.name + ":{}".format(graph_node.index)
else:
tensor_name = graph_node.layer.name + ":0"
feed = dict()
batch_size = [2, 3, 5]
shapes = list()
for b in batch_size:
for input_name, info in self.input_info.items():
(shape, dtype) = cp.deepcopy(info)
input_tensor = self.sess.graph.get_tensor_by_name(input_name +
":0")
if shape.count(-1) > 0:
shape[shape.index(-1)] = b
feed[input_tensor] = numpy.random.random_sample(shape)
output_tensor = self.sess.graph.get_tensor_by_name(tensor_name)
shape = self.sess.run([output_tensor], feed)[0].shape
shapes.append(numpy.array(shape))
compare01 = (shapes[0] == shapes[1])
compare12 = (shapes[1] == shapes[2])
if compare01.all() and compare12.all():
return shape[0].tolist()
if (compare01 == compare12).all():
index = numpy.argwhere(compare01 == False).flatten()
if index.shape[0] != 1:
raise Exception("There's not only one unstable dimension")
if index[0] != 0:
raise Exception("Batch size not in the first dimension")
shapes[0][0] = -1
return shapes[0].tolist()