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Code Issues Releases Activity

dnn/native: add native support for dense

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Signed-off-by: Mingyu Yin <mingyu.yin@intel.com>
This commit is contained in:
Mingyu Yin
2020-09-22 15:11:09 +08:00
committed by Guo, Yejun
parent adcdf0bc60
commit ad2546e3b3
9 changed files with 443 additions and 9 deletions
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126
tools/python/convert_from_tensorflow.py
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@ -48,9 +48,9 @@ class Operand(object):
self.used_count = self.used_count + 1
def __str__(self):
return "{}: (name: {}, iotype: {}, dtype: {}, dims: ({},{},{},{}) used_count: {})".format(self.index,
return "{}: (name: {}, iotype: {}, dtype: {}, dims: {}, used_count: {})".format(self.index,
self.name, self.iotype2str[self.iotype], self.dtype2str[self.dtype],
self.dims[0], self.dims[1], self.dims[2], self.dims[3], self.used_count)
self.dims, self.used_count)
def __lt__(self, other):
return self.index < other.index
@ -71,8 +71,10 @@ class TFConverter:
self.converted_nodes = set()
self.conv2d_scope_names = set()
self.conv2d_scopename_inputname_dict = {}
self.dense_scope_names = set()
self.dense_scopename_inputname_dict = {}
self.op2code = {'Conv2D':1, 'DepthToSpace':2, 'MirrorPad':3, 'Maximum':4,
'MathBinary':5, 'MathUnary':6, 'AvgPool':7}
'MathBinary':5, 'MathUnary':6, 'AvgPool':7, 'MatMul':8}
self.mathbin2code = {'Sub':0, 'Add':1, 'Mul':2, 'RealDiv':3, 'Minimum':4, 'FloorMod':5}
self.mathun2code = {'Abs':0, 'Sin':1, 'Cos':2, 'Tan':3, 'Asin':4,
'Acos':5, 'Atan':6, 'Sinh':7, 'Cosh':8, 'Tanh':9, 'Asinh':10,
@ -126,6 +128,22 @@ class TFConverter:
return knode, bnode, dnode, anode
def get_dense_params(self, dense_scope_name):
knode = self.name_node_dict[dense_scope_name + '/kernel']
bnode = self.name_node_dict.get(dense_scope_name + '/bias')
# the BiasAdd name is possible be changed into the output name,
# if activation is None, and BiasAdd.next is the last op which is Identity
anode = None
if bnode:
if dense_scope_name + '/BiasAdd' in self.edges:
anode = self.edges[dense_scope_name + '/BiasAdd'][0]
if anode.op not in self.conv_activations:
anode = None
else:
anode = None
return knode, bnode, anode
def dump_complex_conv2d_to_file(self, node, f):
assert(node.op == 'Conv2D')
self.layer_number = self.layer_number + 1
@ -181,6 +199,57 @@ class TFConverter:
output_operand_index = self.add_operand(self.edges[bnode.name][0].name, Operand.IOTYPE_OUTPUT)
np.array([input_operand_index, output_operand_index], dtype=np.uint32).tofile(f)
def dump_dense_to_file(self, node, f):
assert(node.op == 'MatMul')
self.layer_number = self.layer_number + 1
self.converted_nodes.add(node.name)
scope_name = TFConverter.get_scope_name(node.name)
#knode for kernel, bnode for bias, anode for activation
knode, bnode, anode = self.get_dense_params(scope_name.split('/')[0])
if bnode is not None:
has_bias = 1
btensor = bnode.attr['value'].tensor
if btensor.tensor_shape.dim[0].size == 1:
bias = struct.pack("f", btensor.float_val[0])
else:
bias = btensor.tensor_content
else:
has_bias = 0
if anode is not None:
activation = anode.op
else:
activation = 'None'
ktensor = knode.attr['value'].tensor
in_channels = ktensor.tensor_shape.dim[0].size
out_channels = ktensor.tensor_shape.dim[1].size
if in_channels * out_channels == 1:
kernel = np.float32(ktensor.float_val[0])
else:
kernel = np.frombuffer(ktensor.tensor_content, dtype=np.float32)
kernel = kernel.reshape(in_channels, out_channels)
kernel = np.transpose(kernel, [1, 0])
np.array([self.op2code[node.op], self.conv_activations[activation], in_channels, out_channels, has_bias], dtype=np.uint32).tofile(f)
kernel.tofile(f)
if has_bias:
f.write(bias)
input_name = self.dense_scopename_inputname_dict[scope_name.split('/')[0]]
input_operand_index = self.add_operand(input_name, Operand.IOTYPE_INPUT)
if anode is not None:
output_operand_index = self.add_operand(anode.name, Operand.IOTYPE_OUTPUT)
else:
if bnode is not None:
output_operand_index = self.add_operand(self.edges[bnode.name][0].name, Operand.IOTYPE_OUTPUT)
else:
output_operand_index = self.add_operand(self.edges[scope_name+'/concat_1'][0].name, Operand.IOTYPE_OUTPUT)
np.array([input_operand_index, output_operand_index], dtype=np.uint32).tofile(f)
def dump_simple_conv2d_to_file(self, node, f):
assert(node.op == 'Conv2D')
@ -343,9 +412,19 @@ class TFConverter:
if node.op == 'Conv2D':
self.dump_complex_conv2d_to_file(node, f)
continue
if self.in_dense_scope(node.name):
if node.op == 'MatMul':
self.dump_dense_to_file(node, f)
continue
if node.op == 'Conv2D':
self.dump_simple_conv2d_to_file(node, f)
continue
if node.name in self.output_names:
input_name = self.id_different_scope_dict[node.name]
if TFConverter.get_scope_name(input_name)!=TFConverter.get_scope_name(node.name):
continue
if node.op == 'AvgPool':
self.dump_avg_pool_to_file(node, f)
elif node.op == 'DepthToSpace':
@ -367,7 +446,7 @@ class TFConverter:
np.array([operand.index, len(operand.name)], dtype=np.uint32).tofile(f)
f.write(operand.name.encode('utf-8'))
np.array([operand.iotype, operand.dtype], dtype=np.uint32).tofile(f)
np.array([operand.dims[0], operand.dims[1], operand.dims[2], operand.dims[3]], dtype=np.uint32).tofile(f)
np.array(operand.dims, dtype=np.uint32).tofile(f)
def dump_to_file(self):
@ -396,6 +475,7 @@ class TFConverter:
def remove_identity(self):
self.id_different_scope_dict = {}
id_nodes = []
id_dict = {}
for node in self.nodes:
@ -408,6 +488,7 @@ class TFConverter:
self.name_node_dict[input].name = name
self.name_node_dict[name] = self.name_node_dict[input]
del self.name_node_dict[input]
self.id_different_scope_dict[name] = input
else:
id_dict[name] = input
@ -449,8 +530,18 @@ class TFConverter:
return False
def generate_conv2d_scope_info(self):
# mostly, conv2d is a sub block in graph, get the scope name
def in_dense_scope(self, name):
inner_scope = TFConverter.get_scope_name(name)
if inner_scope == "":
return False;
for scope in self.dense_scope_names:
index = inner_scope.find(scope)
if index == 0:
return True
return False
def generate_sub_block_op_scope_info(self):
# mostly, conv2d/dense is a sub block in graph, get the scope name
for node in self.nodes:
if node.op == 'Conv2D':
scope = TFConverter.get_scope_name(node.name)
@ -461,8 +552,17 @@ class TFConverter:
if scope + '/kernel' not in self.name_node_dict:
continue
self.conv2d_scope_names.add(scope)
elif node.op == 'MatMul':
scope = TFConverter.get_scope_name(node.name)
# for the case tf.nn.dense is called directly
if scope == '':
continue
# for the case tf.nn.dense is called within a scope
if scope + '/kernel' not in self.name_node_dict and scope.split('/Tensordot')[0] + '/kernel' not in self.name_node_dict:
continue
self.dense_scope_names.add(scope.split('/Tensordot')[0])
# get the input name to the conv2d sub block
# get the input name to the conv2d/dense sub block
for node in self.nodes:
scope = TFConverter.get_scope_name(node.name)
if scope in self.conv2d_scope_names:
@ -470,6 +570,16 @@ class TFConverter:
for inp in node.input:
if TFConverter.get_scope_name(inp) != scope:
self.conv2d_scopename_inputname_dict[scope] = inp
elif scope in self.dense_scope_names:
if node.op == 'MatMul' or node.op == 'Shape':
for inp in node.input:
if TFConverter.get_scope_name(inp) != scope:
self.dense_scopename_inputname_dict[scope] = inp
elif scope.split('/Tensordot')[0] in self.dense_scope_names:
if node.op == 'Transpose':
for inp in node.input:
if TFConverter.get_scope_name(inp).find(scope)<0 and TFConverter.get_scope_name(inp).find(scope.split('/')[0])<0:
self.dense_scopename_inputname_dict[scope.split('/Tensordot')[0]] = inp
def run(self):
@ -477,7 +587,7 @@ class TFConverter:
self.generate_output_names()
self.remove_identity()
self.generate_edges()
self.generate_conv2d_scope_info()
self.generate_sub_block_op_scope_info()
if self.dump4tb:
self.dump_for_tensorboard()