def test_detach(self):
"""Test for ge.detach."""
sgv = ge.sgv(self.c.op, self.a.op)
control_outputs = ge.util.ControlOutputs(self.graph)
ge.detach(sgv, control_inputs=control_outputs)
# make sure the detached graph is as expected.
self.assertTrue(ge.matcher("^foo/c$")
.input_ops("geph__a_0", "geph__b_0")(self.c.op))
def build_pb_fact(pb_location, main_location, breath, quant, lvl):
graph = load_graph(pb_location)
W1 = graph.get_tensor_by_name('prefix/w_in:0')
matmul = graph.get_tensor_by_name('prefix/MatMul:0')
bias = graph.get_tensor_by_name('prefix/b_in:0')
add = graph.get_tensor_by_name('prefix/add:0')
reshape = graph.get_tensor_by_name('prefix/Reshape:0')
# #remove all conncetions from matmul
ge.detach(ge.sgv(matmul.op))
with tf.Session(graph=graph) as sess:
# os.system("mkdir " + main_location + breath + "/" + quant + "/fact_" + str(lvl))
# for op in sess.graph.get_operations():
# print(op.name)
W = W1.eval()
u, s, v, ss = svd_compress_gs(W, lvl)
logEntry("structural_similarity == > " + str(ss))
u1 = tf.matmul(reshape, u, name="prefix/u1")
s1 = tf.matmul(u1, s, name="prefix/s1")
v1 = tf.matmul(s1, v, name="prefix/v1")
ge.connect(ge.sgv(v1.op), ge.sgv(add.op).remap_inputs([0]))
sess.run(tf.variables_initializer([tf.Variable(5, name="dummy" + str(lvl))]))
saver = tf.train.Saver()
# save log for tensorboad
LOGDIR = main_location + '/LOG'
train_writer = tf.summary.FileWriter(LOGDIR)
train_writer.add_graph(sess.graph)
train_writer.close()
# save the freezed model
os.system("mkdir " + main_location + "pb")
tf.train.write_graph(sess.graph_def, main_location + 'pb/', "RNN_" + breath + "_" + quant + "_fact_" + str(lvl) + ".pbtxt")
saver.save(sess, save_path=main_location + "model.ckpt")
input_graph_path = main_location + '/pb/' + "RNN_" + breath + "_" + quant + "_fact_" + str(lvl) + ".pbtxt"
checkpoint_path = main_location + "model.ckpt"
restore_op_name = "save/restore_all"
filename_tensor_name = "save/Const:0"
output_frozen_graph_name = main_location + 'pb/' + "RNN_" + breath + "_" + quant + "_fact_" + str(lvl) + ".pb"
logEntry("Start Freezing the graph")
freeze_graph.freeze_graph(input_graph_path, input_saver="",
input_binary=False, input_checkpoint=checkpoint_path,
output_node_names="prefix/y_", restore_op_name="save/restore_all",
filename_tensor_name="save/Const:0",
output_graph=output_frozen_graph_name, clear_devices=True, initializer_nodes="")
logEntry("End Freezing the graph")
sess.close()
fact_levels = [32, 30, 25, 20, 15, 10, 5]
for lvl in fact_levels:
logEntry(lvl)
# load the orignal graph
graph = load_graph("../model/pb_files/rnn-deep-250-32-2.pb")
W1 = graph.get_tensor_by_name('prefix/w_in:0')
matmul = graph.get_tensor_by_name('prefix/MatMul:0')
bias = graph.get_tensor_by_name('prefix/b_in:0')
add = graph.get_tensor_by_name('prefix/add:0')
reshape = graph.get_tensor_by_name('prefix/Reshape:0')
# #remove all conncetions from matmul
ge.detach(ge.sgv(matmul.op))
with tf.Session(graph=graph) as sess:
os.system("mkdir /flush1/raj034/RNN/model/test_cases/" + breath + "/" + quant + "/fact_" + str(lvl))
# for op in sess.graph.get_operations():
# print(op.name)
W = W1.eval()
u, s, v, ss = svd_compress_gs(W, lvl)
logEntry("structural_similarity == > " + str(ss))
u1 = tf.matmul(reshape, u, name="prefix/u1")
s1 = tf.matmul(u1, s, name="prefix/s1")
v1 = tf.matmul(s1, v, name="prefix/v1")
ge.connect(ge.sgv(v1.op), ge.sgv(add.op).remap_inputs([0]))
def build_pb_fact(pb_location, main_location, breath, quant, lvl):
graph = load_graph(pb_location)
with tf.Session(graph=graph) as sess:
count = 0
pre_layer = [
'Placeholder', 'hidden_layer1', 'hidden_layer2', 'hidden_layer3',
'hidden_layer4', 'hidden_layer5', 'hidden_layer6'
]
svd_num = [2048, 1024, 512, 256, 128, 64, 32]
for layer in [
'hidden_layer1', 'hidden_layer2', 'hidden_layer3',
'hidden_layer4', 'hidden_layer5', 'hidden_layer6',
'hidden_layer7'
]:
if (count == 0):
layer_input = graph.get_tensor_by_name('prefix/' +
pre_layer[count] + ':0')
else:
layer_input = graph.get_tensor_by_name('prefix/' +
pre_layer[count] +
'/Tanh:0')
W = graph.get_tensor_by_name('prefix/' + layer + '/kernel:0')
matmul = graph.get_tensor_by_name('prefix/' + layer + '/MatMul:0')
add = graph.get_tensor_by_name('prefix/' + layer + '/BiasAdd:0')
ge.detach(ge.sgv(matmul.op))
W1 = W.eval()
print(W1.shape)
u_1, s_1, v_1, ss = svd_compress_gs(
W1, math.ceil(svd_num[count] * lvl / 100))
logEntry(
str(lvl) + " ==> layer ==> " + str(count) + " ssim ==> " +
str(ss))
u1 = tf.matmul(layer_input, u_1, name="prefix/" + layer + "/u1")
s1 = tf.matmul(u1, s_1, name="prefix/" + layer + "/s1")
v1 = tf.matmul(s1, v_1, name="prefix/" + layer + "/v1")
ge.connect(ge.sgv(v1.op), ge.sgv(add.op).remap_inputs([0]))
count += 1
sess.run(
tf.variables_initializer([tf.Variable(5,
name="dummy" + str(lvl))]))
saver = tf.train.Saver()
# save log for tensorboad
LOGDIR = main_location + '/LOG'
train_writer = tf.summary.FileWriter(LOGDIR)
train_writer.add_graph(sess.graph)
train_writer.close()
# save the freezed model
os.system("mkdir " + main_location + "pb")
tf.train.write_graph(
sess.graph_def, main_location + 'pb/',
"DNN_" + breath + "_" + quant + "_fact_" + str(lvl) + ".pbtxt")
saver.save(sess, save_path=main_location + "model.ckpt")
input_graph_path = main_location + '/pb/' + "DNN_" + breath + "_" + quant + "_fact_" + str(
lvl) + ".pbtxt"
checkpoint_path = main_location + "model.ckpt"
restore_op_name = "save/restore_all"
filename_tensor_name = "save/Const:0"
output_frozen_graph_name = main_location + 'pb/' + "DNN_" + breath + "_" + quant + "_fact_" + str(
lvl) + ".pb"
logEntry("Start Freezing the graph")
freeze_graph.freeze_graph(input_graph_path,
input_saver="",
input_binary=False,
input_checkpoint=checkpoint_path,
output_node_names="prefix/softmax",
restore_op_name="save/restore_all",
filename_tensor_name="save/Const:0",
output_graph=output_frozen_graph_name,
clear_devices=True,
initializer_nodes="")
logEntry("End Freezing the graph")
sess.close()
def convert(file_path,
inputNodeName,
outputNodeName,
msuffix,
binaryPB,
readMode,
folderPath,
checkpointExt,
checkpointPath,
modelName,
shapeArray,
modifyshapeAttribue,
fixBatchNormal=True):
tf.reset_default_graph()
os.environ['CUDA_VISIBLE_DEVICES'] = ''
config = tf.ConfigProto(allow_soft_placement=True,
device_count={
"GPU": 0,
"CPU": 1
})
runIncommdLine = False
if (os.path.isfile(file_path)):
g_in, graph_def = loadGraph(file_path, binaryPB)
else:
raise ValueError('Can not find : %s ' % folderDir)
fixTrainingBNAndDropout = False
if (fixTrainingBNAndDropout):
fcQuantWeightRemoved = False
import tensorflow.contrib.graph_editor as ge
maxModuleNumber = 6
for i in range(1, maxModuleNumber +
1): #if fire 6 is last fire layer set to 7
blockName = 'fire' + str(i)
if (i == 1):
bnNames = [
'/bn'
] # mobile net First layer Quant model all 7 fires are '/bn'
else:
bnNames = [
'/bn'
] # mobile net ['/dw_bn','/1x1_bn'] Squeeze Net ['/bn']
useConnectMethod = False
for bnName in bnNames:
if (useConnectMethod):
nodeName = blockName + bnName + '/moments/Squeeze_1'
oldInputName = blockName + bnName + '/moments/variance'
newInputName = blockName + bnName + '/moving_variance'
node = g_in.get_operation_by_name(nodeName)
oldInputNode = g_in.get_operation_by_name(oldInputName)
newInputNode = g_in.get_operation_by_name(newInputName)
placeHolderNew = tf.identity(newInputNode.outputs[0])
expDim = tf.expand_dims(
tf.expand_dims(tf.expand_dims(placeHolderNew, 0), 0),
0)
ge.detach(ge.sgv(oldInputNode))
ge.connect(ge.sgv(expDim), ge.sgv(node))
nodeName = blockName + bnName + '/moments/Squeeze'
oldInputName = blockName + bnName + '/moments/mean'
newInputName = blockName + bnName + '/moving_mean'
node = g_in.get_operation_by_name(nodeName)
print(
'%s before edit new node node.node_def .inputs[0]' %
blockName, node.node_def, node.inputs[0])
oldInputNode = g_in.get_operation_by_name(oldInputName)
newInputNode = g_in.get_operation_by_name(newInputName)
placeHolderNew = tf.identity(newInputNode.outputs[0])
expDim = tf.expand_dims(
tf.expand_dims(tf.expand_dims(placeHolderNew, 0), 0),
0)
ge.detach(ge.sgv(oldInputNode))
ge.connect(ge.sgv(expDim), ge.sgv(node))
print(
'%s after edit new node node.node_def .inputs[0]' %
blockName, node.node_def, node.inputs[0])
else:
oldInputName = blockName + bnName + '/moments/Squeeze_1'
newInputName = blockName + bnName + '/moving_variance'
oldInputNode = g_in.get_operation_by_name(oldInputName)
newInputNode = g_in.get_operation_by_name(newInputName)
placeHolderNew = tf.identity(newInputNode.outputs[0])
ge.swap_outputs(ge.sgv(placeHolderNew),
ge.sgv(oldInputNode))
oldInputName = blockName + bnName + '/moments/Squeeze'
newInputName = blockName + bnName + '/moving_mean'
oldInputNode = g_in.get_operation_by_name(oldInputName)
newInputNode = g_in.get_operation_by_name(newInputName)
placeHolderNew = tf.identity(newInputNode.outputs[0])
ge.swap_outputs(ge.sgv(placeHolderNew),
ge.sgv(oldInputNode))
removeWeightQuantize = False
if (removeWeightQuantize):
oldInputName = blockName + '/conv3x3/add'
newInputName = blockName + '/conv3x3/kernels'
#print('%s before edit new node node.node_def .inputs[0]'%blockName, node.node_def , node.inputs[0])
oldInputNode = g_in.get_operation_by_name(oldInputName)
newInputNode = g_in.get_operation_by_name(newInputName)
placeHolderNew = tf.identity(newInputNode.outputs[0])
# expDim=tf.expand_dims(tf.expand_dims(tf.expand_dims(placeHolderNew,0),0),0)
# ge.detach (ge.sgv(oldInputNode))
ge.swap_outputs(
ge.sgv(placeHolderNew),
ge.sgv(oldInputNode)) #reroute_outputs get same results
#Remove FC layer
if (fcQuantWeightRemoved == False):
oldInputName = 'logit/add'
newInputName = 'logit/weights'
#print('%s before edit new node node.node_def .inputs[0]'%blockName, node.node_def , node.inputs[0])
oldInputNode = g_in.get_operation_by_name(oldInputName)
newInputNode = g_in.get_operation_by_name(newInputName)
placeHolderNew = tf.identity(newInputNode.outputs[0])
# expDim=tf.expand_dims(tf.expand_dims(tf.expand_dims(placeHolderNew,0),0),0)
# ge.detach (ge.sgv(oldInputNode))
ge.swap_outputs(ge.sgv(placeHolderNew), ge.sgv(
oldInputNode)) #reroute_outputs get same results
fcQuantWeightRemoved = True
with tf.Session(config=config, graph=g_in) as sess:
graph_def = sess.graph_def
for node in graph_def.node:
if 'dropout/mul' in node.name:
deleteDropOut = True
if (deleteDropOut):
oldInputName = 'dropout/mul'
newInputName = 'fire6/pool/MaxPool'
oldInputNode = g_in.get_operation_by_name(
oldInputName)
newInputNode = g_in.get_operation_by_name(
newInputName)
placeHolderNew = tf.identity(
newInputNode.outputs[0])
ge.swap_outputs(
ge.sgv(placeHolderNew), ge.sgv(oldInputNode)
) #reroute_outputs get same results
else:
for node in graph_def.node:
if node.name == 'dropout/keep_prob':
#node.attr['value'].tensor.float_val=tf.convert_to_tensor (1,dtype=tf.float32)
#node.attr['value'].tensor.float_val=1
node.attr['value'].tensor.CopyFrom(
tensor_util.make_tensor_proto(
1.0, dtype=tf.float32))
#node.attr['value'].value =1.0
# fix batch normal node nodes https://github.com/tensorflow/tensorflow/issues/3628
if (fixBatchNormal):
for node in graph_def.node:
if node.op == 'RefSwitch':
node.op = 'Switch'
for index in range(len(node.input)):
if 'moving_' in node.input[index]:
node.input[index] = node.input[index] + '/read'
elif node.op == 'AssignSub':
node.op = 'Sub'
if 'use_locking' in node.attr: del node.attr['use_locking']
elif node.op == 'AssignAdd':
node.op = 'Add'
if 'use_locking' in node.attr: del node.attr['use_locking']
if ('dilations') in node.attr: del node.attr['dilations']
node.device = ""
#fixVariables not Working
fixVariables = False
if (fixVariables and node.op == 'VariableV2' and
('batchnorm/var' in node.name or 'batchnorm/mean' in node.name)):
outputNodes = find_output_nodes(graph_def, node)
for index in range(len(outputNodes)):
if (outputNodes[index].op == 'Assign'):
#node.output[index] = node.output[index] + '/read'
#outputNodes[index].op ='Identity'
outputNodes[index].name = outputNodes[index].name + '/read'
print('Modified %s ' % outputNodes[index].name)
#################### Step 1 Training to inference simplification , need checkpoint and .pbtxt files from training ######################################################
graphDef = optimize_for_inference_lib.optimize_for_inference(
graph_def,
[inputNodeName], # an array of the input node(s)
[outputNodeName] if type(outputNodeName) is str else
[item for item in outputNodeName], # an array of output nodes
tf.float32.as_datatype_enum)
if (modifyshapeAttribue):
inputOpType = 'Placeholder'
for n in graphDef.node:
#print('node to modify',n.name)
if (n.name == inputNodeName):
print('node to modify', n)
setNodeAttribute(n, 'shape', shapeArray)
if (n.op != inputOpType):
print(
"--Node %s op %s set to op=%s" %
(inputNodeName, n.op, inputOpType), shapeArray)
n.op = inputOpType
print("--Name of the node - %s shape set to " % n.name,
shapeArray)
print('node after modify', n)
modifyClipValue = False
if (modifyClipValue):
for i in range(1, maxModuleNumber + 1):
blockName = 'fire' + str(i)
newClipValue = 127
clipVNodeName = blockName + '/conv3x3/Rint_1/x'
#clipnode= g_in.get_operation_by_name(clipVNodeName)
clipnode = find_node_by_name(graphDef, clipVNodeName)
print('clipnode to modify', clipnode)
setNodeConstValue(graph_def, clipnode, newClipValue)
print("--Name of the node - %s shape set to %f" %
(clipnode.name, newClipValue))
print('clipnode after modify', clipnode)
modifyFCClip = True
if (modifyFCClip and i == maxModuleNumber):
clipVNodeName = 'conv12/Rint_1/x'
clipnodeFC = find_node_by_name(graphDef, clipVNodeName)
#clipnodeFC= g_in.get_operation_by_name(clipVNodeName)
setNodeConstValue(graph_def, clipnodeFC, newClipValue)
print('clipnode after modify', clipnodeFC)
if (runIncommdLine):
copyfile(file_path, file_path + trainModelSuffix)
outputNameSuffix = '%s_frozenforInference.pb' % checkpointExt
inferenceSuffix = '.Inference'
tf.train.write_graph(graphDef,
folderPath,
checkpointPath + modelName + '.pb' + inferenceSuffix,
as_text=False)
tf.train.write_graph(graphDef,
folderPath,
checkpointPath + modelName + '.pbtxt' +
inferenceSuffix,
as_text=True)
pbfileoutput_path = checkpointPath + modelName + outputNameSuffix
checkpointfile_path = checkpointPath + modelName + checkpointExt
pbfile_path = checkpointPath + modelName + msuffix + inferenceSuffix
#################### Step 2 Frozen Inference mode ######################################################
freeze_graph.freeze_graph(
input_graph=pbfile_path,
input_saver='',
input_binary=binaryPB,
input_checkpoint=checkpointfile_path, # an array of the input node(s)
output_node_names=outputNodeName
if type(outputNodeName) is str else ",".join(outputNodeName),
restore_op_name="save/restore_all", #Unused.
filename_tensor_name="save/Const:0", # Unused.
output_graph=pbfileoutput_path, # an array of output nodes
clear_devices=True,
initializer_nodes='')
#################### Step 3 Save in tensor board ######################################################
saveTensorboardForVisualizatoin = False
if (saveTensorboardForVisualizatoin):
modelFullPath = checkpointPath + modelName + outputNameSuffix
tensorboardPath = checkpointPath + '\\tensorboard'
#if not os.path.exists(tensorboardPath):
# os.mkdir(tensorboardPath)
createTensorboard(modelFullPath, tensorboardPath)
