我试图从喀拉拉邦训练自己的项目。但是，由于使用cv2.readNetFromTensorflow或cv2.readNet，我遇到一个错误：函数“ bloblFromTensor”不支持Tensor的数据类型。
我在建立模型之前就使用了K.set_learing_phase（0）
我的模型构建是
def build(width, height, depth, classes):
model = K.models.Sequential()
inputShape = (height, width, depth)
print(" there is the inputShape for hidden layers : {} ".format(inputShape))
# todo if u delete set_learning, the result return it that below
# raise ValueError("Tensor name '{0}' is invalid.".format(node.input[0]))
# dk.set_learning_phase(0)
# first hidden layers
model.add(K.layers.Dense(16, input_shape=inputShape))
model.add(K.layers.BatchNormalization())
# first set of convv => relu => pool layers
model.add(K.layers.Conv2D(32, (7, 7), padding="same", activation="relu"))
model.add(K.layers.MaxPooling2D(pool_size=(2,2)))
model.add(K.layers.BatchNormalization())
model.add(K.layers.Dropout(0.25))
# init dense layer for image resolution
model.add(K.layers.Dense(64, activation="relu"))
model.add(K.layers.Conv2D(64, (3, 3), padding="same", activation="relu"))
model.add(K.layers.MaxPooling2D(pool_size=(2,2)))
model.add(K.layers.BatchNormalization())
model.add(K.layers.Dropout(0.25))
model.add(K.layers.Conv2D(128, (3, 3), activation="relu", padding="same"))
model.add(K.layers.MaxPooling2D(pool_size=(2,2)))
model.add(K.layers.BatchNormalization())
model.add(K.layers.Dropout(0.25))
# (None, 7, 7, 50) -> model.output_shape
a, b, c, d = model.output_shape
print("model ouput_shape giving ", a, b, c, d)
a = b * c * d
print("a -> ", a)
model.add(K.layers.Permute([1, 2, 3]))
model.add(K.layers.Reshape((a,)))
# first set of fc => relu layers
model.add(K.layers.Dense(units=512, activation='relu'))
model.add(K.layers.BatchNormalization())
model.add(K.layers.Dropout(0.5))
# model.add(K.layers.Flatten())
# sofmax classifier
model.add(K.layers.Dense(units=classes, activation='softmax'))
return model
因此，我将fit_generation方法用于培训历史，然后应用
# save file of model
model.save(h5_file)
# freeze the graph to pb file and pbtxt
# ! not showing to console because that so take a long time
frozen_graph = freeze_session(sess, output_names=[out.op.name for out in model.outputs])
# convert to keras file to pb files for using readNetTensorflow
try:
tf.io.write_graph(frozen_graph, learn_path, model_file , as_text=False)
log_file.register(0,"graph file saving to pb is success ")
except ValueError as er:
os.system('clear')
console_log.d("ERROR -> NOT CONVERT TO PB FILE ",er)
log_file.register(1, " ERROR that convert to pbfile : {} ".format(er))
console_log.d("FROZEN GRAPH TO PB FILE ")
# pb file convert to pbtxt file
create_pbtxt(model_file , pbtxt_file, learn_path )
最后，我将删除了某些节点的pb文件转换为pbtxt文件
def create_pbtxt(pb_path, pbtxt_path, path):
# clear console
os.system("clear")
console_log.d("CREATE_TXT FILE")
console_log.d("there is the path file is this - --- > " , pb_path )
# check path file from the news
found_path_file = path + "/" + pb_path
console_log.d("original path that saving here:",found_path_file)
with tf.Session() as sess:
with tf.gfile.GFile(found_path_file, 'rb')as f:
# tf.io.compat
graph_def = tf.GraphDef()
# graph_def.ParseFromString(f.read())
try:
# text_format.Merge(f.read(), graph_def)
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='')
console_log.d("pbtxt file is saving ")
except text_format.ParseError:
console_log.d("error from merge file for text_format")
# inp_node = 'MobilenetV2/MobilenetV2/input'
# oup_node = 'logits/semantic/BiasAdd'
# gragh_def = optimize_for_inference_lib.optimğize_for_inference()
name_array =[ ]
op_array=[]
# print("graph_def_node is here: ", graph_def.node)
# for i in reversed((range(len(graph_def.node) ))):
for i in reversed(range(len(graph_def.node))):
# if op == 'Switch' or op == 'Merge':
# inp = graph_deadnode[i].input[0]
# print("inp : {}".format(inp))
# graph_def node for addConstNodes error that
log_file.register(0, " file is node for reversed : {} ".format(graph_def.node[i].op))
# cv2 test file return addConstNode error from Const node exist (maybe)
if graph_def.node[i].op =='Const':
console_log.d("found const variables!!!")
# console clear
os.system('clear')
del graph_def.node[i]
# if graph_def.op == 'Const':
# os.system("clear")
# console_log.d("node deleting Cons ")
# console_log.d("graph_def.node[i]: ".format(graph_def.node[i]) )
# del graph_def.node[i]
# ! extends adding for any error that return cv2 test file
for attr in ['T', 'data_format', 'Tshape', 'N', 'Tidx', 'Tdim','use_cudnn_on_gpu', 'Index', 'Tperm', 'is_training','Tpaddings']:
if attr in graph_def.node[i].attr:
console_log.d(" THERE IS THE ATTR ")
del graph_def.node[i].attr[attr]
op = graph_def.node[i].op
op_array.append(op)
print("op: {}".format(op))
name = graph_def.node[i].name
name_array.append(name)
print("name : {} ".format(name))
# todo , try to op and name from nodes to logs file
# convert to np array for values save to logs file
op2_array = np.array(op_array)
name2_array = np.array(name_array)
# send values for logs
log_file.register(0," op : {} , name:{} ".format(op2_array, name2_array))
# saving pbtxt file from pb file that convert
tf.io.write_graph( graph_def, path, pbtxt_path, as_text=True)
此问题来自testing.py。当调试光标到达cv2.readNetTensorflow或cv2.readNet时，程序返回“ 函数'blobFromTensor中不支持Tensor的数据类型>“
这是testing.py
# check the pbtxt with or without processing
if flags.text_file is None:
net = cv2.dnn.readNet(pb_file)
else:
txt_file = os.getcwd() + "/" + flags.text_file
print(txt_file)
net = cv2.dnn.readNetFromTensorflow(pb_file,txt_file)
为什么会出现错误？我不知道。请帮助我。
我从此站点fossies.org找到了解决方案
我已将to_remove函数的removeUnusedNodesAndAttr添加到我的代码github中>
因为cv2不支持某些图层节点（我认为这很重要，所以您可以删除它们。