diff --git a/TensorFlow/built-in/cv/image_classification/MiniGo_ID0629_for_TensorFlow/test/set_ranktable.py b/TensorFlow/built-in/cv/image_classification/MiniGo_ID0629_for_TensorFlow/test/set_ranktable.py
new file mode 100644
index 0000000000000000000000000000000000000000..c25b51462c5df2325462786688d4a206ee29fb9a
--- /dev/null
+++ b/TensorFlow/built-in/cv/image_classification/MiniGo_ID0629_for_TensorFlow/test/set_ranktable.py
@@ -0,0 +1,1740 @@
+import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument('-n', '--npu_nums', type=int, default='2', help='nums of npu')
+parser.add_argument('-c', '--conf_path', type=str, default='./', help='the path of server_info')
+FLAGS = parser.parse_args()
+
+import json
+import os
+server = []
+server_conf = []
+server_list = ["0", "1", "2", "3", "4", "5", "6", "7"]
+if os.path.isdir(FLAGS.conf_path):
+	for f in os.listdir(FLAGS.conf_path):
+		if (f.split("_")[-1]).split(".")[0] in server_list and (f.split("_")[-1]).split(".")[1] == 'info' and f.split("_")[0] == 'server':
+			server_conf.append(f)
+			
+
+
+
+
+
+rank_address = []
+for i in range(FLAGS.npu_nums):
+	for x in server_conf:
+		if (x.split("_")[-1]).split(".")[0] == str(i):
+			server.append(x.split("_")[1])
+			l = FLAGS.conf_path + "/" + x
+			with open(l, "r") as a:
+				s = a.readlines()
+				for s_ in s:
+					if 'address_0' in s_:
+						rank_address.append(s_.split("=")[-1][:-1])
+				for s_ in s:
+					if 'address_1' in s_:
+						rank_address.append(s_.split("=")[-1][:-1])
+				for s_ in s:
+					if 'address_2' in s_:
+						rank_address.append(s_.split("=")[-1][:-1])
+				for s_ in s:
+					if 'address_3' in s_:
+						rank_address.append(s_.split("=")[-1][:-1])
+				for s_ in s:
+					if 'address_4' in s_:
+						rank_address.append(s_.split("=")[-1][:-1])
+				for s_ in s:
+					if 'address_5' in s_:
+						rank_address.append(s_.split("=")[-1][:-1])
+				for s_ in s:
+					if 'address_6' in s_:
+						rank_address.append(s_.split("=")[-1][:-1])
+				for s_ in s:
+					if 'address_7' in s_:
+						rank_address.append(s_.split("=")[-1][:-1])
+			
+if FLAGS.npu_nums == 1:
+	rank = {
+		"server_count":"1",
+		"server_list":[
+			{
+				"server_id":server[0],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[0],
+						"rank_id":"0"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[1],
+						"rank_id":"1"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[2],
+						"rank_id":"2"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[3],
+						"rank_id":"3"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[4],
+						"rank_id":"4"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[5],
+						"rank_id":"5"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[6],
+						"rank_id":"6"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[7],
+						"rank_id":"7"
+					}
+				]}],
+		"status":"completed",
+		"version":"1.0"
+	}
+elif FLAGS.npu_nums == 2:
+	rank = {
+		"server_count":"2",
+		"server_list":[
+			{
+				"server_id":server[0],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[0],
+						"rank_id":"0"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[1],
+						"rank_id":"1"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[2],
+						"rank_id":"2"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[3],
+						"rank_id":"3"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[4],
+						"rank_id":"4"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[5],
+						"rank_id":"5"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[6],
+						"rank_id":"6"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[7],
+						"rank_id":"7"
+					}
+				]},
+				
+				
+				{
+				"server_id":server[1],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[8],
+						"rank_id":"8"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[9],
+						"rank_id":"9"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[10],
+						"rank_id":"10"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[11],
+						"rank_id":"11"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[12],
+						"rank_id":"12"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[13],
+						"rank_id":"13"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[14],
+						"rank_id":"14"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[15],
+						"rank_id":"15"
+					}
+				]}
+				],
+		
+		"status":"completed",
+		"version":"1.0"
+	}
+	
+	
+elif FLAGS.npu_nums == 3:
+	rank = {
+		"server_count":"3",
+		"server_list":[
+			{
+				"server_id":server[0],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[0],
+						"rank_id":"0"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[1],
+						"rank_id":"1"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[2],
+						"rank_id":"2"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[3],
+						"rank_id":"3"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[4],
+						"rank_id":"4"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[5],
+						"rank_id":"5"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[6],
+						"rank_id":"6"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[7],
+						"rank_id":"7"
+					}
+				]},
+				
+				
+				{
+				"server_id":server[1],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[8],
+						"rank_id":"8"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[9],
+						"rank_id":"9"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[10],
+						"rank_id":"10"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[11],
+						"rank_id":"11"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[12],
+						"rank_id":"12"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[13],
+						"rank_id":"13"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[14],
+						"rank_id":"14"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[15],
+						"rank_id":"15"
+					}
+				]},
+				
+				{
+				"server_id":server[2],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[16],
+						"rank_id":"16"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[17],
+						"rank_id":"17"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[18],
+						"rank_id":"18"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[19],
+						"rank_id":"19"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[20],
+						"rank_id":"20"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[21],
+						"rank_id":"21"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[22],
+						"rank_id":"22"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[23],
+						"rank_id":"23"
+					}
+				]}
+				],
+		"status":"completed",
+		"version":"1.0"
+	}
+elif FLAGS.npu_nums == 4:
+	rank = {
+		"server_count":"4",
+		"server_list":[
+			{
+				"server_id":server[0],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[0],
+						"rank_id":"0"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[1],
+						"rank_id":"1"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[2],
+						"rank_id":"2"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[3],
+						"rank_id":"3"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[4],
+						"rank_id":"4"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[5],
+						"rank_id":"5"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[6],
+						"rank_id":"6"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[7],
+						"rank_id":"7"
+					}
+				]},
+				
+				
+				{
+				"server_id":server[1],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[8],
+						"rank_id":"8"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[9],
+						"rank_id":"9"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[10],
+						"rank_id":"10"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[11],
+						"rank_id":"11"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[12],
+						"rank_id":"12"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[13],
+						"rank_id":"13"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[14],
+						"rank_id":"14"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[15],
+						"rank_id":"15"
+					}
+				]},
+				{
+				"server_id":server[2],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[16],
+						"rank_id":"16"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[17],
+						"rank_id":"17"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[18],
+						"rank_id":"18"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[19],
+						"rank_id":"19"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[20],
+						"rank_id":"20"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[21],
+						"rank_id":"21"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[22],
+						"rank_id":"22"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[23],
+						"rank_id":"23"
+					}
+				]},
+				{
+				"server_id":server[3],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[24],
+						"rank_id":"24"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[25],
+						"rank_id":"25"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[26],
+						"rank_id":"26"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[27],
+						"rank_id":"27"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[28],
+						"rank_id":"28"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[29],
+						"rank_id":"29"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[30],
+						"rank_id":"30"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[31],
+						"rank_id":"31"
+					}
+				]}
+				],
+		"status":"completed",
+		"version":"1.0"
+	}
+elif FLAGS.npu_nums == 5:
+	rank = {
+		"server_count":"5",
+		"server_list":[
+			{
+				"server_id":server[0],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[0],
+						"rank_id":"0"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[1],
+						"rank_id":"1"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[2],
+						"rank_id":"2"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[3],
+						"rank_id":"3"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[4],
+						"rank_id":"4"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[5],
+						"rank_id":"5"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[6],
+						"rank_id":"6"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[7],
+						"rank_id":"7"
+					}
+				]},
+				
+				
+				{
+				"server_id":server[1],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[8],
+						"rank_id":"8"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[9],
+						"rank_id":"9"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[10],
+						"rank_id":"10"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[11],
+						"rank_id":"11"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[12],
+						"rank_id":"12"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[13],
+						"rank_id":"13"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[14],
+						"rank_id":"14"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[15],
+						"rank_id":"15"
+					}
+				]},
+				{
+				"server_id":server[2],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[16],
+						"rank_id":"16"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[17],
+						"rank_id":"17"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[18],
+						"rank_id":"18"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[19],
+						"rank_id":"19"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[20],
+						"rank_id":"20"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[21],
+						"rank_id":"21"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[22],
+						"rank_id":"22"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[23],
+						"rank_id":"23"
+					}
+				]},
+				{
+				"server_id":server[3],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[24],
+						"rank_id":"24"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[25],
+						"rank_id":"25"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[26],
+						"rank_id":"26"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[27],
+						"rank_id":"27"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[28],
+						"rank_id":"28"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[29],
+						"rank_id":"29"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[30],
+						"rank_id":"30"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[31],
+						"rank_id":"31"
+					}
+				]},
+				{
+				"server_id":server[4],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[32],
+						"rank_id":"32"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[33],
+						"rank_id":"33"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[34],
+						"rank_id":"34"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[35],
+						"rank_id":"35"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[36],
+						"rank_id":"36"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[37],
+						"rank_id":"37"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[38],
+						"rank_id":"38"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[39],
+						"rank_id":"39"
+					}
+				]}
+				],
+		"status":"completed",
+		"version":"1.0"
+	}
+
+
+
+elif FLAGS.npu_nums == 6:
+	rank = {
+		"server_count":"6",
+		"server_list":[
+			{
+				"server_id":server[0],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[0],
+						"rank_id":"0"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[1],
+						"rank_id":"1"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[2],
+						"rank_id":"2"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[3],
+						"rank_id":"3"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[4],
+						"rank_id":"4"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[5],
+						"rank_id":"5"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[6],
+						"rank_id":"6"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[7],
+						"rank_id":"7"
+					}
+				]},
+				
+				
+				{
+				"server_id":server[1],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[8],
+						"rank_id":"8"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[9],
+						"rank_id":"9"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[10],
+						"rank_id":"10"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[11],
+						"rank_id":"11"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[12],
+						"rank_id":"12"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[13],
+						"rank_id":"13"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[14],
+						"rank_id":"14"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[15],
+						"rank_id":"15"
+					}
+				]},
+				{
+				"server_id":server[2],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[16],
+						"rank_id":"16"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[17],
+						"rank_id":"17"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[18],
+						"rank_id":"18"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[19],
+						"rank_id":"19"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[20],
+						"rank_id":"20"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[21],
+						"rank_id":"21"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[22],
+						"rank_id":"22"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[23],
+						"rank_id":"23"
+					}
+				]},
+				{
+				"server_id":server[3],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[24],
+						"rank_id":"24"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[25],
+						"rank_id":"25"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[26],
+						"rank_id":"26"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[27],
+						"rank_id":"27"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[28],
+						"rank_id":"28"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[29],
+						"rank_id":"29"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[30],
+						"rank_id":"30"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[31],
+						"rank_id":"31"
+					}
+				]},
+				{
+				"server_id":server[4],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[32],
+						"rank_id":"32"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[33],
+						"rank_id":"33"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[34],
+						"rank_id":"34"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[35],
+						"rank_id":"35"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[36],
+						"rank_id":"36"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[37],
+						"rank_id":"37"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[38],
+						"rank_id":"38"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[39],
+						"rank_id":"39"
+					}
+				]},
+				{
+				"server_id":server[5],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[40],
+						"rank_id":"40"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[41],
+						"rank_id":"41"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[42],
+						"rank_id":"42"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[43],
+						"rank_id":"43"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[44],
+						"rank_id":"44"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[45],
+						"rank_id":"45"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[46],
+						"rank_id":"46"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[47],
+						"rank_id":"47"
+					}
+				]}
+				],
+		"status":"completed",
+		"version":"1.0"
+	}
+	
+	
+elif FLAGS.npu_nums == 7:
+	rank = {
+		"server_count":"7",
+		"server_list":[
+			{
+				"server_id":server[0],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[0],
+						"rank_id":"0"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[1],
+						"rank_id":"1"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[2],
+						"rank_id":"2"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[3],
+						"rank_id":"3"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[4],
+						"rank_id":"4"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[5],
+						"rank_id":"5"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[6],
+						"rank_id":"6"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[7],
+						"rank_id":"7"
+					}
+				]},
+				
+				
+				{
+				"server_id":server[1],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[8],
+						"rank_id":"8"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[9],
+						"rank_id":"9"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[10],
+						"rank_id":"10"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[11],
+						"rank_id":"11"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[12],
+						"rank_id":"12"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[13],
+						"rank_id":"13"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[14],
+						"rank_id":"14"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[15],
+						"rank_id":"15"
+					}
+				]},
+				{
+				"server_id":server[2],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[16],
+						"rank_id":"16"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[17],
+						"rank_id":"17"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[18],
+						"rank_id":"18"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[19],
+						"rank_id":"19"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[20],
+						"rank_id":"20"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[21],
+						"rank_id":"21"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[22],
+						"rank_id":"22"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[23],
+						"rank_id":"23"
+					}
+				]},
+				{
+				"server_id":server[3],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[24],
+						"rank_id":"24"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[25],
+						"rank_id":"25"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[26],
+						"rank_id":"26"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[27],
+						"rank_id":"27"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[28],
+						"rank_id":"28"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[29],
+						"rank_id":"29"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[30],
+						"rank_id":"30"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[31],
+						"rank_id":"31"
+					}
+				]},
+				{
+				"server_id":server[4],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[32],
+						"rank_id":"32"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[33],
+						"rank_id":"33"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[34],
+						"rank_id":"34"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[35],
+						"rank_id":"35"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[36],
+						"rank_id":"36"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[37],
+						"rank_id":"37"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[38],
+						"rank_id":"38"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[39],
+						"rank_id":"39"
+					}
+				]},
+				{
+				"server_id":server[5],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[40],
+						"rank_id":"40"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[41],
+						"rank_id":"41"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[42],
+						"rank_id":"42"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[43],
+						"rank_id":"43"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[44],
+						"rank_id":"44"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[45],
+						"rank_id":"45"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[46],
+						"rank_id":"46"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[47],
+						"rank_id":"47"
+					}
+				]},
+				{
+				"server_id":server[6],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[48],
+						"rank_id":"48"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[49],
+						"rank_id":"49"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[50],
+						"rank_id":"50"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[51],
+						"rank_id":"51"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[52],
+						"rank_id":"52"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[53],
+						"rank_id":"53"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[54],
+						"rank_id":"54"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[55],
+						"rank_id":"55"
+					}
+				]}
+				],
+		"status":"completed",
+		"version":"1.0"
+	}
+	
+	
+	
+	
+elif FLAGS.npu_nums == 8:
+	rank = {
+		"server_count":"8",
+		"server_list":[
+			{
+				"server_id":server[0],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[0],
+						"rank_id":"0"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[1],
+						"rank_id":"1"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[2],
+						"rank_id":"2"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[3],
+						"rank_id":"3"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[4],
+						"rank_id":"4"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[5],
+						"rank_id":"5"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[6],
+						"rank_id":"6"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[7],
+						"rank_id":"7"
+					}
+				]},
+				
+				
+				{
+				"server_id":server[1],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[8],
+						"rank_id":"8"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[9],
+						"rank_id":"9"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[10],
+						"rank_id":"10"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[11],
+						"rank_id":"11"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[12],
+						"rank_id":"12"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[13],
+						"rank_id":"13"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[14],
+						"rank_id":"14"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[15],
+						"rank_id":"15"
+					}
+				]},
+				{
+				"server_id":server[2],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[16],
+						"rank_id":"16"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[17],
+						"rank_id":"17"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[18],
+						"rank_id":"18"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[19],
+						"rank_id":"19"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[20],
+						"rank_id":"20"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[21],
+						"rank_id":"21"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[22],
+						"rank_id":"22"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[23],
+						"rank_id":"23"
+					}
+				]},
+				{
+				"server_id":server[3],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[24],
+						"rank_id":"24"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[25],
+						"rank_id":"25"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[26],
+						"rank_id":"26"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[27],
+						"rank_id":"27"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[28],
+						"rank_id":"28"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[29],
+						"rank_id":"29"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[30],
+						"rank_id":"30"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[31],
+						"rank_id":"31"
+					}
+				]},
+				{
+				"server_id":server[4],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[32],
+						"rank_id":"32"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[33],
+						"rank_id":"33"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[34],
+						"rank_id":"34"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[35],
+						"rank_id":"35"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[36],
+						"rank_id":"36"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[37],
+						"rank_id":"37"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[38],
+						"rank_id":"38"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[39],
+						"rank_id":"39"
+					}
+				]},
+				{
+				"server_id":server[5],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[40],
+						"rank_id":"40"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[41],
+						"rank_id":"41"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[42],
+						"rank_id":"42"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[43],
+						"rank_id":"43"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[44],
+						"rank_id":"44"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[45],
+						"rank_id":"45"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[46],
+						"rank_id":"46"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[47],
+						"rank_id":"47"
+					}
+				]},
+				{
+				"server_id":server[6],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[48],
+						"rank_id":"48"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[49],
+						"rank_id":"49"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[50],
+						"rank_id":"50"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[51],
+						"rank_id":"51"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[52],
+						"rank_id":"52"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[53],
+						"rank_id":"53"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[54],
+						"rank_id":"54"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[55],
+						"rank_id":"55"
+					}
+				]},
+				{
+				"server_id":server[7],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[56],
+						"rank_id":"56"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[57],
+						"rank_id":"57"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[58],
+						"rank_id":"58"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[59],
+						"rank_id":"59"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[60],
+						"rank_id":"60"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[61],
+						"rank_id":"61"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[62],
+						"rank_id":"62"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[63],
+						"rank_id":"63"
+					}
+				]}
+				],
+		"status":"completed",
+		"version":"1.0"
+	}
+	
+	
+	
+	
+with open("rank_table.json", "w") as f:
+	json.dump(rank, f)
+
+
+
+
+
+
diff --git a/TensorFlow/built-in/cv/image_classification/MiniGo_ID0629_for_TensorFlow/test/train_performance_16p.sh b/TensorFlow/built-in/cv/image_classification/MiniGo_ID0629_for_TensorFlow/test/train_performance_16p.sh
new file mode 100644
index 0000000000000000000000000000000000000000..628a963ee5016fc48114100fc22129de878bedc9
--- /dev/null
+++ b/TensorFlow/built-in/cv/image_classification/MiniGo_ID0629_for_TensorFlow/test/train_performance_16p.sh
@@ -0,0 +1,149 @@
+#!/bin/bash
+
+cur_path=`pwd`/../
+rm -f $cur_path/outputs/models/*
+rm -f $cur_path/estimator_working_dir/*
+
+#基础参数，需要模型审视修改
+#Batch Size
+batch_size=128
+#网络名称，同目录名称
+Network="MiniGo_ID0629_for_TensorFlow"
+#Device数量，单卡默认为1
+RankSize=16
+#训练epoch，可选
+train_epochs=
+#训练step
+train_steps=500
+#学习率
+learning_rate=
+#动态输入模式，不需要修改
+dynamic_input=""
+
+#参数配置 npu param
+precision_mode="allow_fp32_to_fp16"
+over_dump=False
+data_dump_flag=False
+data_dump_step="10"
+profiling=False
+
+data_path="$./outputs/data/selfplay"
+
+if [[ $1 == --help || $1 == -h ]];then
+   echo "usage: ./train_performance_1p.sh $data_path --work_dir="$cur_path/estimator_working_dir" --export_path="$cur_path/outputs/models/000001-first_generation""
+   exit 1
+fi
+
+for para in $*
+do
+   if [[ $para  == --data_path* ]];then
+      data_path=`echo ${para#*=}`
+    elif [[ $para == --bind_core* ]]; then
+        bind_core=`echo ${para#*=}`
+        name_bind="_bindcore"
+    elif [[ $para == --dynamic_input* ]];then
+      dynamic_input=`echo ${para#*=}` 
+	elif [[ $para == --server_index* ]];then
+        server_index=`echo ${para#*=}`
+    elif [[ $para == --conf_path* ]];then
+        conf_path=`echo ${para#*=}`
+    elif [[ $para == --devices_num* ]];then
+	    devices_num=`echo ${para#*=}`
+   fi
+done
+one_node_ip=`find $conf_path -name "server_*0.info"|awk -F "server_" '{print $2}'|awk -F "_" '{print $1}'`
+linux_num=`find $conf_path -name "server_*.info" |wc -l`
+if [[ $data_path  == "" ]];then
+    echo "[Error] para \"data_path\" must be config"
+    exit 1
+fi
+
+export RANK_SIZE=`awk 'BEGIN{printf "%.0f\n",'${devices_num}'*'${linux_num}'}'`
+rank_size=8
+nohup python3 set_ranktable.py --npu_nums=$linux_num --conf_path=$conf_path
+wait
+export RANK_TABLE_FILE=$cur_path/test/rank_table.json
+export JOB_ID=10087
+export DEVICE_INDEX=0
+
+##############执行训练##########
+cd $cur_path
+
+#(Step1)初始化  一定要先运行这一步
+python3 bootstrap.py --work_dir=$cur_path/estimator_working_dir --export_path=$cur_path/outputs/models/000000-bootstrap
+wait
+
+
+start=$(date +%s)
+
+# 8P训练模式
+for((RANK_ID=$((rank_size*server_index));RANK_ID<$((((server_index+1))*rank_size));RANK_ID++));
+do
+    #设置环境变量
+    
+    export ASCEND_DEVICE_ID=`expr ${RANK_ID} - $((rank_size*server_index))`
+    ASCEND_DEVICE_ID=`expr ${RANK_ID} - $((rank_size*server_index))`
+    export RANK_ID=$RANK_ID
+    echo "Device ID: $ASCEND_DEVICE_ID"
+
+    if [ -d $cur_path/test/output/$ASCEND_DEVICE_ID ];then
+        rm -rf $cur_path/test/output/$ASCEND_DEVICE_ID
+        mkdir -p $cur_path/test/output/$ASCEND_DEVICE_ID
+    else
+        mkdir -p $cur_path/test/output/$ASCEND_DEVICE_ID
+    fi
+    echo $ASCEND_DEVICE_ID
+    #(Step3)训练
+    corenum=`cat /proc/cpuinfo |grep 'processor' |wc -l`
+    let a=RANK_ID*${corenum}/8
+    let b=RANK_ID+1
+    let c=b*${corenum}/8-1
+    if [ "x${bind_core}" != x ];then
+        bind_core="taskset -c $a-$c"
+    fi
+    #${bind_core} python3 train.py --training_data_path=$data_path --steps_to_train=$train_steps --train_batch_size=$batch_size --work_dir=$cur_path/estimator_working_dir --export_path=$cur_path/outputs/models/000001-first_generation > $cur_path/test/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log 2>&1 &
+	${bind_core} python3 train.py \
+		--training_data_path=$data_path \
+		--steps_to_train=$train_steps \
+		--train_batch_size=$batch_size \
+		--work_dir=$cur_path/estimator_working_dir \
+		--export_path=$cur_path/outputs/models/000001-first_generation \
+		--dynamic_input=${dynamic_input} > $cur_path/test/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log 2>&1 &
+done
+wait
+
+end=$(date +%s)
+e2etime=$(( $end - $start ))
+
+#echo "Final Performance ms/step : $average_perf"
+echo "Final Training Duration sec : $e2etime"
+
+
+###下面字段用于冒烟看护
+BatchSize=${batch_size}
+#设备类型，自动获取
+DeviceType=`uname -m`
+#用例名称，自动获取
+CaseName=${Network}_bs${BatchSize}_${RankSize}'p'_'perf'
+
+#获取性能
+TrainingTime=`grep "tensorflow:global_step/sec" $cur_path/test/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk 'END {print $2}'`
+wait
+ActualFPS=`awk 'BEGIN{printf "%.2f\n", '${BatchSize}'*'${RankSize}'*'${TrainingTime}'}'`
+
+#从train_*.log中提取Loss到${CaseName}_loss.txt中
+grep "] loss" $cur_path/test/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk '{print $7}' |cut -d , -f 1 >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt
+ActualLoss=`awk 'END {print $1}' $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`
+
+#关键信息打印到CaseName.log中，此处无需修改
+echo "Network = ${Network}" > $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "RankSize = ${RankSize}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "DeviceType = ${DeviceType}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "CaseName = ${CaseName}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "BatchSize = ${batch_size}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainingTime = ${TrainingTime}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualFPS = ${ActualFPS}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualLoss = ${ActualLoss}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainAccuracy = ${ActualLoss}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "E2ETrainingTime = ${e2etime}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "DynamicInput = ${dynamic_input}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
diff --git a/TensorFlow2/built-in/cv/detection/Swin-Transformer_ID2412_for_TensorFlow2.X/swintransformer/train_main_16p.py b/TensorFlow2/built-in/cv/detection/Swin-Transformer_ID2412_for_TensorFlow2.X/swintransformer/train_main_16p.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1ba4ad948b302d6343410092e30bc4691db415d
--- /dev/null
+++ b/TensorFlow2/built-in/cv/detection/Swin-Transformer_ID2412_for_TensorFlow2.X/swintransformer/train_main_16p.py
@@ -0,0 +1,422 @@
+#
+# Copyright 2017 The TensorFlow 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.
+# ============================================================================
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# 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.
+#
+import math, re, os
+import tensorflow as tf
+import numpy as np
+from matplotlib import pyplot as plt
+#from kaggle_datasets import KaggleDatasets
+from sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix
+print("Tensorflow version " + tf.__version__)
+AUTO = tf.data.experimental.AUTOTUNE
+_NUM_TRAIN_FILES=15
+
+
+import sys
+sys.path.append('../swintransformer')
+from model import SwinTransformerModel
+from model import SwinTransformer
+
+import argparse
+import npu_device
+import ast
+parser = argparse.ArgumentParser()
+parser.add_argument("--epochs", default=12, type=int, help="train epochs")
+parser.add_argument("--train_steps", default=0, type=int, help="train steps per epoch")
+parser.add_argument("--nproc_per_node", default=1, type=int, help="npu_device_nums")
+parser.add_argument("--batch_size", default=16, type=int, help="global batch_size")
+parser.add_argument("--rank_size", default=1, type=int, help="rank size")
+parser.add_argument("--device_id", default=0, type=int, help="Ascend device id")
+parser.add_argument('--data_path', default="/home/data", type=str,help='the path to load data')
+parser.add_argument('--ckpt_path', default="/home/data", type=str,help='the path to load ckpt')
+parser.add_argument('--model_name', default="swin_large_224", type=str,help='model_name')
+parser.add_argument('--precision_mode', default="allow_mix_precision", type=str,help='the path to save over dump data')
+parser.add_argument('--over_dump', dest='over_dump', type=ast.literal_eval,
+                    help='if or not over detection, default is False')
+parser.add_argument('--data_dump_flag', dest='data_dump_flag', type=ast.literal_eval,
+                    help='data dump flag, default is False')
+parser.add_argument('--data_dump_step', default="10",
+                    help='data dump step, default is 10')
+parser.add_argument('--profiling', dest='profiling', type=ast.literal_eval,help='if or not profiling for performance debug, default is False')
+parser.add_argument('--profiling_dump_path', default="/home/data", type=str,help='the path to save profiling data')
+parser.add_argument('--over_dump_path', default="/home/data", type=str,help='the path to save over dump data')
+parser.add_argument('--data_dump_path', default="/home/data", type=str,help='the path to save dump data')
+parser.add_argument('--use_mixlist', dest='use_mixlist', type=ast.literal_eval,
+                    help='use_mixlist flag, default is False')
+parser.add_argument('--fusion_off_flag', dest='fusion_off_flag', type=ast.literal_eval,
+                    help='fusion_off flag, default is False')
+parser.add_argument('--mixlist_file', default="ops_info.json", type=str,help='mixlist file name, default is ops_info.json')
+parser.add_argument('--fusion_off_file', default="fusion_switch.cfg", type=str,help='fusion_off file name, default is fusion_switch.cfg')
+args = parser.parse_args()
+
+def npu_config():
+  if args.data_dump_flag:
+    npu_device.global_options().dump_config.enable_dump = True
+    npu_device.global_options().dump_config.dump_path = args.data_dump_path
+    npu_device.global_options().dump_config.dump_step = args.data_dump_step
+    npu_device.global_options().dump_config.dump_mode = "all"
+
+  if args.over_dump:
+      npu_device.global_options().dump_config.enable_dump_debug = True
+      npu_device.global_options().dump_config.dump_path = args.over_dump_path
+      npu_device.global_options().dump_config.dump_debug_mode = "all"
+
+  if args.profiling:
+      npu_device.global_options().profiling_config.enable_profiling = True
+      profiling_options = '{"output":"' + args.profiling_dump_path + '", \
+                          "training_trace":"on", \
+                          "task_trace":"on", \
+                          "aicpu":"on", \
+                          "aic_metrics":"PipeUtilization",\
+                          "fp_point":"", \
+                          "bp_point":""}'
+      npu_device.global_options().profiling_config.profiling_options = profiling_options
+  npu_device.global_options().precision_mode = args.precision_mode
+  if args.use_mixlist and args.precision_mode=='allow_mix_precision':
+    npu_device.global_options().modify_mixlist=args.mixlist_file
+  if args.fusion_off_flag:
+    npu_device.global_options().fusion_switch_file=args.fusion_off_file
+  npu_device.open().as_default()
+
+npu_config()
+
+try: # detect TPUs
+    tpu = tf.distribute.cluster_resolver.TPUClusterResolver.connect() # TPU detection
+    strategy = tf.distribute.TPUStrategy(tpu)
+except ValueError: # detect GPUs
+    #strategy = tf.distribute.MirroredStrategy() # for GPU or multi-GPU machines
+    strategy = tf.distribute.get_strategy() # default strategy that works on CPU and single GPU
+    #strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy() # for clusters of multi-GPU machines
+
+print("Number of accelerators: ", strategy.num_replicas_in_sync)
+
+#need modify according to local path
+#GCS_DS_PATH = KaggleDatasets().get_gcs_path("flower-classification")
+GCS_DS_PATH = args.data_path
+
+IMAGE_SIZE = [224, 224] # At this size, a GPU will run out of memory. Use the TPU.
+                        # For GPU training, please select 224 x 224 px image size.
+EPOCHS = args.epochs
+#BATCH_SIZE = 16 * strategy.num_replicas_in_sync
+BATCH_SIZE = args.batch_size
+TOTAL_BATCH_SIZE=args.nproc_per_node*args.batch_size
+
+GCS_PATH_SELECT = { # available image sizes
+    192: GCS_DS_PATH + '/tfrecords-jpeg-192x192',
+    224: GCS_DS_PATH + '/tfrecords-jpeg-224x224',
+    331: GCS_DS_PATH + '/tfrecords-jpeg-331x331',
+    512: GCS_DS_PATH + '/tfrecords-jpeg-512x512'
+}
+GCS_PATH = GCS_PATH_SELECT[IMAGE_SIZE[0]]
+
+TRAINING_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/train/*.tfrec')
+VALIDATION_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/val/*.tfrec')
+TEST_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/test/*.tfrec') # predictions on this dataset should be submitted for the competition
+#print(TRAINING_FILENAMES)
+#print(VALIDATION_FILENAMES)
+#print(TEST_FILENAMES)
+
+CLASSES = ['pink primrose',    'hard-leaved pocket orchid', 'canterbury bells', 'sweet pea',     'wild geranium',     'tiger lily',           'moon orchid',              'bird of paradise', 'monkshood',        'globe thistle',         # 00 - 09
+           'snapdragon',       "colt's foot",               'king protea',      'spear thistle', 'yellow iris',       'globe-flower',         'purple coneflower',        'peruvian lily',    'balloon flower',   'giant white arum lily', # 10 - 19
+           'fire lily',        'pincushion flower',         'fritillary',       'red ginger',    'grape hyacinth',    'corn poppy',           'prince of wales feathers', 'stemless gentian', 'artichoke',        'sweet william',         # 20 - 29
+           'carnation',        'garden phlox',              'love in the mist', 'cosmos',        'alpine sea holly',  'ruby-lipped cattleya', 'cape flower',              'great masterwort', 'siam tulip',       'lenten rose',           # 30 - 39
+           'barberton daisy',  'daffodil',                  'sword lily',       'poinsettia',    'bolero deep blue',  'wallflower',           'marigold',                 'buttercup',        'daisy',            'common dandelion',      # 40 - 49
+           'petunia',          'wild pansy',                'primula',          'sunflower',     'lilac hibiscus',    'bishop of llandaff',   'gaura',                    'geranium',         'orange dahlia',    'pink-yellow dahlia',    # 50 - 59
+           'cautleya spicata', 'japanese anemone',          'black-eyed susan', 'silverbush',    'californian poppy', 'osteospermum',         'spring crocus',            'iris',             'windflower',       'tree poppy',            # 60 - 69
+           'gazania',          'azalea',                    'water lily',       'rose',          'thorn apple',       'morning glory',        'passion flower',           'lotus',            'toad lily',        'anthurium',             # 70 - 79
+           'frangipani',       'clematis',                  'hibiscus',         'columbine',     'desert-rose',       'tree mallow',          'magnolia',                 'cyclamen ',        'watercress',       'canna lily',            # 80 - 89
+           'hippeastrum ',     'bee balm',                  'pink quill',       'foxglove',      'bougainvillea',     'camellia',             'mallow',                   'mexican petunia',  'bromelia',         'blanket flower',        # 90 - 99
+           'trumpet creeper',  'blackberry lily',           'common tulip',     'wild rose']
+
+# numpy and matplotlib defaults
+np.set_printoptions(threshold=15, linewidth=80)
+
+def batch_to_numpy_images_and_labels(data):
+    images, labels = data
+    numpy_images = images.numpy()
+    numpy_labels = labels.numpy()
+    if numpy_labels.dtype == object: # binary string in this case, these are image ID strings
+        numpy_labels = [None for _ in enumerate(numpy_images)]
+    # If no labels, only image IDs, return None for labels (this is the case for test data)
+    return numpy_images, numpy_labels
+
+def title_from_label_and_target(label, correct_label):
+    if correct_label is None:
+        return CLASSES[label], True
+    correct = (label == correct_label)
+    return "{} [{}{}{}]".format(CLASSES[label], 'OK' if correct else 'NO', u"\u2192" if not correct else '',
+                                CLASSES[correct_label] if not correct else ''), correct
+
+def display_one_flower(image, title, subplot, red=False, titlesize=16):
+    plt.subplot(*subplot)
+    plt.axis('off')
+    plt.imshow(image)
+    if len(title) > 0:
+        plt.title(title, fontsize=int(titlesize) if not red else int(titlesize/1.2), color='red' if red else 'black', fontdict={'verticalalignment':'center'}, pad=int(titlesize/1.5))
+    return (subplot[0], subplot[1], subplot[2]+1)
+    
+def display_batch_of_images(databatch, predictions=None):
+    """This will work with:
+    display_batch_of_images(images)
+    display_batch_of_images(images, predictions)
+    display_batch_of_images((images, labels))
+    display_batch_of_images((images, labels), predictions)
+    """
+    # data
+    images, labels = batch_to_numpy_images_and_labels(databatch)
+    if labels is None:
+        labels = [None for _ in enumerate(images)]
+        
+    # auto-squaring: this will drop data that does not fit into square or square-ish rectangle
+    rows = int(math.sqrt(len(images)))
+    cols = len(images)//rows
+        
+    # size and spacing
+    FIGSIZE = 13.0
+    SPACING = 0.1
+    subplot=(rows,cols,1)
+    if rows < cols:
+        plt.figure(figsize=(FIGSIZE,FIGSIZE/cols*rows))
+    else:
+        plt.figure(figsize=(FIGSIZE/rows*cols,FIGSIZE))
+    
+    # display
+    for i, (image, label) in enumerate(zip(images[:rows*cols], labels[:rows*cols])):
+        title = '' if label is None else CLASSES[label]
+        correct = True
+        if predictions is not None:
+            title, correct = title_from_label_and_target(predictions[i], label)
+        dynamic_titlesize = FIGSIZE*SPACING/max(rows,cols)*40+3 # magic formula tested to work from 1x1 to 10x10 images
+        subplot = display_one_flower(image, title, subplot, not correct, titlesize=dynamic_titlesize)
+    
+    #layout
+    plt.tight_layout()
+    if label is None and predictions is None:
+        plt.subplots_adjust(wspace=0, hspace=0)
+    else:
+        plt.subplots_adjust(wspace=SPACING, hspace=SPACING)
+    plt.show()
+
+def display_confusion_matrix(cmat, score, precision, recall):
+    plt.figure(figsize=(15,15))
+    ax = plt.gca()
+    ax.matshow(cmat, cmap='Reds')
+    ax.set_xticks(range(len(CLASSES)))
+    ax.set_xticklabels(CLASSES, fontdict={'fontsize': 7})
+    plt.setp(ax.get_xticklabels(), rotation=45, ha="left", rotation_mode="anchor")
+    ax.set_yticks(range(len(CLASSES)))
+    ax.set_yticklabels(CLASSES, fontdict={'fontsize': 7})
+    plt.setp(ax.get_yticklabels(), rotation=45, ha="right", rotation_mode="anchor")
+    titlestring = ""
+    if score is not None:
+        titlestring += 'f1 = {:.3f} '.format(score)
+    if precision is not None:
+        titlestring += '\nprecision = {:.3f} '.format(precision)
+    if recall is not None:
+        titlestring += '\nrecall = {:.3f} '.format(recall)
+    if len(titlestring) > 0:
+        ax.text(101, 1, titlestring, fontdict={'fontsize': 18, 'horizontalalignment':'right', 'verticalalignment':'top', 'color':'#804040'})
+    plt.show()
+    
+def display_training_curves(training, validation, title, subplot):
+    if subplot%10==1: # set up the subplots on the first call
+        plt.subplots(figsize=(10,10), facecolor='#F0F0F0')
+        plt.tight_layout()
+    ax = plt.subplot(subplot)
+    ax.set_facecolor('#F8F8F8')
+    ax.plot(training)
+    ax.plot(validation)
+    ax.set_title('model '+ title)
+    ax.set_ylabel(title)
+    #ax.set_ylim(0.28,1.05)
+    ax.set_xlabel('epoch')
+    ax.legend(['train', 'valid.'])
+
+def decode_image(image_data):
+    image = tf.image.decode_jpeg(image_data, channels=3)  # image format uint8 [0,255]
+    image = tf.reshape(image, [*IMAGE_SIZE, 3]) # explicit size needed for TPU
+    return image
+
+def read_labeled_tfrecord(example):
+    LABELED_TFREC_FORMAT = {
+        "image": tf.io.FixedLenFeature([], tf.string), # tf.string means bytestring
+        "class": tf.io.FixedLenFeature([], tf.int64),  # shape [] means single element
+    }
+    example = tf.io.parse_single_example(example, LABELED_TFREC_FORMAT)
+    image = decode_image(example['image'])
+    label = tf.cast(example['class'], tf.int32)
+    return image, label # returns a dataset of (image, label) pairs
+
+def read_unlabeled_tfrecord(example):
+    UNLABELED_TFREC_FORMAT = {
+        "image": tf.io.FixedLenFeature([], tf.string), # tf.string means bytestring
+        "id": tf.io.FixedLenFeature([], tf.string),  # shape [] means single element
+        # class is missing, this competitions's challenge is to predict flower classes for the test dataset
+    }
+    example = tf.io.parse_single_example(example, UNLABELED_TFREC_FORMAT)
+    image = decode_image(example['image'])
+    idnum = example['id']
+    return image, idnum # returns a dataset of image(s)
+
+def load_dataset(filenames, labeled=True, ordered=False):
+    # Read from TFRecords. For optimal performance, reading from multiple files at once and
+    # disregarding data order. Order does not matter since we will be shuffling the data anyway.
+
+    ignore_order = tf.data.Options()
+    if not ordered:
+        ignore_order.experimental_deterministic = False # disable order, increase speed
+    
+    dataset = tf.data.TFRecordDataset(filenames, num_parallel_reads=AUTO) # automatically interleaves reads from multiple files
+    #dataset= dataset.interleave(tf.data.TFRecordDataset,cycle_length=10,num_parallel_calls=1)
+    dataset = dataset.with_options(ignore_order) # uses data as soon as it streams in, rather than in its original order
+    #dataset= dataset.interleave(tf.data.TFRecordDataset,cycle_length=10,num_parallel_calls=10)
+    dataset = dataset.map(read_labeled_tfrecord if labeled else read_unlabeled_tfrecord, num_parallel_calls=AUTO)
+    # returns a dataset of (image, label) pairs if labeled=True or (image, id) pairs if labeled=False
+    return dataset
+
+def data_augment(image, label):
+    # data augmentation. Thanks to the dataset.prefetch(AUTO) statement in the next function (below),
+    # this happens essentially for free on TPU. Data pipeline code is executed on the "CPU" part
+    # of the TPU while the TPU itself is computing gradients.
+    image = tf.image.random_flip_left_right(image)
+    #image = tf.image.random_saturation(image, 0, 2)
+    return image, label   
+
+def get_training_dataset():
+    dataset = load_dataset(TRAINING_FILENAMES, labeled=True)
+    dataset, BATCH_SIZE = npu_device.distribute.shard_and_rebatch_dataset(dataset,TOTAL_BATCH_SIZE)
+    print('111111111111122221',str(BATCH_SIZE))
+    dataset = dataset.map(data_augment, num_parallel_calls=AUTO)
+    dataset = dataset.repeat() # the training dataset must repeat for several epochs
+    dataset = dataset.shuffle(2048)
+    dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)
+    dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size)
+    return dataset
+
+def get_validation_dataset(ordered=False):
+    dataset = load_dataset(VALIDATION_FILENAMES, labeled=True, ordered=ordered)
+    dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)
+    dataset = dataset.cache()
+    dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size)
+    return dataset
+
+def get_test_dataset(ordered=False):
+    dataset = load_dataset(TEST_FILENAMES, labeled=False, ordered=ordered)
+    dataset = dataset.batch(BATCH_SIZE)
+    dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size)
+    return dataset
+
+def count_data_items(filenames):
+    # the number of data items is written in the name of the .tfrec files, i.e. flowers00-230.tfrec = 230 data items
+    n = [int(re.compile(r"-([0-9]*)\.").search(filename).group(1)) for filename in filenames]
+    return np.sum(n)
+
+NUM_TRAINING_IMAGES = count_data_items(TRAINING_FILENAMES)
+NUM_VALIDATION_IMAGES = count_data_items(VALIDATION_FILENAMES)
+NUM_TEST_IMAGES = count_data_items(TEST_FILENAMES)
+STEPS_PER_EPOCH = args.train_steps if args.train_steps else NUM_TRAINING_IMAGES // BATCH_SIZE
+VALIDATION_STEPS = -(-NUM_VALIDATION_IMAGES // BATCH_SIZE) # The "-(-//)" trick rounds up instead of down :-)
+TEST_STEPS = -(-NUM_TEST_IMAGES // BATCH_SIZE)             # The "-(-//)" trick rounds up instead of down :-)
+print('Dataset: {} training images, {} validation images, {} unlabeled test images'.format(NUM_TRAINING_IMAGES, NUM_VALIDATION_IMAGES, NUM_TEST_IMAGES))
+
+'''
+# data dump
+print("Training data shapes:")
+for image, label in get_training_dataset().take(1):
+    print(image.numpy().shape, label.numpy().shape)
+    print(image.numpy())
+    print(label.numpy())
+print("Training data label examples:", label.numpy())
+print("Validation data shapes:")
+for image, label in get_validation_dataset().take(3):
+    print(image.numpy().shape, label.numpy().shape)
+print("Validation data label examples:", label.numpy())
+print("Test data shapes:")
+for image, idnum in get_test_dataset().take(3):
+    print(image.numpy().shape, idnum.numpy().shape)
+print("Test data IDs:", idnum.numpy().astype('U')) # U=unicode string
+'''
+
+# Peek at training data
+training_dataset = get_training_dataset()
+training_dataset = training_dataset.unbatch().batch(20)
+train_batch = iter(training_dataset)
+
+# run this cell again for next set of images
+#display_batch_of_images(next(train_batch))
+
+test_dataset = get_test_dataset()
+test_dataset = test_dataset.unbatch().batch(20)
+test_batch = iter(test_dataset)
+
+# run this cell again for next set of images
+#display_batch_of_images(next(test_batch))
+
+with strategy.scope():
+    img_adjust_layer = tf.keras.layers.Lambda(lambda data: tf.keras.applications.imagenet_utils.preprocess_input(tf.cast(data, tf.float32), mode="torch"), input_shape=[*IMAGE_SIZE, 3])
+    pretrained_model = SwinTransformer(args.ckpt_path, args.model_name, num_classes=len(CLASSES), include_top=False, pretrained=True, use_tpu=False)
+    
+    model = tf.keras.Sequential([
+        img_adjust_layer,
+        pretrained_model,
+        tf.keras.layers.Dense(len(CLASSES), activation='softmax')
+    ])
+opt = tf.keras.optimizers.Adam(learning_rate=1e-4, epsilon=1e-8)
+opt = npu_device.distribute.npu_distributed_keras_optimizer_wrapper(opt)
+model.compile(
+    optimizer=opt,
+    loss = 'sparse_categorical_crossentropy',
+    metrics=['sparse_categorical_accuracy']
+)
+
+training_vars = model.trainable_variables
+npu_device.distribute.broadcast(training_vars, root_rank=0)
+model.summary()
+current_iteration=0
+while current_iteration < EPOCHS: 
+    history = model.fit(get_training_dataset(), initial_epoch=current_iteration, epochs=current_iteration + 1, steps_per_epoch=STEPS_PER_EPOCH)
+    current_iteration = current_iteration+1
+    current_step = current_iteration*STEPS_PER_EPOCH-1
+
+cmdataset = get_validation_dataset(ordered=True) # since we are splitting the dataset and iterating separately on images and labels, order matters.
+images_ds = cmdataset.map(lambda image, label: image)
+labels_ds = cmdataset.map(lambda image, label: label).unbatch()
+cm_correct_labels = next(iter(labels_ds.batch(NUM_VALIDATION_IMAGES))).numpy() # get everything as one batch
+cm_probabilities = model.predict(images_ds, steps=VALIDATION_STEPS)
+cm_predictions = np.argmax(cm_probabilities, axis=-1)
+print("Correct   labels: ", cm_correct_labels.shape, cm_correct_labels)
+print("Predicted labels: ", cm_predictions.shape, cm_predictions)
+
+cmat = confusion_matrix(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)))
+score = f1_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)), average='macro')
+precision = precision_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)), average='macro')
+recall = recall_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)), average='macro')
+cmat = (cmat.T / cmat.sum(axis=1)).T # normalized
+#display_confusion_matrix(cmat, score, precision, recall)
+print('f1 score: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(score, precision, recall))                                                                                                                                           # 100 - 102
diff --git a/TensorFlow2/built-in/cv/detection/Swin-Transformer_ID2412_for_TensorFlow2.X/test/8p.json b/TensorFlow2/built-in/cv/detection/Swin-Transformer_ID2412_for_TensorFlow2.X/test/8p.json
new file mode 100644
index 0000000000000000000000000000000000000000..2e2fc25f515295921d79bd23bd95cc3f4607aff8
--- /dev/null
+++ b/TensorFlow2/built-in/cv/detection/Swin-Transformer_ID2412_for_TensorFlow2.X/test/8p.json
@@ -0,0 +1,52 @@
+{
+  "server_count":"1",
+  "server_list":[
+    {
+      "server_id":"127.0.0.1",
+      "device":[
+	{
+          "device_id":"0",
+	  "device_ip":"192.168.100.101",
+	  "rank_id":"0"
+	},
+	{
+          "device_id":"1",
+          "device_ip":"192.168.101.101",
+          "rank_id":"1"
+        },
+	{
+          "device_id":"2",
+          "device_ip":"192.168.102.101",
+          "rank_id":"2"
+        },
+	{
+          "device_id":"3",
+          "device_ip":"192.168.103.101",
+          "rank_id":"3"
+        },
+	{
+          "device_id":"4",
+          "device_ip":"192.168.100.100",
+          "rank_id":"4"
+        },
+	{
+          "device_id":"5",
+          "device_ip":"192.168.101.100",
+          "rank_id":"5"
+        },
+	{
+          "device_id":"6",
+          "device_ip":"192.168.102.100",
+          "rank_id":"6"
+        },
+	{
+          "device_id":"7",
+          "device_ip":"192.168.103.100",
+          "rank_id":"7"
+        }
+      ]
+    }
+  ],
+  "status":"completed",
+  "version":"1.0"
+}
diff --git a/TensorFlow2/built-in/cv/detection/Swin-Transformer_ID2412_for_TensorFlow2.X/test/set_ranktable.py b/TensorFlow2/built-in/cv/detection/Swin-Transformer_ID2412_for_TensorFlow2.X/test/set_ranktable.py
new file mode 100644
index 0000000000000000000000000000000000000000..c25b51462c5df2325462786688d4a206ee29fb9a
--- /dev/null
+++ b/TensorFlow2/built-in/cv/detection/Swin-Transformer_ID2412_for_TensorFlow2.X/test/set_ranktable.py
@@ -0,0 +1,1740 @@
+import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument('-n', '--npu_nums', type=int, default='2', help='nums of npu')
+parser.add_argument('-c', '--conf_path', type=str, default='./', help='the path of server_info')
+FLAGS = parser.parse_args()
+
+import json
+import os
+server = []
+server_conf = []
+server_list = ["0", "1", "2", "3", "4", "5", "6", "7"]
+if os.path.isdir(FLAGS.conf_path):
+	for f in os.listdir(FLAGS.conf_path):
+		if (f.split("_")[-1]).split(".")[0] in server_list and (f.split("_")[-1]).split(".")[1] == 'info' and f.split("_")[0] == 'server':
+			server_conf.append(f)
+			
+
+
+
+
+
+rank_address = []
+for i in range(FLAGS.npu_nums):
+	for x in server_conf:
+		if (x.split("_")[-1]).split(".")[0] == str(i):
+			server.append(x.split("_")[1])
+			l = FLAGS.conf_path + "/" + x
+			with open(l, "r") as a:
+				s = a.readlines()
+				for s_ in s:
+					if 'address_0' in s_:
+						rank_address.append(s_.split("=")[-1][:-1])
+				for s_ in s:
+					if 'address_1' in s_:
+						rank_address.append(s_.split("=")[-1][:-1])
+				for s_ in s:
+					if 'address_2' in s_:
+						rank_address.append(s_.split("=")[-1][:-1])
+				for s_ in s:
+					if 'address_3' in s_:
+						rank_address.append(s_.split("=")[-1][:-1])
+				for s_ in s:
+					if 'address_4' in s_:
+						rank_address.append(s_.split("=")[-1][:-1])
+				for s_ in s:
+					if 'address_5' in s_:
+						rank_address.append(s_.split("=")[-1][:-1])
+				for s_ in s:
+					if 'address_6' in s_:
+						rank_address.append(s_.split("=")[-1][:-1])
+				for s_ in s:
+					if 'address_7' in s_:
+						rank_address.append(s_.split("=")[-1][:-1])
+			
+if FLAGS.npu_nums == 1:
+	rank = {
+		"server_count":"1",
+		"server_list":[
+			{
+				"server_id":server[0],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[0],
+						"rank_id":"0"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[1],
+						"rank_id":"1"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[2],
+						"rank_id":"2"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[3],
+						"rank_id":"3"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[4],
+						"rank_id":"4"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[5],
+						"rank_id":"5"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[6],
+						"rank_id":"6"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[7],
+						"rank_id":"7"
+					}
+				]}],
+		"status":"completed",
+		"version":"1.0"
+	}
+elif FLAGS.npu_nums == 2:
+	rank = {
+		"server_count":"2",
+		"server_list":[
+			{
+				"server_id":server[0],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[0],
+						"rank_id":"0"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[1],
+						"rank_id":"1"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[2],
+						"rank_id":"2"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[3],
+						"rank_id":"3"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[4],
+						"rank_id":"4"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[5],
+						"rank_id":"5"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[6],
+						"rank_id":"6"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[7],
+						"rank_id":"7"
+					}
+				]},
+				
+				
+				{
+				"server_id":server[1],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[8],
+						"rank_id":"8"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[9],
+						"rank_id":"9"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[10],
+						"rank_id":"10"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[11],
+						"rank_id":"11"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[12],
+						"rank_id":"12"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[13],
+						"rank_id":"13"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[14],
+						"rank_id":"14"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[15],
+						"rank_id":"15"
+					}
+				]}
+				],
+		
+		"status":"completed",
+		"version":"1.0"
+	}
+	
+	
+elif FLAGS.npu_nums == 3:
+	rank = {
+		"server_count":"3",
+		"server_list":[
+			{
+				"server_id":server[0],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[0],
+						"rank_id":"0"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[1],
+						"rank_id":"1"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[2],
+						"rank_id":"2"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[3],
+						"rank_id":"3"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[4],
+						"rank_id":"4"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[5],
+						"rank_id":"5"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[6],
+						"rank_id":"6"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[7],
+						"rank_id":"7"
+					}
+				]},
+				
+				
+				{
+				"server_id":server[1],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[8],
+						"rank_id":"8"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[9],
+						"rank_id":"9"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[10],
+						"rank_id":"10"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[11],
+						"rank_id":"11"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[12],
+						"rank_id":"12"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[13],
+						"rank_id":"13"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[14],
+						"rank_id":"14"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[15],
+						"rank_id":"15"
+					}
+				]},
+				
+				{
+				"server_id":server[2],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[16],
+						"rank_id":"16"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[17],
+						"rank_id":"17"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[18],
+						"rank_id":"18"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[19],
+						"rank_id":"19"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[20],
+						"rank_id":"20"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[21],
+						"rank_id":"21"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[22],
+						"rank_id":"22"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[23],
+						"rank_id":"23"
+					}
+				]}
+				],
+		"status":"completed",
+		"version":"1.0"
+	}
+elif FLAGS.npu_nums == 4:
+	rank = {
+		"server_count":"4",
+		"server_list":[
+			{
+				"server_id":server[0],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[0],
+						"rank_id":"0"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[1],
+						"rank_id":"1"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[2],
+						"rank_id":"2"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[3],
+						"rank_id":"3"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[4],
+						"rank_id":"4"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[5],
+						"rank_id":"5"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[6],
+						"rank_id":"6"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[7],
+						"rank_id":"7"
+					}
+				]},
+				
+				
+				{
+				"server_id":server[1],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[8],
+						"rank_id":"8"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[9],
+						"rank_id":"9"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[10],
+						"rank_id":"10"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[11],
+						"rank_id":"11"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[12],
+						"rank_id":"12"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[13],
+						"rank_id":"13"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[14],
+						"rank_id":"14"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[15],
+						"rank_id":"15"
+					}
+				]},
+				{
+				"server_id":server[2],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[16],
+						"rank_id":"16"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[17],
+						"rank_id":"17"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[18],
+						"rank_id":"18"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[19],
+						"rank_id":"19"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[20],
+						"rank_id":"20"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[21],
+						"rank_id":"21"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[22],
+						"rank_id":"22"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[23],
+						"rank_id":"23"
+					}
+				]},
+				{
+				"server_id":server[3],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[24],
+						"rank_id":"24"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[25],
+						"rank_id":"25"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[26],
+						"rank_id":"26"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[27],
+						"rank_id":"27"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[28],
+						"rank_id":"28"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[29],
+						"rank_id":"29"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[30],
+						"rank_id":"30"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[31],
+						"rank_id":"31"
+					}
+				]}
+				],
+		"status":"completed",
+		"version":"1.0"
+	}
+elif FLAGS.npu_nums == 5:
+	rank = {
+		"server_count":"5",
+		"server_list":[
+			{
+				"server_id":server[0],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[0],
+						"rank_id":"0"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[1],
+						"rank_id":"1"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[2],
+						"rank_id":"2"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[3],
+						"rank_id":"3"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[4],
+						"rank_id":"4"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[5],
+						"rank_id":"5"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[6],
+						"rank_id":"6"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[7],
+						"rank_id":"7"
+					}
+				]},
+				
+				
+				{
+				"server_id":server[1],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[8],
+						"rank_id":"8"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[9],
+						"rank_id":"9"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[10],
+						"rank_id":"10"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[11],
+						"rank_id":"11"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[12],
+						"rank_id":"12"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[13],
+						"rank_id":"13"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[14],
+						"rank_id":"14"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[15],
+						"rank_id":"15"
+					}
+				]},
+				{
+				"server_id":server[2],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[16],
+						"rank_id":"16"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[17],
+						"rank_id":"17"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[18],
+						"rank_id":"18"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[19],
+						"rank_id":"19"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[20],
+						"rank_id":"20"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[21],
+						"rank_id":"21"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[22],
+						"rank_id":"22"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[23],
+						"rank_id":"23"
+					}
+				]},
+				{
+				"server_id":server[3],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[24],
+						"rank_id":"24"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[25],
+						"rank_id":"25"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[26],
+						"rank_id":"26"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[27],
+						"rank_id":"27"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[28],
+						"rank_id":"28"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[29],
+						"rank_id":"29"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[30],
+						"rank_id":"30"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[31],
+						"rank_id":"31"
+					}
+				]},
+				{
+				"server_id":server[4],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[32],
+						"rank_id":"32"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[33],
+						"rank_id":"33"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[34],
+						"rank_id":"34"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[35],
+						"rank_id":"35"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[36],
+						"rank_id":"36"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[37],
+						"rank_id":"37"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[38],
+						"rank_id":"38"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[39],
+						"rank_id":"39"
+					}
+				]}
+				],
+		"status":"completed",
+		"version":"1.0"
+	}
+
+
+
+elif FLAGS.npu_nums == 6:
+	rank = {
+		"server_count":"6",
+		"server_list":[
+			{
+				"server_id":server[0],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[0],
+						"rank_id":"0"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[1],
+						"rank_id":"1"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[2],
+						"rank_id":"2"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[3],
+						"rank_id":"3"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[4],
+						"rank_id":"4"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[5],
+						"rank_id":"5"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[6],
+						"rank_id":"6"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[7],
+						"rank_id":"7"
+					}
+				]},
+				
+				
+				{
+				"server_id":server[1],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[8],
+						"rank_id":"8"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[9],
+						"rank_id":"9"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[10],
+						"rank_id":"10"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[11],
+						"rank_id":"11"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[12],
+						"rank_id":"12"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[13],
+						"rank_id":"13"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[14],
+						"rank_id":"14"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[15],
+						"rank_id":"15"
+					}
+				]},
+				{
+				"server_id":server[2],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[16],
+						"rank_id":"16"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[17],
+						"rank_id":"17"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[18],
+						"rank_id":"18"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[19],
+						"rank_id":"19"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[20],
+						"rank_id":"20"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[21],
+						"rank_id":"21"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[22],
+						"rank_id":"22"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[23],
+						"rank_id":"23"
+					}
+				]},
+				{
+				"server_id":server[3],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[24],
+						"rank_id":"24"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[25],
+						"rank_id":"25"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[26],
+						"rank_id":"26"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[27],
+						"rank_id":"27"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[28],
+						"rank_id":"28"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[29],
+						"rank_id":"29"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[30],
+						"rank_id":"30"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[31],
+						"rank_id":"31"
+					}
+				]},
+				{
+				"server_id":server[4],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[32],
+						"rank_id":"32"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[33],
+						"rank_id":"33"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[34],
+						"rank_id":"34"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[35],
+						"rank_id":"35"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[36],
+						"rank_id":"36"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[37],
+						"rank_id":"37"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[38],
+						"rank_id":"38"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[39],
+						"rank_id":"39"
+					}
+				]},
+				{
+				"server_id":server[5],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[40],
+						"rank_id":"40"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[41],
+						"rank_id":"41"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[42],
+						"rank_id":"42"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[43],
+						"rank_id":"43"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[44],
+						"rank_id":"44"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[45],
+						"rank_id":"45"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[46],
+						"rank_id":"46"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[47],
+						"rank_id":"47"
+					}
+				]}
+				],
+		"status":"completed",
+		"version":"1.0"
+	}
+	
+	
+elif FLAGS.npu_nums == 7:
+	rank = {
+		"server_count":"7",
+		"server_list":[
+			{
+				"server_id":server[0],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[0],
+						"rank_id":"0"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[1],
+						"rank_id":"1"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[2],
+						"rank_id":"2"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[3],
+						"rank_id":"3"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[4],
+						"rank_id":"4"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[5],
+						"rank_id":"5"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[6],
+						"rank_id":"6"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[7],
+						"rank_id":"7"
+					}
+				]},
+				
+				
+				{
+				"server_id":server[1],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[8],
+						"rank_id":"8"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[9],
+						"rank_id":"9"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[10],
+						"rank_id":"10"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[11],
+						"rank_id":"11"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[12],
+						"rank_id":"12"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[13],
+						"rank_id":"13"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[14],
+						"rank_id":"14"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[15],
+						"rank_id":"15"
+					}
+				]},
+				{
+				"server_id":server[2],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[16],
+						"rank_id":"16"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[17],
+						"rank_id":"17"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[18],
+						"rank_id":"18"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[19],
+						"rank_id":"19"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[20],
+						"rank_id":"20"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[21],
+						"rank_id":"21"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[22],
+						"rank_id":"22"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[23],
+						"rank_id":"23"
+					}
+				]},
+				{
+				"server_id":server[3],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[24],
+						"rank_id":"24"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[25],
+						"rank_id":"25"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[26],
+						"rank_id":"26"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[27],
+						"rank_id":"27"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[28],
+						"rank_id":"28"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[29],
+						"rank_id":"29"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[30],
+						"rank_id":"30"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[31],
+						"rank_id":"31"
+					}
+				]},
+				{
+				"server_id":server[4],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[32],
+						"rank_id":"32"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[33],
+						"rank_id":"33"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[34],
+						"rank_id":"34"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[35],
+						"rank_id":"35"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[36],
+						"rank_id":"36"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[37],
+						"rank_id":"37"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[38],
+						"rank_id":"38"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[39],
+						"rank_id":"39"
+					}
+				]},
+				{
+				"server_id":server[5],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[40],
+						"rank_id":"40"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[41],
+						"rank_id":"41"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[42],
+						"rank_id":"42"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[43],
+						"rank_id":"43"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[44],
+						"rank_id":"44"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[45],
+						"rank_id":"45"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[46],
+						"rank_id":"46"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[47],
+						"rank_id":"47"
+					}
+				]},
+				{
+				"server_id":server[6],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[48],
+						"rank_id":"48"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[49],
+						"rank_id":"49"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[50],
+						"rank_id":"50"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[51],
+						"rank_id":"51"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[52],
+						"rank_id":"52"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[53],
+						"rank_id":"53"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[54],
+						"rank_id":"54"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[55],
+						"rank_id":"55"
+					}
+				]}
+				],
+		"status":"completed",
+		"version":"1.0"
+	}
+	
+	
+	
+	
+elif FLAGS.npu_nums == 8:
+	rank = {
+		"server_count":"8",
+		"server_list":[
+			{
+				"server_id":server[0],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[0],
+						"rank_id":"0"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[1],
+						"rank_id":"1"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[2],
+						"rank_id":"2"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[3],
+						"rank_id":"3"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[4],
+						"rank_id":"4"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[5],
+						"rank_id":"5"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[6],
+						"rank_id":"6"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[7],
+						"rank_id":"7"
+					}
+				]},
+				
+				
+				{
+				"server_id":server[1],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[8],
+						"rank_id":"8"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[9],
+						"rank_id":"9"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[10],
+						"rank_id":"10"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[11],
+						"rank_id":"11"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[12],
+						"rank_id":"12"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[13],
+						"rank_id":"13"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[14],
+						"rank_id":"14"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[15],
+						"rank_id":"15"
+					}
+				]},
+				{
+				"server_id":server[2],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[16],
+						"rank_id":"16"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[17],
+						"rank_id":"17"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[18],
+						"rank_id":"18"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[19],
+						"rank_id":"19"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[20],
+						"rank_id":"20"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[21],
+						"rank_id":"21"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[22],
+						"rank_id":"22"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[23],
+						"rank_id":"23"
+					}
+				]},
+				{
+				"server_id":server[3],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[24],
+						"rank_id":"24"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[25],
+						"rank_id":"25"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[26],
+						"rank_id":"26"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[27],
+						"rank_id":"27"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[28],
+						"rank_id":"28"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[29],
+						"rank_id":"29"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[30],
+						"rank_id":"30"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[31],
+						"rank_id":"31"
+					}
+				]},
+				{
+				"server_id":server[4],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[32],
+						"rank_id":"32"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[33],
+						"rank_id":"33"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[34],
+						"rank_id":"34"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[35],
+						"rank_id":"35"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[36],
+						"rank_id":"36"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[37],
+						"rank_id":"37"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[38],
+						"rank_id":"38"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[39],
+						"rank_id":"39"
+					}
+				]},
+				{
+				"server_id":server[5],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[40],
+						"rank_id":"40"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[41],
+						"rank_id":"41"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[42],
+						"rank_id":"42"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[43],
+						"rank_id":"43"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[44],
+						"rank_id":"44"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[45],
+						"rank_id":"45"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[46],
+						"rank_id":"46"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[47],
+						"rank_id":"47"
+					}
+				]},
+				{
+				"server_id":server[6],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[48],
+						"rank_id":"48"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[49],
+						"rank_id":"49"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[50],
+						"rank_id":"50"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[51],
+						"rank_id":"51"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[52],
+						"rank_id":"52"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[53],
+						"rank_id":"53"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[54],
+						"rank_id":"54"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[55],
+						"rank_id":"55"
+					}
+				]},
+				{
+				"server_id":server[7],
+				"device":[
+					{
+						"device_id":"0",
+						"device_ip":rank_address[56],
+						"rank_id":"56"
+					},
+					{
+						"device_id":"1",
+						"device_ip":rank_address[57],
+						"rank_id":"57"
+					},
+					{
+						"device_id":"2",
+						"device_ip":rank_address[58],
+						"rank_id":"58"
+					},
+					{
+						"device_id":"3",
+						"device_ip":rank_address[59],
+						"rank_id":"59"
+					},
+					{
+						"device_id":"4",
+						"device_ip":rank_address[60],
+						"rank_id":"60"
+					},
+					{
+						"device_id":"5",
+						"device_ip":rank_address[61],
+						"rank_id":"61"
+					},
+					{
+						"device_id":"6",
+						"device_ip":rank_address[62],
+						"rank_id":"62"
+					},
+					{
+						"device_id":"7",
+						"device_ip":rank_address[63],
+						"rank_id":"63"
+					}
+				]}
+				],
+		"status":"completed",
+		"version":"1.0"
+	}
+	
+	
+	
+	
+with open("rank_table.json", "w") as f:
+	json.dump(rank, f)
+
+
+
+
+
+
diff --git a/TensorFlow2/built-in/cv/detection/Swin-Transformer_ID2412_for_TensorFlow2.X/test/train_performance_16p.sh b/TensorFlow2/built-in/cv/detection/Swin-Transformer_ID2412_for_TensorFlow2.X/test/train_performance_16p.sh
new file mode 100644
index 0000000000000000000000000000000000000000..1db12d29f1c866a33ac05d09e95d91870fac7d81
--- /dev/null
+++ b/TensorFlow2/built-in/cv/detection/Swin-Transformer_ID2412_for_TensorFlow2.X/test/train_performance_16p.sh
@@ -0,0 +1,230 @@
+#!/bin/bash
+cur_path=`pwd`
+#失败用例打屏
+export RANK_SIZE=16
+export JOB_ID=10087
+#基础参数，需要模型审视修改
+#网络名称，同目录名称
+Network="Swin-Transformer_ID2412_for_TensorFlow2.X"
+#Device数量，单卡默认为1
+RankSize=1
+#Batch Size
+batch_size=256
+#训练epoch，可选
+epochs=3
+#训练step
+train_steps=10
+conf_path=""
+server_index=""
+############维测参数##############
+precision_mode="allow_mix_precision"
+#维持参数，以下不需要修改
+over_dump=False
+if [[ $over_dump == True ]];then
+    over_dump_path=$cur_path/overflow_dump #此处cur_path为代码根目录
+    mkdir -p ${over_dump_path}
+fi
+data_dump_flag=False
+data_dump_step="10"
+profiling=False
+use_mixlist=True
+mixlist_file="../configs/ops_info.json"
+fusion_off_flag=False
+fusion_off_file="../configs/fusion_switch.cfg"
+############维测参数##############
+
+#参数配置
+data_path=""
+ckpt_path=""
+model_name=""
+
+if [[ $1 == --help || $1 == --h ]];then
+    echo"usage:./train_full_1p.sh <args>"
+    echo " "
+    echo "parameter explain:
+    --precision_mode         precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision)
+    --over_dump                 if or not over detection, default is False
+    --data_dump_flag         data dump flag, default is False
+    --data_dump_step         data dump step, default is 10
+    --profiling                 if or not profiling for performance debug, default is False
+    --autotune               whether to enable autotune, default is False
+    --data_path                 source data of training
+    --ckpt_path                 source ckpt of training
+    -h/--help                 show help message
+    "
+   exit 1
+fi
+
+##############执行训练##########
+cd $cur_path
+if [ -d $cur_path/output ];then
+   rm -rf $cur_path/output/*
+   mkdir -p $cur_path/output/$ASCEND_DEVICE_ID
+else
+   mkdir -p $cur_path/output/$ASCEND_DEVICE_ID
+fi
+wait
+
+for para in $*
+do
+    if [[ $para == --data_path* ]];then
+        data_path=`echo ${para#*=}`
+    elif [[ $para == --ckpt_path* ]];then
+        ckpt_path=`echo ${para#*=}`
+    elif [[ $para == --precision_mode* ]];then
+        precision_mode=`echo ${para#*=}`
+    elif [[ $para == --over_dump* ]];then
+        over_dump=`echo ${para#*=}`
+        over_dump_path=${cur_path}/output/overflow_dump
+        mkdir -p ${over_dump_path}
+    elif [[ $para == --data_dump_flag* ]];then
+        data_dump_flag=`echo ${para#*=}`
+        data_dump_path=${cur_path}/output/data_dump
+        mkdir -p ${data_dump_path}
+    elif [[ $para == --data_dump_step* ]];then
+        data_dump_step=`echo ${para#*=}`
+    elif [[ $para == --profiling* ]];then
+        profiling=`echo ${para#*=}`
+        profiling_dump_path=${cur_path}/output/profiling
+        mkdir -p ${profiling_dump_path}
+    elif [[ $para == --data_path* ]];then
+        data_path=`echo ${para#*=}`
+    elif [[ $para == --use_mixlist* ]];then
+        use_mixlist=`echo ${para#*=}`
+    elif [[ $para == --mixlist_file* ]];then
+        mixlist_file=`echo ${para#*=}`
+    elif [[ $para == --fusion_off_flag* ]];then
+        fusion_off_flag=`echo ${para#*=}`
+    elif [[ $para == --fusion_off_file* ]];then
+        fusion_off_file=`echo ${para#*=}`
+    elif [[ $para == --server_index* ]];then
+        server_index=`echo ${para#*=}`
+    elif [[ $para == --conf_path* ]];then
+        conf_path=`echo ${para#*=}`
+    fi
+done
+
+
+if [[ $data_path  == "" ]];then
+   echo "[Error] para \"data_path\" must be config"
+   exit 1
+elif [[ $ckpt_path == "" ]];then
+   echo "[Error] para \"ckpt_path\" must be config"
+   exit 1
+fi
+rank_size=8
+#export RANK_TABLE_FILE=$cur_path/../scripts/rank_table_16p.json
+nohup python3 $cur_path/set_ranktable.py --npu_nums=$((RANK_SIZE/rank_size)) --conf_path=$conf_path
+export RANK_TABLE_FILE=${cur_path}/rank_table.json
+export HCCL_CONNECT_TIMEOUT=600
+export RANK_INDEX=0
+RANK_ID_START=0
+RANK_SIZE=16
+
+
+
+
+
+
+
+start=$(date +%s)
+
+RANK_ID_START=0
+for((RANK_ID=$((rank_size*server_index));RANK_ID<$((((server_index+1))*rank_size));RANK_ID++));
+do
+    # 设置环境变量
+    export RANK_ID=$RANK_ID
+    export ASCEND_DEVICE_ID=`expr ${RANK_ID} - $((rank_size*server_index))`
+    ASCEND_DEVICE_ID=`expr ${RANK_ID} - $((rank_size*server_index))`
+    export DEVICE_ID=${ASCEND_DEVICE_ID}
+    echo "Device ID: $ASCEND_DEVICE_ID"
+    if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then
+        rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID}
+        mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID
+    else
+        mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID
+    fi
+
+	python3 ../swintransformer/train_main_16p.py \
+        --data_path=${data_path} \
+        --ckpt_path=${ckpt_path} \
+        --model_name="swin_large_224" \
+        --epochs=${epochs} \
+        --train_steps=${train_steps} \
+        --precision_mode=${precision_mode} \
+        --over_dump=${over_dump} \
+        --over_dump_path=${over_dump_path} \
+        --data_dump_flag=${data_dump_flag} \
+        --data_dump_step=${data_dump_step} \
+        --data_dump_path=${data_dump_path} \
+        --profiling=${profiling} \
+        --use_mixlist=${use_mixlist} \
+        --fusion_off_flag=${fusion_off_flag} \
+        --mixlist_file=${mixlist_file} \
+        --fusion_off_file=${fusion_off_file} \
+        --profiling_dump_path=${profiling_dump_path}} \
+	--batch_size 16 \
+        --nproc_per_node=16 	>$cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log 2>&1 &
+done
+wait
+end=$(date +%s)
+e2etime=$(( $end - $start ))
+
+#echo "Final Performance ms/step : $average_perf"
+echo "Final Training Duration sec : $e2etime"
+
+###下面字段用于冒烟看护
+BatchSize=${batch_size}
+#设备类型，自动获取
+DeviceType=`uname -m`
+#用例名称，自动获取
+CaseName=${Network}_bs${BatchSize}_${RankSize}'p'_'perf'
+
+echo "------------------ Final result ------------------"
+#输出性能FPS，需要模型审视修改
+cp -f $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log.bak
+sed -i 's/\x0d/\n/g' $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log.bak
+single_batch_step_sec=`grep ms/step  $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log.bak | awk 'END {print $5}' | awk -F 'm' '{print $1}'`
+
+FPS=`echo ${single_batch_step_sec} ${batch_size} | awk '{print $2 * 1000 / $1}'`
+
+#打印，不需要修改
+echo "Final Performance images/sec : $FPS"
+
+grep "ms/step" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log.bak|awk '{print$8}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt
+ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`
+#输出训练精度,需要模型审视修改
+grep "f1 score" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_acc.txt
+train_accuracy=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_acc.txt |awk '{print $3}' |awk -F "," '{print $1}'`
+#打印，不需要修改
+echo "Final Train Accuracy : ${train_accuracy}"
+echo "E2E Training Duration sec : $e2e_time"
+
+##获取性能数据
+#吞吐量，不需要修改
+ActualFPS=${FPS}
+#单迭代训练时长，不需要修改
+TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'*'${RankSize}'*1000/'${FPS}'}'`
+
+##获取错误信息
+#系统错误信息
+error_msg="the shape of grad must equal with var"
+#判断错误信息是否和历史状态一致，此处无需修改
+Status=`grep "${error_msg}" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | wc -l`
+#失败阶段，枚举值图准备FAIL/图拆分FAIL/图优化FAIL/图编译FAIL/图执行FAIL/流程OK
+ModelStatus="流程OK"
+#DTS单号或者issue链接
+DTS_Number=""
+
+#关键信息打印到CaseName.log中，此处无需修改
+echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "RankSize = ${RankSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "E2ETrainingTime = ${e2etime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+
diff --git a/TensorFlow2/built-in/cv/detection/Swin-Transformer_ID2412_for_TensorFlow2.X/test/train_performance_8p.sh b/TensorFlow2/built-in/cv/detection/Swin-Transformer_ID2412_for_TensorFlow2.X/test/train_performance_8p.sh
new file mode 100644
index 0000000000000000000000000000000000000000..9ea83e280f03da2f33cd0f83f99a548c7812677b
--- /dev/null
+++ b/TensorFlow2/built-in/cv/detection/Swin-Transformer_ID2412_for_TensorFlow2.X/test/train_performance_8p.sh
@@ -0,0 +1,211 @@
+#!/bin/bash
+cur_path=`pwd`
+
+#失败用例打屏
+export RANK_SIZE=8
+export JOB_ID=10087
+export RANK_TABLE_FILE=8p.json
+#基础参数，需要模型审视修改
+#网络名称，同目录名称
+Network="Swin-Transformer_ID2412_for_TensorFlow2.X"
+#Device数量，单卡默认为1
+RankSize=1
+#Batch Size
+batch_size=128
+#训练epoch，可选
+epochs=3
+#训练step
+train_steps=10
+
+############维测参数##############
+precision_mode="allow_mix_precision"
+#维持参数，以下不需要修改
+over_dump=False
+if [[ $over_dump == True ]];then
+    over_dump_path=$cur_path/overflow_dump #此处cur_path为代码根目录
+    mkdir -p ${over_dump_path}
+fi
+data_dump_flag=False
+data_dump_step="10"
+profiling=False
+use_mixlist=True
+mixlist_file="../configs/ops_info.json"
+fusion_off_flag=False
+fusion_off_file="../configs/fusion_switch.cfg"
+############维测参数##############
+
+#参数配置
+data_path="/npu/traindata/dataset_swin/"
+ckpt_path="/npu/traindata/pretrain/"
+model_name=""
+
+if [[ $1 == --help || $1 == --h ]];then
+    echo"usage:./train_full_1p.sh <args>"
+    echo " "
+    echo "parameter explain:
+    --precision_mode         precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision)
+    --over_dump                 if or not over detection, default is False
+    --data_dump_flag         data dump flag, default is False
+    --data_dump_step         data dump step, default is 10
+    --profiling                 if or not profiling for performance debug, default is False
+    --autotune               whether to enable autotune, default is False
+    --data_path                 source data of training
+    --ckpt_path                 source ckpt of training
+    -h/--help                 show help message
+    "
+   exit 1
+fi
+
+##############执行训练##########
+cd $cur_path
+if [ -d $cur_path/output ];then
+   rm -rf $cur_path/output/*
+   mkdir -p $cur_path/output/$ASCEND_DEVICE_ID
+else
+   mkdir -p $cur_path/output/$ASCEND_DEVICE_ID
+fi
+wait
+
+for para in $*
+do
+    if [[ $para == --data_path* ]];then
+        data_path=`echo ${para#*=}`
+    elif [[ $para == --ckpt_path* ]];then
+        ckpt_path=`echo ${para#*=}`
+    elif [[ $para == --precision_mode* ]];then
+        precision_mode=`echo ${para#*=}`
+    elif [[ $para == --over_dump* ]];then
+        over_dump=`echo ${para#*=}`
+        over_dump_path=${cur_path}/output/overflow_dump
+        mkdir -p ${over_dump_path}
+    elif [[ $para == --data_dump_flag* ]];then
+        data_dump_flag=`echo ${para#*=}`
+        data_dump_path=${cur_path}/output/data_dump
+        mkdir -p ${data_dump_path}
+    elif [[ $para == --data_dump_step* ]];then
+        data_dump_step=`echo ${para#*=}`
+    elif [[ $para == --profiling* ]];then
+        profiling=`echo ${para#*=}`
+        profiling_dump_path=${cur_path}/output/profiling
+        mkdir -p ${profiling_dump_path}
+    elif [[ $para == --data_path* ]];then
+        data_path=`echo ${para#*=}`
+    elif [[ $para == --use_mixlist* ]];then
+        use_mixlist=`echo ${para#*=}`
+    elif [[ $para == --mixlist_file* ]];then
+        mixlist_file=`echo ${para#*=}`
+    elif [[ $para == --fusion_off_flag* ]];then
+        fusion_off_flag=`echo ${para#*=}`
+    elif [[ $para == --fusion_off_file* ]];then
+        fusion_off_file=`echo ${para#*=}`
+    fi
+done
+
+
+if [[ $data_path  == "" ]];then
+   echo "[Error] para \"data_path\" must be config"
+   exit 1
+elif [[ $ckpt_path == "" ]];then
+   echo "[Error] para \"ckpt_path\" must be config"
+   exit 1
+fi
+
+
+start=$(date +%s)
+
+RANK_ID_START=0
+for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++));
+do	
+    export RANK_ID=$RANK_ID
+    export ASCEND_DEVICE_ID=$RANK_ID
+    echo "Device ID: $ASCEND_DEVICE_ID"
+    if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then
+        rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID}
+        mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID
+    else
+        mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID
+    fi
+
+	python3 ../swintransformer/train_main_16p.py \
+        --data_path=${data_path} \
+        --ckpt_path=${ckpt_path} \
+        --model_name="swin_large_224" \
+        --epochs=${epochs} \
+        --train_steps=${train_steps} \
+        --precision_mode=${precision_mode} \
+        --over_dump=${over_dump} \
+        --over_dump_path=${over_dump_path} \
+        --data_dump_flag=${data_dump_flag} \
+        --data_dump_step=${data_dump_step} \
+        --data_dump_path=${data_dump_path} \
+        --profiling=${profiling} \
+        --use_mixlist=${use_mixlist} \
+        --fusion_off_flag=${fusion_off_flag} \
+        --mixlist_file=${mixlist_file} \
+        --fusion_off_file=${fusion_off_file} \
+        --profiling_dump_path=${profiling_dump_path}} \
+	--batch_size 16 \
+	--nproc_per_node=8 >$cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log 2>&1 &
+done
+wait
+end=$(date +%s)
+e2etime=$(( $end - $start ))
+
+#echo "Final Performance ms/step : $average_perf"
+echo "Final Training Duration sec : $e2etime"
+
+###下面字段用于冒烟看护
+BatchSize=${batch_size}
+#设备类型，自动获取
+DeviceType=`uname -m`
+#用例名称，自动获取
+CaseName=${Network}_bs${BatchSize}_${RankSize}'p'_'perf'
+
+echo "------------------ Final result ------------------"
+#输出性能FPS，需要模型审视修改
+cp -f $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log.bak
+sed -i 's/\x0d/\n/g' $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log.bak
+single_batch_step_sec=`grep ms/step  $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log.bak | awk 'END {print $5}' | awk -F 'm' '{print $1}'`
+
+FPS=`echo ${single_batch_step_sec} ${batch_size} | awk '{print $2 * 1000 / $1}'`
+
+#打印，不需要修改
+echo "Final Performance images/sec : $FPS"
+
+grep "ms/step" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log.bak|awk '{print$8}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt
+ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`
+#输出训练精度,需要模型审视修改
+grep "f1 score" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_acc.txt
+train_accuracy=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_acc.txt |awk '{print $3}' |awk -F "," '{print $1}'`
+#打印，不需要修改
+echo "Final Train Accuracy : ${train_accuracy}"
+echo "E2E Training Duration sec : $e2e_time"
+
+##获取性能数据
+#吞吐量，不需要修改
+ActualFPS=${FPS}
+#单迭代训练时长，不需要修改
+TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'*'${RankSize}'*1000/'${FPS}'}'`
+
+##获取错误信息
+#系统错误信息
+error_msg="the shape of grad must equal with var"
+#判断错误信息是否和历史状态一致，此处无需修改
+Status=`grep "${error_msg}" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | wc -l`
+#失败阶段，枚举值图准备FAIL/图拆分FAIL/图优化FAIL/图编译FAIL/图执行FAIL/流程OK
+ModelStatus="流程OK"
+#DTS单号或者issue链接
+DTS_Number=""
+
+#关键信息打印到CaseName.log中，此处无需修改
+echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "RankSize = ${RankSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "E2ETrainingTime = ${e2etime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+