applying --verbose to hide dubug info

demo.py

@@ -2,8 +2,9 @@
 # Licensed under the MIT license.
 from nn_meter.utils.utils import try_import_torchvision_models
 from nn_meter import load_predictor_config, load_latency_predictors
-import yaml
 import argparse
+import os
+import logging
 
 
 def test_ir_graphs(args, predictor):
@@ -16,7 +17,7 @@ def test_ir_graphs(args, predictor):
     models = glob("data/testmodels/**.json")
     for model in models:
         latency = predictor.predict(model)
-        print(model.split("/")[-1], latency)
+        logging.debug(os.path.basename(model), latency)
 
 
 def test_pb_models(args, predictor):
@@ -29,7 +30,7 @@ def test_pb_models(args, predictor):
     models = glob("data/testmodels/**.pb")
     for model in models:
         latency = predictor.predict(model)
-        print(model.split("/")[-1], latency)
+        logging.debug(os.path.basename(model), latency)
 
 
 def test_onnx_models(args, predictor):
@@ -42,7 +43,7 @@ def test_onnx_models(args, predictor):
     models = glob("data/testmodels/**.onnx")
     for model in models:
         latency = predictor.predict(model)
-        print(model.split("/")[-1], latency)
+        logging.debug(os.path.basename(model), latency)
 
 
 def test_pytorch_models(args, predictor):
@@ -73,27 +74,21 @@ def test_pytorch_models(args, predictor):
     models.append(resnext50_32x4d)
     models.append(wide_resnet50_2)
     models.append(mnasnet)
-    print("start to test")
+    logging.debug("start to test")
     for model in models:
         latency = predictor.predict(
             model, model_type="torch", input_shape=(1, 3, 224, 224)
         )
-        print(model.__class__.__name__, latency)
+        logging.debug(model.__class__.__name__, latency)
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser("predict model latency on device")
-    parser.add_argument(
-        "--input_model",
-        type=str,
-        required=True,
-        help="Path to input model. ONNX, FrozenPB or JSON",
-    )
     parser.add_argument(
         "--predictor", 
         type=str, 
         required=True, 
-        help="name of target predictor (hardware)"
+        help="name of target predictor (hardware)",
     )
     parser.add_argument(
         "--predictor-version",
@@ -107,9 +102,51 @@ if __name__ == "__main__":
         default="nn_meter/configs/predictors.yaml",
         help="config file to store current supported edge platform",
     )
+    group = parser.add_mutually_exclusive_group() # Jiahang: can't handle model_type == "torch" now.
+    group.add_argument(
+        "--tensorflow",
+        type=str,
+        # required=True,
+        help="Path to input Tensorflow model (*.pb)"
+    )
+    group.add_argument(
+        "--onnx",
+        type=str,
+        # required=True,
+        help="Path to input ONNX model (*.onnx)"
+    )
+    group.add_argument(
+        "--nn-meter-ir",
+        type=str,
+        # required=True,
+        help="Path to input nn-Meter IR model (*.json)"
+    )
+    group.add_argument(
+        "--nni-ir",
+        type=str,
+        # required=True,
+        help="Path to input NNI IR model (*.json)"
+    )
+    parser.add_argument(
+        "-v", "--verbose", 
+        help="increase output verbosity",
+        action="store_true"
+    )
     args = parser.parse_args()
 
+    if args.verbose:
+        logging.basicConfig(level=logging.DEBUG)
+
     pred_info = load_predictor_config(args.config, args.predictor, args.predictor_version)
     predictor = load_latency_predictors(pred_info)
-    latency = predictor.predict(args.input_model)
-    print('predict latency', latency)
+    if args.tensorflow:
+        input_model, model_type = args.tensorflow, "pb"
+    elif args.onnx:
+        input_model, model_type = args.onnx, "onnx"
+    elif args.nn_meter_ir:
+        input_model, model_type = args.nn_meter_ir, "json"
+    elif args.nni_ir:
+        input_model, model_type = args.nni_ir, "json"
+    
+    latency = predictor.predict(input_model, model_type)
+    logging.info('predict latency: %f' % latency)

nn_meter/ir_converters/frozenpb_converter/frozenpb_parser.py

@@ -89,13 +89,13 @@ class FrozenPbParser:
                         graph[graph_node]["attr"]["type"] == "Split"
                         and ":" not in graph_node
                     ):
-                        logging.info("Find split main node %s." % graph_node)
+                        logging.debug("Find split main node %s." % graph_node)
                         split_node_name = graph_node
                         for node_name in graph.keys():
                             idx = re.findall(r"%s:(\d+)" % split_node_name, node_name)
                             if len(idx) > 0:
                                 idx = int(idx[0])
-                                logging.info("Find split child node %s." % node_name)
+                                logging.debug("Find split child node %s." % node_name)
                                 graph[graph_node]["outbounds"] += graph[node_name][
                                     "outbounds"
                                 ]
@@ -194,7 +194,7 @@ class FrozenPbParser:
                         attr_as_node[node.op]["node_name"](node.name), target_node.name
                     )
                     if len(node_attr) > 0:
-                        logging.info("Find regex matching node %s" % node.name)
+                        logging.debug("Find regex matching node %s" % node.name)
                         for attr_name in target_node.attr.keys():
                             if (
                                 attr_name == "value"

nn_meter/ir_converters/frozenpb_converter/protobuf_helper.py

@@ -82,7 +82,7 @@ class ProtobufHelper:
                     weight_op in graph.keys()
                     and graph[weight_op]["attr"]["type"] != "Identity"
                 ):
-                    logging.info(
+                    logging.debug(
                         "Find node %s with its weight op %s."
                         % (node["attr"]["name"], weight_op)
                     )

nn_meter/ir_converters/frozenpb_converter/shape_inference.py

@@ -69,7 +69,7 @@ class ShapeInference:
             Padding type, now support SAME and VALID.
         """
 
-        logging.info(
+        logging.debug(
             "Calculating padding shape, input shape: %s, kernel size: %s, strides: %s, padding: %s."
             % (str(input_shape), str(k_size), str(strides), str(padding))
         )
@@ -156,7 +156,7 @@ class ShapeInference:
         node   : dict
             The node in Graph IR in dict format.
         """
-        logging.info("Propogate through op %s.", node["attr"]["name"])
+        logging.debug("Propogate through op %s.", node["attr"]["name"])
         in_shape = [graphe[node["inbounds"][0]]["attr"]["output_shape"][0]]
         return in_shape, in_shape
 
@@ -279,13 +279,13 @@ class ShapeInference:
         """
         if len(node["inbounds"]) != 1:
             logging.warning("Failed to get input node of %s." % (node["attr"]["name"]))
-            logging.info(node)
+            logging.debug(node)
             return
 
         input_shape = copy.deepcopy(
             graphe[node["inbounds"][0]]["attr"]["output_shape"][0]
         )
-        logging.info(
+        logging.debug(
             "Get input shape of %s from %s, input shape:%s."
             % (node["attr"]["name"], node["inbounds"][0], input_shape)
         )
@@ -297,12 +297,12 @@ class ShapeInference:
                 "Invalid strides %s of node %s."
                 % (str(node["attr"]["attr"]["strides"]), node["attr"]["name"])
             )
-            logging.info(node)
+            logging.debug(node)
             return
 
         strides = node["attr"]["attr"]["strides"]
         padding = node["attr"]["attr"]["padding"].decode("utf-8")
-        logging.info(
+        logging.debug(
             "Op:%s, stride:%s, padding:%s."
             % (node["attr"]["name"], str(strides), str(padding))
         )
@@ -407,18 +407,18 @@ class ShapeInference:
         weight_node = ph.find_weights_root(graphe, node)
         if len(weight_node) != 1:
             logging.warning("Failed to get shape of node %s." % (node["attr"]["name"]))
-            logging.info(node)
+            logging.debug(node)
             return
 
         input_node = [x for x in node["inbounds"] if x != weight_node]
         input_node = [x for x in input_node if graphe[x]["attr"]["type"] != "Identity"]
         if len(input_node) != 1:
             logging.warning("Failed to get input node of %s." % (node["attr"]["name"]))
-            logging.info(node)
+            logging.debug(node)
             return
 
         input_shape = copy.deepcopy(graphe[input_node[0]]["attr"]["output_shape"][0])
-        logging.info(
+        logging.debug(
             "Get input shape of %s from %s, input shape:%s."
             % (node["attr"]["name"], input_node[0], input_shape)
         )
@@ -429,10 +429,10 @@ class ShapeInference:
                 "Failed to parse weight shape %s of node %s."
                 % (str(weight_shape), node["attr"]["name"])
             )
-            logging.info(node)
+            logging.debug(node)
             return
 
-        logging.info(
+        logging.debug(
             "Get weight shape of %s from %s, input shape:%s."
             % (node["attr"]["name"], weight_node, weight_shape)
         )
@@ -445,13 +445,13 @@ class ShapeInference:
                 "Invalid strides %s of node %s."
                 % (str(node["attr"]["attr"]["strides"]), node["attr"]["name"])
             )
-            logging.info(node)
+            logging.debug(node)
             return
 
         strides = node["attr"]["attr"]["strides"]
         dilation = node["attr"]["attr"]["dilations"]
         padding = node["attr"]["attr"]["padding"].decode("utf-8")
-        logging.info(
+        logging.debug(
             "Op:%s, stride:%s, dilation:%s, padding:%s."
             % (node["attr"]["name"], str(strides), str(dilation), str(padding))
         )
@@ -490,18 +490,18 @@ class ShapeInference:
         weight_node = ph.find_weights_root(graphe, node)
         if len(weight_node) != 1:
             logging.warning("Failed to get shape of node %s." % (node["attr"]["name"]))
-            logging.info(node)
+            logging.debug(node)
             return
 
         input_node = [x for x in node["inbounds"] if x != weight_node]
         input_node = [x for x in input_node if graphe[x]["attr"]["type"] != "Identity"]
         if len(input_node) != 1:
             logging.warning("Failed to get input node of %s." % (node["attr"]["name"]))
-            logging.info(node)
+            logging.debug(node)
             return
 
         input_shape = copy.deepcopy(graphe[input_node[0]]["attr"]["output_shape"][0])
-        logging.info(
+        logging.debug(
             "Get input shape of %s from %s, input shape:%s."
             % (node["attr"]["name"], input_node[0], input_shape)
         )
@@ -512,10 +512,10 @@ class ShapeInference:
                 "Failed to parse weight shape %s of node %s."
                 % (str(weight_shape), node["attr"]["name"])
             )
-            logging.info(node)
+            logging.debug(node)
             return
 
-        logging.info(
+        logging.debug(
             "Get weight shape of %s from %s, input shape:%s."
             % (node["attr"]["name"], weight_node, weight_shape)
         )
@@ -528,14 +528,14 @@ class ShapeInference:
                 "Invalid strides %s of node %s."
                 % (str(node["attr"]["attr"]["strides"]), node["attr"]["name"])
             )
-            logging.info(node)
+            logging.debug(node)
             return
 
         strides = node["attr"]["attr"]["strides"]
         dilation = node["attr"]["attr"]["dilations"]
         padding = node["attr"]["attr"]["padding"].decode("utf-8")
 
-        logging.info(
+        logging.debug(
             "Op:%s, stride:%s, dilation:%s, padding:%s."
             % (node["attr"]["name"], str(strides), str(dilation), str(padding))
         )
@@ -573,7 +573,7 @@ class ShapeInference:
         """
         input_shape = graphe[node["inbounds"][0]]["attr"]["output_shape"][0]
         output_shape = input_shape
-        logging.info(
+        logging.debug(
             "Get input shape of %s from %s, input shape:%s."
             % (node["attr"]["name"], node["inbounds"][0], output_shape)
         )
@@ -582,7 +582,7 @@ class ShapeInference:
         output_shape[2] = 0
 
         reduction_indices = node["attr"]["attr"]["reduction_indices"]
-        logging.info("Get Reduction Indices %s.", str(reduction_indices))
+        logging.debug("Get Reduction Indices %s.", str(reduction_indices))
 
         reduction_cnt = 0
         for reduction in sorted(reduction_indices):
@@ -648,7 +648,7 @@ class ShapeInference:
         weight_node = ph.find_weights_root(graphe, node)
         if len(weight_node) != 1:
             logging.warning("Failed to get shape of node %s." % (node["attr"]["name"]))
-            logging.info(node)
+            logging.debug(node)
             return
 
         weight_shape = graphe[weight_node[0]]["attr"]["attr"]["tensor_shape"]
@@ -657,10 +657,10 @@ class ShapeInference:
                 "Failed to parse weight shape %s of node %s."
                 % (str(weight_shape), node["attr"]["name"])
             )
-            logging.info(node)
+            logging.debug(node)
             return
 
-        logging.info(
+        logging.debug(
             "Get weight shape of %s from %s, input shape:%s."
             % (node["attr"]["name"], weight_node, weight_shape)
         )
@@ -669,11 +669,11 @@ class ShapeInference:
         input_node = [x for x in input_node if graphe[x]["attr"]["type"] != "Identity"]
         if len(input_node) != 1:
             logging.warning("Failed to get input node of %s." % (node["attr"]["name"]))
-            logging.info(node)
+            logging.debug(node)
             return
 
         input_shape = copy.deepcopy(graphe[input_node[0]]["attr"]["output_shape"][0])
-        logging.info(
+        logging.debug(
             "Get input shape of %s from %s, input shape:%s."
             % (node["attr"]["name"], input_node[0], input_shape)
         )
@@ -683,7 +683,7 @@ class ShapeInference:
                 "Weight shape and input shape not matched for %s."
                 % (node["attr"]["name"])
             )
-            logging.info(node)
+            logging.debug(node)
             return
 
         output_shape = copy.deepcopy(input_shape)
@@ -707,7 +707,7 @@ class ShapeInference:
             The node in Graph IR in dict format.
         """
         if "shape" in node["attr"]["attr"].keys():
-            logging.info(
+            logging.debug(
                 "Shape attr find in %s op, propogate with normal.", node["attr"]["name"]
             )
             input_shape = copy.deepcopy(
@@ -715,7 +715,7 @@ class ShapeInference:
             )
             exp_output_shape = copy.deepcopy(node["attr"]["attr"]["shape"])
         else:
-            logging.info(
+            logging.debug(
                 "Shape attr not find in %s op, try finding the shape node.",
                 node["attr"]["name"],
             )
@@ -730,7 +730,7 @@ class ShapeInference:
                         for sl in graphe[in_node]["attr"]["attr"]["constant"]
                         for it in sl
                     ]
-                    logging.info(
+                    logging.debug(
                         "Fetched expected output shape from Pack op %s"
                         % str(exp_output_shape)
                     )
@@ -768,7 +768,7 @@ class ShapeInference:
             in_shape = graphe[in_node]["attr"]["output_shape"][0]
             if in_shape != []:
                 input_shape.append(in_shape)
-                logging.info(
+                logging.debug(
                     "Get input shape of %s from %s, input shape:%s."
                     % (node["attr"]["name"], in_node, input_shape[-1])
                 )
@@ -830,7 +830,7 @@ class ShapeInference:
                 input_shape = copy.deepcopy(graphe[in_node]["attr"]["output_shape"][0])
 
         split_dim = node["attr"]["attr"]["split_dim"][0]
-        logging.info("Fetched Split dim for %s is %s.", node["attr"]["name"], split_dim)
+        logging.debug("Fetched Split dim for %s is %s.", node["attr"]["name"], split_dim)
         output_node_cnt = len(node["outbounds"])
 
         output_shape = copy.deepcopy(input_shape)
@@ -854,17 +854,17 @@ class ShapeInference:
         for in_node in node["inbounds"]:
             if graphe[in_node]["attr"]["type"] == "Const":
                 perm = copy.deepcopy(graphe[in_node]["attr"]["attr"]["constant"])
-                logging.info("Fetched perm sequence from Const op %s" % str(perm))
+                logging.debug("Fetched perm sequence from Const op %s" % str(perm))
             elif graphe[in_node]["attr"]["type"] == "Pack":
                 perm = [1] + [
                     it
                     for sl in graphe[in_node]["attr"]["attr"]["constant"]
                     for it in sl
                 ]
-                logging.info("Fetched perm sequence from Pack op %s" % str(perm))
+                logging.debug("Fetched perm sequence from Pack op %s" % str(perm))
             else:
                 input_shape = copy.deepcopy(graphe[in_node]["attr"]["output_shape"][0])
-                logging.info(
+                logging.debug(
                     "Fetched input shape from %s,  %s" % (in_node, str(input_shape))
                 )
 
@@ -946,17 +946,17 @@ class ShapeInference:
                     )
                 if input_shape is not None:
                     graph[node_name]["attr"]["input_shape"] = copy.deepcopy(input_shape)
-                logging.info(
+                logging.debug(
                     "Input shape of %s op is %s." % (node_name, str(input_shape))
                 )
-                logging.info(
+                logging.debug(
                     "Output shape of %s op is %s." % (node_name, str(output_shape))
                 )
             else:
                 logging.error("%s not support yet." % graphe.get_node_type(node_name))
-                logging.info("------ node content --------")
-                logging.info(graph[node_name])
-                logging.info("----------------------------")
+                logging.debug("------ node content --------")
+                logging.debug(graph[node_name])
+                logging.debug("----------------------------")
 
         # Pass #2
         # This is a patching for back-end, since backend extract shapes from
@@ -973,11 +973,11 @@ class ShapeInference:
                     )
                 if input_shape is not None:
                     graph[node_name]["attr"]["input_shape"] = copy.deepcopy(input_shape)
-                logging.info(
+                logging.debug(
                     "Second Pass: Input shape of %s op is %s."
                     % (node_name, str(input_shape))
                 )
-                logging.info(
+                logging.debug(
                     "Second Pass: Output shape of %s op is %s."
                     % (node_name, str(output_shape))
                 )

nn_meter/kerneldetection/detection/detector.py

@@ -5,6 +5,7 @@ from nn_meter.kerneldetection.rulelib.rule_splitter import RuleSplitter
 from nn_meter.utils.graphe_tool import Graphe
 from nn_meter.kerneldetection.utils.constants import DUMMY_TYPES
 from nn_meter.kerneldetection.utils.ir_tools import convert_nodes
+# import logging
 
 
 class KernelDetector:
@@ -33,7 +34,7 @@ class KernelDetector:
 
     def _bb_to_kernel(self, bb):
         types = [self.graph.get_node_type(node) for node in bb]
-        # print(types)
+        # logging.debug(types)
         types = [t for t in types if t and t not in DUMMY_TYPES]
 
         if types:

nn_meter/nn_meter.py

@@ -11,6 +11,7 @@ import argparse
 import pkg_resources
 from shutil import copyfile
 from packaging import version
+import logging
 
 
 __user_config_folder__ = os.path.expanduser('~/.nn_meter/config')
@@ -40,7 +41,7 @@ def list_latency_predictors():
         with open(fn_pred) as fp:
             return yaml.load(fp, yaml.FullLoader)
     except FileNotFoundError:
-        print(f"config file {fn_pred} not found, created")
+        logging.debug(f"config file {fn_pred} not found, created")
         create_user_configs()
         return list_latency_predictors()
 
@@ -60,7 +61,7 @@ def load_predictor_config(config, predictor, predictor_version):
                 if version.parse(preds_info[i]['version']) > latest_version:
                     latest_version = version.parse(preds_info[i]['version'])
                     latest_version_idx = i
-            print(f'WARNING: There are multiple version for {predictor}, use the latest one ({str(latest_version)})')
+            logging.warning(f'There are multiple version for {predictor}, use the latest one ({str(latest_version)})')
             return preds_info[latest_version_idx]
         else:
             raise NotImplementedError('No predictor that meet the required version, please try again.')
@@ -84,7 +85,7 @@ class nnMeter:
             graph = model_file_to_graph(model, model_type)
         else:
             graph = model_to_graph(model, model_type, input_shape=input_shape)
-       # print(graph)
+        # logging.debug(graph)
         self.kd.load_graph(graph)
 
         py = nn_predict(self.kernel_predictors, self.kd.kernels)
@@ -120,12 +121,12 @@ def nn_meter_cli():
 
     if args.list_predictors:
         preds = list_latency_predictors()
-        print("Supported latency predictors:")
+        logging.info("Supported latency predictors:")
         for p in preds:
-            print(f"{p['name']}: version={p['version']}")
+            logging.info(f"{p['name']}: version={p['version']}")
         return
 
     pred_info = load_predictor_config(args.config, args.predictor, args.predictor_version)
     predictor = load_latency_predictors(pred_info)
     latency = predictor.predict(args.input_model)
-    print('predict latency', latency)
+    logging.info('predict latency', latency)

nn_meter/prediction/load_predictors.py

@@ -4,6 +4,7 @@ from glob import glob
 from zipfile import ZipFile
 from tqdm import tqdm
 import requests
+import logging
 
 
 def loading_to_local(pred_info, dir="data/predictorzoo"):
@@ -29,11 +30,11 @@ def loading_to_local(pred_info, dir="data/predictorzoo"):
     for p in ps:
         pname =  os.path.basename(p).replace(".pkl", "")
         with open(p, "rb") as f:
-            print("load predictor", p)
+            logging.debug("load predictor %s" % p)
             model = pickle.load(f)
             predictors[pname] = model
     fusionrule = os.path.join(ppath, "rule_" + hardware + ".json")
-    print(fusionrule)
+    logging.debug(fusionrule)
     if not os.path.isfile(fusionrule):
         raise ValueError(
             "check your fusion rule path, file " + fusionrule + " does not exist！"
@@ -54,7 +55,7 @@ def download_from_url(urladdr, ppath):
     if not os.path.isdir(ppath):
         os.makedirs(ppath)
 
-    print("download from " + urladdr)
+    logging.debug("download from " + urladdr)
     response = requests.get(urladdr, stream=True)
     total_size_in_bytes = int(response.headers.get("content-length", 0))
     block_size = 2048  # 2 Kibibyte
@@ -76,7 +77,7 @@ def check_predictors(ppath, kernel_predictors):
 
     model: a pytorch/onnx/tensorflow model object or a str containing path to the model file
     """
-    print("checking local kernel predictors at " + ppath)
+    logging.debug("checking local kernel predictors at " + ppath)
     if os.path.isdir(ppath):
         filenames = glob(os.path.join(ppath, "**.pkl"))
         # check if all the pkl files are included

nn_meter/prediction/predictors/extract_feature.py

@@ -2,7 +2,7 @@
 # Licensed under the MIT license.
 import numpy as np
 from sklearn.metrics import mean_squared_error
-
+import logging
 
 def get_accuracy(y_pred, y_true, threshold=0.01):
     a = (y_true - y_pred) / y_true
@@ -57,7 +57,7 @@ def get_predict_features(config):
     mdicts = {}
     layer = 0
     for item in config:
-        print(item)
+        logging.debug(item)
     for item in config:
         op = item["op"]
         if "conv" in op or "maxpool" in op or "avgpool" in op:

nn_meter/utils/graphe_tool.py

@@ -3,6 +3,7 @@
 import copy
 import json
 import numpy as np
+import logging
 
 
 class NumpyEncoder(json.JSONEncoder):
@@ -136,7 +137,7 @@ class Graphe:
         if name in self.graph.keys() and "attr" in self.graph[name].keys():
             return self.graph[name]["attr"]["type"]
         else:
-            print(name, self.graph[name])
+            logging.debug(name, self.graph[name])
             return None
 
     def get_root_node(self, subgraph):

nn_meter/utils/utils.py

@@ -5,6 +5,7 @@ from zipfile import ZipFile
 from tqdm import tqdm
 import requests
 from packaging import version
+import logging
 
 
 def download_from_url(urladdr, ppath):
@@ -20,7 +21,7 @@ def download_from_url(urladdr, ppath):
     if not os.path.isdir(ppath):
         os.makedirs(ppath)
 
-    print("download from " + urladdr)
+    logging.info("download from " + urladdr)
     response = requests.get(urladdr, stream=True)
     total_size_in_bytes = int(response.headers.get("content-length", 0))
     block_size = 2048  # 2 Kibibyte
@@ -39,10 +40,10 @@ def try_import_onnx(require_version = "1.9.0"):
     try:
         import onnx
         if version.parse(onnx.__version__) != version.parse(require_version):
-            print(f'WARNING: onnx=={onnx.__version__} is not well tested now, well tested version: onnx=={require_version}' )
+            logging.warning(f'onnx=={onnx.__version__} is not well tested now, well tested version: onnx=={require_version}' )
         return onnx
     except ImportError:
-        print(f'You have not install the onnx package, please install onnx=={require_version} and try again.')
+        logging.error(f'You have not install the onnx package, please install onnx=={require_version} and try again.')
         exit()
 
 
@@ -50,10 +51,10 @@ def try_import_torch(require_version = "1.8.1"):
     try:
         import torch
         if version.parse(torch.__version__) != version.parse(require_version):
-            print(f'WARNING: torch=={torch.__version__} is not well tested now, well tested version: torch=={require_version}' )
+            logging.warning(f'torch=={torch.__version__} is not well tested now, well tested version: torch=={require_version}' )
         return torch
     except ImportError:
-        print(f'You have not install the torch package, please install torch=={require_version} and try again.')
+        logging.error(f'You have not install the torch package, please install torch=={require_version} and try again.')
         exit()
 
 
@@ -61,10 +62,10 @@ def try_import_tensorflow(require_version = "1.9.0"):
     try:
         import tensorflow
         if version.parse(tensorflow.__version__) != version.parse(require_version):
-            print(f'WARNING: tensorflow=={tensorflow.__version__} is not well tested now, well tested version: tensorflow=={require_version}' )
+            logging.warning(f'tensorflow=={tensorflow.__version__} is not well tested now, well tested version: tensorflow=={require_version}' )
         return tensorflow
     except ImportError:
-        print(f'You have not install the tensorflow package, please install tensorflow=={require_version} and try again.')
+        logging.error(f'You have not install the tensorflow package, please install tensorflow=={require_version} and try again.')
         exit()
 
 
@@ -73,6 +74,6 @@ def try_import_torchvision_models():
         import torchvision
         return torchvision.models
     except ImportError:
-        print(f'You have not install the torchvision package, please install torchvision and try again.')
+        logging.error(f'You have not install the torchvision package, please install torchvision and try again.')
         exit()