add profiler and max_iter for mask_rcnn (#3930)

PaddleCV/rcnn/train.py

@@ -40,6 +40,7 @@ import collections
 
 import paddle
 import paddle.fluid as fluid
+from paddle.fluid import profiler
 import reader
 import models.model_builder as model_builder
 import models.resnet as resnet
@@ -197,6 +198,14 @@ def train():
                 sys.stdout.flush()
                 if (iter_id + 1) % cfg.TRAIN.snapshot_iter == 0:
                     save_model("model_iter{}".format(iter_id))
+            
+                #profiler tools, used for benchmark
+                if args.is_profiler and iter_id == 10:
+                    profiler.start_profiler("All")
+                elif args.is_profiler and iter_id == 15: 
+                    profiler.stop_profiler("total", args.profiler_path)
+                    return
+
             end_time = time.time()
             total_time = end_time - start_time
             last_loss = np.array(outs[0]).mean()
@@ -232,6 +241,12 @@ def train():
                 save_model("model_iter{}".format(iter_id))
             if (iter_id + 1) == cfg.max_iter:
                 break
+            #profiler tools, used for benchmark
+            if args.is_profiler and iter_id == 10:
+                profiler.start_profiler("All")
+            elif args.is_profiler and iter_id == 15:
+                profiler.stop_profiler("total", args.profiler_path)
+                return
         end_time = time.time()
         total_time = end_time - start_time
         last_loss = np.array(outs[0]).mean()

PaddleCV/rcnn/utility.py

@@ -149,6 +149,11 @@ def parse_args():
     add_arg('variance',         float,  [1.,1.,1.,1.],    "The variance of anchors.")
     add_arg('rpn_stride',       float,  [16.,16.],    "Stride of the feature map that RPN is attached.")
     add_arg('rpn_nms_thresh',    float,   0.7,          "NMS threshold used on RPN proposals")
+
+    #NOTE: args for profiler, used for benchmark
+    add_arg('is_profiler',              int,    0,                      "the profiler switch.(used for benchmark)")
+    add_arg('profiler_path',            str,    './',                   "the profiler output file path.(used for benchmark)")
+
     # TRAIN VAL INFER
     add_arg('MASK_ON', bool, False, "Option for different models. If False, choose faster_rcnn. If True, choose mask_rcnn")
     add_arg('im_per_batch',       int,   1,        "Minibatch size.")