[~] Refactor

python/tasks/mlb_player.py

@@ -489,8 +489,8 @@ def kernel_7(
             feed = Variable(torch.from_numpy(img_test_pad)).cuda()
             output1, output2 = model(feed)
 
-            print(output1.size())
-            print(output2.size())
+            #print(output1.size())
+            #print(output2.size())
 
             heatmap = nn.UpsamplingBilinear2d((img_raw.shape[0], img_raw.shape[1])).cuda()(output2)
 
@@ -752,7 +752,14 @@ def kernel_7(
         model_pose = torch.nn.DataParallel(model_pose, device_ids=range(torch.cuda.device_count()))
         cudnn.benchmark = True
 
-    def estimate_pose(img_ori, name=None):
+    def estimate_pose(
+        img_ori,
+        name=None,
+        scale_param=None,
+    ):
+        if scale_param is None:
+            scale_param = [0.5, 1.0, 1.5, 2.0]
+
         if name is None:
             name = tempfile.mktemp(
                 dir='/kaggle/working',
@@ -763,7 +770,6 @@ def kernel_7(
         )
 
         # People might be at different scales in the image, perform inference at multiple scales to boost results
-        scale_param = [0.5, 1.0, 1.5, 2.0]
 
         # Predict Heatmaps for approximate joint position
         # Use Part Affinity Fields (PAF's) as guidance to link joints to form skeleton
@@ -811,3 +817,20 @@ def kernel_8(
         arch_image = o
         img_ori = o_7['cv2'].imread(arch_image)
         o_7['estimate_pose'](img_ori)
+
+def kernel_9_benchmark(
+    o_7,
+):
+    t1 = o_7['cv2'].imread('../input/indonesian-traditional-dance/tgagrakanyar/tga_0000.jpg'
+    t5 = 10
+    t2 = datetime.datetime.now()
+    for k in range(t5):
+        o_7['estimate_pose'](
+            img_ori=t1,
+            scale_param=[1.0],
+        )
+    t3 = datetime.datetime.now()
+    t4 = (t3 - t2).totalseconds() / t5
+    pprint.pprint(
+        ['kernel_9_benchmark', dict(t4=t4, t5=t5)]
+    )