merge eval results in all processes. (#1160)

apps/kg/eval.py

@@ -139,13 +139,26 @@ def main(args):
     args.step = 0
     args.max_step = 0
     if args.num_proc > 1:
+        queue = mp.Queue(args.num_proc)
         procs = []
         for i in range(args.num_proc):
-            proc = mp.Process(target=test, args=(args, model, [test_sampler_heads[i], test_sampler_tails[i]]))
+            proc = mp.Process(target=test, args=(args, model, [test_sampler_heads[i], test_sampler_tails[i]],
+                              'Test', queue))
             procs.append(proc)
             proc.start()
         for proc in procs:
             proc.join()
+
+        total_metrics = {}
+        for i in range(args.num_proc):
+            metrics = queue.get()
+            for k, v in metrics.items():
+                if i == 0:
+                    total_metrics[k] = v / args.num_proc
+                else:
+                    total_metrics[k] += v / args.num_proc
+        for k, v in metrics.items():
+            print('Test average {} at [{}/{}]: {}'.format(k, args.step, args.max_step, v))
     else:
         test(args, model, [test_sampler_head, test_sampler_tail])
 

apps/kg/train.py

@@ -263,16 +263,32 @@ def run(args, logger):
 
     # test
     if args.test:
+        start = time.time()
         if args.num_proc > 1:
+            queue = mp.Queue(args.num_proc)
             procs = []
             for i in range(args.num_proc):
-                proc = mp.Process(target=test, args=(args, model, [test_sampler_heads[i], test_sampler_tails[i]]))
+                proc = mp.Process(target=test, args=(args, model, [test_sampler_heads[i], test_sampler_tails[i]],
+                                  'Test', queue))
                 procs.append(proc)
                 proc.start()
+
+            total_metrics = {}
+            for i in range(args.num_proc):
+                metrics = queue.get()
+                for k, v in metrics.items():
+                    if i == 0:
+                        total_metrics[k] = v / args.num_proc
+                    else:
+                        total_metrics[k] += v / args.num_proc
+            for k, v in metrics.items():
+                print('Test average {} at [{}/{}]: {}'.format(k, args.step, args.max_step, v))
+
             for proc in procs:
                 proc.join()
         else:
             test(args, model, [test_sampler_head, test_sampler_tail])
+        print('test:', time.time() - start)
 
 if __name__ == '__main__':
     args = ArgParser().parse_args()

apps/kg/train_mxnet.py

@@ -61,7 +61,7 @@ def train(args, model, train_sampler, valid_samplers=None):
     # clear cache
     logs = []
 
-def test(args, model, test_samplers, mode='Test'):
+def test(args, model, test_samplers, mode='Test', queue=None):
     logs = []
 
     for sampler in test_samplers:

apps/kg/train_pytorch.py

@@ -80,10 +80,9 @@ def train(args, model, train_sampler, valid_samplers=None):
             test(args, model, valid_samplers, mode='Valid')
             print('test:', time.time() - start)
 
-def test(args, model, test_samplers, mode='Test'):
+def test(args, model, test_samplers, mode='Test', queue=None):
     if args.num_proc > 1:
         th.set_num_threads(1)
-    start = time.time()
     with th.no_grad():
         logs = []
         for sampler in test_samplers:
@@ -96,9 +95,10 @@ def test(args, model, test_samplers, mode='Test'):
         if len(logs) > 0:
             for metric in logs[0].keys():
                 metrics[metric] = sum([log[metric] for log in logs]) / len(logs)
-
-        for k, v in metrics.items():
-            print('{} average {} at [{}/{}]: {}'.format(mode, k, args.step, args.max_step, v))
-    print('test:', time.time() - start)
+        if queue is not None:
+            queue.put(metrics)
+        else:
+            for k, v in metrics.items():
+                print('{} average {} at [{}/{}]: {}'.format(mode, k, args.step, args.max_step, v))
     test_samplers[0] = test_samplers[0].reset()
     test_samplers[1] = test_samplers[1].reset()