mean average precision meter (#265)

Summary:
Pull Request resolved: #265

Average Precision (AP) summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold, with the increase in recall from the previous threshold used as the weight.

See AP formal definition (https://scikit-learn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html).

Mean Average Precision is an average of AP over different classes.

In this diff, we implement Mean Average Precision meter that is useful for multi-label classification task.

Differential Revision: D18715190

fbshipit-source-id: 83cffde189d4e2f100523c1d33353d85fd4815e7

classy_vision/generic/distributed_util.py

@@ -82,7 +82,7 @@ def all_reduce_sum(tensor):
     return tensor
 
 
-def gather_from_all(tensor):
+def gather_tensors_from_all(tensor):
     """
     Wrapper over torch.distributed.all_gather for performing
     'gather' of 'tensor' over all processes in both distributed /
@@ -91,11 +91,21 @@ def gather_from_all(tensor):
     if tensor.ndim == 0:
         # 0 dim tensors cannot be gathered. so unsqueeze
         tensor = tensor.unsqueeze(0)
-    gathered_tensor = [
-        torch.zeros_like(tensor) for _ in range(torch.distributed.get_world_size())
-    ]
-    torch.distributed.all_gather(gathered_tensor, tensor)
-    gathered_tensor = torch.cat(gathered_tensor, 0)
+
+    if is_distributed_training_run():
+        gathered_tensors = [
+            torch.zeros_like(tensor) for _ in range(torch.distributed.get_world_size())
+        ]
+        torch.distributed.all_gather(gathered_tensors, tensor)
+    else:
+        gathered_tensors = [tensor]
+
+    return gathered_tensors
+
+
+def gather_from_all(tensor):
+    gathered_tensors = gather_tensors_from_all()
+    gathered_tensor = torch.cat(gathered_tensors, 0)
     return gathered_tensor
 
 

classy_vision/hooks/tensorboard_plot_hook.py

@@ -129,7 +129,15 @@ def on_phase_end(
                 log.warn(f"Skipping meter {meter.name} with value: {meter.value}")
                 continue
             for name, value in meter.value.items():
-                meter_key = f"{phase_type}_{meter.name}_{name}"
-                self.tb_writer.add_scalar(meter_key, value, global_step=phase_type_idx)
+                if isinstance(value, int) or isinstance(value, float):
+                    meter_key = f"{phase_type}_{meter.name}_{name}"
+                    self.tb_writer.add_scalar(
+                        meter_key, value, global_step=phase_type_idx
+                    )
+                else:
+                    log.warn(
+                        f"Skipping meter name {meter.name}_{name} with value: {value}"
+                    )
+                    continue
 
         logging.info(f"Done plotting to Tensorboard")

test/generic/meter_test_utils.py

@@ -49,10 +49,13 @@ def _meter_worker(qin, qout, meter, is_train, world_size, rank, filename):
             continue
 
         if signal == UPDATE_SIGNAL:
+            print("signal UPDATE_SIGNAL")
             meter.update(val[0], val[1], is_train=is_train)
 
         elif signal == VALUE_SIGNAL:
+            print("signal VALUE_SIGNAL")
             meter.sync_state()
+            print("meter.value: %s" % str(meter.value))
             qout.put(meter.value)
 
         elif signal == SHUTDOWN_SIGNAL:
@@ -106,23 +109,45 @@ def _apply_updates_and_test_meter(
             self.assertTrue(
                 key in meter_value, msg="{0} not in meter value!".format(key)
             )
-            self.assertAlmostEqual(
-                meter_value[key],
-                val,
-                places=4,
-                msg="{0} meter value mismatch!".format(key),
-            )
+            if torch.is_tensor(meter_value[key]):
+                self.assertTrue(
+                    torch.all(torch.eq(meter_value[key], val)),
+                    msg="{0} meter value mismatch!".format(key),
+                )
+            else:
+                self.assertAlmostEqual(
+                    meter_value[key],
+                    val,
+                    places=4,
+                    msg="{0} meter value mismatch!".format(key),
+                )
 
     def _values_match_expected_value(self, value0, value1, expected_value):
         for key, val in expected_value.items():
             self.assertTrue(key in value0, msg="{0} not in meter value!".format(key))
-            self.assertAlmostEqual(
-                value0[key], val, places=4, msg="{0} meter value mismatch!".format(key)
-            )
             self.assertTrue(key in value1, msg="{0} not in meter value!".format(key))
-            self.assertAlmostEqual(
-                value1[key], val, places=4, msg="{0} meter value mismatch!".format(key)
-            )
+            if torch.is_tensor(val):
+                self.assertTrue(
+                    torch.all(torch.eq(value0[key], val)),
+                    "{0} meter value mismatch!".format(key),
+                )
+                self.assertTrue(
+                    torch.all(torch.eq(value1[key], val)),
+                    "{0} meter value mismatch!".format(key),
+                )
+            else:
+                self.assertAlmostEqual(
+                    value0[key],
+                    val,
+                    places=4,
+                    msg="{0} meter value mismatch!".format(key),
+                )
+                self.assertAlmostEqual(
+                    value1[key],
+                    val,
+                    places=4,
+                    msg="{0} meter value mismatch!".format(key),
+                )
 
     def meter_update_and_reset_test(
         self, meter, model_outputs, targets, expected_value, **kwargs
@@ -192,25 +217,43 @@ def meter_get_set_classy_state_test(
         meter1.sync_state()
         value1 = meter1.value
         for key, val in value0.items():
-            self.assertNotEqual(
-                value1[key], val, msg="{0} meter values should not be same!".format(key)
-            )
+            if torch.is_tensor(value1[key]):
+                self.assertFalse(
+                    torch.all(torch.eq(value1[key], val)),
+                    msg="{0} meter values should not be same!".format(key),
+                )
+            else:
+                self.assertNotEqual(
+                    value1[key],
+                    val,
+                    msg="{0} meter values should not be same!".format(key),
+                )
 
         meter0.set_classy_state(meter1.get_classy_state())
         value0 = meter0.value
         for key, val in value0.items():
-            self.assertAlmostEqual(
-                value1[key],
-                val,
-                places=4,
-                msg="{0} meter value mismatch after state transfer!".format(key),
-            )
-            self.assertAlmostEqual(
-                value1[key],
-                expected_value[key],
-                places=4,
-                msg="{0} meter value mismatch from ground truth!".format(key),
-            )
+            if torch.is_tensor(value1[key]):
+                self.assertTrue(
+                    torch.all(torch.eq(value1[key], val)),
+                    msg="{0} meter value mismatch after state transfer!".format(key),
+                )
+                self.assertTrue(
+                    torch.all(torch.eq(value1[key], expected_value[key])),
+                    msg="{0} meter value mismatch from ground truth!".format(key),
+                )
+            else:
+                self.assertAlmostEqual(
+                    value1[key],
+                    val,
+                    places=4,
+                    msg="{0} meter value mismatch after state transfer!".format(key),
+                )
+                self.assertAlmostEqual(
+                    value1[key],
+                    expected_value[key],
+                    places=4,
+                    msg="{0} meter value mismatch from ground truth!".format(key),
+                )
 
     def _spawn_all_meter_workers(self, world_size, meters, is_train):
         filename = tempfile.NamedTemporaryFile(delete=True).name