Unify the metrics implementation between low-level and high-level API.

python/CMakeLists.txt

@@ -91,6 +91,7 @@ set(PADDLE_PYTHON_PACKAGE_DIR ${CMAKE_CURRENT_BINARY_DIR}/dist/)
 if (WITH_TESTING)
   add_subdirectory(paddle/reader/tests)
   add_subdirectory(paddle/dataset/tests)
+  add_subdirectory(paddle/tests)
   add_subdirectory(paddle/fluid/tests)
   add_subdirectory(paddle/fluid/contrib/tests)
   add_subdirectory(paddle/fluid/contrib/slim/tests)

python/paddle/fluid/tests/unittests/test_layers.py

@@ -3686,5 +3686,32 @@ def test_basic_gru(self):
                         batch_first=batch_first)
 
 
+class TestMetricsDetectionMap(unittest.TestCase):
+    def test_detection_map(self):
+        program = fluid.Program()
+        with program_guard(program):
+            detect_res = fluid.layers.data(
+                name='detect_res',
+                shape=[10, 6],
+                append_batch_size=False,
+                dtype='float32')
+            label = fluid.layers.data(
+                name='label',
+                shape=[10, 1],
+                append_batch_size=False,
+                dtype='float32')
+            box = fluid.layers.data(
+                name='bbox',
+                shape=[10, 4],
+                append_batch_size=False,
+                dtype='float32')
+            map_eval = fluid.metrics.DetectionMAP(
+                detect_res, label, box, class_num=21)
+            cur_map, accm_map = map_eval.get_map_var()
+            self.assertIsNotNone(cur_map)
+            self.assertIsNotNone(accm_map)
+        print(str(program))
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/incubate/hapi/__init__.py

@@ -20,7 +20,6 @@
 from . import model
 from .model import *
 
-from . import metrics
 from . import datasets
 from . import distributed
 from . import vision
@@ -39,7 +38,6 @@
     'datasets',
     'distributed',
     'download',
-    'metrics',
     'vision',
     'text',
     'utils',

python/paddle/incubate/hapi/callbacks.py

@@ -305,8 +305,8 @@ class ProgBarLogger(Callback):
 
             optim = fluid.optimizer.Adam(0.001)
             model.prepare(optimizer=optim,
-                        loss_function=paddle.nn.CrossEntropyLoss(),
-                        metrics=hapi.metrics.Accuracy())
+                        loss=paddle.nn.CrossEntropyLoss(),
+                        metrics=paddle.metric.Accuracy())
 
             callback = hapi.callbacks.ProgBarLogger(log_freq=10)
             model.fit(train_dataset, batch_size=64, callbacks=callback)
@@ -441,8 +441,8 @@ class ModelCheckpoint(Callback):
 
             optim = fluid.optimizer.Adam(0.001)
             model.prepare(optimizer=optim,
-                        loss_function=paddle.nn.CrossEntropyLoss(),
-                        metrics=hapi.metrics.Accuracy())
+                        loss=paddle.nn.CrossEntropyLoss(),
+                        metrics=paddle.metric.Accuracy())
 
             callback = hapi.callbacks.ModelCheckpoint(save_dir='./temp')
             model.fit(train_dataset, batch_size=64, callbacks=callback)