test=develop, refine ocr attention model (#17763)

* test=develop, refine ocr attention model

* test=develop, refine code, remove cpu only part

test=develop, refine code, remove cpu only part

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

@@ -90,7 +90,7 @@ class ConvBNPool(fluid.dygraph.Layer):
             3,
             padding=1,
             param_attr=conv_param_0,
-            bias_attr=None,
+            bias_attr=False,
             act=None,
             use_cudnn=use_cudnn)
         self.bn_0_layer = BatchNorm(
@@ -102,7 +102,7 @@ class ConvBNPool(fluid.dygraph.Layer):
             filter_size=3,
             padding=1,
             param_attr=conv_param_1,
-            bias_attr=None,
+            bias_attr=False,
             act=None,
             use_cudnn=use_cudnn)
         self.bn_1_layer = BatchNorm(
@@ -301,7 +301,11 @@ class SimpleAttention(fluid.dygraph.Layer):
                        decoder_size,
                        act=None,
                        bias_attr=False)
-        self.fc_2 = FC(self.full_name(), 1, act=None, bias_attr=False)
+        self.fc_2 = FC(self.full_name(),
+                       1,
+                       num_flatten_dims=2,
+                       act=None,
+                       bias_attr=False)
 
     def _build_once(self, encoder_vec, encoder_proj, decoder_state):
         pass
@@ -317,19 +321,17 @@ class SimpleAttention(fluid.dygraph.Layer):
                                                 decoder_state_expand)
         concated = fluid.layers.tanh(x=concated)
         attention_weight = self.fc_2(concated)
+
         weights_reshape = fluid.layers.reshape(
-            x=attention_weight, shape=[-1], inplace=False)
+            x=attention_weight,
+            shape=[attention_weight.shape[0], attention_weight.shape[1]],
+            inplace=False)
+
+        weights_reshape = fluid.layers.softmax(weights_reshape)
         scaled = fluid.layers.elementwise_mul(
             x=encoder_vec, y=weights_reshape, axis=0)
-        scaled = fluid.layers.transpose(scaled, [0, 2, 1])
-        scaled = fluid.layers.reshape(
-            scaled, [-1, scaled.shape[1], scaled.shape[2], 1], inplace=False)
-        context = fluid.layers.pool2d(
-            input=scaled,
-            pool_size=[scaled.shape[2], scaled.shape[3]],
-            pool_type='avg')
-        context = fluid.layers.reshape(
-            context, [-1, context.shape[1]], inplace=False)
+        context = fluid.layers.reduce_sum(scaled, dim=1)
+
         return context
 
 
@@ -381,7 +383,7 @@ class GRUDecoderWithAttention(fluid.dygraph.Layer):
             out = self.out_layer(h)
             res.append(out)
 
-        res1 = fluid.layers.concat(res, axis=0)
+        res1 = fluid.layers.concat(res, axis=1)
 
         return res1
 
@@ -427,7 +429,11 @@ class TestDygraphOCRAttention(unittest.TestCase):
     def test_while_op(self):
         seed = 90
         epoch_num = 2
-        batch_num = 20
+        if core.is_compiled_with_cuda():
+            batch_num = 20
+        else:
+            print("in CPU")
+            batch_num = 2
         np.random.seed = seed
         image_np = np.random.randn(Config.batch_size, Config.DATA_SHAPE[0],
                                    Config.DATA_SHAPE[1],
@@ -441,7 +447,6 @@ class TestDygraphOCRAttention(unittest.TestCase):
                 i * Config.max_length,
                 dtype='int64').reshape([1, Config.max_length])))
 
-        print(label_in_np.shape)
         label_out_np = np.arange(
             0, Config.max_length,
             dtype='int64').reshape([1, Config.max_length])
@@ -450,7 +455,6 @@ class TestDygraphOCRAttention(unittest.TestCase):
                 (i - 1) * Config.max_length,
                 i * Config.max_length,
                 dtype='int64').reshape([1, Config.max_length])))
-        print(label_out_np.shape)
         #if Config.use_gpu:
         #    place = fluid.CUDAPlace(0)
         #else:
@@ -484,6 +488,8 @@ class TestDygraphOCRAttention(unittest.TestCase):
                     dy_prediction = ocr_attention(img, label_in)
                     label_out = fluid.layers.reshape(
                         label_out, [-1, 1], inplace=False)
+                    dy_prediction = fluid.layers.reshape(
+                        dy_prediction, [label_out.shape[0], -1], inplace=False)
                     loss = fluid.layers.cross_entropy(
                         input=dy_prediction, label=label_out)
                     avg_loss = fluid.layers.reduce_sum(loss)
@@ -536,6 +542,9 @@ class TestDygraphOCRAttention(unittest.TestCase):
 
             static_prediction = ocr_attention(images, static_label_in)
 
+            static_prediction = fluid.layers.reshape(
+                static_prediction, shape=[-1, Config.num_classes + 2])
+
             cost = fluid.layers.cross_entropy(
                 input=static_prediction, label=static_label_out)
             static_avg_loss = fluid.layers.reduce_sum(cost)
@@ -558,8 +567,6 @@ class TestDygraphOCRAttention(unittest.TestCase):
                 static_param_init_value[static_param_name_list[i]] = out[i]
 
             fetch_list = [static_avg_loss.name]
-            # print(static_test.name)
-            # fetch_list = [static_avg_loss.name, static_test.name]
             fetch_list.extend(static_param_name_list)
             fetch_list.extend(static_grad_name_list)
             for epoch in range(epoch_num):