update readme && fix codestyle problem

Author: duyiqi17

models/recall/mind/README.md

@@ -107,15 +107,15 @@ python -u static_infer.py -m config.yaml -top_n 50  #对测试数据进行预测
 在全量数据下模型的指标如下：
 | 模型 |  batch_size | epoch_num| Recall@50 | NDCG@50 | HitRate@50 |Time of each epoch |
 | :------| :------ | :------ | :------| :------ | :------|  :------ | 
-| mind | 128 | 20 | 8.43% | 13.28% | 17.22% | 398.64s(CPU) |
-
+| mind(paddle实现) | 128 | 50 | 5.52% | 4.31% | 11.49% | 356.43s(CPU) |
 
 1. 确认您当前所在目录为PaddleRec/models/recall/mind
 2. 进入paddlerec/datasets/AmazonBook目录下执行run.sh脚本，会下载处理完成的AmazonBook数据集，并解压到指定目录
 ```bash
 cd ../../../datasets/AmazonBook
 sh run.sh
 ``` 
+
 3. 安装依赖，我们使用[faiss](https://github.com/facebookresearch/faiss)来进行向量召回
 ```bash
 # CPU-only version(pip)

models/recall/mind/config_bigdata.yaml

@@ -18,15 +18,15 @@ runner:
   use_gpu: False
   use_auc: False
   train_batch_size: 128
-  epochs: 20
+  epochs: 50
   print_interval: 500
   model_save_path: "output_model_mind_all"
   infer_batch_size: 128
   infer_reader_path: "mind_infer_reader" # importlib format
   test_data_dir: "../../../datasets/AmazonBook/valid"
   infer_load_path: "output_model_mind_all"
-  infer_start_epoch: 19
-  infer_end_epoch: 20
+  infer_start_epoch: 49
+  infer_end_epoch: 50
 
   # distribute_config
   # sync_mode: "async"

models/recall/mind/net.py

@@ -21,6 +21,7 @@
 class Mind_SampledSoftmaxLoss_Layer(nn.Layer):
     """SampledSoftmaxLoss with LogUniformSampler
     """
+
     def __init__(self,
                  num_classes,
                  n_sample,
@@ -83,13 +84,13 @@ def forward(self, inputs, labels, weights, bias):
         sample_b = all_b[-n_sample:]
 
         # [B, D] * [B, 1,D]
-        true_logist = paddle.sum(paddle.multiply(
-            true_w, inputs.unsqueeze(1)), axis=-1) + true_b
+        true_logist = paddle.sum(paddle.multiply(true_w, inputs.unsqueeze(1)),
+                                 axis=-1) + true_b
         # print(true_logist)
-   
+
         sample_logist = paddle.matmul(
-            inputs, sample_w, transpose_y=True)  + sample_b
-      
+            inputs, sample_w, transpose_y=True) + sample_b
+
         if self.remove_accidental_hits:
             hit = (paddle.equal(labels[:, :], neg_samples))
             padding = paddle.ones_like(sample_logist) * -1e30
@@ -115,6 +116,7 @@ def forward(self, inputs, labels, weights, bias):
 class Mind_Capsual_Layer(nn.Layer):
     """Mind_Capsual_Layer
     """
+
     def __init__(self,
                  input_units,
                  output_units,
@@ -189,11 +191,13 @@ def forward(self, item_his_emb, seq_len):
 
         low_capsule_new_tile = paddle.tile(low_capsule_new, [1, 1, self.k_max])
         low_capsule_new_tile = paddle.reshape(
-            low_capsule_new_tile, [-1, self.maxlen, self.k_max, self.output_units])
-        low_capsule_new_tile = paddle.transpose(
-            low_capsule_new_tile, [0, 2, 1, 3])
+            low_capsule_new_tile,
+            [-1, self.maxlen, self.k_max, self.output_units])
+        low_capsule_new_tile = paddle.transpose(low_capsule_new_tile,
+                                                [0, 2, 1, 3])
         low_capsule_new_tile = paddle.reshape(
-            low_capsule_new_tile, [-1, self.k_max, self.maxlen, self.output_units])
+            low_capsule_new_tile,
+            [-1, self.k_max, self.maxlen, self.output_units])
         low_capsule_new_nograd = paddle.assign(low_capsule_new_tile)
         low_capsule_new_nograd.stop_gradient = True
 
@@ -209,8 +213,9 @@ def forward(self, item_his_emb, seq_len):
             high_capsule_tmp = paddle.matmul(W, low_capsule_new_nograd)
             # print(low_capsule_new_nograd.shape)
             high_capsule = self.squash(high_capsule_tmp)
-            B_delta = paddle.matmul(low_capsule_new_nograd,
-                                    paddle.transpose(high_capsule, [0, 1, 3, 2]))
+            B_delta = paddle.matmul(
+                low_capsule_new_nograd,
+                paddle.transpose(high_capsule, [0, 1, 3, 2]))
             B_delta = paddle.reshape(
                 B_delta, shape=[-1, self.k_max, self.maxlen])
             B += B_delta
@@ -220,8 +225,8 @@ def forward(self, item_his_emb, seq_len):
         W = paddle.unsqueeze(W, axis=2)
         interest_capsule = paddle.matmul(W, low_capsule_new_tile)
         interest_capsule = self.squash(interest_capsule)
-        high_capsule = paddle.reshape(
-            interest_capsule, [-1, self.k_max, self.output_units])
+        high_capsule = paddle.reshape(interest_capsule,
+                                      [-1, self.k_max, self.output_units])
 
         high_capsule = F.relu(self.relu_layer(high_capsule))
         return high_capsule, W, seq_len
@@ -277,12 +282,16 @@ def __init__(self,
     def label_aware_attention(self, keys, query):
         """label_aware_attention
         """
-        weight = paddle.matmul(keys, paddle.reshape(query, [-1, paddle.shape(query)[-1], 1])) #[B, K, dim] * [B, dim, 1] == [B, k, 1]
+        weight = paddle.matmul(keys,
+                               paddle.reshape(query, [
+                                   -1, paddle.shape(query)[-1], 1
+                               ]))  #[B, K, dim] * [B, dim, 1] == [B, k, 1]
         weight = paddle.squeeze(weight, axis=-1)
         weight = paddle.pow(weight, self.pow_p)  # [x,k_max]
-        weight = F.softmax(weight) #[x, k_max]
-        weight = paddle.unsqueeze(weight, 1) #[B, 1, k_max]
-        output = paddle.matmul(weight, keys) #[B, 1, k_max] * [B, k_max, dim] => [B, 1, dim]
+        weight = F.softmax(weight)  #[x, k_max]
+        weight = paddle.unsqueeze(weight, 1)  #[B, 1, k_max]
+        output = paddle.matmul(
+            weight, keys)  #[B, 1, k_max] * [B, k_max, dim] => [B, 1, dim]
         return output.squeeze(1), weight
 
     def forward(self, hist_item, seqlen, labels=None):