Merge pull request #8 from keezen/master

rename and add kmax pooling module

fastNLP/modules/convolution/AvgPool1d.py

@@ -1,22 +0,0 @@
-# python: 3.6
-# encoding: utf-8
-
-import torch.nn as nn
-# import torch.nn.functional as F
-
-
-class AvgPool1d(nn.Module):
-    """1-d average pooling module."""
-
-    def __init__(self, kernel_size, stride=None, padding=0,
-                 ceil_mode=False, count_include_pad=True):
-        super(AvgPool1d, self).__init__()
-        self.pool = nn.AvgPool1d(
-            kernel_size=kernel_size,
-            stride=stride,
-            padding=padding,
-            ceil_mode=ceil_mode,
-            count_include_pad=count_include_pad)
-
-    def forward(self, x):
-        return self.pool(x)

fastNLP/modules/convolution/MaxPool1d.py

@@ -1,23 +0,0 @@
-# python: 3.6
-# encoding: utf-8
-
-import torch.nn as nn
-# import torch.nn.functional as F
-
-
-class MaxPool1d(nn.Module):
-    """1-d max-pooling module."""
-
-    def __init__(self, kernel_size, stride=None, padding=0,
-                 dilation=1, return_indices=False, ceil_mode=False):
-        super(MaxPool1d, self).__init__()
-        self.maxpool = nn.MaxPool1d(
-            kernel_size=kernel_size,
-            stride=stride,
-            padding=padding,
-            dilation=dilation,
-            return_indices=return_indices,
-            ceil_mode=ceil_mode)
-
-    def forward(self, x):
-        return self.maxpool(x)

fastNLP/modules/convolution/avg_pool.py

@@ -0,0 +1,24 @@
+# python: 3.6
+# encoding: utf-8
+
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class AvgPool(nn.Module):
+    """1-d average pooling module."""
+
+    def __init__(self, stride=None, padding=0):
+        super(AvgPool, self).__init__()
+        self.stride = stride
+        self.padding = padding
+
+    def forward(self, x):
+        # [N,C,L] -> [N,C]
+        kernel_size = x.size(2)
+        x = F.max_pool1d(
+            input=x,
+            kernel_size=kernel_size,
+            stride=self.stride,
+            padding=self.padding)
+        return x.squeeze(dim=-1)

fastNLP/modules/convolution/Conv1d.py → fastNLP/modules/convolution/conv.py

@@ -5,7 +5,7 @@ import torch.nn as nn
 # import torch.nn.functional as F
 
 
-class Conv1d(nn.Module):
+class Conv(nn.Module):
     """
     Basic 1-d convolution module.
     """
@@ -13,7 +13,7 @@ class Conv1d(nn.Module):
     def __init__(self, in_channels, out_channels, kernel_size,
                  stride=1, padding=0, dilation=1,
                  groups=1, bias=True):
-        super(Conv1d, self).__init__()
+        super(Conv, self).__init__()
         self.conv = nn.Conv1d(
             in_channels=in_channels,
             out_channels=out_channels,
@@ -25,4 +25,4 @@ class Conv1d(nn.Module):
             bias=bias)
 
     def forward(self, x):
-        return self.conv(x)
+        return self.conv(x)  # [N,C,L]

fastNLP/modules/convolution/kmax_pool.py

@@ -0,0 +1,20 @@
+# python: 3.6
+# encoding: utf-8
+
+import torch
+import torch.nn as nn
+# import torch.nn.functional as F
+
+
+class KMaxPool(nn.Module):
+    """K max-pooling module."""
+
+    def __init__(self, k):
+        super(KMaxPool, self).__init__()
+        self.k = k
+
+    def forward(self, x):
+        # [N,C,L] -> [N,C*k]
+        x, index = torch.topk(x, self.k, dim=-1, sorted=False)
+        x = torch.reshape(x, (x.size(0), -1))
+        return x

fastNLP/modules/convolution/max_pool.py

@@ -0,0 +1,26 @@
+# python: 3.6
+# encoding: utf-8
+
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class MaxPool(nn.Module):
+    """1-d max-pooling module."""
+
+    def __init__(self, stride=None, padding=0, dilation=1):
+        super(MaxPool, self).__init__()
+        self.stride = stride
+        self.padding = padding
+        self.dilation = dilation
+
+    def forward(self, x):
+        # [N,C,L] -> [N,C]
+        kernel_size = x.size(2)
+        x = F.max_pool1d(
+            input=x,
+            kernel_size=kernel_size,
+            stride=self.stride,
+            padding=self.padding,
+            dilation=self.dilation)
+        return x.squeeze(dim=-1)