Merge pull request #7 from keezen/master

add conv and pooling module

fastNLP/modules/convolution/AvgPool1d.py

@@ -0,0 +1,22 @@
+# 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/Conv1d.py

@@ -0,0 +1,28 @@
+# python: 3.6
+# encoding: utf-8
+
+import torch.nn as nn
+# import torch.nn.functional as F
+
+
+class Conv1d(nn.Module):
+    """
+    Basic 1-d convolution module.
+    """
+
+    def __init__(self, in_channels, out_channels, kernel_size,
+                 stride=1, padding=0, dilation=1,
+                 groups=1, bias=True):
+        super(Conv1d, self).__init__()
+        self.conv = nn.Conv1d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias)
+
+    def forward(self, x):
+        return self.conv(x)

fastNLP/modules/convolution/MaxPool1d.py

@@ -0,0 +1,23 @@
+# 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)