From fa9667a24019242f76f7eb712d6b070abaaf8ebe Mon Sep 17 00:00:00 2001
From: xiaoxuheng <73049735+xiaoxuheng@users.noreply.github.com>
Date: Sat, 24 Dec 2022 17:33:25 +0800
Subject: [PATCH] Add files via upload

---
 nn/Pooling.py | 66 +++++++++++++++++++++++++++------------------------
 1 file changed, 35 insertions(+), 31 deletions(-)

diff --git a/nn/Pooling.py b/nn/Pooling.py
index 924b126..199751a 100644
--- a/nn/Pooling.py
+++ b/nn/Pooling.py
@@ -2,36 +2,39 @@
 
 import numpy as np
 
-from Test_Parameter import Test_Parameter
-from Module import Module
+from .Parameter import Parameter
+from .Module import Module
 
 
 class Pooling(Module):
-    def __init__(self, row, col, poolsize, poolstride, mode="max"):
-        self.parameter = Test_Parameter((row, col))
+    def __init__(self, pool_size=2, mode="max"):
         self.inputs = []
         self.data = None
-        self.poolsize = poolsize
-        self.poolstride = poolstride
+        self.pool_size = pool_size
+        self.pool_stride = pool_size
         self.mode = mode
-        self.maxpos = []
+        self.max_pos = []
+        self.gradient = None
 
     def forward(self, x):
         self.inputs.append(x)
-        if self.mode == "max":
-            self.data = self.pooling()
+        self.data = self.pooling()
         return self
 
     def backward(self, grad):
-        temp = copy.copy(self.inputs)
-        in_row, in_col = np.shape(self.inputs)
-        for i in range(0, in_row):
-            for j in range(0, in_col):
-                if self.maxpos.count((i, j)) == 0:
-                    temp[i][j] = 0
-        self.parameter.gradient = temp
+        self.gradient = copy.copy(self.inputs[0].data)
+        in_row, in_col = np.shape(self.inputs[0].data)
+        if self.mode == "max":
+            for i in range(0, in_row):
+                for j in range(0, in_col):
+                    if self.max_pos.count((i, j)) == 0:
+                        self.gradient[i][j] = 0
+        elif self.mode == "mean":
+            for i in range(0, in_row):
+                for j in range(0, in_col):
+                    self.gradient[i, j] = self.data[int(i/self.pool_size), int(j/self.pool_size)]/self.pool_size/self.pool_size
         if isinstance(self.inputs[0], Module):
-            self.inputs[0].backward(self.parameter.gradient)
+            self.inputs[0].backward(self.gradient)
 
     def __call__(self, x):
         return self.forward(x)
@@ -48,11 +51,11 @@ def pooling(self):
            Padding mode - 'edge'
         """
         # inputMap sizes
-        in_row, in_col = np.shape(self.inputs)
+        in_row, in_col = np.shape(self.inputs[0].data)
 
         # outputMap sizes
-        out_row, out_col = int(np.floor(in_row / self.poolstride)), int(np.floor(in_col / self.poolstride))
-        row_remainder, col_remainder = np.mod(in_row, self.poolstride), np.mod(in_col, self.poolstride)
+        out_row, out_col = int(np.floor(in_row / self.pool_stride)), int(np.floor(in_col / self.pool_stride))
+        row_remainder, col_remainder = np.mod(in_row, self.pool_stride), np.mod(in_col, self.pool_stride)
         if row_remainder != 0:
             out_row += 1
         if col_remainder != 0:
@@ -60,27 +63,28 @@ def pooling(self):
         outputMap = np.zeros((out_row, out_col))
 
         # padding
-        temp_map = np.lib.pad(self.inputs, ((0, self.poolsize - row_remainder), (0, self.poolsize - col_remainder)), 'edge')
+        temp_map = np.lib.pad(self.inputs[0].data, ((0, self.pool_size - row_remainder), (0, self.pool_size - col_remainder)), 'edge')
 
-        # max pooling
+        # pooling
         if self.mode == "max":
             for r_idx in range(0, out_row):
                 for c_idx in range(0, out_col):
-                    startX = c_idx * self.poolstride
-                    startY = r_idx * self.poolstride
-                    poolField = temp_map[startY:startY + self.poolsize, startX:startX + self.poolsize]
+                    startX = c_idx * self.pool_stride
+                    startY = r_idx * self.pool_stride
+                    poolField = temp_map[startY:startY + self.pool_size, startX:startX + self.pool_size]
                     poolOut = np.max(poolField)
-                    temp = np.where(self.inputs == poolOut)
-                    self.maxpos.append((temp[0][0], temp[1][0]))
+                    temp = np.where(temp_map[startY:startY + self.pool_size, startX:startX + self.pool_size] == poolOut)
+                    self.max_pos.append((startY+temp[0][0], startX+temp[1][0]))
                     outputMap[r_idx, c_idx] = poolOut
         elif self.mode == "mean":
             for r_idx in range(0, out_row):
                 for c_idx in range(0, out_col):
-                    startX = c_idx * self.poolstride
-                    startY = r_idx * self.poolstride
-                    poolField = temp_map[startY:startY + self.poolsize, startX:startX + self.poolsize]
+                    startX = c_idx * self.pool_stride
+                    startY = r_idx * self.pool_stride
+                    poolField = temp_map[startY:startY + self.pool_size, startX:startX + self.pool_size]
                     poolOut = np.sum(poolField)
-                    outputMap[r_idx, c_idx] = poolOut / self.poolsize / self.poolsize
+                    mean_value = poolOut / self.pool_size / self.pool_size
+                    outputMap[r_idx, c_idx] = mean_value
 
         # retrun outputMap
         return outputMap