add drawing dilated convolution pixels to be used

Author: wz

others_project/draw_dilated_conv/main.py

@@ -0,0 +1,98 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.colors import LinearSegmentedColormap
+
+
+def dilated_conv_one_pixel(center: (int, int),
+                           feature_map: np.ndarray,
+                           k: int = 3,
+                           r: int = 1,
+                           v: int = 1):
+    """
+    膨胀卷积核中心在指定坐标center处时，统计哪些像素被利用到，
+    并在利用到的像素位置处加上增量v
+    Args:
+        center: 膨胀卷积核中心的坐标
+        feature_map: 记录每个像素使用次数的特征图
+        k: 膨胀卷积核的kernel大小
+        r: 膨胀卷积的dilation rate
+        v: 使用次数增量
+    """
+    assert divmod(3, 2)[1] == 1
+
+    # left-top: (x, y)
+    left_top = (center[0] - ((k - 1) // 2) * r, center[1] - ((k - 1) // 2) * r)
+    for i in range(k):
+        for j in range(k):
+            feature_map[left_top[1] + i * r][left_top[0] + j * r] += v
+
+
+def dilated_conv_all_map(dilated_map: np.ndarray,
+                         k: int = 3,
+                         r: int = 1):
+    """
+    根据输出特征矩阵中哪些像素被使用以及使用次数，
+    配合膨胀卷积k和r计算输入特征矩阵哪些像素被使用以及使用次数
+    Args:
+        dilated_map: 记录输出特征矩阵中每个像素被使用次数的特征图
+        k: 膨胀卷积核的kernel大小
+        r: 膨胀卷积的dilation rate
+    """
+    new_map = np.zeros_like(dilated_map)
+    for i in range(dilated_map.shape[0]):
+        for j in range(dilated_map.shape[1]):
+            if dilated_map[i][j] > 0:
+                dilated_conv_one_pixel((j, i), new_map, k=k, r=r, v=dilated_map[i][j])
+
+    return new_map
+
+
+def plot_map(matrix: np.ndarray):
+    plt.figure()
+
+    c_list = ['white', 'blue', 'red']
+    new_cmp = LinearSegmentedColormap.from_list('chaos', c_list)
+    plt.imshow(matrix, cmap=new_cmp)
+
+    ax = plt.gca()
+    ax.set_xticks(np.arange(-0.5, matrix.shape[1], 1), minor=True)
+    ax.set_yticks(np.arange(-0.5, matrix.shape[0], 1), minor=True)
+
+    # 显示color bar
+    plt.colorbar()
+
+    # 在图中标注数量
+    thresh = 5
+    for x in range(matrix.shape[1]):
+        for y in range(matrix.shape[0]):
+            # 注意这里的matrix[y, x]不是matrix[x, y]
+            info = int(matrix[y, x])
+            ax.text(x, y, info,
+                    verticalalignment='center',
+                    horizontalalignment='center',
+                    color="white" if info > thresh else "black")
+    ax.grid(which='minor', color='black', linestyle='-', linewidth=1.5)
+    plt.show()
+    plt.close()
+
+
+def main():
+    # bottom to top
+    dilated_rates = [1, 2, 3]
+    # init feature map
+    size = 31
+    m = np.zeros(shape=(size, size), dtype=np.int32)
+    center = size // 2
+    m[center][center] = 1
+    # print(m)
+    # plot_map(m)
+
+    for index, dilated_r in enumerate(dilated_rates[::-1]):
+        new_map = dilated_conv_all_map(m, r=dilated_r)
+        m = new_map
+    print(m)
+    plot_map(m)
+
+
+if __name__ == '__main__':
+    main()