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convolution2D.py
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convolution2D.py
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# encoding: utf-8
from src.layers.layer import Layer
from src.unit import BasicUnit
import src.initialization
import numpy as np
import numba as nb
from src.util import *
import time
class Convolution2D(Layer):
# input_size:tuple or list include height,width,channel
def __init__(self, filters, kernel_size, input_size=0, name='', pad=0, stride=1):
super().__init__(kernel_size +(filters, ), input_size, name)
self.pad = pad
self.filters = filters
self.stride = stride
self.kernel_size = kernel_size
def build(self, loc_idx, input_size, init_param_method='glorot_normal'):
pass
super().build(loc_idx, input_size, init_param_method)
# self.W = self.kernel_size + (self.input_size[2], self.filters)
self.b = np.zeros((1, 1, 1, self.filters))
self.W = src.initialization.get(init_param_method)((self.input_size[2], *self.kernel_size, self.filters))
# self.W = src.initialization.get(init_param_method)(self.kernel_size + (self.input_size[2], self.filters))
n_H_prev, n_W_prev ,n_C_prev = self.input_size
(n_C_prev, f, f, n_C) = self.W.shape
n_H = int((n_H_prev - f + 2 * self.pad) / self.stride) + 1
n_W = int((n_W_prev - f + 2 * self.pad) / self.stride) + 1
self.output_size = (n_H, n_W, n_C)
@nb.jit
def forward(self, X):
W = self.W
b = self.b
stride = self.stride
pad = self.pad
(n_C_prev, f, f, n_C) = W.shape
m, n_H_prev, n_W_prev, n_C_prev = X.shape
n_H = int((n_H_prev - f + 2 * pad) / stride) + 1
n_W = int((n_W_prev - f + 2 * pad) / stride) + 1
# X_pad = np.pad(X, ((0, 0), (pad, pad), (pad, pad), (0, 0)), 'constant', constant_values=0)
# Z = np.zeros((m * n_H * n_W, f * f * n_C_prev), dtype=np.double)
# st = time.time()
# XX = src.ext.sample_pyx.img2col(X_pad, Z,n_H,n_W, stride, f)
# XX = img2col(X_pad, Z, n_H, n_W, stride, f)
# 先将X转换为二维矩阵，然后进行前向乘积运算，加快速度。原始实现方法看simple_convolution2D.py
XX = img2col(X, pad, stride, f)
# print('cnn ,forward,',(time.time()-st))
# WW = WW.reshape(f*f*n_C_prev, n_C)
WW = W.reshape(f * f * n_C_prev, n_C)
self.XX = XX
self.WW = WW
Z = BasicUnit.forward(XX, WW, b)
return Z.reshape(m, n_H, n_W, n_C)
def backward(self, X, dA):
pass
return speed_backward(self, X,dA)
def speed_forward(model, X):
W = model.W
b = model.b
stride = model.stride
pad = model.pad
(n_C_prev, f, f, n_C) = W.shape
m, n_H_prev, n_W_prev, n_C_prev = X.shape
n_H = int((n_H_prev - f + 2 * pad) / stride) + 1
n_W = int((n_W_prev - f + 2 * pad) / stride) + 1
# WW = W.swapaxes(2,1)
# WW = WW.swapaxes(1,0)
XX = img2col(X, pad, stride, f)
# WW = WW.reshape(f*f*n_C_prev, n_C)
WW = W.reshape(f*f*n_C_prev, n_C)
model.XX = XX
model.WW = WW
Z = BasicUnit.forward(XX, WW, b)
return Z.reshape(m, n_H, n_W, n_C)
@nb.jit
def speed_backward(model,X, dA):
W = model.W
b = model.b
stride = model.stride
pad = model.pad
(n_C_prev, f, f, n_C) = W.shape
m, n_H_prev, n_W_prev, n_C_prev = X.shape
n_H = int((n_H_prev - f + 2 * pad) / stride) + 1
n_W = int((n_W_prev - f + 2 * pad) / stride) + 1
dA = dA.reshape(m * n_H * n_W, n_C)
dAA, dW, db = BasicUnit.backward(model.XX, dA, model.WW)
# Z = np.zeros((m, n_H_prev + 2 * pad, n_W_prev + 2 * pad, n_C_prev), dtype=np.float64)
# 将dAA回原先的m,h,w,c格式。原始实现方法看simple_convolution2D.py
dAA = col2img(dAA, (n_H_prev, n_W_prev, n_C_prev), pad, stride, f)
# dAA = col2img(dAA,Z, (n_H_prev, n_W_prev, n_C_prev), n_H, n_W, pad, stride, f)
dW = dW.reshape(n_C_prev, f, f, n_C)
return dAA, dW, db
@nb.jit(nopython=True)
def conv_single_step(X, W, b):
# 对一个裁剪图像进行卷积
# X.shape = f, f, prev_channel_size
return np.sum(np.multiply(X, W) + b)
# @nb.jit('double[:,:,:](double[:,:,:],int16)', nopython=True)
def zero_pad(X, pad):
"""
X -- shape (m, n_H, n_W, n_C)
Returns:
X_pad -- padded image of shape (m, n_H + 2*pad, n_W + 2*pad, n_C)
"""
X_pad = np.pad(X, ((0, 0), (pad, pad), (pad, pad), (0, 0)), 'constant', constant_values=0)
return X_pad
if __name__ == '__main__':
pass
import time
st = time.time()
for i in range(1000):
con = Convolution2D(8,(2,2), pad=2)
con.build(1,(4,4,3),'randn')
np.random.seed(1)
A_prev = np.random.randn(10, 4, 4, 3)
W = np.random.randn(3, 2, 2, 8)
b = np.random.randn(1, 1, 1, 8)
con.W = W
con.b = b
ret = con.forward(A_prev)
# print(A_prev[0])
# print(ret[0])
print(ret.shape,i)
print(np.mean(ret))
np.random.seed(1)
dA, dW, db = con.backward(A_prev, ret)
# print("dA_mean =", np.mean(dA),dA.shape)
# print("dW_mean =", np.mean(dW),dW.shape)
# print("db_mean =", np.mean(db),db.shape)
print(time.time()-st)