def _backward(din, w_value, x_value, col, col_w, pad, stride):
FN, C, FH, FW = w_value.shape
din = din.transpose(0, 2, 3, 1).reshape(-1, FN)
db = np.sum(din, axis=0)
dw = np.dot(col.T, din)
dw = dw.transpose(1, 0).reshape(FN, C, FH, FW)
dcol = np.dot(din, col_w.T)
dx = tff.col2im(dcol, x_value.shape, FH, FW, stride, pad)
return dw, dx, db
def _backward(din, x_value, filter_h, filter_w, pad, stride, arg_max):
din = din.transpose(0, 2, 3, 1)
pool_size = filter_h * filter_w
dmax = np.zeros((din.size, pool_size))
dmax[np.arange(arg_max.size), arg_max.flatten()] = din.flatten()
dmax = dmax.reshape(din.shape + (pool_size, ))
dcol = dmax.reshape(dmax.shape[0] * dmax.shape[1] * dmax.shape[2], -1)
dx = tff.col2im(dcol, x_value.shape, filter_h, filter_w, stride, pad)
return dx
def _backward(din, w_value, x_value, pad, stride, arg_max):
FN, C, FH, FW = w_value.shape
din = din.transpose(0, 2, 3, 1)
pool_size = FH * FW
dmax = np.zeros((din.size, pool_size))
dmax[np.arange(arg_max.size), arg_max.flatten()] = din.flatten()
dmax = dmax.reshape(din.shape + (pool_size, ))
dcol = dmax.reshape(dmax.shape[0] * dmax.shape[1] * dmax.shape[2], -1)
dx = tff.col2im(dcol, x_value.shape, FH, FW, stride, pad)
return dx
def backward(self, din, is_numba):
if is_numba:
self._backward(din, self.x_value, self.filter_h, self.filter_w,
self.pad, self.stride, self.arg_max)
else:
din = din.transpose(0, 2, 3, 1)
pool_size = self.filter_h * self.filter_w
dmax = np.zeros((din.size, pool_size))
dmax[np.arange(self.arg_max.size),
self.arg_max.flatten()] = din.flatten()
dmax = dmax.reshape(din.shape + (pool_size, ))
dcol = dmax.reshape(dmax.shape[0] * dmax.shape[1] * dmax.shape[2],
-1)
dx = tff.col2im(dcol, self.x_value.shape, self.filter_h,
self.filter_w, self.stride, self.pad)
return dx
def backward(self, din, is_numba):
if is_numba:
dw, dx, db = self._backward(din, self.w_value, self.x_value,
self.col, self.col_w, self.pad,
self.stride)
self.dw = dw
self.db = db
return dx
else:
FN, C, FH, FW = self.w_value.shape
din = din.transpose(0, 2, 3, 1).reshape(-1, FN)
self.db = np.sum(din, axis=0)
self.dw = np.dot(self.col.T, din)
self.dw = self.dw.transpose(1, 0).reshape(FN, C, FH, FW)
dcol = np.dot(din, self.col_w.T)
dx = tff.col2im(dcol, self.x_value.shape, FH, FW, self.stride,
self.pad)
return dx