def test_backward():
# try to assign back
col_data = np.reshape(range(27),(1,3,3,3)).astype('float32')
sz = col_data.shape
col_data = np.zeros((1,sz[1],sz[2],sz[3]*_ksize*_ksize),'float32')
padded_data = np.zeros((1,4,4,3),'float32')
wrapper.im2col_backward(padded_data, col_data,_ksize, _stride)
print padded_data
def backward(self, bottom, top, propagate_down):
"""Runs the backward pass."""
top_diff = top[0].diff()
padded_data = self._padded.data()
col_data = self._col.data()
bottom_data = bottom[0].data()
if bottom_data.ndim != 4:
raise ValueError('Bottom data should be a 4-dim tensor.')
kernel_diff = self._kernels.init_diff()
if self._has_bias:
bias_diff = self._bias.init_diff()
# bias diff is fairly easy to compute: just sum over all other
# dimensions
np.sum(top_diff.reshape(top_diff.size / top_diff.shape[-1],
top_diff.shape[-1]),
axis=0, out=bias_diff)
if propagate_down:
bottom_diff = bottom[0].init_diff(setzero=False)
col_diff = self._col.init_diff()
if self._pad_size == 0:
padded_diff = self._padded.mirror_diff(bottom_diff)
else:
padded_diff = self._padded.init_diff(setzero=False)
if self._large_mem:
# we have the col_data all pre-stored, making things more efficient.
blasdot.dot_firstdims(col_data, top_diff, out=kernel_diff)
if propagate_down:
blasdot.dot_lastdim(top_diff, self._kernels.data().T,
out=col_diff)
wrapper.im2col_backward(padded_diff, col_diff,
self._ksize, self._stride)
else:
kernel_diff_buffer = np.zeros_like(kernel_diff)
for i in range(bottom_data.shape[0]):
# although it is a backward layer, we still need to compute
# the intermediate results using forward calls.
wrapper.im2col_forward(padded_data[i:i+1], col_data,
self._ksize, self._stride)
blasdot.dot_firstdims(col_data, top_diff[i],
out=kernel_diff_buffer)
kernel_diff += kernel_diff_buffer
if propagate_down:
blasdot.dot_lastdim(top_diff[i], self._kernels.data().T,
out=col_diff)
# im2col backward
wrapper.im2col_backward(padded_diff[i:i+1], col_diff,
self._ksize, self._stride)
# finally, copy results to the bottom diff.
if propagate_down:
if self._pad_size != 0:
bottom_diff[:] = padded_diff[:,
self._pad_size:-self._pad_size,
self._pad_size:-self._pad_size]
# finally, add the regularization term
if self._reg is not None:
return self._reg.reg(self._kernels, bottom_data.shape[0])
else:
return 0.
def backward(self, bottom, top, propagate_down):
"""Computes the backward pass."""
if not propagate_down:
return 0.
top_diff = top[0].diff()
bottom_diff = bottom[0].init_diff(setzero=False)
wrapper.im2col_backward(bottom_diff, top_diff, self._psize,
self._stride)
return 0.
def backward(self, bottom, top, propagate_down):
"""Computes the backward pass."""
if not propagate_down:
return 0.
top_diff = top[0].diff()
bottom_diff = bottom[0].init_diff(setzero=False)
wrapper.im2col_backward(bottom_diff, top_diff, self._psize,
self._stride)
return 0.
def data_backward(self,bottom,top,l_conv1):
"""Runs the backward pass."""
# bottom:(blob) data
# top:(blob) conv1_neuron
# l_conv1: (layer) conv1
top_diff = top[0].diff()
t_sz = top_diff.shape
t_sz0 = np.prod(t_sz[:-1])
top_diff = np.reshape(top_diff,(t_sz0,t_sz[-1]))
padded_data = l_conv1._padded.data()
col_data = l_conv1._col.data() # stored in layers['conv1']
kernel_data = l_conv1._kernels.data()
k_sz = kernel_data.shape
col_diff = np.zeros((t_sz0,k_sz[0]),'float32');
for i in range(k_sz[1]):
col_diff += np.dot(top_diff[:,i:i+1],kernel_data[:,i:i+1].T)
# accumulate back to image col2im
padded_diff = np.empty_like(bottom[0].data())
print padded_diff.dtype
print (t_sz[0],t_sz[1],t_sz[2],k_sz[0])
if l_conv1._large_mem:
wrapper.im2col_backward(padded_diff, np.reshape(col_diff,(t_sz[0],t_sz[1],t_sz[2],k_sz[0])),
l_conv1._ksize, l_conv1._stride)
else:
raise NotImplementedError(str(type(self)) + " does not implement small memory im2col_backward.")
"""
kernel_diff_buffer = np.empty_like(kernel_diff)
for i in range(bottom_data.shape[0]):
# although it is a backward layer, we still need to compute
# the intermediate results using forward calls.
wrapper.im2col_forward(padded_data[i:i+1], col_data,
l_conv1._ksize, l_conv1._stride)
blasdot.dot_firstdims(col_data, top_diff[i],
out=kernel_diff_buffer)
kernel_diff += kernel_diff_buffer
if propagate_down:
blasdot.dot_lastdim(top_diff[i], l_conv1._kernels.data().T,
out=col_diff)
# im2col backward
wrapper.im2col_backward(padded_diff[i:i+1], col_diff,
l_conv1._ksize, l_conv1._stride)
"""
return padded_diff
def forward(self, bottom, top):
"""Runs the forward pass."""
bottom_data = bottom[0].data()
if bottom_data.ndim != 4:
raise ValueError('Bottom data should be a 4-dim tensor.')
if not self._kernels.has_data():
# initialize the kernels
self._kernels.init_data(
(bottom_data.shape[-1],
self._ksize * self._ksize * self._num_channels),
bottom_data.dtype)
# initialize the buffers.
self._col.init_data((1, bottom_data.shape[1], bottom_data.shape[2],
self._kernels.data().shape[1]),
dtype = bottom_data.dtype)
pad_height = self._ksize + (bottom_data.shape[1] - 1) \
* self._stride
pad_width = self._ksize + (bottom_data.shape[2] - 1) \
* self._stride
if self._mode != 'valid':
padded_data = self._padded.init_data(
(1, pad_height, pad_width, self._num_channels),
dtype = bottom_data.dtype)
top_data = top[0].init_data(
(bottom_data.shape[0], pad_height - self._border * 2,
pad_width - self._border * 2, self._num_channels),
dtype=bottom_data.dtype)
# process data individually
for i in range(bottom_data.shape[0]):
# first, compute the convolution as a gemm operation
blasdot.dot_lastdim(bottom_data[i:i+1], self._kernels.data(),
out=self._col.data())
if self._mode != 'valid':
# do col2im
wrapper.im2col_backward(padded_data, self._col.data(),
self._ksize, self._stride)
top_data[i] = padded_data[0, self._border:-self._border,
self._border:-self._border]
else:
wrapper.im2col_backward(top_data[i:i+1], self._col.data(),
self._ksize, self._stride)
return
def backward(self, bottom, top, propagate_down):
"""Runs the backward pass."""
top_diff = top[0].diff()
padded_data = self._padded.data()
col_data = self._col.data()
bottom_data = bottom[0].data()
if bottom_data.ndim != 4:
raise ValueError('Bottom data should be a 4-dim tensor.')
kernel_diff = self._kernels.init_diff()
if self._has_bias:
bias_diff = self._bias.init_diff()
# bias diff is fairly easy to compute: just sum over all other
# dimensions
np.sum(top_diff.reshape(top_diff.size / top_diff.shape[-1],
top_diff.shape[-1]),
axis=0,
out=bias_diff)
if propagate_down:
bottom_diff = bottom[0].init_diff(setzero=False)
col_diff = self._col.init_diff()
if self._pad_size == 0:
padded_diff = self._padded.mirror_diff(bottom_diff)
else:
padded_diff = self._padded.init_diff(setzero=False)
if self._large_mem:
# we have the col_data all pre-stored, making things more efficient.
blasdot.dot_firstdims(col_data, top_diff, out=kernel_diff)
if propagate_down:
blasdot.dot_lastdim(top_diff,
self._kernels.data().T,
out=col_diff)
wrapper.im2col_backward(padded_diff, col_diff, self._ksize,
self._stride)
else:
kernel_diff_buffer = np.zeros_like(kernel_diff)
for i in range(bottom_data.shape[0]):
# although it is a backward layer, we still need to compute
# the intermediate results using forward calls.
wrapper.im2col_forward(padded_data[i:i + 1], col_data,
self._ksize, self._stride)
blasdot.dot_firstdims(col_data,
top_diff[i],
out=kernel_diff_buffer)
kernel_diff += kernel_diff_buffer
if propagate_down:
blasdot.dot_lastdim(top_diff[i],
self._kernels.data().T,
out=col_diff)
# im2col backward
wrapper.im2col_backward(padded_diff[i:i + 1], col_diff,
self._ksize, self._stride)
# finally, copy results to the bottom diff.
if propagate_down:
if self._pad_size != 0:
bottom_diff[:] = padded_diff[:, self._pad_size:-self._pad_size,
self._pad_size:-self._pad_size]
# finally, add the regularization term
if self._reg is not None:
return self._reg.reg(self._kernels, bottom_data.shape[0])
else:
return 0.
import numpy as np
# foward
_ksize = 2
_stride = 1
def test_forward():
# same as matlab
padded_data = np.reshape(range(48),(1,4,4,3)).astype('float32')
sz = padded_data.shape
col_data = np.zeros((1,(sz[1]-_ksize)/_stride+1,(sz[2]-_ksize)/_stride+1,sz[3]*_ksize*_ksize),'float32')
wrapper.im2col_forward(padded_data, col_data,_ksize, _stride)
print col_data
def test_backward():
# try to assign back
col_data = np.reshape(range(27),(1,3,3,3)).astype('float32')
sz = col_data.shape
col_data = np.zeros((1,sz[1],sz[2],sz[3]*_ksize*_ksize),'float32')
padded_data = np.zeros((1,4,4,3),'float32')
wrapper.im2col_backward(padded_data, col_data,_ksize, _stride)
print padded_data
#col_data = np.reshape(range(12),(1,2,2,3)).astype('float32')
col_data = np.reshape(range(108),(1,3,3,3*4)).astype('float32')
#col_data = np.reshape(range(27),(1,3,3,3)).astype('float32')
sz = col_data.shape
padded_data = np.zeros((1,4,4,3),'float32')
wrapper.im2col_backward(padded_data, col_data,_ksize, _stride)
print padded_data