import tensorflow.contrib.slim as slim
from .baseop import BaseOp
import tensorflow as tf
import numpy as np
class reorg(BaseOp):
def _forward(self):
inp = self.inp.out
shape = inp.get_shape().as_list()
_, h, w, c = shape
s = self.lay.stride
out = list()
for i in range(int(h/s)):
row_i = list()
for j in range(int(w/s)):
si, sj = s * i, s * j
boxij = inp[:, si: si+s, sj: sj+s,:]
flatij = tf.reshape(boxij, [-1,1,1,c*s*s])
row_i += [flatij]
out += [tf.concat(row_i, 2)]
self.out = tf.concat(out, 1)
def forward(self):
inp = self.inp.out
s = self.lay.stride
self.out = tf.extract_image_patches(
inp, [1,s,s,1], [1,s,s,1], [1,1,1,1], 'VALID')
def speak(self):
args = [self.lay.stride] * 2
msg = 'local flatten {}x{}'
return msg.format(*args)
class local(BaseOp):
def forward(self):
pad = [[self.lay.pad, self.lay.pad]] * 2;
temp = tf.pad(self.inp.out, [[0, 0]] + pad + [[0, 0]])
k = self.lay.w['kernels']
ksz = self.lay.ksize
half = int(ksz / 2)
out = list()
for i in range(self.lay.h_out):
row_i = list()
for j in range(self.lay.w_out):
kij = k[i * self.lay.w_out + j]
i_, j_ = i + 1 - half, j + 1 - half
tij = temp[:, i_ : i_ + ksz, j_ : j_ + ksz,:]
row_i.append(
tf.nn.conv2d(tij, kij,
padding = 'VALID',
strides = [1] * 4))
out += [tf.concat(row_i, 2)]
self.out = tf.concat(out, 1)
def speak(self):
l = self.lay
args = [l.ksize] * 2 + [l.pad] + [l.stride]
args += [l.activation]
msg = 'loca {}x{}p{}_{} {}'.format(*args)
return msg
class convolutional(BaseOp):
def forward(self):
pad = [[self.lay.pad, self.lay.pad]] * 2;
temp = tf.pad(self.inp.out, [[0, 0]] + pad + [[0, 0]])
temp = tf.nn.conv2d(temp, self.lay.w['kernel'], padding = 'VALID',
name = self.scope, strides = [1] + [self.lay.stride] * 2 + [1])
if self.lay.batch_norm:
temp = self.batchnorm(self.lay, temp)
self.out = tf.nn.bias_add(temp, self.lay.w['biases'])
def batchnorm(self, layer, inp):
if not self.var:
temp = (inp - layer.w['moving_mean'])
temp /= (np.sqrt(layer.w['moving_variance']) + 1e-5)
temp *= layer.w['gamma']
return temp
else:
args = dict({
'center' : False, 'scale' : True,
'epsilon': 1e-5, 'scope' : self.scope,
'updates_collections' : None,
'is_training': layer.h['is_training'],
'param_initializers': layer.w
})
return slim.batch_norm(inp, **args)
def speak(self):
l = self.lay
args = [l.ksize] * 2 + [l.pad] + [l.stride]
args += [l.batch_norm * '+bnorm']
args += [l.activation]
msg = 'conv {}x{}p{}_{} {} {}'.format(*args)
return msg
class conv_select(convolutional):
def speak(self):
l = self.lay
args = [l.ksize] * 2 + [l.pad] + [l.stride]
args += [l.batch_norm * '+bnorm']
args += [l.activation]
msg = 'sele {}x{}p{}_{} {} {}'.format(*args)
return msg
class conv_extract(convolutional):
def speak(self):
l = self.lay
args = [l.ksize] * 2 + [l.pad] + [l.stride]
args += [l.batch_norm * '+bnorm']
args += [l.activation]
msg = 'extr {}x{}p{}_{} {} {}'.format(*args)
return msg
