def __call__(self, inputs):
with tf.name_scope(self.name):
with tf.name_scope('preprocessing'):
pad_1 = tf.pad(inputs, np.array([[0,0],[2,2],[2,2],[0,0]]))
conv_1 = conv2d(pad_1, 64, kernel_size=6, strides = 2, name = '256to128')
res_1 = residual(conv_1, 128)
pool_1 = tf.contrib.layers.max_pool2d(res_1, [2,2], [2,2], padding= 'VALID')
res_2 = residual(pool_1, 128)
res_3 = residual(res_2, self.nFeat)
# Supervision Table
hg = [None] * self.nbStack
ll = [None] * self.nbStack
ll_ = [None] * self.nbStack
drop = [None] * self.nbStack
out = [None] * self.nbStack
out_ = [None] * self.nbStack
sum_ = [None] * self.nbStack
with tf.name_scope('stacks'):
with tf.name_scope('hourglass.1'):
hg[0] = self.hourglass(res_3, self.nLow, self.nFeat, 'hourglass')
ll[0] = convBnrelu(hg[0], self.nFeat, name= 'conv_1')
ll_[0] = conv2d(ll[0],self.nFeat,1,1,'VALID','ll')
drop[0] = tf.layers.dropout(ll_[0], rate = 0.1, training = self.train)
out[0] = conv2d(ll[0],self.outDim,1,1,'VALID','out')
out_[0] = conv2d(out[0],self.nFeat,1,1,'VALID','out_')
sum_[0] = tf.add_n([drop[0], out_[0], res_3])
for i in range(1, self.nbStack-1):
with tf.name_scope('hourglass.' + str(i+1)):
hg[i] = self.hourglass(sum_[i-1], self.nLow, self.nFeat, 'hourglass')
ll[i] = convBnrelu(hg[i], self.nFeat, name='conv_1')
ll_[i] = conv2d(ll[i],self.nFeat,1,1,'VALID','ll')
drop[i] = tf.layers.dropout(ll_[i],rate=0.1, training = self.train)
out[i] = conv2d(ll[i],self.outDim,1,1,'VALID','out')
out_[i] = conv2d(out[i],self.nFeat,1,1,'VALID','out_')
sum_[i] = tf.add_n([drop[i], out_[i], sum_[i-1]])
with tf.name_scope('hourglass.' + str(self.nbStack)):
hg[self.nbStack-1] = self.hourglass(sum_[self.nbStack - 2], self.nLow, self.nFeat, 'hourglass')
ll[self.nbStack-1] = convBnrelu(hg[self.nbStack - 1], self.nFeat, name='conv_1')
drop[self.nbStack-1] = tf.layers.dropout(ll[self.nbStack-1], rate=0.1, training = self.train)
out[self.nbStack-1] = conv2d(drop[self.nbStack-1],self.outDim,1,1,'VALID', 'out')
return tf.stack(out, name = 'output')
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