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convnet.py
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convnet.py
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import tensorflow as tf
PARAMETERS_NAME = ["conv_%d_w", \
"conv_%d_b", \
"prelu_%d_%d_alpha", \
"bn_%d_%d_offset", \
"bn_%d_%d_scale", \
"bn_%d_%d_mv_mean", \
"bn_%d_%d_mv_var", \
"in_%d_%d_offset", \
"in_%d_%d_scale", \
"ln_%d_%d_offset", \
"ln_%d_%d_scale"]
# .#####...######..##......##..##.
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# .##..##..######..######...####..
# ................................
def relu_layer():
return dict(name='relu')
def exe_relu_layer(tensor):
tensor = tf.nn.relu(tensor)
return tensor
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# ........................................
def prelu_layer():
return dict(name='prelu')
def exe_prelu_layer(tensor, net_info, l_index, is_first, act_o):
p_index = 2
parameter_count = 1
alphas_l = []
for i in range(act_o['size']):
alphas = tf.get_variable(name=PARAMETERS_NAME[p_index] % (l_index, i), \
shape=tensor.get_shape()[-1], \
initializer=tf.constant_initializer(0.0))
alphas_l.append(alphas)
alphas = alphas_l[act_o['index']]
pos = tf.nn.relu(tensor)
neg = alphas * (tensor - abs(tensor)) * 0.5
tensor = pos + neg
if is_first:
net_info.weights.extend(alphas_l)
for i in range(parameter_count):
for j in range(act_o['size']):
net_info.parameter_names.append(PARAMETERS_NAME[p_index + i] % (l_index, j))
return tensor
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# ........................................
def lrelu_layer(leak):
return dict(
name='lrelu',
leak=leak)
def exe_lrelu_layer(tensor, layer_o):
leak = layer_o['leak']
f1 = 0.5 * (1 + leak)
f2 = 0.5 * (1 - leak)
tensor = f1 * tensor + f2 * abs(tensor)
return tensor
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# ................................
def selu_layer():
return dict(name='selu')
def exe_selu_layer(tensor):
#alpha = 1.6732632423543772848170429916717
#scale = 1.0507009873554804934193349852946
alpha, scale = (1.0198755295894968, 1.0026538655307724)
return scale*tf.where(tensor>=0.0, tensor, alpha*tf.nn.elu(tensor))
# .#####...##..##.
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# ................
def bn_layer(use_offset=False, use_scale=False, epsilon=1e-5, decay=0.9):
return dict(
name='bn',
use_offset=use_offset,
use_scale=use_scale,
epsilon=epsilon,
decay=decay)
def exe_bn_layer(tensor, layer_o, net_info, l_index, is_first, is_training, trainable, act_o):
p_index = 3
parameter_count = 4
shape = [tensor.get_shape()[-1]]
offset_trainable = layer_o['use_offset'] if trainable else False
scale_trainable = layer_o['use_scale'] if trainable else False
pars = []
for i in range(act_o['size']):
offset = tf.get_variable(name=PARAMETERS_NAME[p_index ] % (l_index, i), shape=shape, initializer=tf.constant_initializer(0.0), trainable=offset_trainable)
scale = tf.get_variable(name=PARAMETERS_NAME[p_index+1] % (l_index, i), shape=shape, initializer=tf.constant_initializer(1.0), trainable=scale_trainable)
mv_mean = tf.get_variable(name=PARAMETERS_NAME[p_index+2] % (l_index, i), shape=shape, initializer=tf.constant_initializer(0.0), trainable=False)
mv_var = tf.get_variable(name=PARAMETERS_NAME[p_index+3] % (l_index, i), shape=shape, initializer=tf.constant_initializer(1.0), trainable=False)
pars.append([offset, scale, mv_mean, mv_var])
offset, scale, mv_mean, mv_var = pars[act_o['index']]
if is_first:
for ps in pars:
net_info.weights.extend(ps)
for i in range(parameter_count):
for j in range(act_o['size']):
net_info.parameter_names.append(PARAMETERS_NAME[p_index + i] % (l_index, j))
if is_training:
batch_mean, batch_var = tf.nn.moments(tensor, [0, 1, 2])
train_mean = tf.assign(mv_mean,
mv_mean * layer_o['decay'] + batch_mean * (1 - layer_o['decay']))
train_var = tf.assign(mv_var,
mv_var * layer_o['decay'] + batch_var * (1 - layer_o['decay']))
with tf.control_dependencies([train_mean, train_var]):
tensor = tf.nn.batch_normalization(tensor, batch_mean, batch_var, offset, scale, layer_o['epsilon'])
else:
tensor = tf.nn.batch_normalization(tensor, mv_mean, mv_var, offset, scale, layer_o['epsilon'])
return tensor
# .######..##..##.
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# ................
def in_layer(use_offset=False, use_scale=False, epsilon=1e-5):
return dict(
name='in',
use_offset=use_offset,
use_scale=use_scale,
epsilon=epsilon)
def exe_in_layer(tensor, layer_o, net_info, l_index, is_first, trainable, act_o):
p_index = 7
shape = [tensor.get_shape()[-1]]
offset_trainable = layer_o['use_offset'] if trainable else False
scale_trainable = layer_o['use_scale'] if trainable else False
pars = []
for i in range(act_o['size']):
offset = tf.get_variable(name=PARAMETERS_NAME[p_index ] % (l_index, i), shape=shape, initializer=tf.constant_initializer(0.0), trainable=offset_trainable)
scale = tf.get_variable(name=PARAMETERS_NAME[p_index+1] % (l_index, i), shape=shape, initializer=tf.constant_initializer(1.0), trainable=scale_trainable)
pars.append([offset, scale])
offset, scale = pars[act_o['index']]
if is_first:
for ps in pars:
net_info.weights.extend(ps)
parameter_count = 2
for i in range(parameter_count):
for j in range(act_o['size']):
net_info.parameter_names.append(PARAMETERS_NAME[p_index + i] % (l_index, j))
t_list = tf.unstack(tensor)
result = []
for t in t_list:
batch_mean, batch_var = tf.nn.moments(t, [0, 1])
t = tf.nn.batch_normalization(t, batch_mean, batch_var, offset, scale, layer_o['epsilon'])
result.append(t)
return tf.stack(result)
# mean, var = tf.nn.moments(tensor, [1, 2], keep_dims=True)
# normalized = tf.div(tf.sub(tensor, mean), tf.sqrt(tf.add(var, layer_o['epsilon'])))
# return scale * normalized + offset
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# ................
def ln_layer(use_offset=False, use_scale=False, epsilon=1e-5):
return dict(
name='ln',
use_offset=use_offset,
use_scale=use_scale,
epsilon=epsilon)
def exe_ln_layer(tensor, layer_o, net_info, l_index, is_first, trainable, act_o):
p_index = 9
shape = [1, 1, tensor.get_shape()[-1]]
offset_trainable = layer_o['use_offset'] if trainable else False
scale_trainable = layer_o['use_scale'] if trainable else False
pars = []
for i in range(act_o['size']):
offset = tf.get_variable(name=PARAMETERS_NAME[p_index ] % (l_index, i), shape=shape, initializer=tf.constant_initializer(0.0), trainable=offset_trainable)
scale = tf.get_variable(name=PARAMETERS_NAME[p_index+1] % (l_index, i), shape=shape, initializer=tf.constant_initializer(1.0), trainable=scale_trainable)
pars.append([offset, scale])
offset, scale = pars[act_o['index']]
if is_first:
for ps in pars:
net_info.weights.extend(ps)
parameter_count = 2
for i in range(parameter_count):
for j in range(act_o['size']):
net_info.parameter_names.append(PARAMETERS_NAME[p_index + i] % (l_index, j))
mean, var = tf.nn.moments(tensor, [1, 2, 3], keep_dims=True)
result = tf.nn.batch_normalization(tensor, mean, var, offset, scale, layer_o['epsilon'])
return result
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# ................................
def conv_layer(kernel, stride, filter, pad_mode, initializer, dropout=1, padding='VALID'):
return dict(
name='conv',
kernel=kernel,
stride=stride,
filter=filter,
pad_mode=pad_mode,
initializer=initializer,
dropout=dropout,
padding=padding)
def exe_conv_layer(tensor, layer_o, net_info, l_index, is_first, is_training, trainable, seed):
p_index = 0
parameter_count = 2
kernel = layer_o['kernel']
stride = layer_o['stride']
filter = layer_o['filter']
pad_mode = layer_o['pad_mode']
dropout = layer_o['dropout']
initializer = layer_o['initializer']
padding = layer_o['padding']
conv_w = tf.get_variable(PARAMETERS_NAME[p_index ] % l_index, \
[kernel, kernel, tensor.get_shape()[-1], filter], \
initializer=initializer, \
trainable=trainable)
conv_b = tf.get_variable(PARAMETERS_NAME[p_index+1] % l_index, \
[filter], \
initializer=tf.constant_initializer(0), \
trainable=trainable)
pad_size = (kernel - 1) // 2
if pad_size > 0 and pad_mode is not None:
tensor = tf.pad(tensor, [[0, 0], [pad_size, pad_size], [pad_size, pad_size], [0, 0]], pad_mode)
if is_training and dropout < 1:
tensor = tf.nn.dropout(tensor, dropout, seed=seed)
tensor = tf.nn.bias_add(tf.nn.conv2d(tensor, conv_w, strides=[1,stride,stride,1], padding=padding), conv_b)
if is_first:
net_info.weights.extend((conv_w, conv_b))
for i in range(parameter_count):
net_info.parameter_names.append(PARAMETERS_NAME[p_index + i] % l_index)
return tensor
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# ........................................................................................................
def conv_res_layer(index, kernel, stride, initializer, dropout=1, padding='VALID'):
return dict(
name='conv_res',
index=index,
kernel=kernel,
stride=stride,
dropout=dropout,
initializer=initializer,
padding=padding)
def exe_conv_res_layer(res_tensor, layer_o, tensor_list, net_info, l_index, is_first, is_training, trainable, seed):
p_index = 0
parameter_count = 2
index = layer_o['index']
kernel = layer_o['kernel']
stride = layer_o['stride']
dropout = layer_o['dropout']
initializer = layer_o['initializer']
padding = layer_o['padding']
filter = res_tensor.get_shape()[-1]
tensor = tensor_list[index]
conv_w = tf.get_variable(PARAMETERS_NAME[p_index ] % l_index, \
[kernel, kernel, tensor.get_shape()[-1], filter], \
initializer=initializer, \
trainable=trainable)
conv_b = tf.get_variable(PARAMETERS_NAME[p_index+1] % l_index, \
[filter], \
initializer=tf.constant_initializer(0), \
trainable=trainable)
if is_training and dropout < 1:
tensor = tf.nn.dropout(tensor, dropout, seed=seed)
tensor = tf.nn.bias_add(tf.nn.conv2d(tensor, conv_w, strides=[1,stride,stride,1], padding=padding), conv_b)
if is_first:
net_info.weights.extend((conv_w, conv_b))
for i in range(parameter_count):
net_info.parameter_names.append(PARAMETERS_NAME[p_index + i] % l_index)
tensor = tf.add(res_tensor, tensor)
return tensor
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# ................................................................
def res_layer(index, axis):
return dict(
name='res',
index=index,
axis=axis)
def exe_res_layer(tensor, layer_o, tensor_list):
index = layer_o['index']
axis = layer_o['axis']
res_tensor = tensor_list[index]
l = [res_tensor[:, :, :, i] for i in axis]
res_tensor = tf.stack(l, -1)
tensor = tf.add(tensor, res_tensor)
return tensor
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# .................................................................
def max_pool_layer(kernel, stride, padding='VALID'):
return dict(
name='max_pool',
kernel=kernel,
stride=stride,
padding=padding)
def exe_max_pool_layer(tensor, layer_o):
kernel = layer_o['kernel']
stride = layer_o['stride']
padding = layer_o['padding']
tensor = tf.nn.max_pool(tensor, [1, kernel, kernel, 1], [1, stride, stride, 1], padding=padding)
return tensor
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# ................................................................
def avg_pool_layer(kernel, stride, padding='VALID'):
return dict(
name='avg_pool',
kernel=kernel,
stride=stride,
padding=padding)
def exe_avg_pool_layer(tensor, layer_o):
kernel = layer_o['kernel']
stride = layer_o['stride']
padding = layer_o['padding']
tensor = tf.nn.avg_pool(tensor, [1, kernel, kernel, 1], [1, stride, stride, 1], padding=padding)
return tensor
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# ................................................
def resize_layer(scale, method, align_corners=False):
return dict(
name='resize',
scale=scale,
method=method,
align_corners=align_corners)
def exe_resize_layer(tensor, layer_o):
scale = layer_o['scale']
method = layer_o['method']
align_corners = layer_o['align_corners']
t_shape = tensor.get_shape().as_list()
if t_shape[1] == None or t_shape[2] == None:
t_shape = tf.shape(tensor)
t_size = [t_shape[1] * scale, t_shape[2] * scale]
tensor = tf.image.resize_images(tensor, t_size, method=method, align_corners=align_corners)
return tensor
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# ................................................
def concat_layer(index):
return dict(
name='concat',
index=index)
def exe_concat_layer(tensor, layer_o, tensor_list):
index = layer_o['index']
concat_t = tensor_list[index]
tensor = tf.concat([tensor, concat_t], 3)
return tensor
# ..####...##.......####...#####....####...##...............####....####...##..##...####....####...######.
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# ........................................................................................................
def global_concat_layer(index):
return dict(
name='g_concat',
index=index)
def exe_global_concat_layer(tensor, layer_o, tensor_list):
index = layer_o['index']
h = tf.shape(tensor)[1]
w = tf.shape(tensor)[2]
concat_t = tf.squeeze(tensor_list[index], [1, 2])
dims = concat_t.get_shape()[-1]
batch_l = tf.unstack(concat_t, axis=0)
bs = []
for batch in batch_l:
batch = tf.tile(batch, [h * w])
batch = tf.reshape(batch, [h, w, -1])
bs.append(batch)
concat_t = tf.stack(bs)
concat_t.set_shape(concat_t.get_shape().as_list()[:3] + [dims])
tensor = tf.concat([tensor, concat_t], 3)
return tensor
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def reshape_layer(shape):
return dict(
name='reshape',
shape=shape)
def exe_reshape_layer(tensor, layer_o):
shape = reshape['shape']
shape = [tensor.get_shape().as_list()[0]] + shape
tensor = tf.reshape(tensor, shape)
return tensor
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def clip_layer(min_v=0, max_v=1):
return dict(
name='clip',
min_v=min_v,
max_v=max_v)
def exe_clip_layer(tensor, layer_o):
min_v = layer_o['min_v']
max_v = layer_o['max_v']
tensor = tf.clip_by_value(tensor, min_v, max_v)
return tensor
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def sigmoid_layer():
return dict(name='sigmoid')
def exe_sigmoid_layer(tensor):
return tf.nn.sigmoid(tensor)
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def softmax_layer():
return dict(name='softmax')
def exe_softmax_layer(tensor):
return tf.nn.softmax(tensor)
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def squeeze_layer(axis):
return dict(name='squeeze', axis=axis)
def exe_squeeze_layer(tensor, layer_o):
axis = layer_o['axis']
return tf.squeeze(tensor, axis)
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def abs_layer():
return dict(name='abs')
def exe_abs_layer(tensor):
return tf.abs(tensor)
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def tanh_layer():
return dict(name='tanh')
def exe_tanh_layer(tensor):
return tf.tanh(tensor)
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def inv_tanh_layer():
return dict(name='inv_tanh')
def exe_inv_tanh_layer(tensor):
return -tf.log((2.0 / (tensor + 1 + 1e-100)) - 1) * 0.5
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def add_layer(value):
return dict(name='add', value=value)
def exe_add_layer(tensor, layer_o):
value = layer_o['value']
return tf.add(tensor, value)
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def mul_layer(value):
return dict(name='mul', value=value)
def exe_mul_layer(tensor, layer_o):
value = layer_o['value']
return tf.mul(tensor, value)
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def null_layer():
return dict(name='null')
def exe_null_layer(tensor):
return tensor
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def reduce_mean_layer(axis=None, keep_dims=False):
return dict(name='reduce_mean', axis=axis, keep_dims=keep_dims)
def exe_reduce_mean_layer(tensor, layer_o):
axis = layer_o['axis']
keep_dims = layer_o['keep_dims']
return tf.reduce_mean(tensor, axis, keep_dims)
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def residual_block(input_p, output_p, stride, initializer, index):
result = []
bottle_p = output_p // 4
result.append(bn_layer(True, True))
result.append(prelu_layer())
result.append(conv_layer(1, stride, bottle_p, None, initializer))
result.append(bn_layer(True, True))
result.append(prelu_layer())
result.append(conv_layer(3, 1, bottle_p, "CONSTANT", initializer))
result.append(bn_layer(True, True))
result.append(prelu_layer())
result.append(conv_layer(1, 1, output_p, None, initializer))
if input_p == output_p:
result.append(res_layer(index))
else:
result.append(conv_res_layer(index + 2, 1, stride, initializer))
return result
def residual_layer(count, input_p, output_p, stride, initializer, index):
result = residual_block(input_p, output_p, stride, initializer, index)
for _ in range(count - 1):
index = index + 10
result = result + residual_block(output_p, output_p, 1, initializer, index)
return result