def resnet18_imagenet(image):
with remap_variables(new_get_variable), \
argscope(Conv2D, use_bias=False,
kernel_initializer=tf.variance_scaling_initializer(scale=2.0, mode='fan_out')):
# Note that this pads the image by [2, 3] instead of [3, 2].
# Similar things happen in later stride=2 layers as well.
l = Conv2D('conv0', image, 64, 7, strides=2, activation=BNReLU)
l = MaxPooling('pool0',
l,
pool_size=3,
strides=2,
padding='SAME')
l = resnet_group('group0', l, resnet_basicblock, 64, 2, 1)
l = activate(l)
l = resnet_group('group1', l, resnet_basicblock, 128, 2, 2)
l = activate(l)
l = resnet_group('group2', l, resnet_basicblock, 256, 2, 2)
l = activate(l)
l = resnet_group('group3', l, resnet_basicblock, 512, 2, 2)
l = GlobalAvgPooling('gap', l)
logits = FullyConnected(
'linear',
l,
1000,
kernel_initializer=tf.random_normal_initializer(
stddev=0.01))
# tmp = tf.trainable_variables()
return logits
def weight_standardization_context(enable=True):
"""
Implement Centered Weight Normalization
(http://openaccess.thecvf.com/content_ICCV_2017/papers/Huang_Centered_Weight_Normalization_ICCV_2017_paper.pdf)
or Weight Standardization (https://arxiv.org/abs/1903.10520)
Usage:
with weight_standardization_context():
l = Conv2D('conv', l)
...
"""
if enable:
def weight_standardization(v):
if (not v.name.endswith('/W:0')) or v.shape.ndims != 4:
return v
mean, var = tf.nn.moments(v, [0, 1, 2], keep_dims=True)
v = (v - mean) / (tf.sqrt(var) + 1e-5)
return v
with remap_variables(weight_standardization):
yield
else:
yield
def get_logits(self, image):
if BITW == 't':
fw, fa, fg = get_dorefa(32, 32, 32)
fw = ternarize
else:
fw, fa, fg = get_dorefa(BITW, BITA, BITG)
# monkey-patch tf.get_variable to apply fw
def new_get_variable(v):
name = v.op.name
# don't binarize first and last layer
if not name.endswith('W') or 'conv0' in name or 'fct' in name:
return v
else:
logger.info("Quantizing weight {}".format(v.op.name))
return fw(v)
def nonlin(x):
if BITA == 32:
return tf.nn.relu(x) # still use relu for 32bit cases
return tf.clip_by_value(x, 0.0, 1.0)
def activate(x):
return fa(nonlin(x))
with remap_variables(new_get_variable), \
argscope([Conv2D, BatchNorm, MaxPooling], data_format='channels_first'), \
argscope(BatchNorm, momentum=0.9, epsilon=1e-4), \
argscope(Conv2D, use_bias=False):
logits = (LinearWrap(image).Conv2D(
'conv0', 96, 12, strides=4, padding='VALID',
use_bias=True).apply(activate).Conv2D(
'conv1', 256, 5, padding='SAME',
split=2).apply(fg).BatchNorm('bn1').MaxPooling(
'pool1', 3, 2, padding='SAME').apply(activate).Conv2D(
'conv2', 384,
3).apply(fg).BatchNorm('bn2').MaxPooling(
'pool2', 3, 2,
padding='SAME').apply(activate).Conv2D(
'conv3', 384, 3, split=2).apply(fg).
BatchNorm('bn3').apply(activate).Conv2D(
'conv4', 256, 3,
split=2).apply(fg).BatchNorm('bn4').MaxPooling(
'pool4', 3, 2,
padding='VALID').apply(activate).FullyConnected(
'fc0', 4096).apply(fg).BatchNorm('bnfc0').
apply(activate).FullyConnected(
'fc1', 4096,
use_bias=False).apply(fg).BatchNorm('bnfc1').apply(
nonlin).FullyConnected('fct',
1000,
use_bias=True)())
add_param_summary(('.*/W', ['histogram', 'rms']))
tf.nn.softmax(logits, name='output') # for prediction
return logits
def get_logits(self, image):
def weight_standardization(v):
if not self.use_WS:
return v
if (not v.name.endswith('/W:0')) or v.shape.ndims != 4:
return v
mean, var = tf.nn.moments(v, [0, 1, 2], keep_dims=True)
v = (v - mean) / (tf.sqrt(var) + 1e-5)
return v
num_blocks = {50: [3, 4, 6, 3], 101: [3, 4, 23, 3]}[self.depth]
block_func = resnet_bottleneck
with argscope([Conv2D, MaxPooling, GlobalAvgPooling], data_format=self.data_format), \
varreplace.remap_variables(weight_standardization):
return resnet_backbone(image, num_blocks, resnet_group, block_func)
def weight_standardization_context(enable):
if enable:
def weight_standardization(v):
if (not v.name.endswith('/W:0')) or v.shape.ndims != 4:
return v
print("WS on " + v.name)
mean, std = tf.nn.moments(v, [0, 1, 2], keep_dims=True)
v = (v - mean) / (std + 1e-5)
return v
with remap_variables(weight_standardization):
yield
else:
yield
def alexnet(image):
with remap_variables(new_get_variable), \
argscope([Conv2D, BatchNorm, MaxPooling], data_format='channels_first'), \
argscope(BatchNorm, momentum=0.9, epsilon=1e-4), \
argscope(Conv2D, use_bias=False):
logits = (
LinearWrap(image).Conv2D(
'conv0',
96,
12,
strides=4,
padding='VALID',
use_bias=True).apply(fg).Conv2D(
'conv1', 256, 5, padding='SAME',
split=2).apply(fg).BatchNorm('bn1').MaxPooling(
'pool1', 3, 2,
padding='SAME').apply(activate).Conv2D(
'conv2', 384,
3).apply(fg).BatchNorm('bn2').MaxPooling(
'pool2', 3, 2,
padding='SAME').apply(activate).Conv2D(
'conv3', 384, 3, split=2).
apply(fg).BatchNorm('bn3').apply(activate).Conv2D(
'conv4', 256, 3,
split=2).apply(fg).BatchNorm('bn4').MaxPooling(
'pool4', 3, 2,
padding='VALID').apply(activate).FullyConnected(
'fc0',
4096).apply(fg).BatchNorm('bnfc0').apply(
activate).FullyConnected('fc1',
4096,
use_bias=False).
apply(fg).BatchNorm('bnfc1').apply(nonlin).FullyConnected(
'fct', self.class_num, use_bias=True)())
return logits
def build_model(input_quant_wei_layer, intput_quant_wei_lambda,
input_quant_wei_delta, input_quant_wei_levels):
global id_target_quant_layer
global q_lambda
global q_delta
global num_quant_levels
id_target_quant_layer = input_quant_wei_layer
q_lambda = intput_quant_wei_lambda
q_delta = input_quant_wei_delta
num_quant_levels = input_quant_wei_levels
with tf.name_scope('main_params'):
global_step = tf.Variable(initial_value=0,
trainable=False,
name='global_step')
learning_rate = tf.placeholder(tf.float32,
shape=[],
name='learning_rate')
with remap_variables(quant_wei_uni_dead_zone), tf.variable_scope(
'conv1') as scope:
conv1 = tf.layers.conv2d(
inputs=data_x,
filters=32,
kernel_size=[1, 64],
padding='VALID',
use_bias=False,
kernel_regularizer=tf.contrib.layers.l2_regularizer(WEIGHT_DECAY))
bn1 = tf.layers.batch_normalization(conv1, training=isTraining)
relu1 = tf.nn.relu(bn1)
with remap_variables(quant_wei_uni_dead_zone), tf.variable_scope(
'conv2') as scope:
conv2 = tf.layers.conv2d(
inputs=relu1,
filters=64,
kernel_size=[1, 32],
padding='VALID',
use_bias=False,
kernel_regularizer=tf.contrib.layers.l2_regularizer(WEIGHT_DECAY))
bn2 = tf.layers.batch_normalization(conv2, training=isTraining)
relu2 = tf.nn.relu(bn2)
with remap_variables(quant_wei_uni_dead_zone), tf.variable_scope(
'conv3') as scope:
conv3 = tf.layers.conv2d(
inputs=relu2,
filters=128,
kernel_size=[1, 16],
padding='VALID',
use_bias=False,
kernel_regularizer=tf.contrib.layers.l2_regularizer(WEIGHT_DECAY))
bn3 = tf.layers.batch_normalization(conv3, training=isTraining)
relu3 = tf.nn.relu(bn3)
with remap_variables(quant_wei_uni_dead_zone), tf.variable_scope(
'fully_connected') as scope:
flat = tf.layers.flatten(relu3)
logits = tf.layers.dense(
inputs=flat,
units=NUM_CLASSES,
name=scope.name,
use_bias=False,
kernel_regularizer=tf.contrib.layers.l2_regularizer(WEIGHT_DECAY))
y_pred_cls = tf.argmax(logits, axis=1)
gtlabel = tf.one_hot(label_y, NUM_CLASSES)
# LOSS AND OPTIMIZER
cross_entropy_loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits,
labels=gtlabel))
loss = cross_entropy_loss + tf.add_n(
tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate,
epsilon=1e-3).minimize(
loss, global_step=global_step)
# PREDICTION AND ACCURACY CALCULATION
def get_eval_op(preds, labels):
correct_prediction = tf.equal(preds, labels)
return tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
eval_op = get_eval_op(y_pred_cls, label_y)
return loss, optimizer, eval_op, global_step, learning_rate
def _build_graph(self, inputs):
image, label, ious, ious_weights, valids, bndboxes = inputs
image = tf.round(image)
fw, fa, fg = get_dorefa(BITW, BITA, BITG)
old_get_variable = tf.get_variable
def monitor(x, name):
if MONITOR == 1:
return tf.Print(x, [x],
message='\n\n' + name + ': ',
summarize=1000,
name=name)
else:
return x
def new_get_variable(v):
name = v.op.name
# if not name.endswith('W') or 'conv1' in name or 'conv_obj' in name or 'conv_box' in name:
if not name.endswith(
'W') or 'conv_obj' in name or 'conv_box' in name:
return v
else:
logger.info("Quantizing weight {}".format(v.op.name))
if MONITOR == 1:
return tf.Print(fw(v), [fw(v)],
message='\n\n' + v.name +
', Quantized weights are:',
summarize=100)
else:
return fw(v)
def activate(x):
if BITA == 32:
return tf.nn.relu(x)
else:
return fa(tf.nn.relu(x))
def bn_activate(name, x):
x = BatchNorm(name, x)
x = monitor(x, name + '_noact_out')
return activate(x)
def halffire(name, x, num_squeeze_filters, num_expand_3x3_filters,
skip):
out_squeeze = Conv2D('squeeze_conv_' + name,
x,
out_channel=num_squeeze_filters,
kernel_shape=1,
stride=1,
padding='SAME')
out_squeeze = bn_activate('bn_squeeze_' + name, out_squeeze)
out_expand_3x3 = Conv2D('expand_3x3_conv_' + name,
out_squeeze,
out_channel=num_expand_3x3_filters,
kernel_shape=3,
stride=1,
padding='SAME')
out_expand_3x3 = bn_activate('bn_expand_3x3_' + name,
out_expand_3x3)
if skip == 0:
return out_expand_3x3
else:
return tf.add(x, out_expand_3x3)
def halffire_noact(name, x, num_squeeze_filters,
num_expand_3x3_filters):
out_squeeze = Conv2D('squeeze_conv_' + name,
x,
out_channel=num_squeeze_filters,
kernel_shape=1,
stride=1,
padding='SAME')
out_squeeze = bn_activate('bn_squeeze_' + name, out_squeeze)
out_expand_3x3 = Conv2D('expand_3x3_conv_' + name,
out_squeeze,
out_channel=num_expand_3x3_filters,
kernel_shape=3,
stride=1,
padding='SAME')
return out_expand_3x3
def halffire_final(l, name):
l = halffire('fire4' + name, l, NUM_SQUEEZE_FILTERS,
NUM_EXPAND_FILTERS, 0)
l = halffire('fire5' + name, l, NUM_SQUEEZE_FILTERS,
NUM_EXPAND_FILTERS, 0)
l = halffire('fire6' + name, l, NUM_SQUEEZE_FILTERS,
NUM_EXPAND_FILTERS, 0)
l = halffire('fire7' + name, l, NUM_SQUEEZE_FILTERS,
NUM_EXPAND_FILTERS, 0)
return l
def decision(l, name):
classify = Conv2D('conv_class' + name,
l,
out_channel=12,
kernel_shape=1,
stride=1,
padding='SAME')
classify = bn_activate('bn_class' + name, classify)
classify = monitor(classify, 'conv_class_out' + name)
logits = GlobalAvgPooling('pool_class' + name, classify)
l = tf.concat([l, classify], axis=3)
objdetect = Conv2D('conv_obj' + name,
l,
out_channel=1,
kernel_shape=1,
stride=1,
padding='SAME')
bndbox = Conv2D('conv_box' + name,
l,
out_channel=4,
kernel_shape=1,
stride=1,
padding='SAME')
return logits, objdetect, bndbox
def first_layer(x):
l = Conv2D('conv1',
x,
out_channel=16,
kernel_shape=3,
stride=1,
padding='SAME')
l = bn_activate('bn1', l)
l = monitor(l, 'conv1_out')
return l
with remap_variables(new_get_variable), \
argscope([Conv2D, FullyConnected], use_bias=False, nl=tf.identity), \
argscope(BatchNorm, decay=0.9, epsilon=1e-4):
image = monitor(image, 'image_out')
if DEMO_DATASET == 0:
l = first_layer(image)
else:
l = tf.stop_gradient(first_layer(image))
l = MaxPooling('pool1', l, shape=3, stride=2, padding='SAME')
l = monitor(l, 'pool1_out')
l = halffire('fire1', l, NUM_SQUEEZE_FILTERS, NUM_EXPAND_FILTERS,
0)
l = monitor(l, 'fire1_out')
l = MaxPooling('pool2', l, shape=3, stride=2, padding='SAME')
l = monitor(l, 'pool2_out')
l = halffire('fire2', l, NUM_SQUEEZE_FILTERS, NUM_EXPAND_FILTERS,
0)
l = monitor(l, 'fire2_out')
l = MaxPooling('pool3', l, shape=3, stride=2, padding='SAME')
l = monitor(l, 'pool3_out')
l = halffire('fire3', l, NUM_SQUEEZE_FILTERS, NUM_EXPAND_FILTERS,
0)
l = monitor(l, 'fire3_out')
l = MaxPooling('pool4', l, shape=3, stride=2, padding='SAME')
l = monitor(l, 'pool4_out')
l1 = halffire_final(l, '1')
l1 = monitor(l1, 'final1_out')
l2 = halffire_final(l, '2')
l2 = monitor(l2, 'final2_out')
l3 = halffire_final(l, '3')
l3 = monitor(l3, 'final3_out')
l4 = halffire_final(l, '4')
l4 = monitor(l4, 'final4_out')
logits1, objdetect1, bndbox1 = decision(l1, '1')
logits2, objdetect2, bndbox2 = decision(l2, '2')
logits3, objdetect3, bndbox3 = decision(l3, '3')
logits4, objdetect4, bndbox4 = decision(l4, '4')
# Classification
logits = (logits1 + logits2 + logits3 + logits4) / 4
class_loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=label),
name='cross_entropy_loss')
wrong = prediction_incorrect(logits, label, 1, name='wrong-top1')
add_moving_summary(tf.reduce_mean(wrong, name='train-error-top1'))
# Object Detection
objdetect = (objdetect1 + objdetect2 + objdetect3 + objdetect4) / 4
objdetect = tf.identity(objdetect, name='objdetect_out')
objdetect_loss = tf.losses.hinge_loss(labels=ious,
logits=objdetect,
weights=ious_weights)
bndbox = (bndbox1 + bndbox2 + bndbox3 + bndbox4) / 4
bndbox = tf.identity(bndbox, name='bndbox_out')
bndbox_loss = tf.losses.mean_squared_error(labels=bndboxes,
predictions=tf.multiply(
bndbox,
valids,
name='mult0'))
if DEMO_DATASET == 0:
cost = class_loss + 5 * objdetect_loss + bndbox_loss
else:
cost = 1000 * objdetect_loss + bndbox_loss
add_moving_summary(class_loss, objdetect_loss, bndbox_loss, cost)
self.cost = cost
tf.get_variable = old_get_variable
def _build_graph(self, inputs):
image, label = inputs
is_training = get_current_tower_context().is_training
fw, fa, fg = get_dorefa(BITW, BITA, BITG)
# monkey-patch tf.get_variable to apply fw
def binarize_weight(v):
name = v.op.name
# don't binarize first and last layer
if not name.endswith('W') or 'conv0' in name or 'fc' in name:
return v
else:
logger.info("Binarizing weight {}".format(v.op.name))
return fw(v)
def cabs(x):
return tf.minimum(1.0, tf.abs(x), name='cabs')
def activate(x):
return fa(cabs(x))
image = image / 256.0
with remap_variables(binarize_weight), \
argscope(BatchNorm, decay=0.9, epsilon=1e-4), \
argscope(Conv2D, use_bias=False, nl=tf.identity):
logits = (
LinearWrap(image).Conv2D('conv0',
48,
5,
padding='VALID',
use_bias=True).MaxPooling(
'pool0', 2,
padding='SAME').apply(activate)
# 18
.Conv2D('conv1', 64, 3, padding='SAME').apply(fg).BatchNorm(
'bn1').apply(activate).Conv2D(
'conv2', 64, 3,
padding='SAME').apply(fg).BatchNorm('bn2').MaxPooling(
'pool1', 2, padding='SAME').apply(activate)
# 9
.Conv2D(
'conv3', 128, 3,
padding='VALID').apply(fg).BatchNorm('bn3').apply(activate)
# 7
.Conv2D('conv4', 128, 3, padding='SAME').apply(fg).
BatchNorm('bn4').apply(activate).Conv2D(
'conv5', 128, 3,
padding='VALID').apply(fg).BatchNorm('bn5').apply(activate)
# 5
.tf.nn.dropout(0.5 if is_training else 1.0).Conv2D(
'conv6', 512, 5, padding='VALID').apply(fg).BatchNorm(
'bn6').apply(cabs).FullyConnected('fc1',
10,
nl=tf.identity)())
tf.nn.softmax(logits, name='output')
# compute the number of failed samples
wrong = prediction_incorrect(logits, label)
# monitor training error
add_moving_summary(tf.reduce_mean(wrong, name='train_error'))
cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=label)
cost = tf.reduce_mean(cost, name='cross_entropy_loss')
# weight decay on all W of fc layers
wd_cost = regularize_cost('fc.*/W', l2_regularizer(1e-7))
add_param_summary(('.*/W', ['histogram', 'rms']))
self.cost = tf.add_n([cost, wd_cost], name='cost')
add_moving_summary(cost, wd_cost, self.cost)
def resnet18_cifar(input_tensor,
is_training=True,
pooling_and_fc=True,
reuse=False,
kernel_initializer=tf.contrib.layers.
variance_scaling_initializer()):
with remap_variables(new_get_variable):
x = tf.layers.conv2d(input_tensor,
64, (3, 3),
strides=(1, 1),
kernel_initializer=kernel_initializer,
use_bias=False,
padding='SAME',
name='conv1_1/3x3_s1',
reuse=reuse)
x = tf.layers.batch_normalization(x,
training=is_training,
name='bn1_1/3x3_s1',
reuse=reuse)
x = tf.nn.relu(x)
x1 = identity_block2d(x,
3, [48, 64, 64],
stage=2,
block='1b',
is_training=is_training,
reuse=reuse,
kernel_initializer=kernel_initializer)
x1 = identity_block2d(x1,
3, [48, 64, 64],
stage=3,
block='1c',
is_training=is_training,
reuse=reuse,
kernel_initializer=kernel_initializer)
x2 = conv_block_2d(x1,
3, [96, 128, 128],
stage=3,
block='2a',
strides=(2, 2),
is_training=is_training,
reuse=reuse,
kernel_initializer=kernel_initializer)
x2 = activate(x2)
x2 = identity_block2d(x2,
3, [96, 128, 128],
stage=3,
block='2b',
is_training=is_training,
reuse=reuse,
kernel_initializer=kernel_initializer)
x3 = conv_block_2d(x2,
3, [128, 256, 256],
stage=4,
block='3a',
strides=(2, 2),
is_training=is_training,
reuse=reuse,
kernel_initializer=kernel_initializer)
x3 = activate(x3)
x3 = identity_block2d(x3,
3, [128, 256, 256],
stage=4,
block='3b',
is_training=is_training,
reuse=reuse,
kernel_initializer=kernel_initializer)
x4 = conv_block_2d(x3,
3, [256, 512, 512],
stage=5,
block='4a',
strides=(2, 2),
is_training=is_training,
reuse=reuse,
kernel_initializer=kernel_initializer)
x4 = activate(x4)
x4 = identity_block2d(x4,
3, [256, 512, 512],
stage=5,
block='4b',
is_training=is_training,
reuse=reuse,
kernel_initializer=kernel_initializer)
print('before gap: ', x4)
x4 = tf.reduce_mean(x4, [1, 2])
print('after gap: ', x4)
# flatten = tf.contrib.layers.flatten(x4)
prob = tf.layers.dense(
x4,
self.class_num,
reuse=reuse,
kernel_initializer=tf.contrib.layers.xavier_initializer())
# tmp = tf.trainable_variables()
# prob = tf.layers.batch_normalization(prob, training=is_training, name='fbn', reuse=reuse)
print('prob', prob)
return prob
def _build_graph(self, inputs):
conf = Config()
is_training = get_current_tower_context().is_training
input, nextinput = inputs
initializer = tf.random_uniform_initializer(-conf.init_scale,
conf.init_scale)
def get_basic_cell():
# cell = rnn.BasicLSTMCell(num_units=conf.hidden_size, forget_bias=0.0, reuse=tf.get_variable_scope().reuse)
cell = ttq_rnn.TtqLSTMCell(
num_units=conf.hidden_size,
thre=0.05, #)
forget_bias=1.0,
reuse=tf.get_variable_scope().reuse)
if is_training and conf.keep_prob < 1:
cell = rnn.DropoutWrapper(cell,
output_keep_prob=conf.keep_prob)
return cell
cell = rnn.MultiRNNCell(
[get_basic_cell() for _ in range(conf.num_layers)])
def get_v(n):
return tf.get_variable(
n,
[conf.batch_size, conf.hidden_size], #,[BATCH, HIDDEN_SIZE],
trainable=False,
initializer=tf.constant_initializer())
def replace_w(x):
if x.op.name.endswith('W'):
print("\nBefore quantize name: " + x.op.name)
return tw_ternarize(x, 0.05) # tanh to round to [-1,+1]
#return bit_utils.quantize_w(tf.tanh(x), bit=self._w_bit)
elif x.op.name.endswith('b'):
print("\nBefore quantize name: " + x.op.name)
return tw_ternarize_bias(x, 0.05)
else:
print("\nNOT Quantizing:" + x.op.name)
print(x.shape)
print(type(x))
tf.summary.histogram(x.name, x)
return x
# Parameters of gates are concatenated into one multiply for efficiency.
# with bit_utils.replace_variable(replace_w):
self.state = state_var = \
(rnn.LSTMStateTuple(get_v('c0'), get_v('h0')),
rnn.LSTMStateTuple(get_v('c1'), get_v('h1')))
embeddingW = tf.get_variable(
'embedding', [conf.vocab_size, conf.hidden_size],
initializer=initializer) #tf.random_uniform_initializer())
input_feature = tf.nn.embedding_lookup(
embeddingW, input) # B x seqlen x hiddensize
if is_training and conf.keep_prob < 1:
input_feature = Dropout(input_feature, conf.keep_prob)
print("\n\nThe STATE:")
print(self.state)
with tf.variable_scope('LSTM', initializer=initializer):
input_list = tf.unstack(input_feature, num=conf.num_steps,
axis=1) # seqlen x (Bxhidden)
outputs, last_state = rnn.static_rnn(cell,
input_list,
state_var,
scope='rnn')
update_state_ops = []
for k in range(conf.num_layers):
update_state_ops.extend([
tf.assign(state_var[k].c, last_state[k].c),
tf.assign(state_var[k].h, last_state[k].h)
])
# seqlen x (Bxrnnsize)
output = tf.reshape(tf.concat(outputs, 1),
[-1, conf.hidden_size]) # (Bxseqlen) x hidden
with varreplace.remap_variables(replace_w):
logits = FullyConnected('fc',
output,
conf.vocab_size,
nl=tf.identity,
W_init=initializer,
b_init=initializer)
xent_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=tf.reshape(nextinput, [-1]))
with tf.control_dependencies(update_state_ops):
self.cost = tf.truediv(tf.reduce_sum(xent_loss),
tf.cast(conf.batch_size, tf.float32),
name='cost') # log-perplexity
perpl = tf.exp(self.cost / conf.num_steps, name='perplexity')
summary.add_moving_summary(perpl, self.cost)
def _build_graph(self, inputs):
image, label = inputs
is_training = get_current_tower_context().is_training
fw, fa, fg = get_dorefa(BITW, BITA, BITG)
# monkey-patch tf.get_variable to apply fw
def binarize_weight(v):
name = v.op.name
# don't binarize first and last layer
if not name.endswith('W') or 'conv0' in name or 'fc' in name:
return v
else:
logger.info("Binarizing weight {}".format(v.op.name))
return fw(v)
def cabs(x):
return tf.minimum(1.0, tf.abs(x), name='cabs')
def activate(x):
return fa(cabs(x))
image = image / 256.0
with remap_variables(binarize_weight), \
argscope(BatchNorm, decay=0.9, epsilon=1e-4), \
argscope(Conv2D, use_bias=False, nl=tf.identity):
logits = (LinearWrap(image)
.Conv2D('conv0', 48, 5, padding='VALID', use_bias=True)
.MaxPooling('pool0', 2, padding='SAME')
.apply(activate)
# 18
.Conv2D('conv1', 64, 3, padding='SAME')
.apply(fg)
.BatchNorm('bn1').apply(activate)
.Conv2D('conv2', 64, 3, padding='SAME')
.apply(fg)
.BatchNorm('bn2')
.MaxPooling('pool1', 2, padding='SAME')
.apply(activate)
# 9
.Conv2D('conv3', 128, 3, padding='VALID')
.apply(fg)
.BatchNorm('bn3').apply(activate)
# 7
.Conv2D('conv4', 128, 3, padding='SAME')
.apply(fg)
.BatchNorm('bn4').apply(activate)
.Conv2D('conv5', 128, 3, padding='VALID')
.apply(fg)
.BatchNorm('bn5').apply(activate)
# 5
.tf.nn.dropout(0.5 if is_training else 1.0)
.Conv2D('conv6', 512, 5, padding='VALID')
.apply(fg).BatchNorm('bn6')
.apply(cabs)
.FullyConnected('fc1', 10, nl=tf.identity)())
tf.nn.softmax(logits, name='output')
# compute the number of failed samples
wrong = prediction_incorrect(logits, label)
# monitor training error
add_moving_summary(tf.reduce_mean(wrong, name='train_error'))
cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label)
cost = tf.reduce_mean(cost, name='cross_entropy_loss')
# weight decay on all W of fc layers
wd_cost = regularize_cost('fc.*/W', l2_regularizer(1e-7))
add_param_summary(('.*/W', ['histogram', 'rms']))
self.cost = tf.add_n([cost, wd_cost], name='cost')
add_moving_summary(cost, wd_cost, self.cost)
def _build_graph(self, inputs):
image, label = inputs
image = tf.expand_dims(image, 3)
image = image * 2 - 1 # center the pixels values at zero
is_training = get_current_tower_context().is_training
fw, fa, fg = get_dorefa(BITW, BITA, BITG)
# monkey-patch tf.get_variable to apply fw
def binarize_weight(v):
name = v.op.name
# don't binarize first and last layer
if not name.endswith('W') or 'conv0' in name or 'fc' in name:
return v
else:
logger.info("Binarizing weight {}".format(v.op.name))
return fw(v)
def cabs(x):
return tf.minimum(1.0, tf.abs(x), name='cabs')
def activate(x):
return fa(cabs(x))
with remap_variables(binarize_weight), \
argscope(Conv2D, kernel_shape=3, nl=tf.nn.relu, out_channel=32):
logits = (LinearWrap(image).Conv2D('conv0').MaxPooling(
'pool0', 2).apply(activate).Conv2D('conv1').apply(fg).Conv2D(
'conv2').apply(fg).MaxPooling('pool1', 2).apply(activate).
Conv2D('conv3').apply(fg).apply(cabs).FullyConnected(
'fc0', 512, activation=tf.nn.relu).Dropout(
'dropout',
0.5).FullyConnected('fc1',
10,
activation=tf.identity)())
tf.nn.softmax(logits, name='output')
# a vector of length B with loss of each sample
cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=label)
cost = tf.reduce_mean(
cost, name='cross_entropy_loss') # the average cross-entropy loss
correct = tf.cast(tf.nn.in_top_k(logits, label, 1),
tf.float32,
name='correct')
accuracy = tf.reduce_mean(correct, name='accuracy')
# This will monitor training error (in a moving_average fashion):
# 1. write the value to tensosrboard
# 2. write the value to stat.json
# 3. print the value after each epoch
train_error = tf.reduce_mean(1 - correct, name='train_error')
summary.add_moving_summary(train_error, accuracy)
# Use a regex to find parameters to apply weight decay.
# Here we apply a weight decay on all W (weight matrix) of all fc layers
wd_cost = tf.multiply(1e-5,
regularize_cost('fc.*/W', tf.nn.l2_loss),
name='regularize_loss')
self.cost = tf.add_n([wd_cost, cost], name='total_cost')
summary.add_moving_summary(cost, wd_cost, self.cost)
# monitor histogram of all weight (of conv and fc layers) in tensorboard
summary.add_param_summary(('.*/W', ['histogram', 'rms']))
def _build_graph(self, inputs):
image, label = inputs
image = image / 256.0
fw, fa, fg = get_dorefa(BITW, BITA, BITG)
def new_get_variable(v):
name = v.op.name
# don't binarize first and last layer
if not name.endswith('W') or 'conv1' in name or 'fct' in name:
return v
else:
logger.info("Binarizing weight {}".format(v.op.name))
return fw(v)
def nonlin(x):
return tf.clip_by_value(x, 0.0, 1.0)
def activate(x):
return fa(nonlin(x))
def resblock(x, channel, stride):
def get_stem_full(x):
return (LinearWrap(x)
.Conv2D('c3x3a', channel, 3)
.BatchNorm('stembn')
.apply(activate)
.Conv2D('c3x3b', channel, 3)())
channel_mismatch = channel != x.get_shape().as_list()[3]
if stride != 1 or channel_mismatch or 'pool1' in x.name:
# handling pool1 is to work around an architecture bug in our model
if stride != 1 or 'pool1' in x.name:
x = AvgPooling('pool', x, stride, stride)
x = BatchNorm('bn', x)
x = activate(x)
shortcut = Conv2D('shortcut', x, channel, 1)
stem = get_stem_full(x)
else:
shortcut = x
x = BatchNorm('bn', x)
x = activate(x)
stem = get_stem_full(x)
return shortcut + stem
def group(x, name, channel, nr_block, stride):
with tf.variable_scope(name + 'blk1'):
x = resblock(x, channel, stride)
for i in range(2, nr_block + 1):
with tf.variable_scope(name + 'blk{}'.format(i)):
x = resblock(x, channel, 1)
return x
with remap_variables(new_get_variable), \
argscope(BatchNorm, decay=0.9, epsilon=1e-4), \
argscope(Conv2D, use_bias=False, nl=tf.identity):
logits = (LinearWrap(image)
# use explicit padding here, because our training framework has
# different padding mechanisms from TensorFlow
.tf.pad([[0, 0], [3, 2], [3, 2], [0, 0]])
.Conv2D('conv1', 64, 7, stride=2, padding='VALID', use_bias=True)
.tf.pad([[0, 0], [1, 1], [1, 1], [0, 0]], 'SYMMETRIC')
.MaxPooling('pool1', 3, 2, padding='VALID')
.apply(group, 'conv2', 64, 2, 1)
.apply(group, 'conv3', 128, 2, 2)
.apply(group, 'conv4', 256, 2, 2)
.apply(group, 'conv5', 512, 2, 2)
.BatchNorm('lastbn')
.apply(nonlin)
.GlobalAvgPooling('gap')
.tf.multiply(49) # this is due to a bug in our model design
.FullyConnected('fct', 1000)())
tf.nn.softmax(logits, name='output')
ImageNetModel.compute_loss_and_error(logits, label)
def _build_graph(self, inputs):
image, label = inputs
image = image / 256.0
fw, fa = get_quantize(BITW, BITA)
old_get_variable = tf.get_variable
def new_get_variable(v):
name = v.op.name
# don't binarize first and last layer
if not name.endswith('W') or 'conv1' in name or 'fct' in name:
return v
else:
logger.info("Binarizing weight {}".format(v.op.name))
return fw(v)
def nonlin(x):
return tf.clip_by_value(x, 0.0, 1.0)
def activate(x):
return fa(nonlin(x * 0.1))
def resblock(x,
channel,
stride,
bottleneck_dividend=4,
stem_type='full'):
bottleneck_channel_s = channel // bottleneck_dividend
def get_stem_bottleneck(x):
return (LinearWrap(x).Conv2D(
'c1x1shrink', bottleneck_channel_s,
1).BatchNorm('stembn1').apply(activate).Conv2D(
'c3x3', bottleneck_channel_s,
3).BatchNorm('stembn2').apply(activate).Conv2D(
'c1x1expand', channel, 1)())
def get_stem_full(x):
return (LinearWrap(x).Conv2D(
'c3x3a', channel,
3).BatchNorm('stembn').apply(activate).Conv2D(
'c3x3b', channel, 3)())
get_stem = dict(bottleneck=get_stem_bottleneck,
full=get_stem_full)[stem_type]
channel_mismatch = channel != x.get_shape().as_list()[3]
if stride != 1 or channel_mismatch or 'pool1' in x.name:
# handling pool1 is to work around an architecture bug in our model
if stride != 1 or 'pool1' in x.name:
x = AvgPooling('pool', x, stride, stride)
x = BatchNorm('bn', x)
x = activate(x)
shortcut = Conv2D('shortcut', x, channel, 1)
stem = get_stem(x)
else:
shortcut = x
x = BatchNorm('bn', x)
x = activate(x)
stem = get_stem(x)
return shortcut + stem
def group(x, name, channel, nr_block, stride):
with tf.variable_scope(name + 'blk1'):
x = resblock(x, channel, stride)
for i in range(2, nr_block + 1):
with tf.variable_scope(name + 'blk{}'.format(i)):
x = resblock(x, channel, 1)
return x
with remap_variables(new_get_variable), \
argscope(BatchNorm, decay=0.9, epsilon=1e-4), \
argscope(Conv2D, use_bias=False, nl=tf.identity):
logits = (
LinearWrap(image)
# use explicit padding here, because our training framework has
# different padding mechanisms from TensorFlow
.tf.pad([[0, 0], [3, 2], [3, 2], [0, 0]]).Conv2D(
'conv1', 64, 7, stride=2, padding='VALID',
use_bias=True).tf.pad(
[[0, 0], [1, 1], [1, 1], [0, 0]],
'SYMMETRIC').MaxPooling(
'pool1',
3, 2, padding='VALID').apply(
group, 'conv2', 64,
2, 1).apply(group, 'conv3', 128, 2, 2).apply(
group, 'conv4', 256, 2,
2).apply(group, 'conv5', 512, 2,
2).BatchNorm('lastbn').
apply(nonlin).GlobalAvgPooling('gap').tf.multiply(
49) # this is due to a bug in our model design
.FullyConnected('fct', 1000)())
prob = tf.nn.softmax(logits, name='output')
cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=label)
cost = tf.reduce_mean(cost, name='cross_entropy_loss')
wrong = prediction_incorrect(logits, label, 1, name='wrong-top1')
add_moving_summary(tf.reduce_mean(wrong, name='train-error-top1'))
wrong = prediction_incorrect(logits, label, 5, name='wrong-top5')
add_moving_summary(tf.reduce_mean(wrong, name='train-error-top5'))
wd_cost = regularize_cost('fc.*/W',
l2_regularizer(5e-6),
name='regularize_cost')
add_param_summary(('.*/W', ['histogram', 'rms']))
self.cost = tf.add_n([cost, wd_cost], name='cost')
add_moving_summary(cost, wd_cost, self.cost)
def _build_graph(self, inputs):
image, label = inputs
image = image / 255.0
fw, fa, fg = get_dorefa(BITW, BITA, BITG)
# monkey-patch tf.get_variable to apply fw
def new_get_variable(v):
name = v.op.name
# don't binarize first and last layer
if not name.endswith('W') or 'conv0' in name or 'fct' in name:
return v
else:
logger.info("Binarizing weight {}".format(v.op.name))
return fw(v)
def nonlin(x):
if BITA == 32:
return tf.nn.relu(x) # still use relu for 32bit cases
return tf.clip_by_value(x, 0.0, 1.0)
def activate(x):
return fa(nonlin(x))
with remap_variables(new_get_variable), \
argscope(BatchNorm, decay=0.9, epsilon=1e-4), \
argscope([Conv2D, FullyConnected], use_bias=False, nl=tf.identity):
logits = (LinearWrap(image)
.Conv2D('conv0', 96, 12, stride=4, padding='VALID')
.apply(activate)
.Conv2D('conv1', 256, 5, padding='SAME', split=2)
.apply(fg)
.BatchNorm('bn1')
.MaxPooling('pool1', 3, 2, padding='SAME')
.apply(activate)
.Conv2D('conv2', 384, 3)
.apply(fg)
.BatchNorm('bn2')
.MaxPooling('pool2', 3, 2, padding='SAME')
.apply(activate)
.Conv2D('conv3', 384, 3, split=2)
.apply(fg)
.BatchNorm('bn3')
.apply(activate)
.Conv2D('conv4', 256, 3, split=2)
.apply(fg)
.BatchNorm('bn4')
.MaxPooling('pool4', 3, 2, padding='VALID')
.apply(activate)
.FullyConnected('fc0', 4096)
.apply(fg)
.BatchNorm('bnfc0')
.apply(activate)
.FullyConnected('fc1', 4096)
.apply(fg)
.BatchNorm('bnfc1')
.apply(nonlin)
.FullyConnected('fct', 1000, use_bias=True)())
tf.nn.softmax(logits, name='output')
cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label)
cost = tf.reduce_mean(cost, name='cross_entropy_loss')
wrong = prediction_incorrect(logits, label, 1, name='wrong-top1')
add_moving_summary(tf.reduce_mean(wrong, name='train-error-top1'))
wrong = prediction_incorrect(logits, label, 5, name='wrong-top5')
add_moving_summary(tf.reduce_mean(wrong, name='train-error-top5'))
# weight decay on all W of fc layers
wd_cost = regularize_cost('fc.*/W', l2_regularizer(5e-6), name='regularize_cost')
add_param_summary(('.*/W', ['histogram', 'rms']))
self.cost = tf.add_n([cost, wd_cost], name='cost')
add_moving_summary(cost, wd_cost, self.cost)
def _build_graph(self, inputs):
image, label = inputs
image = image / 255.0
fw, fa = get_quantize(BITW, BITA)
old_get_variable = tf.get_variable
# monkey-patch tf.get_variable to apply fw
def new_get_variable(v):
name = v.op.name
# don't binarize first and last layer
if not name.endswith('W') or 'conv0' in name or 'fct' in name:
return v
else:
logger.info("Binarizing weight {}".format(v.op.name))
return fw(v)
def nonlin(x):
if BITA == 32:
return tf.nn.relu(x) # still use relu for 32bit cases
return tf.clip_by_value(x, 0.0, 1.0)
def activate(x):
return fa(nonlin(x * 0.1))
def inception_bn(name,
x,
nr_c0_conv_1x1,
nr_c1_conv_1x1,
nr_c1_conv_3x3,
nr_c2_conv_1x1,
nr_c2_conv_5x5,
nr_c3_conv_1x1,
nonlinearity=tf.nn.relu,
internal_nonlin=None,
do_proc=True):
if internal_nonlin is None:
internal_nonlin = nonlinearity
outputs = []
with tf.variable_scope(name) as scope:
c0 = Conv2D('column_0_conv_1x1', x, nr_c0_conv_1x1, 1)
c0 = BatchNorm('bn_0_1x1', c0)
if do_proc:
c0 = activate(c0)
outputs.append(c0)
c1_1x1 = Conv2D('column_1_conv_1x1', x, nr_c1_conv_1x1, 1)
c1_1x1 = BatchNorm('bn_1_1x1', c1_1x1)
c1_1x1 = activate(c1_1x1)
c1_3x3 = Conv2D('column_1_conv_3x3', c1_1x1, nr_c1_conv_3x3, 3)
c1_3x3 = BatchNorm('bn_1_3x3', c1_3x3)
if do_proc:
c1_3x3 = activate(c1_3x3)
outputs.append(c1_3x3)
c2_1x1 = Conv2D('column_2_conv_1x1', x, nr_c2_conv_1x1, 1)
c2_1x1 = BatchNorm('bn_2_1x1', c2_1x1)
c2_1x1 = activate(c2_1x1)
c2_5x5 = Conv2D('column_2_conv_5x5', c2_1x1, nr_c2_conv_5x5, 5)
c2_5x5 = BatchNorm('bn_2_5x5', c2_5x5)
if do_proc:
c2_5x5 = activate(c2_5x5)
outputs.append(c2_5x5)
c3_maxpool = MaxPooling('column_3_maxpool',
x,
3,
1,
padding='SAME')
c3_1x1 = Conv2D('column_3_conv_1x1', c3_maxpool,
nr_c3_conv_1x1, 1)
c3_1x1 = BatchNorm('bn_3_1x1', c3_1x1)
if do_proc:
c3_1x1 = activate(c3_1x1)
outputs.append(c3_1x1)
return tf.concat(outputs, 3, name='concat')
with remap_variables(new_get_variable), \
argscope(BatchNorm, decay=0.9, epsilon=1e-4), \
argscope([Conv2D, FullyConnected], use_bias=False, nl=tf.identity):
nl = tf.identity
l = (LinearWrap(image).Conv2D(
'conv1_1', 64, 7, stride=2,
padding='SAME').BatchNorm('bn1_1').MaxPooling(
'pool1', 3, 2, padding='SAME').apply(activate).Conv2D(
'conv2_1', 64, 1, padding='SAME').BatchNorm(
'bn2_1').apply(activate).Conv2D(
'conv2_2', 192, 3,
padding='SAME').BatchNorm('bn2_2').MaxPooling(
'pool2', 3, 2,
padding='SAME').apply(activate)())
l = inception_bn('inception_3_1', l, 64, 96, 128, 16, 32, 32, nl)
l = inception_bn('inception_3_2', l, 128, 128, 192, 32, 96, 64, nl)
l = MaxPooling('pool3', l, 3, 2, padding='SAME')
l = inception_bn('inception_4_1', l, 192, 96, 208, 16, 48, 64, nl)
l = inception_bn('inception_4_2', l, 160, 112, 224, 24, 64, 64, nl)
l = inception_bn('inception_4_3', l, 128, 128, 256, 24, 64, 64, nl)
l = inception_bn('inception_4_4', l, 112, 144, 288, 32, 64, 64, nl)
l = inception_bn('inception_4_5', l, 256, 160, 320, 32, 128, 128,
nl)
l = MaxPooling('pool4', l, 3, 2, padding='SAME')
l = inception_bn('inception_5_1', l, 256, 160, 320, 32, 128, 128,
nl)
l = inception_bn('inception_5_2',
l,
384,
192,
384,
48,
128,
128,
nl,
do_proc=False)
l = GlobalAvgPooling('gap', l)
l = activate(l)
l = FullyConnected('fct', l, 1000, use_bias=True)
logits = l
prob = tf.nn.softmax(logits, name='output')
cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=label)
cost = tf.reduce_mean(cost, name='cross_entropy_loss')
wrong = prediction_incorrect(logits, label, 1, name='wrong-top1')
add_moving_summary(tf.reduce_mean(wrong, name='train-error-top1'))
wrong = prediction_incorrect(logits, label, 5, name='wrong-top5')
add_moving_summary(tf.reduce_mean(wrong, name='train-error-top5'))
# weight decay on all W of fc layers
wd_cost = regularize_cost('fc.*/W',
l2_regularizer(5e-6),
name='regularize_cost')
add_param_summary(('.*/W', ['histogram', 'rms']))
self.cost = tf.add_n([cost, wd_cost], name='cost')
add_moving_summary(cost, wd_cost, self.cost)
def get_logits(self, image):
if BITW == 't':
fw, fa, fg = get_dorefa(32, 32, 32)
fw = ternarize
else:
fw, fa, fg = get_dorefa(BITW, BITA, BITG)
# monkey-patch tf.get_variable to apply fw
def new_get_variable(v):
name = v.op.name
# don't binarize first and last layer
if not name.endswith('W') or 'conv0' in name or 'fct' in name:
return v
else:
logger.info("Quantizing weight {}".format(v.op.name))
return fw(v)
def nonlin(x):
if BITA == 32:
return tf.nn.relu(x) # still use relu for 32bit cases
return tf.clip_by_value(x, 0.0, 1.0)
def activate(x):
return fa(nonlin(x))
with remap_variables(new_get_variable), \
argscope([Conv2D, BatchNorm, MaxPooling], data_format='channels_first'), \
argscope(BatchNorm, momentum=0.9, epsilon=1e-4), \
argscope(Conv2D, use_bias=False):
logits = (LinearWrap(image)
.Conv2D('conv0', 96, 12, strides=4, padding='VALID', use_bias=True)
.apply(activate)
.Conv2D('conv1', 256, 5, padding='SAME', split=2)
.apply(fg)
.BatchNorm('bn1')
.MaxPooling('pool1', 3, 2, padding='SAME')
.apply(activate)
.Conv2D('conv2', 384, 3)
.apply(fg)
.BatchNorm('bn2')
.MaxPooling('pool2', 3, 2, padding='SAME')
.apply(activate)
.Conv2D('conv3', 384, 3, split=2)
.apply(fg)
.BatchNorm('bn3')
.apply(activate)
.Conv2D('conv4', 256, 3, split=2)
.apply(fg)
.BatchNorm('bn4')
.MaxPooling('pool4', 3, 2, padding='VALID')
.apply(activate)
.FullyConnected('fc0', 4096)
.apply(fg)
.BatchNorm('bnfc0')
.apply(activate)
.FullyConnected('fc1', 4096, use_bias=False)
.apply(fg)
.BatchNorm('bnfc1')
.apply(nonlin)
.FullyConnected('fct', 1000, use_bias=True)())
add_param_summary(('.*/W', ['histogram', 'rms']))
tf.nn.softmax(logits, name='output') # for prediction
return logits
def build_graph(self, image, label):
is_training = get_current_tower_context().is_training
fw, fa, fg = get_dorefa(BITW, BITA, BITG) #获取对三个参量量化的函数变量
# monkey-patch tf.get_variable to apply fw
def binarize_weight(v): #注意，对模型的第一层和最后一层，一般是不做任何量化的。
name = v.op.name
# don't binarize first and last layer
if not name.endswith('W') or 'conv0' in name or 'fc' in name:
return v
else:
logger.info("Binarizing weight {}".format(v.op.name))
return fw(v)
def nonlin(x): #这里是clip_Relu
if BITA == 32:
return tf.nn.relu(x)
return tf.clip_by_value(x, 0.0, 1.0)
def activate(x): #这里是对A先做clip_Relu，再做量化
return fa(nonlin(x))
image = image / 256.0
with remap_variables(binarize_weight), \
argscope(BatchNorm, momentum=0.9, epsilon=1e-4,center=True, scale=True,), \
argscope(Conv2D, use_bias=False):#这行代码是对所有的variables对过binarize_weight函数、设置BN和Conv的参数
logits = (
LinearWrap(image) #LinearWrap用来搭建线性模型，其中apply的是函数句柄，可以向其中传递参数；
.Conv2D('conv0', 48, 5, padding='VALID', use_bias=True
) #conv0 input:[none,40,40,3] output:[none,36,36,48]
.MaxPooling('pool0', 2, padding='SAME'
) #pooling input[none:36,36,48] output:[18,18,48]
.apply(activate) #对Activation进行量化。
# 18
.Conv2D('conv1', 64, 3, padding='SAME'
) #input[none,18,18,48] output[none,18,18,64]
.apply(fg) #对导数进行量化
.BatchNorm('bn1').apply(activate).Conv2D(
'conv2', 64, 3, padding='SAME'
) #input[none 18,18,64] output[none,18,18,64]
.apply(fg).BatchNorm('bn2').MaxPooling(
'pool1', 2,
padding='SAME') #input[none,18,18,64] output[none,9,9,64]
.apply(activate)
# 9
.Conv2D(
'conv3', 128, 3,
padding='VALID') #input[none,9,9,64] output[none,7,7,128]
.apply(fg).BatchNorm('bn3').apply(activate)
# 7
.Conv2D(
'conv4', 128, 3,
padding='SAME') #input[none,7,7,128] output[none,7,7,128]
.apply(fg).BatchNorm('bn4').apply(activate).Conv2D(
'conv5', 128, 3,
padding='VALID') #input[none,7,7,128] output[none,5,5,128]
.apply(fg).BatchNorm('bn5').apply(activate)
# 5
.Dropout(rate=0.5 if is_training else 0.0).Conv2D(
'conv6', 512, 5,
padding='VALID') #input[none,5,5,128] output[none,1,1,512]
.apply(fg).BatchNorm('bn6').apply(
nonlin) #这里只做了clip_relu.并没有过量化。
.FullyConnected('fc1', 10)()) #fc1 output[none,10]
tf.nn.softmax(logits, name='output')
# compute the number of failed samples
wrong = tf.cast(tf.logical_not(tf.nn.in_top_k(logits, label, 1)),
tf.float32,
name='wrong_tensor')
# monitor training error
add_moving_summary(tf.reduce_mean(wrong, name='train_error'))
cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=label)
cost = tf.reduce_mean(cost, name='cross_entropy_loss')
# weight decay on all W of fc layers
wd_cost = regularize_cost('fc.*/W', l2_regularizer(1e-7))
add_param_summary(('.*/W', ['histogram', 'rms']))
total_cost = tf.add_n([cost, wd_cost], name='cost')
add_moving_summary(cost, wd_cost, total_cost)
return total_cost
def _build_graph(self, inputs):
image, label, ious, valids, bndboxes = inputs
image = tf.round(image)
fw, fa, fg = get_dorefa(BITW, BITA, BITG)
old_get_variable = tf.get_variable
def monitor(x, name):
if MONITOR == 1:
return tf.Print(x, [x], message='\n\n' + name + ': ', summarize=1000, name=name)
else:
return x
def new_get_variable(v):
name = v.op.name
if not name.endswith('W') or 'conv1' in name or 'conv_obj' in name or 'conv_box' in name:
return v
else:
logger.info("Quantizing weight {}".format(v.op.name))
if MONITOR == 1:
return tf.Print(fw(v), [fw(v)], message='\n\n' + v.name + ', Quantized weights are:', summarize=100)
else:
return fw(v)
def activate(x):
if BITA == 32:
return tf.nn.relu(x)
else:
return fa(tf.nn.relu(x))
def bn_activate(name, x):
x = BatchNorm(name, x)
x = monitor(x, name + '_noact_out')
return activate(x)
def halffire(name, x, num_squeeze_filters, num_expand_3x3_filters, skip):
out_squeeze = Conv2D('squeeze_conv_' + name, x, out_channel=num_squeeze_filters, kernel_shape=1, stride=1, padding='SAME')
out_squeeze = bn_activate('bn_squeeze_' + name, out_squeeze)
out_expand_3x3 = Conv2D('expand_3x3_conv_' + name, out_squeeze, out_channel=num_expand_3x3_filters, kernel_shape=3, stride=1, padding='SAME')
out_expand_3x3 = bn_activate('bn_expand_3x3_' + name, out_expand_3x3)
if skip == 0:
return out_expand_3x3
else:
return tf.add(x, out_expand_3x3)
def halffire_noact(name, x, num_squeeze_filters, num_expand_3x3_filters):
out_squeeze = Conv2D('squeeze_conv_' + name, x, out_channel=num_squeeze_filters, kernel_shape=1, stride=1, padding='SAME')
out_squeeze = bn_activate('bn_squeeze_' + name, out_squeeze)
out_expand_3x3 = Conv2D('expand_3x3_conv_' + name, out_squeeze, out_channel=num_expand_3x3_filters, kernel_shape=3, stride=1, padding='SAME')
return out_expand_3x3
with remap_variables(new_get_variable), \
argscope([Conv2D, FullyConnected], use_bias=False, nl=tf.identity), \
argscope(BatchNorm, decay=0.9, epsilon=1e-4):
image = monitor(image, 'image_out')
l = Conv2D('conv1', image, out_channel=32, kernel_shape=3, stride=2, padding='SAME')
l = bn_activate('bn1', l)
l = monitor(l, 'conv1_out')
l = MaxPooling('pool1', l, shape=3, stride=2, padding='SAME')
l = monitor(l, 'pool1_out')
l = halffire('fire1', l, NUM_SQUEEZE_FILTERS, NUM_EXPAND_FILTERS, 0)
l = monitor(l, 'fire1_out')
l = MaxPooling('pool2', l, shape=3, stride=2, padding='SAME')
l = monitor(l, 'pool2_out')
l = halffire('fire2', l, NUM_SQUEEZE_FILTERS, NUM_EXPAND_FILTERS, 0)
l = monitor(l, 'fire2_out')
l = MaxPooling('pool3', l, shape=3, stride=2, padding='SAME')
l = monitor(l, 'pool3_out')
l = halffire('fire3', l, NUM_SQUEEZE_FILTERS, NUM_EXPAND_FILTERS, 0)
l = monitor(l, 'fire3_out')
l = halffire('fire4', l, NUM_SQUEEZE_FILTERS, NUM_EXPAND_FILTERS, 0)
l = monitor(l, 'fire4_out')
l = halffire('fire5', l, NUM_SQUEEZE_FILTERS, NUM_EXPAND_FILTERS, 0)
l = monitor(l, 'fire5_out')
l = halffire('fire6', l, NUM_SQUEEZE_FILTERS, NUM_EXPAND_FILTERS, 0)
l = monitor(l, 'fire6_out')
l = halffire('fire7', l, NUM_SQUEEZE_FILTERS, NUM_EXPAND_FILTERS, 0)
l = monitor(l, 'fire7_out')
# Classification
classify = Conv2D('conv_class', l, out_channel=12, kernel_shape=1, stride=1, padding='SAME')
classify = bn_activate('bn_class', classify)
classify = monitor(classify, 'conv_class_out')
logits = GlobalAvgPooling('pool_class', classify)
class_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label)
class_loss = tf.reduce_mean(class_loss, name='cross_entropy_loss')
wrong = prediction_incorrect(logits, label, 1, name='wrong-top1')
add_moving_summary(tf.reduce_mean(wrong, name='train-error-top1'))
# Object Detection
l = tf.concat([l, classify], axis=3)
objdetect = Conv2D('conv_obj', l, out_channel=1, kernel_shape=1, stride=1, padding='SAME')
objdetect = tf.identity(objdetect, name='objdetect_out')
objdetect_loss = tf.losses.hinge_loss(labels=ious, logits=objdetect)
bndbox = Conv2D('conv_box', l, out_channel=4, kernel_shape=1, stride=1, padding='SAME')
bndbox = tf.identity(bndbox, name='bndbox_out')
bndbox = tf.multiply(bndbox, valids, name='mult0')
bndbox_loss = tf.losses.mean_squared_error(labels=bndboxes, predictions=bndbox)
# weight decay on all W of fc layers
# reg_cost = regularize_cost('(fire7|conv_obj|conv_box).*/W', l2_regularizer(1e-5), name='regularize_cost')
# cost = class_loss*objdetect_loss*bndbox_loss
# cost = class_loss + objdetect_loss + bndbox_loss + reg_cost
cost = class_loss + 10*objdetect_loss + bndbox_loss
add_moving_summary(class_loss, objdetect_loss, bndbox_loss, cost)
self.cost = cost
tf.get_variable = old_get_variable
def build_graph(self, image, label):
is_training = get_current_tower_context().is_training
fw, fa, fg = get_dorefa(BITW, BITA, BITG)
# monkey-patch tf.get_variable to apply fw
def binarize_weight(v):
name = v.op.name
# don't binarize first and last layer
if not name.endswith('W') or 'conv0' in name or 'weak' in name or 'fc' in name:
return v
else:
logger.info("Binarizing weight {}".format(v.op.name))
return fw(v)
#return ternarize(v)
def cabs(x):
return tf.minimum(1.0, tf.abs(x), name='cabs')
def activate(x):
return fa(cabs(x))
def merge(x, y):
#return x + y
#return x - y
return tf.concat([x,y], axis=3)
image = image / 256.0; k=3; zp=0.25; zp2=zp / 1
#scale = tf.train.exponential_decay(learning_rate=1.0, global_step=get_global_step_var(), decay_steps=4721*5, decay_rate=0.5, staircase=True, name='scale')
#scale = tf.where(scale>0.001, scale, tf.zeros_like(scale))
scale = tf.train.cosine_decay(learning_rate=1.0, global_step=get_global_step_var(), decay_steps=4721*50, alpha=0.0)
tf.summary.scalar('scale', scale); endconv=[]; endweak=[]
#scale2 = tf.train.cosine_decay(learning_rate=1.0, global_step=get_global_step_var(), decay_steps=4721*50, alpha=0.0)
#scale3 = tf.train.cosine_decay(learning_rate=1.0, global_step=get_global_step_var(), decay_steps=4721*80, alpha=0.0)
with remap_variables(binarize_weight), \
argscope(BatchNorm, momentum=0.9, epsilon=1e-4), \
argscope(Conv2D, use_bias=False):
net=Conv2D('conv0', image, np.round(48*zp), 5, padding='VALID', use_bias=True)
net=MaxPooling('pool0', net, 2, padding='SAME'); net=activate(net)
net1=Conv2D('conv1', net, np.round(64*zp), 3, padding='SAME'); net1=BatchNorm('bn1', net1); endconv.append(net1)
net2=Conv2D('weak1', net, np.round(64*zp2), k, padding='SAME'); net2=BatchNorm('bn12', net2); endweak.append(net2); # net2=tf.nn.relu(net2)
net=merge(activate(net1), scale*net2)
#net=activate(net1)
net1=Conv2D('conv2', net, np.round(64*zp), 3, padding='SAME'); net1=BatchNorm('bn2', net1); endconv.append(net1)
net2=Conv2D('weak2', net, np.round(64*zp2), k, padding='SAME'); net2=BatchNorm('bn22', net2); endweak.append(net2); # net2=tf.nn.relu(net2)
net1=MaxPooling('pool1', net1, 2, padding='SAME'); net2=MaxPooling('pool12', net2, 2, padding='SAME');
net=merge(activate(net1), scale*net2)
net=activate(net1)
net1=Conv2D('conv3', net, np.round(128*zp), 3, padding='VALID'); net1=BatchNorm('bn3', net1); endconv.append(net1)
net2=Conv2D('weak3', net, np.round(128*zp2), k, padding='VALID'); net2=BatchNorm('bn32', net2); endweak.append(net2); # net2=tf.nn.relu(net2)
net=merge(activate(net1), scale*net2)
#net=activate(net1)
net1=Conv2D('conv4', net, np.round(128*zp), 3, padding='SAME'); net1=BatchNorm('bn4', net1); endconv.append(net1)
net2=Conv2D('weak4', net, np.round(128*zp2), k, padding='SAME'); net2=BatchNorm('bn42', net2); endweak.append(net2); # net2=tf.nn.relu(net2)
net=merge(activate(net1), scale*net2)
# net=activate(net1)
net1=Conv2D('conv5', net, np.round(128*zp), 3, padding='VALID'); net1=BatchNorm('bn5', net1); endconv.append(net1)
net2=Conv2D('weak5', net, np.round(128*zp2), k, padding='VALID'); net2=BatchNorm('bn52', net2); endweak.append(net2); # net2=tf.nn.relu(net2)
net=merge(activate(net1), scale*net2)
#net=activate(net1)
net=tf.nn.dropout(net, 0.5 if is_training else 1.0)
net1=Conv2D('conv6', net, np.round(512*zp), 5, padding='VALID'); net1=BatchNorm('bn6', net1); endconv.append(net1)
net2=Conv2D('weak6', net, np.round(512*zp2), 5, padding='VALID'); net2=BatchNorm('bn62', net2); endweak.append(net2); # net2=tf.nn.relu(net2)
net=merge(cabs(net1), scale*net2)
# net=cabs(net1)
logits=FullyConnected('fc1', net, 10)
tf.nn.softmax(logits, name='output')
# compute the number of failed samples
wrong = tf.cast(tf.logical_not(tf.nn.in_top_k(logits, label, 1)), tf.float32, name='wrong_tensor')
# monitor training error
add_moving_summary(tf.reduce_mean(wrong, name='train_error'))
cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label)
cost = tf.reduce_mean(cost, name='cross_entropy_loss')
# weight decay on all W of fc layers
wd_cost = regularize_cost('fc.*/W', l2_regularizer(1e-7))
add_param_summary(('.*/W', ['histogram', 'rms']))
total_cost = tf.add_n([cost, wd_cost], name='cost')
add_moving_summary(cost, wd_cost, total_cost)
for i in range(len(endweak)):
add_moving_summary(tf.reduce_mean(tf.abs(endconv[i]), name='mean_conv_'+str(i+1) ) )
add_moving_summary(tf.reduce_mean(tf.abs(endweak[i]), name='mean_weak_'+str(i+1) ) )
return total_cost
def ResNet18(image,
label,
scope,
is_training,
dataset='cifar',
reuse=False,
Distill=None,
bit_a=32,
bit_w=32,
bit_g=32):
end_points = {}
nChannels = []
if 'cifar' in dataset or 'svhn' in dataset:
nChannels = [64, 64, 128, 256, 512]
elif 'imagenet' in dataset:
nChannels = [64, 256, 512, 1024, 2048]
assert len(nChannels) > 0, "empty channels!!"
stride = [1, 2, 2, 2]
# 4 (stride) * n * 2 + 2. shortcut is not involved.
# 32 -> 16 -> 8 -> 4
n = 2
if scope == 'Teacher':
with tf.variable_scope(scope):
std = tf.contrib.layers.conv2d(image,
nChannels[0], [3, 3],
1,
scope='base_conv',
trainable=True,
reuse=reuse)
std = tf.contrib.layers.batch_norm(std,
scope='bn0',
trainable=True,
is_training=is_training,
reuse=reuse)
std = tf.nn.relu(std)
for i in range(len(stride)):
std = NetworkBlock(std,
ResBlock,
n,
nChannels[i + 1],
stride[i],
is_training=is_training,
reuse=reuse,
name='Resblock%d' % i)
fc = tf.reduce_mean(std, [1, 2])
logits = tf.contrib.layers.fully_connected(
fc,
label.get_shape().as_list()[-1],
weights_initializer=tf.contrib.layers.xavier_initializer(),
biases_initializer=tf.zeros_initializer(),
trainable=True,
scope='full',
reuse=reuse)
end_points['Logits'] = logits
elif scope == 'Student':
fw, fa, fg = get_dorefa(bit_w, bit_a, bit_g)
# monkey-patch tf.get_variable to apply fw
def new_get_variable(v):
name = v.op.name
# don't binarize first and last layer
if not name.endswith(
'weights') or 'base_conv' in name or 'full' in name:
return v
else:
tf.logging.info("Quantizing weight {} at bits {}".format(
v.op.name, bit_w))
return fw(v)
def nonlin(x):
if bit_a == 32:
return tf.nn.relu(x) # still use relu for 32-bit cases
return tf.clip_by_value(x, 0.0, 1.0)
def activate(x):
tf.logging.info("Quantizing activations {} at bits {}".format(
x.name, bit_a))
return fa(nonlin(x))
with tf.variable_scope(scope), remap_variables(new_get_variable):
std = tf.contrib.layers.conv2d(image,
nChannels[0], [3, 3],
1,
scope='base_conv',
trainable=True,
reuse=reuse)
std = tf.contrib.layers.batch_norm(std,
scope='bn0',
trainable=True,
is_training=is_training,
reuse=reuse)
for i in range(len(stride)):
std = NetworkBlock(std,
ResBlock,
n,
nChannels[i + 1],
stride[i],
activate,
is_training=is_training,
reuse=reuse,
name='Resblock%d' % i,
scope=scope)
fc = tf.reduce_mean(std, [1, 2])
logits = tf.contrib.layers.fully_connected(
fc,
label.get_shape().as_list()[-1],
weights_initializer=tf.contrib.layers.xavier_initializer(),
biases_initializer=tf.zeros_initializer(),
trainable=True,
scope='full',
reuse=reuse)
end_points['Logits'] = logits
if Distill is not None:
if Distill == 'DML':
teacher_train = True
else:
is_training = False
teacher_train = False
with tf.variable_scope('Teacher'):
with tf.contrib.framework.arg_scope(
[tf.contrib.layers.conv2d, tf.contrib.layers.fully_connected],
variables_collections=[
tf.GraphKeys.GLOBAL_VARIABLES, 'Teacher'
]):
with tf.contrib.framework.arg_scope(
[tf.contrib.layers.batch_norm],
variables_collections=[
tf.GraphKeys.GLOBAL_VARIABLES, 'Teacher'
]):
tch = tf.contrib.layers.conv2d(image,
nChannels[0], [3, 3],
1,
scope='base_conv',
trainable=teacher_train,
reuse=reuse)
tch = tf.contrib.layers.batch_norm(tch,
scope='bn0',
trainable=teacher_train,
is_training=is_training,
reuse=reuse)
tch = tf.nn.relu(tch)
for i in range(len(stride)):
tch = NetworkBlock(tch,
ResBlock,
n,
nChannels[i + 1],
stride[i],
is_training=is_training,
reuse=reuse,
name='Resblock%d' % i)
fc = tf.reduce_mean(tch, [1, 2])
logits_tch = tf.contrib.layers.fully_connected(
fc,
label.get_shape().as_list()[-1],
weights_initializer=tf.contrib.layers.
xavier_initializer(),
biases_initializer=tf.zeros_initializer(),
trainable=teacher_train,
scope='full',
reuse=reuse)
end_points['Logits_tch'] = logits_tch
with tf.variable_scope('Distillation'):
feats = tf.get_collection('feat')
student_feats = feats[:len(feats) // 2]
teacher_feats = feats[len(feats) // 2:]
feats_noact = tf.get_collection('feat_noact')
student_feats_noact = feats[:len(feats_noact) // 2]
teacher_feats_noact = feats[len(feats_noact) // 2:]
if Distill == 'Soft_logits':
tf.add_to_collection(
'dist', Response.Soft_logits(logits, logits_tch, 3))
elif Distill == 'DML':
tf.add_to_collection('dist', Response.DML(logits, logits_tch))
elif Distill == 'FT':
tf.add_to_collection(
'dist',
Response.Factor_Transfer(student_feats_noact[-1],
teacher_feats_noact[-1]))
elif Distill == 'FitNet':
tf.add_to_collection(
'dist', Multiple.FitNet(student_feats, teacher_feats))
elif Distill == 'AT':
tf.add_to_collection(
'dist',
Multiple.Attention_transfer(student_feats, teacher_feats))
elif Distill == 'AB':
tf.add_to_collection(
'dist',
Multiple.AB_distillation(student_feats, teacher_feats, 1.,
3e-3))
elif Distill == 'FSP':
tf.add_to_collection('dist',
Shared.FSP(student_feats, teacher_feats))
elif Distill[:3] == 'KD-':
tf.add_to_collection(
'dist',
Shared.KD_SVD(student_feats, teacher_feats, Distill[-3:]))
elif Distill == 'RKD':
tf.add_to_collection(
'dist', Relation.RKD(logits, logits_tch, l=[5e1, 1e2]))
elif Distill == 'MHGD':
tf.add_to_collection(
'dist', Relation.MHGD(student_feats, teacher_feats))
elif Distill == 'MHGD-RKD':
tf.add_to_collection(
'dist',
Relation.MHGD(student_feats, teacher_feats) +
Relation.RKD(logits, logits_tch, l=[5e1, 1e2]))
elif Distill == 'MHGD-RKD-SVD':
tf.add_to_collection(
'dist',
Relation.MHGD(student_feats, teacher_feats) +
Relation.RKD(logits, logits_tch, l=[5e1, 1e2]) +
Shared.KD_SVD(student_feats, teacher_feats, "SVD"))
return end_points
def build_graph(self, image, label):
image = image / 256.0
is_training = get_current_tower_context().is_training
fw, fa, fg = get_dorefa(BITW, BITA, BITG)
def new_get_variable(v):
name = v.op.name
# don't binarize first and last layer
if not name.endswith('W') or 'conv1' in name or 'fct' in name:
return v
else:
logger.info("Binarizing weight {}".format(v.op.name))
return fw(v)
def nonlin(x):
return tf.clip_by_value(x, 0.0, 1.0)
def activate(x):
return fa(nonlin(x))
def resblock(x, channel, stride):
def get_stem_full(x):
return (LinearWrap(x)
.Conv2D('c3x3a', channel, 3)
.quan_all_L2norm('stembn')
.apply(activate)
.Conv2D('c3x3b', channel, 3)())
channel_mismatch = channel != x.get_shape().as_list()[3]
if stride != 1 or channel_mismatch or 'pool1' in x.name:
# handling pool1 is to work around an architecture bug in our model
if stride != 1 or 'pool1' in x.name:
x = AvgPooling('pool', x, stride, stride)
x = quan_all_L2norm('bn', x)
x = activate(x)
shortcut = Conv2D('shortcut', x, channel, 1)
stem = get_stem_full(x)
else:
shortcut = x
x = quan_all_L2norm('bn', x)
x = activate(x)
stem = get_stem_full(x)
return shortcut + stem
def group(x, name, channel, nr_block, stride):
with tf.variable_scope(name + 'blk1'):
x = resblock(x, channel, stride)
for i in range(2, nr_block + 1):
with tf.variable_scope(name + 'blk{}'.format(i)):
x = resblock(x, channel, 1)
return x
with remap_variables(new_get_variable), \
argscope(quan_all_L2norm, momentum=0.9, eps=1e-4,train=is_training), \
argscope(Conv2D, use_bias=False, nl=tf.identity):
logits = (LinearWrap(image)
# use explicit padding here, because our private training framework has
# different padding mechanisms from TensorFlow
.tf.pad([[0, 0], [3, 2], [3, 2], [0, 0]])
.Conv2D('conv1', 64, 7, stride=2, padding='VALID', use_bias=True)
.tf.pad([[0, 0], [1, 1], [1, 1], [0, 0]], 'SYMMETRIC')
.MaxPooling('pool1', 3, 2, padding='VALID')
.apply(group, 'conv2', 64, 2, 1)
.apply(group, 'conv3', 128, 2, 2)
.apply(group, 'conv4', 256, 2, 2)
.apply(group, 'conv5', 512, 2, 2)
.quan_all_L2norm('lastbn')
.apply(nonlin)
.GlobalAvgPooling('gap')
#.tf.multiply(49) # this is due to a bug in our model design
.FullyConnected('fct', 10)())
tf.nn.softmax(logits, name='output')
# compute the number of failed samples
cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label)
cost = tf.reduce_mean(cost, name='cross_entropy_loss')
wrong = tf.cast(tf.logical_not(tf.nn.in_top_k(logits, label, 1)), tf.float32, name='wrong_vector')
# monitor training error
add_moving_summary(tf.reduce_mean(wrong, name='train_error'))
# weight decay on all W of fc layers
#wd_w = tf.train.exponential_decay(0.0002, get_global_step_var(),480000, 0.2, True)
#wd_cost = tf.multiply(wd_w, regularize_cost('.*/W', tf.nn.l2_loss), name='wd_cost')
#add_moving_summary(cost, wd_cost)
add_param_summary(('.*/W', ['histogram'])) # monitor W
return tf.add_n([cost], name='cost')
def build_graph(self, image, label):
# get quantization function
# quantize weights
qw = quantize_weight(int(self.quantizer_config['BITW']),
self.quantizer_config['name'],
self.quantizer_config['W_opts'],
self.quantizer_config)
# quantize activation
if self.quantizer_config['BITA'] in ['32', 32]:
qa = tf.identity
else:
qa = quantize_activation(int(self.quantizer_config['BITA']),
self.quantizer_config['name'],
self.quantizer_config)
# quantize gradient
qg = quantize_gradient(int(self.quantizer_config['BITG']))
def new_get_variable(v):
name = v.op.name
# don't quantize first and last layer
if not name.endswith('/W') or 'conv1' in name or 'fct' in name:
return v
else:
logger.info("Quantizing weight {}".format(v.op.name))
return qw(v)
def activate(x):
return qa(self.activation(x))
@layer_register(use_scope=True)
def DWConv2D(inputs,
channel,
kernel_size=3,
stride=1,
padding='SAME',
data_format=None,
dilations=None):
#output = tf.keras.layers.DepthwiseConv2D(kernel_size, strides=(stride,stride), padding='same', use_bias=False)(inputs)
#print(output.name, ': ', inputs.shape, ' --> ', output.shape)
#return output
curr_channel = inputs.get_shape().as_list()[3]
var = tf.get_variable(
name='dwconv_kernel',
shape=[kernel_size, kernel_size, curr_channel, 1],
initializer=tf.glorot_uniform_initializer)
output = tf.nn.depthwise_conv2d(inputs,
var,
strides=(1, stride, stride, 1),
padding=padding)
print(output.name, ': ', inputs.shape, ' --> ', output.shape)
return output
@layer_register(use_scope=True)
def SE_block(input_feature, ratio=8):
kernel_initializer = tf.contrib.layers.variance_scaling_initializer(
)
bias_initializer = tf.constant_initializer(value=0.0)
channel = input_feature.get_shape()[-1]
# Global average pooling
squeeze = tf.reduce_mean(input_feature, axis=[1, 2], keepdims=True)
excitation = tf.layers.dense(inputs=squeeze,
units=channel // ratio,
activation=tf.nn.relu,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
name='bottleneck_fc')
excitation = tf.layers.dense(inputs=excitation,
units=channel,
activation=tf.nn.sigmoid,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
name='recover_fc')
scale = input_feature * excitation
return scale
def SepConv(x, name, nr_block, channel, kernel_size, stride):
with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
x = DWConv2D('dwconv', x, channel, kernel_size, stride)
x = Conv2D('pwconv', x, channel, 1, strides=(stride, stride))
return x
def block(x, channel, kernel_size, stride, extension, SE):
channel_match = channel == x.get_shape().as_list()[3]
shortcut = x
if x.get_shape().as_list()[3] < 20:
x = Conv2D('pwconv_a',
x,
channel * extension,
1,
strides=(1, 1))
else:
x = Conv2D('pwconv_a1', x, 20, 1, strides=(1, 1))
x = Conv2D('pwconv_a2',
x,
channel * extension,
1,
strides=(1, 1))
x = BatchNorm('bn_a', x)
x = activate(x)
x = DWConv2D('dwconv_b', x, channel * extension, kernel_size,
stride)
x = BatchNorm('bn_b', x)
x = activate(x)
if SE:
x = SE_block('se_block', x)
if channel < 20:
x = Conv2D('pwconv_c', x, channel, 1, strides=(1, 1))
else:
x = Conv2D('pwconv_c1', x, 20, 1, strides=(1, 1))
x = Conv2D('pwconv_c2', x, channel, 1, strides=(1, 1))
x = BatchNorm('bn_c', x)
if stride == 1 and channel_match:
x = x + shortcut
return x
def group(x, name, nr_block, channel, kernel_size, stride, extension,
SE):
with tf.variable_scope(name + 'blk1', reuse=tf.AUTO_REUSE):
x = block(x, channel, kernel_size, stride, extension, SE)
for i in range(2, nr_block + 1):
with tf.variable_scope(name + 'blk{}'.format(i),
reuse=tf.AUTO_REUSE):
x = block(x, channel, kernel_size, 1, extension, SE)
return x
with remap_variables(new_get_variable), \
argscope(BatchNorm, decay=0.99, epsilon=1e-3), \
argscope(Conv2D, use_bias=False, nl=tf.identity,
kernel_initializer=tf.variance_scaling_initializer(scale=float(self.initializer_config['scale']),
mode=self.initializer_config['mode'])):
logits = (
LinearWrap(image).Conv2D('conv1', 32, 3) # size=32
.apply(group, 'mbconv2', 1, 16, 3, 1, 6, False) # size=32
.apply(group, 'mbconv3', 2, 24, 3, 1, 6, False) # size=16
.apply(group, 'mbconv4', 3, 32, 3, 2, 6, False) # size=8
.apply(group, 'mbconv5', 4, 64, 3, 2, 6, False) # size=4
.apply(group, 'mbconv6', 3, 96, 3, 1, 6, False) # size=4
.apply(group, 'mbconv7', 3, 160, 3, 2, 6, False) # size=2
.apply(group, 'mbconv8', 1, 320, 3, 1, 6, False) # size=2
.Conv2D('conv9/pwconv_a1', 20, 1, strides=(1, 1)).Conv2D(
'conv9/pwconv_a2', 1280, 1,
strides=(1, 1)).BatchNorm('last_bn').apply(
activate).GlobalAvgPooling('gap').FullyConnected(
'fct', self.nb_classes)())
prob = tf.nn.softmax(logits, name='output')
cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=label)
cost = tf.reduce_mean(cost, name='cross_entropy_loss')
# regularization
if self.regularizer_config['name'] not in [None, 'None']:
reg_func = getattr(regularizers,
self.regularizer_config['name'])().get_func(
self.regularizer_config,
self.quantizer_config)
reg_cost = tf.multiply(float(self.regularizer_config['lmbd']),
regularize_cost('.*/W', reg_func),
name='reg_cost')
#reg_cost = tf.multiply(float(self.regularizer_config['lmbd']), regularize_cost_from_collection(), name='reg_cost')
total_cost = tf.add_n([cost, reg_cost], name='total_cost')
else:
total_cost = cost
# summary
def add_summary(logits, cost):
err_top1 = tf.cast(tf.logical_not(tf.nn.in_top_k(logits, label,
1)),
tf.float32,
name='err_top1')
add_moving_summary(
tf.reduce_mean(err_top1, name='train_error_top1'))
err_top5 = tf.cast(tf.logical_not(tf.nn.in_top_k(logits, label,
5)),
tf.float32,
name='err_top5')
add_moving_summary(
tf.reduce_mean(err_top5, name='train_error_top5'))
add_moving_summary(cost)
add_param_summary(('.*/W', ['histogram'])) # monitor W
add_summary(logits, cost)
return total_cost
def _build_graph(self, inputs):
inp, label = inputs
is_training = get_current_tower_context().is_training
fw, fa = get_dorefa(self.bitw, self.bita)
def binarize_weight(v):
name = v.op.name
if not (name.endswith('W') or name.endswith('b')):
logger.info("Not quantizing {}".format(name))
return v
elif not self.quant_ends and 'conv0' in name:
logger.info("Not quantizing {}".format(name))
return v
elif not self.quant_ends and 'last_linear' in name:
logger.info("Not quantizing {}".format(name))
return v
elif not self.quant_ends and (self.net_fn == fcn1_net or self.net_fn == fcn2_net) and 'linear0' in name:
logger.info("Not quantizing {}".format(name))
return v
else:
logger.info("Quantizing weight {}".format(name))
return fw(v)
def nonlin(x, name="activate"):
if self.bita == 32:
return fa(tf.nn.relu(BNWithTrackedMults(x)))
else:
return fa(tf.clip_by_value(BNWithTrackedMults(x), 0.0, 1.0))
with remap_variables(binarize_weight), \
argscope([FullyConnectedWithTrackedMults], network_complexity=self.network_complexity), \
argscope([Conv2DWithTrackedMults], network_complexity=self.network_complexity), \
argscope([BNReLUWithTrackedMults], network_complexity=self.network_complexity), \
argscope([BNWithTrackedMults], network_complexity=self.network_complexity), \
argscope(BatchNorm, decay=0.9, epsilon=1e-4):
l = self.net_fn(inp, nonlin, self.n_context)
logits = FullyConnectedWithTrackedMults('last_linear', l, out_dim=self.n_spks, nl=tf.identity)
prob = tf.nn.softmax(logits, name='output')
# used for validation accuracy of utterance
identity_guesses = flatten(tf.argmax(prob, axis=1))
uniq_identities, _, count = tf.unique_with_counts(identity_guesses)
idx_to_identity_with_most_votes = tf.argmax(count)
chosen_identity = tf.gather(uniq_identities, idx_to_identity_with_most_votes)
wrong = tf.expand_dims(tf.not_equal(chosen_identity, tf.cast(label[0], tf.int64)), axis=0, name='utt-wrong')
cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label)
cost = tf.reduce_mean(cost, name='cross_entropy_loss')
add_moving_summary(cost)
wrong = prediction_incorrect(logits, label, 1, name='wrong-top1')
add_moving_summary(tf.reduce_mean(wrong, name='train-error-top1'))
with tf.name_scope('original-weight-summaries'):
add_param_summary(('.*/W', ['rms', 'histogram']))
add_param_summary(('.*/b', ['rms', 'histogram']))
with tf.name_scope('activation-summaries'):
def fn(name):
return (name.endswith('output') or name.endswith('output:0')) and "Inference" not in name and 'quantized' not in name
tensors = get_tensors_from_graph(tf.get_default_graph(), fn)
logger.info("Adding activation tensors to summary: {}".format(tensors))
for tensor in tensors:
add_tensor_summary(tensor, ['rms', 'histogram'])
wd_w = tf.train.exponential_decay(0.0002, get_global_step_var(), 480000, 0.2, True)
wd_cost = tf.multiply(wd_w, regularize_cost('.*/W', tf.nn.l2_loss), name='wd_cost')
add_moving_summary(wd_cost)
self.cost = tf.add_n([cost, wd_cost], name='cost')
tf.constant([self.network_complexity['mults']], name='TotalMults')
tf.constant([self.network_complexity['weights']], name='TotalWeights')
logger.info("Parameter count: {}".format(self.network_complexity))
def _build_graph(self, inputs):
image, label = inputs
"""Add a single channel here"""
image = tf.expand_dims(image, 3)
image = image * 256
image = tf.round(image)
fw, fa, fg = get_dorefa(BITW, BITA, BITG)
old_get_variable = tf.get_variable
def monitor(x, name):
if MONITOR == 1:
return tf.Print(x, [x],
message='\n\n' + name + ': ',
summarize=1000,
name=name)
else:
return x
def new_get_variable(v):
name = v.op.name
if not name.endswith('W') or 'conv0' in name or 'fc1' in name:
return v
else:
logger.info("Quantizing weight {}".format(v.op.name))
if MONITOR == 1:
return tf.Print(fw(v), [fw(v)],
message='\n\n' + v.name +
', Quantized weights are:',
summarize=100)
else:
return fw(v)
def activate(x):
if BITA == 32:
return tf.nn.relu(x)
else:
return fa(tf.nn.relu(x))
with remap_variables(new_get_variable), \
argscope(Conv2D, kernel_shape=3, use_bias=False, nl=tf.identity, out_channel=32):
logits = (LinearWrap(image).apply(monitor, 'image_out').Conv2D(
'conv0').apply(fg).BatchNorm('bn0').apply(activate).apply(
monitor, 'conv0_out').MaxPooling('pool0', 2).apply(
monitor, 'pool0_out').Conv2D('conv1').apply(
fg).BatchNorm('bn1').apply(activate).apply(
monitor, 'conv1_out').Conv2D('conv2').apply(
fg).BatchNorm('bn2').apply(activate).apply(
monitor, 'conv2_out').MaxPooling(
'pool1', 2).apply(
monitor,
'pool1_out').Conv2D('conv3').
apply(fg).BatchNorm('bn3').apply(activate).apply(
monitor, 'conv3_out').FullyConnected(
'fc0',
use_bias=False,
out_dim=20,
nl=tf.identity).apply(activate).apply(
monitor, 'fc0_out').FullyConnected(
'fc1',
use_bias=False,
out_dim=10,
nl=tf.identity).apply(
monitor, 'fc1_out')())
prob = tf.nn.softmax(logits, name='prob')
cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=label)
cost = tf.reduce_mean(cost, name='cross_entropy_loss')
wrong = symbf.prediction_incorrect(logits, label, name='incorrect')
accuracy = symbf.accuracy(logits, label, name='accuracy')
train_error = tf.reduce_mean(wrong, name='train_error')
summary.add_moving_summary(train_error, accuracy)
wd_cost = tf.multiply(1e-5,
regularize_cost('fc.*/W', tf.nn.l2_loss),
name='regularize_loss')
self.cost = tf.add_n([wd_cost, cost], name='total_cost')
summary.add_moving_summary(cost, wd_cost, self.cost)
def build_graph(self, image, label):
image = image / 255.0
fw, fa, fg = get_dorefa(BITW, BITA, BITG)
# monkey-patch tf.get_variable to apply fw
def new_get_variable(v):
name = v.op.name
# don't binarize first and last layer
if not name.endswith('W') or 'conv0' in name or 'fct' in name:
return v
else:
logger.info("Binarizing weight {}".format(v.op.name))
return fw(v)
def nonlin(x):
if BITA == 32:
return tf.nn.relu(x) # still use relu for 32bit cases
return tf.clip_by_value(x, 0.0, 1.0)
def activate(x):
return fa(nonlin(x))
with remap_variables(new_get_variable), \
argscope(BatchNorm, momentum=0.9, epsilon=1e-4), \
argscope(Conv2D, use_bias=False):
logits = (LinearWrap(image).Conv2D(
'conv0', 96, 12, strides=4,
padding='VALID').apply(activate).Conv2D(
'conv1', 256, 5, padding='SAME',
split=2).apply(fg).BatchNorm('bn1').MaxPooling(
'pool1', 3, 2, padding='SAME').apply(activate).Conv2D(
'conv2', 384,
3).apply(fg).BatchNorm('bn2').MaxPooling(
'pool2', 3, 2,
padding='SAME').apply(activate).Conv2D(
'conv3', 384, 3, split=2).apply(fg).
BatchNorm('bn3').apply(activate).Conv2D(
'conv4', 256, 3,
split=2).apply(fg).BatchNorm('bn4').MaxPooling(
'pool4', 3, 2,
padding='VALID').apply(activate).FullyConnected(
'fc0', 4096).apply(fg).BatchNorm('bnfc0').
apply(activate).FullyConnected(
'fc1', 4096,
use_bias=False).apply(fg).BatchNorm('bnfc1').apply(
nonlin).FullyConnected('fct',
1000,
use_bias=True)())
tf.nn.softmax(logits, name='output')
cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=label)
cost = tf.reduce_mean(cost, name='cross_entropy_loss')
wrong = prediction_incorrect(logits, label, 1, name='wrong-top1')
add_moving_summary(tf.reduce_mean(wrong, name='train-error-top1'))
wrong = prediction_incorrect(logits, label, 5, name='wrong-top5')
add_moving_summary(tf.reduce_mean(wrong, name='train-error-top5'))
# weight decay on all W of fc layers
wd_cost = regularize_cost('fc.*/W',
l2_regularizer(5e-6),
name='regularize_cost')
add_param_summary(('.*/W', ['histogram', 'rms']))
total_cost = tf.add_n([cost, wd_cost], name='cost')
add_moving_summary(cost, wd_cost, total_cost)
return total_cost
def build_graph(self, image, label):
is_training = get_current_tower_context().is_training
fw, fa, fg = get_dorefa(BITW, BITA, BITG)
# monkey-patch tf.get_variable to apply fw
def binarize_weight(v):
name = v.op.name
# don't binarize first and last layer
if not name.endswith('W') or 'conv0' in name or 'fc' in name:
return v
else:
logger.info("Binarizing weight {}".format(v.op.name))
return fw(v)
def nonlin(x):
if BITA == 32:
return tf.nn.relu(x)
return tf.clip_by_value(x, 0.0, 1.0)
def activate(x):
return fa(nonlin(x))
image = image / 256.0
with remap_variables(binarize_weight), \
argscope(BatchNorm, momentum=0.9, epsilon=1e-4), \
argscope(Conv2D, use_bias=False):
logits = (
LinearWrap(image).Conv2D('conv0',
48,
5,
padding='VALID',
use_bias=True).MaxPooling(
'pool0', 2,
padding='SAME').apply(activate)
# 18
.Conv2D('conv1', 64, 3, padding='SAME').apply(fg).BatchNorm(
'bn1').apply(activate).Conv2D(
'conv2', 64, 3,
padding='SAME').apply(fg).BatchNorm('bn2').MaxPooling(
'pool1', 2, padding='SAME').apply(activate)
# 9
.Conv2D(
'conv3', 128, 3,
padding='VALID').apply(fg).BatchNorm('bn3').apply(activate)
# 7
.Conv2D('conv4', 128, 3, padding='SAME').apply(fg).
BatchNorm('bn4').apply(activate).Conv2D(
'conv5', 128, 3,
padding='VALID').apply(fg).BatchNorm('bn5').apply(activate)
# 5
.Dropout(rate=0.5 if is_training else 0.0).Conv2D(
'conv6', 512, 5, padding='VALID').apply(fg).BatchNorm(
'bn6').apply(nonlin).FullyConnected('fc1', 10)())
tf.nn.softmax(logits, name='output')
# compute the number of failed samples
wrong = tf.cast(tf.logical_not(tf.nn.in_top_k(logits, label, 1)),
tf.float32,
name='wrong_tensor')
# monitor training error
add_moving_summary(tf.reduce_mean(wrong, name='train_error'))
cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=label)
cost = tf.reduce_mean(cost, name='cross_entropy_loss')
# weight decay on all W of fc layers
wd_cost = regularize_cost('fc.*/W', l2_regularizer(1e-7))
add_param_summary(('.*/W', ['histogram', 'rms']))
total_cost = tf.add_n([cost, wd_cost], name='cost')
add_moving_summary(cost, wd_cost, total_cost)
return total_cost
def AlexNetCifar(image,
label,
scope,
is_training,
dataset='cifar',
reuse=False,
Distill=None,
bit_a=32,
bit_w=32,
bit_g=32):
end_points = {}
if scope == 'Teacher':
with tf.variable_scope(scope):
image = tf.pad(image, [[0, 0], [5, 5], [5, 5], [0, 0]])
std = tf.contrib.layers.conv2d(image,
64, [11, 11],
1,
scope='conv0',
padding='VALID',
trainable=True,
reuse=reuse)
std = tf.contrib.layers.batch_norm(std,
scope='bn0',
trainable=True,
is_training=is_training,
reuse=reuse)
std = tf.nn.relu(std)
std = tf.layers.max_pooling2d(std, 2, strides=2, padding='SAME')
std = tf.contrib.layers.conv2d(std,
192, [5, 5],
padding='SAME',
trainable=True,
reuse=reuse)
std = tf.contrib.layers.batch_norm(std,
scope='bn1',
trainable=True,
is_training=is_training,
reuse=reuse)
std = tf.nn.relu(std)
std = tf.layers.max_pooling2d(std, 2, strides=2, padding='SAME')
tf.add_to_collection('feat', std)
std = tf.contrib.layers.conv2d(std,
384, [3, 3],
padding='SAME',
trainable=True,
reuse=reuse)
std = tf.contrib.layers.batch_norm(std,
scope='bn2',
trainable=True,
is_training=is_training,
reuse=reuse)
std = tf.nn.relu(std)
tf.add_to_collection('feat', std)
std = tf.contrib.layers.conv2d(std,
256, [3, 3],
padding='SAME',
trainable=True,
reuse=reuse)
std = tf.contrib.layers.batch_norm(std,
scope='bn3',
trainable=True,
is_training=is_training,
reuse=reuse)
std = tf.nn.relu(std)
tf.add_to_collection('feat', std)
std = tf.contrib.layers.conv2d(std,
256, [3, 3],
padding='SAME',
trainable=True,
reuse=reuse)
std = tf.contrib.layers.batch_norm(std,
scope='bn4',
trainable=True,
is_training=is_training,
reuse=reuse)
std = tf.nn.relu(std)
std = tf.layers.max_pooling2d(std, 2, strides=2, padding='SAME')
tf.add_to_collection('feat', std)
fc = tf.layers.flatten(std, name='fc_flat')
fc1 = tf.contrib.layers.fully_connected(fc,
4096,
scope='fc0',
trainable=True,
reuse=reuse)
fc1 = tf.contrib.layers.batch_norm(fc1,
scope='bn_fc0',
trainable=True,
is_training=is_training,
reuse=reuse)
fc1 = tf.nn.relu(fc1)
fc2 = tf.contrib.layers.fully_connected(fc1,
4096,
scope='fc1',
trainable=True,
reuse=reuse)
fc2 = tf.contrib.layers.batch_norm(fc2,
scope='bn_fc1',
trainable=True,
is_training=is_training,
reuse=reuse)
fc2 = tf.nn.relu(fc2)
logits = tf.contrib.layers.fully_connected(
fc2,
label.get_shape().as_list()[-1],
scope='fct',
trainable=True,
reuse=reuse)
end_points['Logits'] = logits
else:
fw, fa, fg = get_dorefa(bit_w, bit_a, bit_g)
def new_get_variable(v):
name = v.op.name
# don't binarize first and last layer
if not name.endswith(
'weights') or 'conv0' in name or 'fct' in name:
return v
else:
tf.logging.info("Quantizing weight {} at bits {}".format(
v.op.name, bit_w))
return fw(v)
def nonlin(x):
if bit_a == 32:
return tf.nn.relu(x) # still use relu for 32-bit cases
return tf.clip_by_value(x, 0.0, 1.0)
def activate(x):
tf.logging.info("Quantizing activations {} at bits {}".format(
x.name, bit_a))
return fa(nonlin(x))
with tf.variable_scope(scope), remap_variables(new_get_variable):
image = tf.pad(image, [[0, 0], [5, 5], [5, 5], [0, 0]])
std = tf.contrib.layers.conv2d(image,
64, [11, 11],
1,
scope='conv0',
padding='VALID',
trainable=True,
reuse=reuse)
std = tf.contrib.layers.batch_norm(std,
scope='bn0',
trainable=True,
is_training=is_training,
reuse=reuse)
std = tf.nn.relu(std)
std = activate(std)
std = tf.layers.max_pooling2d(std, 2, strides=2, padding='SAME')
std = tf.contrib.layers.conv2d(std,
192, [5, 5],
padding='SAME',
trainable=True,
reuse=reuse)
std = tf.contrib.layers.batch_norm(std,
scope='bn1',
trainable=True,
is_training=is_training,
reuse=reuse)
std = tf.nn.relu(std)
std = activate(std)
std = tf.layers.max_pooling2d(std, 2, strides=2, padding='SAME')
tf.add_to_collection('feat', std)
std = tf.contrib.layers.conv2d(std,
384, [3, 3],
padding='SAME',
trainable=True,
reuse=reuse)
std = tf.contrib.layers.batch_norm(std,
scope='bn2',
trainable=True,
is_training=is_training,
reuse=reuse)
std = tf.nn.relu(std)
std = activate(std)
tf.add_to_collection('feat', std)
std = tf.contrib.layers.conv2d(std,
256, [3, 3],
padding='SAME',
trainable=True,
reuse=reuse)
std = tf.contrib.layers.batch_norm(std,
scope='bn3',
trainable=True,
is_training=is_training,
reuse=reuse)
std = tf.nn.relu(std)
std = activate(std)
tf.add_to_collection('feat', std)
std = tf.contrib.layers.conv2d(std,
256, [3, 3],
padding='SAME',
trainable=True,
reuse=reuse)
std = tf.contrib.layers.batch_norm(std,
scope='bn4',
trainable=True,
is_training=is_training,
reuse=reuse)
std = tf.nn.relu(std)
std = activate(std)
std = tf.layers.max_pooling2d(std, 2, strides=2, padding='SAME')
tf.add_to_collection('feat', std)
fc = tf.layers.flatten(std, name='fc_flat')
fc1 = tf.contrib.layers.fully_connected(fc,
4096,
scope='fc0',
trainable=True,
reuse=reuse)
fc1 = tf.contrib.layers.batch_norm(fc1,
scope='bn_fc0',
trainable=True,
is_training=is_training,
reuse=reuse)
fc1 = tf.nn.relu(fc1)
fc1 = activate(fc1)
fc2 = tf.contrib.layers.fully_connected(fc1,
4096,
scope='fc1',
trainable=True,
reuse=reuse)
fc2 = tf.contrib.layers.batch_norm(fc2,
scope='bn_fc1',
trainable=True,
is_training=is_training,
reuse=reuse)
fc2 = tf.nn.relu(fc2)
logits = tf.contrib.layers.fully_connected(
fc2,
label.get_shape().as_list()[-1],
scope='fct',
trainable=True,
reuse=reuse)
end_points['Logits'] = logits
if Distill is not None:
if Distill == 'DML':
teacher_train = True
else:
is_training = False
teacher_train = False
with tf.variable_scope('Teacher'):
with tf.contrib.framework.arg_scope(
[tf.contrib.layers.conv2d, tf.contrib.layers.fully_connected],
variables_collections=[
tf.GraphKeys.GLOBAL_VARIABLES, 'Teacher'
]):
with tf.contrib.framework.arg_scope(
[tf.contrib.layers.batch_norm],
variables_collections=[
tf.GraphKeys.GLOBAL_VARIABLES, 'Teacher'
]):
std = tf.contrib.layers.conv2d(image,
64, [11, 11],
1,
scope='conv0',
padding='VALID',
trainable=True,
reuse=reuse)
std = tf.contrib.layers.batch_norm(std,
scope='bn0',
trainable=True,
is_training=is_training,
reuse=reuse)
std = tf.nn.relu(std)
std = tf.layers.max_pooling2d(std,
2,
strides=2,
padding='SAME')
# tf.add_to_collection('feat', std)
std = tf.contrib.layers.conv2d(std,
192, [5, 5],
padding='SAME',
trainable=teacher_train,
reuse=reuse)
std = tf.contrib.layers.batch_norm(std,
scope='bn1',
trainable=teacher_train,
is_training=is_training,
reuse=reuse)
std = tf.nn.relu(std)
std = tf.layers.max_pooling2d(std,
2,
strides=2,
padding='SAME')
tf.add_to_collection('feat', std)
std = tf.contrib.layers.conv2d(std,
384, [3, 3],
padding='SAME',
trainable=teacher_train,
reuse=reuse)
std = tf.contrib.layers.batch_norm(std,
scope='bn2',
trainable=teacher_train,
is_training=is_training,
reuse=reuse)
std = tf.nn.relu(std)
tf.add_to_collection('feat', std)
std = tf.contrib.layers.conv2d(std,
256, [3, 3],
padding='SAME',
trainable=teacher_train,
reuse=reuse)
std = tf.contrib.layers.batch_norm(std,
scope='bn3',
trainable=teacher_train,
is_training=is_training,
reuse=reuse)
std = tf.nn.relu(std)
tf.add_to_collection('feat', std)
std = tf.contrib.layers.conv2d(std,
256, [3, 3],
padding='SAME',
trainable=teacher_train,
reuse=reuse)
std = tf.contrib.layers.batch_norm(std,
scope='bn4',
trainable=teacher_train,
is_training=is_training,
reuse=reuse)
std = tf.nn.relu(std)
std = tf.layers.max_pooling2d(std,
2,
strides=2,
padding='SAME')
tf.add_to_collection('feat', std)
fc_tch = tf.layers.flatten(std, name='fc_flat')
fc1 = tf.contrib.layers.fully_connected(
fc_tch,
4096,
scope='fc0',
trainable=teacher_train,
reuse=reuse)
fc1 = tf.contrib.layers.batch_norm(fc1,
scope='bn_fc0',
trainable=teacher_train,
is_training=is_training,
reuse=reuse)
fc1 = tf.nn.relu(fc1)
fc2 = tf.contrib.layers.fully_connected(
fc1,
4096,
scope='fc1',
trainable=teacher_train,
reuse=reuse)
fc2 = tf.contrib.layers.batch_norm(fc2,
scope='bn_fc1',
trainable=teacher_train,
is_training=is_training,
reuse=reuse)
fc2 = tf.nn.relu(fc2)
logits_tch = tf.contrib.layers.fully_connected(
fc2,
label.get_shape().as_list()[-1],
scope='fct',
trainable=teacher_train,
reuse=reuse)
end_points['Logits_tch'] = logits_tch
with tf.variable_scope('Distillation'):
feats = tf.get_collection('feat')
student_feats = feats[:len(feats) // 2]
teacher_feats = feats[len(feats) // 2:]
feats_noact = tf.get_collection('feat_noact')
student_feats_noact = feats[:len(feats_noact) // 2]
teacher_feats_noact = feats[len(feats_noact) // 2:]
if Distill == 'Soft_logits':
tf.add_to_collection(
'dist', Response.Soft_logits(logits, logits_tch, 3))
elif Distill == 'DML':
tf.add_to_collection('dist', Response.DML(logits, logits_tch))
elif Distill == 'FT':
tf.add_to_collection(
'dist',
Response.Factor_Transfer(student_feats_noact[-1],
teacher_feats_noact[-1]))
elif Distill == 'FitNet':
tf.add_to_collection(
'dist', Multiple.FitNet(student_feats, teacher_feats))
elif Distill == 'AT':
tf.add_to_collection(
'dist',
Multiple.Attention_transfer(student_feats, teacher_feats))
elif Distill == 'AB':
tf.add_to_collection(
'dist',
Multiple.AB_distillation(student_feats, teacher_feats, 1.,
3e-3))
elif Distill == 'FSP':
tf.add_to_collection('dist',
Shared.FSP(student_feats, teacher_feats))
elif Distill[:3] == 'KD-':
tf.add_to_collection(
'dist',
Shared.KD_SVD(student_feats, teacher_feats, Distill[-3:]))
elif Distill == 'RKD':
tf.add_to_collection(
'dist', Relation.RKD(logits, logits_tch, l=[5e1, 1e2]))
elif Distill == 'MHGD':
tf.add_to_collection(
'dist', Relation.MHGD(student_feats, teacher_feats))
elif Distill == 'MHGD-RKD':
tf.add_to_collection(
'dist',
Relation.MHGD(student_feats, teacher_feats) +
Relation.RKD(logits, logits_tch, l=[5e1, 1e2]))
elif Distill == 'MHGD-RKD-SVD':
tf.add_to_collection(
'dist',
Relation.MHGD(student_feats, teacher_feats) +
Relation.RKD(logits, logits_tch, l=[5e1, 1e2]) +
Shared.KD_SVD(student_feats, teacher_feats, "SVD"))
return end_points
def build_graph(self, image, label):
# get quantization function
# quantize weights
qw = quantize_weight(int(self.quantizer_config['BITW']),
self.quantizer_config['name'],
self.quantizer_config['W_opts'],
self.quantizer_config)
# quantize activation
if self.quantizer_config['BITA'] in ['32', 32]:
qa = tf.identity
else:
qa = quantize_activation(int(self.quantizer_config['BITA']),
self.quantizer_config['name'],
self.quantizer_config)
# quantize gradient
qg = quantize_gradient(int(self.quantizer_config['BITG']))
def new_get_variable(v):
name = v.op.name
# don't quantize first and last layer
if not name.endswith('/W') or 'conv1' in name or 'fct' in name:
return v
else:
logger.info("Quantizing weight {}".format(v.op.name))
return qw(v)
def activate(x):
return qa(self.activation(x))
def resblock(x, channel, stride):
def get_stem_full(x):
return (LinearWrap(x).Conv2D(
'stem_conv_a', channel,
3).BatchNorm('stem_bn').apply(activate).Conv2D(
'stem_conv_b', channel, 3)())
channel_mismatch = channel != x.get_shape().as_list()[3]
if stride != 1 or channel_mismatch:
if stride != 1:
x = AvgPooling('avgpool', x, stride, stride)
x = BatchNorm('bn', x)
x = activate(x)
shortcut = Conv2D('shortcut', x, channel, 1)
stem = get_stem_full(x)
else:
shortcut = x
x = BatchNorm('bn', x)
x = activate(x)
stem = get_stem_full(x)
return shortcut + stem
def group(x, name, channel, nr_block, stride):
with tf.variable_scope(name + 'blk1', reuse=tf.AUTO_REUSE):
x = resblock(x, channel, stride)
for i in range(2, nr_block + 1):
with tf.variable_scope(name + 'blk{}'.format(i),
reuse=tf.AUTO_REUSE):
x = resblock(x, channel, 1)
return x
def resblock_idt(x, channel, stride, first):
def get_r(x):
if 'InferenceTower' in x.op.name:
idx = x.op.name.index('/')
n = x.op.name[idx + 1::]
elif 'tower' in x.op.name:
idx = x.op.name.index('/')
n = x.op.name[idx + 1::]
else:
n = x.op.name
n0 = n.split('blk')[0]
n1 = n0 + 'blk1/shortcut/maxW'
n2 = n.split('/output')[0] + '/maxW'
if int(
self.quantizer_config['BITW']
) != 32: # and eval(self.quantizer_config['W_opts']['fix_max']):
n1 += '_stop_grad'
n2 += '_stop_grad'
maxs = tf.get_collection('maxs')
for tensor in maxs:
tn = tensor.op.name
if n1 == tn:
m1 = tensor
elif n2 == tn:
m2 = tensor
r = m2 / m1
temp = self.quantizer_config['mulR']
if temp == '2R':
r2 = (1 / r) * (2.0**tf.floor(tf.log(r) / tf.log(2.0)))
elif temp == 'R':
r2 = 1 / r
return r2
def get_stem_full(x):
return (LinearWrap(x).Conv2D(
'stem_conv_a', channel, 1,
strides=(1,
1)).BatchNorm('stem_bn1').apply(activate).Conv2D(
'stem_conv_b',
channel,
3,
strides=(stride,
stride)).BatchNorm('stem_bn2').apply(
activate).Conv2D('stem_conv_c',
channel * 4,
1,
strides=(1,
1))())
#channel_mismatch = channel != x.get_shape().as_list()[3]
#if stride != 1 or channel_mismatch:
if first:
#shortcut = tf.concat([x[::, 0::2, 0::2, ::], x[::, 1::2, 1::2, ::]], -1)
x = BatchNorm('bn', x)
x = activate(x)
#if stride != 1:
# shortcut = Conv2D('shortcut', x, channel, 1, strides=(stride, stride))
#else:
# shortcut = Conv2D('shortcut', x, channel, 1)
shortcut = Conv2D('shortcut',
x,
channel * 4,
1,
strides=(stride, stride))
stem = get_stem_full(x)
else:
shortcut = x
x = BatchNorm('bn', x)
x = activate(x)
stem = get_stem_full(x)
if self.quantizer_config['mulR'] in ['2R', 'R']:
r = get_r(stem)
stem = stem * r
return shortcut + stem
def group_v2(x, name, channel, nr_block, stride):
with tf.variable_scope(name + 'blk1', reuse=tf.AUTO_REUSE):
x = resblock_idt(x, channel, stride, True)
for i in range(2, nr_block + 1):
with tf.variable_scope(name + 'blk{}'.format(i),
reuse=tf.AUTO_REUSE):
x = resblock_idt(x, channel, 1, False)
return x
with remap_variables(new_get_variable), \
argscope(BatchNorm, decay=0.9, epsilon=1e-4), \
argscope(Conv2D, use_bias=False, nl=tf.identity,
kernel_initializer=tf.variance_scaling_initializer(scale=float(self.initializer_config['scale']),
mode=self.initializer_config['mode'])):
logits = (
LinearWrap(image).Conv2D('conv1', 64, 7, strides=2) # size=112
.MaxPooling('pool1', pool_size=3, strides=2,
padding="SAME") # size=56
#.BatchNorm('bn1')
#.apply(activate)
.apply(group_v2, 'res1', 64, 3, 1) # size=56
.apply(group_v2, 'res2', 128, 4, 2) # size=28
.apply(group_v2, 'res3', 256, 6, 2) # size=14
.apply(group_v2, 'res4', 512, 3, 2) # size=7
.BatchNorm('last_bn').apply(activate).GlobalAvgPooling(
'gap').FullyConnected('fct', self.nb_classes)())
prob = tf.nn.softmax(logits, name='output')
cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=label)
cost = tf.reduce_mean(cost, name='cross_entropy_loss')
# regularization
if self.regularizer_config['name'] not in [None, 'None']:
reg_func = getattr(regularizers,
self.regularizer_config['name'])().get_func(
self.regularizer_config,
self.quantizer_config)
reg_cost = tf.multiply(float(self.regularizer_config['lmbd']),
regularize_cost('.*/W', reg_func),
name='reg_cost')
total_cost = tf.add_n([cost, reg_cost], name='total_cost')
else:
total_cost = cost
# summary
def add_summary(logits, cost):
err_top1 = tf.cast(tf.logical_not(tf.nn.in_top_k(logits, label,
1)),
tf.float32,
name='err_top1')
add_moving_summary(
tf.reduce_mean(err_top1, name='train_error_top1'))
err_top5 = tf.cast(tf.logical_not(tf.nn.in_top_k(logits, label,
5)),
tf.float32,
name='err_top5')
add_moving_summary(
tf.reduce_mean(err_top5, name='train_error_top5'))
add_moving_summary(cost)
add_param_summary(('.*/W', ['histogram'])) # monitor W
add_summary(logits, cost)
return total_cost
def _build_graph(self, inputs):
image, label = inputs
fw, fa, fg = get_dorefa(BITW, BITA, BITG)
old_get_variable = tf.get_variable
def monitor(x, name):
if MONITOR == 1:
return tf.Print(x, [x],
message='\n\n' + name + ': ',
summarize=1000,
name=name)
else:
return x
def new_get_variable(v):
name = v.op.name
if not name.endswith('W') or 'conv1_1' in name or 'fc8' in name:
return v
else:
logger.info("Quantizing weight {}".format(v.op.name))
if MONITOR == 1:
return tf.Print(fw(v), [fw(v)],
message='\n\n' + v.name +
', Quantized weights are:',
summarize=100)
else:
return fw(v)
def bn_activate(name, x):
X = BatchNorm(name, x)
x = monitor(x, name + '_noact_out')
return activate(x)
def activate(x):
if BITA == 32:
return tf.nn.relu(x)
else:
return fa(tf.nn.relu(x))
# VGG 16
with remap_variables(new_get_variable), \
argscope(Conv2D, kernel_shape=3, use_bias=False, nl = tf.identity):
logits = (
LinearWrap(image).apply(monitor, 'image_out').Conv2D(
'conv1_1',
64).apply(fg).BatchNorm('bn1_1').apply(activate).apply(
monitor, 'conv1_1_out').Conv2D('conv1_2', 64).apply(
fg).BatchNorm('bn1_2').apply(activate).apply(
monitor,
'conv1_2_out').MaxPooling('pool1', 2).apply(
monitor, 'pool1_out')
# 112
.Conv2D(
'conv2_1',
128).apply(fg).BatchNorm('bn2_1').apply(activate).apply(
monitor, 'conv2_1_out').Conv2D('conv2_2', 128).apply(
fg).BatchNorm('bn2_2').apply(activate).apply(
monitor, 'conv2_2_out').MaxPooling(
'pool2', 2).apply(monitor, 'pool2_out')
# 56
.Conv2D(
'conv3_1',
256).apply(fg).BatchNorm('bn3_1').apply(activate).apply(
monitor, 'conv3_1_out').Conv2D(
'conv3_2', 256).apply(fg).BatchNorm('bn3_2').
apply(activate).apply(monitor, 'conv3_2_out').Conv2D(
'conv3_3',
256).apply(fg).BatchNorm('bn3_3').apply(activate).apply(
monitor, 'conv3_3_out').MaxPooling('pool3', 2).apply(
monitor, 'pool3_out')
# 28
.Conv2D(
'conv4_1',
512).apply(fg).BatchNorm('bn4_1').apply(activate).apply(
monitor, 'conv4_1_out').Conv2D(
'conv4_2', 512).apply(fg).BatchNorm('bn4_2').
apply(activate).apply(monitor, 'conv4_2_out').Conv2D(
'conv4_3',
512).apply(fg).BatchNorm('bn4_3').apply(activate).apply(
monitor, 'conv4_3_out').MaxPooling('pool4', 2).apply(
monitor, 'pool4_out')
# 14
.Conv2D(
'conv5_1',
512).apply(fg).BatchNorm('bn5_1').apply(activate).apply(
monitor, 'conv5_1_out').Conv2D(
'conv5_2', 512).apply(fg).BatchNorm('bn5_2').
apply(activate).apply(monitor, 'conv5_2_out').Conv2D(
'conv5_3',
512).apply(fg).BatchNorm('bn5_3').apply(activate).apply(
monitor, 'conv5_3_out').MaxPooling('pool5', 2).apply(
monitor, 'pool5_out').FullyConnected(
'fc6', use_bias=False,
out_dim=512).apply(activate).apply(
monitor, 'fc6_out').FullyConnected(
'fc7', use_bias=False,
out_dim=512).apply(activate).apply(
monitor, 'fc7_out').FullyConnected(
'fc8',
use_bias=False,
out_dim=self.cifar_classnum,
nl=tf.identity).apply(
monitor, 'fc8_out')())
prob = tf.nn.softmax(logits, name='prob')
cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=label)
cost = tf.reduce_mean(cost, name='cross_entropy_loss')
wrong = symbf.prediction_incorrect(logits, label, name='incorrect')
accuracy = symbf.accuracy(logits, label, name='accuracy')
train_error = tf.reduce_mean(wrong, name='train_error')
summary.add_moving_summary(train_error, accuracy)
wd_cost = tf.multiply(1e-5,
regularize_cost('fc.*/W', tf.nn.l2_loss),
name='regularize_loss')
self.cost = tf.add_n([wd_cost, cost], name='total_cost')
summary.add_moving_summary(cost, wd_cost, self.cost)
def _build_graph(self, inputs):
image, label = inputs
image = image / 256.0
fw, fa, fg = get_dorefa(BITW, BITA, BITG)
old_get_variable = tf.get_variable
def new_get_variable(v):
name = v.op.name
# don't binarize first and last layer
if not name.endswith('W') or 'conv1' in name or 'fct' in name:
return v
else:
logger.info("Binarizing weight {}".format(v.op.name))
return fw(v)
def nonlin(x):
return tf.clip_by_value(x, 0.0, 1.0)
def activate(x):
return fa(nonlin(x))
def resblock(x, channel, stride):
def get_stem_full(x):
return (LinearWrap(x)
.Conv2D('c3x3a', channel, 3)
.BatchNorm('stembn')
.apply(activate)
.Conv2D('c3x3b', channel, 3)())
channel_mismatch = channel != x.get_shape().as_list()[3]
if stride != 1 or channel_mismatch or 'pool1' in x.name:
# handling pool1 is to work around an architecture bug in our model
if stride != 1 or 'pool1' in x.name:
x = AvgPooling('pool', x, stride, stride)
x = BatchNorm('bn', x)
x = activate(x)
shortcut = Conv2D('shortcut', x, channel, 1)
stem = get_stem_full(x)
else:
shortcut = x
x = BatchNorm('bn', x)
x = activate(x)
stem = get_stem_full(x)
return shortcut + stem
def group(x, name, channel, nr_block, stride):
with tf.variable_scope(name + 'blk1'):
x = resblock(x, channel, stride)
for i in range(2, nr_block + 1):
with tf.variable_scope(name + 'blk{}'.format(i)):
x = resblock(x, channel, 1)
return x
with remap_variables(new_get_variable), \
argscope(BatchNorm, decay=0.9, epsilon=1e-4), \
argscope(Conv2D, use_bias=False, nl=tf.identity):
logits = (LinearWrap(image)
# use explicit padding here, because our training framework has
# different padding mechanisms from TensorFlow
.tf.pad([[0, 0], [3, 2], [3, 2], [0, 0]])
.Conv2D('conv1', 64, 7, stride=2, padding='VALID', use_bias=True)
.tf.pad([[0, 0], [1, 1], [1, 1], [0, 0]], 'SYMMETRIC')
.MaxPooling('pool1', 3, 2, padding='VALID')
.apply(group, 'conv2', 64, 2, 1)
.apply(group, 'conv3', 128, 2, 2)
.apply(group, 'conv4', 256, 2, 2)
.apply(group, 'conv5', 512, 2, 2)
.BatchNorm('lastbn')
.apply(nonlin)
.GlobalAvgPooling('gap')
.tf.multiply(49) # this is due to a bug in our model design
.FullyConnected('fct', 1000)())
prob = tf.nn.softmax(logits, name='output')
wrong = prediction_incorrect(logits, label, 1, name='wrong-top1')
wrong = prediction_incorrect(logits, label, 5, name='wrong-top5')
def _build_graph(self, inputs):
input, label = inputs
fw, fa, fg = get_dorefa(FLAGS.bit_w, FLAGS.bit_a, 32)
old_get_variable = tf.get_variable
# monkey-patch tf.get_variable to apply fw
def new_get_variable(v):
name = v.op.name
logger.info("Binarizing weight {}".format(v.op.name))
return fw(v, FLAGS.force_quantization)
def nonlin(x):
if FLAGS.bit_a == 32 and not FLAGS.use_clip:
return tf.nn.relu(x) # still use relu for 32bit cases
return tf.clip_by_value(x, 0.0, 1.0)
def activate(x):
return fa(nonlin(x))
activations = []
with remap_variables(new_get_variable), \
argscope([Conv2D, FullyConnected], use_bias=False, nl=tf.identity):
curr_layer = LinearWrap(input)
for i in range(FLAGS.n_layers):
curr_layer = (curr_layer.FullyConnected(
'fc' + str(i),
FLAGS.state_size).LayerNorm('ln_fc' +
str(i)).apply(activate))
activations.append(curr_layer.tensor())
curr_layer = (curr_layer.Dropout('dropout', FLAGS.dropout))
logits = (curr_layer.FullyConnected(
'fc' + str(FLAGS.n_layers),
256).LayerNorm('lnfc' + str(FLAGS.n_layers)).apply(
nonlin).FullyConnected('fct', self.n_spks,
use_bias=True)())
print_all_tf_vars()
prob = tf.nn.softmax(logits, name='output')
# used for validation accuracy of utterance
identity_guesses = flatten(tf.argmax(prob, axis=1))
uniq_identities, _, count = tf.unique_with_counts(identity_guesses)
idx_to_identity_with_most_votes = tf.argmax(count)
chosen_identity = tf.gather(uniq_identities,
idx_to_identity_with_most_votes)
wrong = tf.expand_dims(tf.not_equal(chosen_identity,
tf.cast(label[0], tf.int64)),
axis=0,
name='utt-wrong')
cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=label)
cost = tf.reduce_mean(cost, name='cross_entropy_loss')
wrong = prediction_incorrect(logits, label, 1, name='wrong-top1')
add_moving_summary(tf.reduce_mean(wrong, name='train-error-top1'))
# weight decay on all W of fc layers
wd_cost = regularize_cost('fc.*/W',
l2_regularizer(5e-6),
name='regularize_cost')
add_param_summary(('.*/W', ['histogram', 'rms']))
self.cost = tf.add_n([cost, wd_cost], name='cost')
add_moving_summary(cost, wd_cost, self.cost)
for activation in activations:
add_activation_summary(activation)
tf.summary.histogram(activation.name, activation)
def build_graph(self, image, label):
# get quantization function
# quantize weights
qw = quantize_weight(int(self.quantizer_config['BITW']), self.quantizer_config['name'], self.quantizer_config['W_opts'], self.quantizer_config)
# quantize activation
if self.quantizer_config['BITA'] in ['32', 32]:
qa = tf.identity
else:
qa = quantize_activation(int(self.quantizer_config['BITA']))
# quantize gradient
qg = quantize_gradient(int(self.quantizer_config['BITG']))
def new_get_variable(v):
name = v.op.name
# don't quantize first and last layer
if not name.endswith('/W') or 'conv1' in name or 'fct' in name:
return v
else:
logger.info("Quantizing weight {}".format(v.op.name))
return qw(v)
def activate(x):
return qa(self.activation(x))
with remap_variables(new_get_variable), \
argscope(BatchNorm, decay=0.9, epsilon=1e-4), \
argscope(Conv2D, use_bias=False, nl=tf.identity,
kernel_initializer=tf.variance_scaling_initializer(scale=float(self.initializer_config['scale']),
mode=self.initializer_config['mode'])):
logits = (LinearWrap(image)
.Conv2D('conv1', 96, 3)
.BatchNorm('bn1')
.apply(activate)
.Conv2D('conv2', 256, 3, padding='SAME', split=2)
.BatchNorm('bn2')
.apply(activate)
.MaxPooling('pool2', 2, 2, padding='VALID') # size=16
.Conv2D('conv3', 384, 3)
.BatchNorm('bn3')
.apply(activate)
.MaxPooling('pool2', 2, 2, padding='VALID') # size=8
.Conv2D('conv4', 384, 3, split=2)
.BatchNorm('bn4')
.apply(activate)
.Conv2D('conv5', 256, 3, split=2)
.BatchNorm('bn5')
.apply(activate)
.MaxPooling('pool5', 2, 2, padding='VALID') # size=4
.FullyConnected('fc1', 4096, use_bias=False)
.BatchNorm('bnfc1')
.apply(activate)
.FullyConnected('fc2', 4096, use_bias=False)
.BatchNorm('bnfc2')
.apply(activate)
.FullyConnected('fct', self.nb_classes, use_bias=True)())
prob = tf.nn.softmax(logits, name='output')
cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label)
cost = tf.reduce_mean(cost, name='cross_entropy_loss')
# regularization
if self.regularizer_config['name'] not in [None, 'None']:
reg_func = getattr(regularizers, self.regularizer_config['name'])().get_func(self.regularizer_config)
reg_cost = tf.multiply(float(self.regularizer_config['lmbd']), regularize_cost('.*/W', reg_func), name='reg_cost')
total_cost = tf.add_n([cost, reg_cost], name='total_cost')
else:
total_cost = cost
# summary
def add_summary(logits, cost):
err_top1 = tf.cast(tf.logical_not(tf.nn.in_top_k(logits, label, 1)), tf.float32, name='err_top1')
add_moving_summary(tf.reduce_mean(err_top1, name='train_error_top1'))
err_top5 = tf.cast(tf.logical_not(tf.nn.in_top_k(logits, label, 5)), tf.float32, name='err_top5')
add_moving_summary(tf.reduce_mean(err_top5, name='train_error_top5'))
add_moving_summary(cost)
add_param_summary(('.*/W', ['histogram'])) # monitor W
add_summary(logits, cost)
return total_cost
