def _build_graph(self, inputs):
image, label = inputs
image = image_preprocess(image, bgr=True)
image = tf.transpose(image, [0, 3, 1, 2])
def bottleneck_se(l, ch_out, stride, preact):
l, shortcut = apply_preactivation(l, preact)
l = Conv2D('conv1', l, ch_out, 1, nl=BNReLU)
l = Conv2D('conv2', l, ch_out, 3, stride=stride, nl=BNReLU)
l = Conv2D('conv3', l, ch_out * 4, 1)
squeeze = GlobalAvgPooling('gap', l)
squeeze = FullyConnected('fc1',
squeeze,
ch_out // 4,
nl=tf.identity)
squeeze = FullyConnected('fc2',
squeeze,
ch_out * 4,
nl=tf.nn.sigmoid)
l = l * tf.reshape(squeeze, [-1, ch_out * 4, 1, 1])
return l + resnet_shortcut(shortcut, ch_out * 4, stride)
defs = RESNET_CONFIG[DEPTH]
with argscope([Conv2D, MaxPooling, GlobalAvgPooling, BatchNorm],
data_format='NCHW'):
logits = resnet_backbone(image, defs, bottleneck_se)
loss = compute_loss_and_error(logits, label)
wd_loss = regularize_cost('.*/W',
l2_regularizer(1e-4),
name='l2_regularize_loss')
add_moving_summary(loss, wd_loss)
self.cost = tf.add_n([loss, wd_loss], name='cost')
def _build_graph(self, inputs):
image, label = inputs
image = image_preprocess(image, bgr=True)
image = tf.transpose(image, [0, 3, 1, 2])
cfg = {
18: ([2, 2, 2, 2], preresnet_basicblock),
34: ([3, 4, 6, 3], preresnet_basicblock),
}
defs, block_func = cfg[DEPTH]
with argscope(Conv2D, nl=tf.identity, use_bias=False,
W_init=variance_scaling_initializer(mode='FAN_OUT')), \
argscope([Conv2D, MaxPooling, GlobalAvgPooling, BatchNorm], data_format='NCHW'):
convmaps = (LinearWrap(image)
.Conv2D('conv0', 64, 7, stride=2, nl=BNReLU)
.MaxPooling('pool0', shape=3, stride=2, padding='SAME')
.apply(preresnet_group, 'group0', block_func, 64, defs[0], 1)
.apply(preresnet_group, 'group1', block_func, 128, defs[1], 2)
.apply(preresnet_group, 'group2', block_func, 256, defs[2], 2)
.apply(preresnet_group, 'group3new', block_func, 512, defs[3], 1)())
print(convmaps)
logits = (LinearWrap(convmaps)
.GlobalAvgPooling('gap')
.FullyConnected('linearnew', 1000, nl=tf.identity)())
loss = compute_loss_and_error(logits, label)
wd_cost = regularize_cost('.*/W', l2_regularizer(1e-4), name='l2_regularize_loss')
add_moving_summary(loss, wd_cost)
self.cost = tf.add_n([loss, wd_cost], name='cost')
def _build_graph(self, inputs):
image, label = inputs
image = image_preprocess(image, bgr=True)
if self.data_format == 'NCHW':
image = tf.transpose(image, [0, 3, 1, 2])
defs, block_func = RESNET_CONFIG[DEPTH]
with argscope([Conv2D, MaxPooling, GlobalAvgPooling, BatchNorm], data_format=self.data_format):
logits = resnet_backbone(image, defs, block_func)
loss = compute_loss_and_error(logits, label)
wd_loss = regularize_cost('.*/W', l2_regularizer(1e-4), name='l2_regularize_loss')
add_moving_summary(loss, wd_loss)
self.cost = tf.add_n([loss, wd_loss], name='cost')
def _build_graph(self, inputs):
image, label = inputs
# It should actually use bgr=True here, but for compatibility with
# pretrained models, we keep the wrong version.
image = image_preprocess(image, bgr=False)
if self.data_format == 'NCHW':
image = tf.transpose(image, [0, 3, 1, 2])
defs, block_func = RESNET_CONFIG[DEPTH]
with argscope([Conv2D, MaxPooling, GlobalAvgPooling, BatchNorm], data_format=self.data_format):
logits = resnet_backbone(image, defs, block_func)
loss = compute_loss_and_error(logits, label)
wd_loss = regularize_cost('.*/W', l2_regularizer(1e-4), name='l2_regularize_loss')
add_moving_summary(loss, wd_loss)
self.cost = tf.add_n([loss, wd_loss], name='cost')
