def branch1(self, x, numOut, s):
with tf.variable_scope("conv1"):
conv1 = utils.relu(utils.Bn(utils.conv2d(x, numOut/4, d_h=s, d_w=s), training=self.is_training))
with tf.variable_scope("conv2"):
conv2 = utils.relu(utils.Bn(utils.conv2d(conv1, numOut/4, 3, 3), training=self.is_training))
with tf.variable_scope("conv3"):
conv3 = utils.Bn(utils.conv2d(conv2, numOut), training=self.is_training)
return conv3
def inference(self, x):
with tf.variable_scope("conv0"):
conv1 = utils.relu(utils.Bn(utils.conv2d(x, 64, 7, 7, 2, 2, bias=True), training=self.is_training))
with tf.name_scope("pool1"):
pool1 = utils.max_pool(conv1, 3, 3, 2, 2)
with tf.variable_scope("group0"):
res2a = self.residual(pool1, 256, name='block0')
res2b = self.residual(res2a, 256, name='block1')
res2c = self.residual(res2b, 256, name='block2')
with tf.variable_scope("group1"):
res3a = self.residual(res2c, 512, 2, name='block0')
res3b = self.residual(res3a, 512, name='block1')
res3c = self.residual(res3b, 512, name='block2')
res3d = self.residual(res3c, 512, name='block3')
with tf.variable_scope("group2"):
res4a = self.residual(res3d, 1024, 2, name='block0')
res4b = self.residual(res4a, 1024, name='block1')
res4c = self.residual(res4b, 1024, name='block2')
res4d = self.residual(res4c, 1024, name='block3')
res4e = self.residual(res4d, 1024, name='block4')
res4f = self.residual(res4e, 1024, name='block5')
with tf.variable_scope("group3"):
res5a = self.residual(res4f, 2048, 2, name='block0')
res5b = self.residual(res5a, 2048, name='block1')
res5c = self.residual(res5b, 2048, name='block2')
with tf.name_scope("pool5"):
pool5 = utils.global_pool(res5c)
with tf.variable_scope("linear"):
dropout = tf.nn.dropout(pool5, keep_prob=self.keep_prob)
out = utils.linear(dropout, 1000)
return out
def inference(self, x, grah):
with tf.variable_scope("conv0"):
if self.res_name == "resnet50":
net = utils.relu(
utils.Bn(utils.conv2d(x, 64, 7, 7, 2, 2, bias=True),
training=self.is_training))
else:
net = utils.relu(
utils.Bn(utils.conv2d(x, 64, 7, 7, 2, 2, bias=False),
training=self.is_training))
with tf.name_scope("pool1"):
net = utils.max_pool(net, 3, 3, 2, 2)
with tf.variable_scope("group0"):
for i in range(grah[0]):
net = self.residual(net, 256, name='block' + str(i))
with tf.variable_scope("group1"):
for i in range(grah[1]):
if i == 0:
net = self.residual(net, 512, 2, name='block' + str(i))
else:
net = self.residual(net, 512, name='block' + str(i))
with tf.variable_scope("group2"):
for i in range(grah[2]):
if i == 0:
net = self.residual(net, 1024, 2, name='block' + str(i))
else:
net = self.residual(net, 1024, name='block' + str(i))
with tf.variable_scope("group3"):
for i in range(grah[3]):
if i == 0:
net = self.residual(net, 2048, 2, name='block' + str(i))
else:
net = self.residual(net, 2048, name='block' + str(i))
with tf.name_scope("pool5"):
net = utils.global_pool(net)
with tf.variable_scope("linear"):
net = tf.nn.dropout(net, keep_prob=self.keep_prob)
net = utils.linear(net, 1000)
return net
def inference(self, x):
with tf.variable_scope("conv0"):
conv1 = utils.relu(utils.Bn(utils.conv2d(x, 64, 7, 7, 2, 2, bias=False), training=self.is_training))
with tf.name_scope("pool1"):
pool1 = utils.max_pool(conv1, 3, 3, 2, 2)
with tf.variable_scope("group0"):
res2a = self.residual(pool1, 64, branch=True, name='block0')
res2b = self.residual(res2a, 64, name='block1')
with tf.variable_scope("group1"):
res3a = self.residual(res2b, 128, 2, name='block0')
res3b = self.residual(res3a, 128, name='block1')
with tf.variable_scope("group2"):
res4a = self.residual(res3b, 256, 2, name='block0')
res4b = self.residual(res4a, 256, name='block1')
with tf.variable_scope("group3"):
res5a = self.residual(res4b, 512, 2, name='block0')
res5b = self.residual(res5a, 512, name='block1')
with tf.name_scope("pool5"):
pool5 = utils.global_pool(res5b)
with tf.variable_scope("linear"):
dropout = tf.nn.dropout(pool5, keep_prob=self.keep_prob)
out = utils.linear(dropout, 1000)
return out
def branch2(self, x, numOut, s):
with tf.variable_scope("convshortcut"):
return utils.Bn(utils.conv2d(x, numOut, d_h=s, d_w=s),
training=self.is_training)