def resnet(inpt, n):
if n < 20 or (n - 20) % 12 != 0:
print "ResNet depth invalid."
return
num_conv = (n - 20) / 12 + 1
layers = []
num_residual = 16
chOut =0
with tf.variable_scope('conv_start'):
conv00 = conv_layer(inpt, [3, 3, 3, 16], 1)
layers.append(conv00)
for lv in range(num_residual/2 -2 ):
with tf.variable_scope('conv_%d' % (lv)):
chOut += 168
print layers[-1].get_shape().as_list()[1:]
conv1_1_x = residual_block(layers[-1], 16+chOut,True)
print conv1_1_x.get_shape().as_list()[1:]
conv1_1 = residual_block(conv1_1_x,16+chOut, False)
layers.append(conv1_1_x)
layers.append(conv1_1)
#print layers[-1].get_shape().as_list()[1:]
assert layers[-1].get_shape().as_list()[1:] == [7, 7, 1024]
return layers[-1]
def resnet(inpt, n):
if n < 20 or (n - 20) % 12 != 0:
print "ResNet depth invalid."
return
num_conv = (n - 20) / 12 + 1
layers = {}
layer_list = []
with tf.variable_scope('conv1'):
conv1, conv1_w = conv_layer(inpt, [3, 3, 3, 16], 1)
layers['conv1'] = conv1
for weights in conv1_w:
layer_list.append(weights)
for i in range(num_conv):
with tf.variable_scope('conv2_%d' % (i + 1)):
conv2_x, conv2_x_w = residual_block(layers['conv1'], 16, False)
conv2, conv2_w = residual_block(conv2_x, 16, False)
layers['conv2_x'] = conv2_x
layers['conv2'] = conv2
for weights in conv2_x_w:
layer_list.append(weights)
for weights in conv2_w:
layer_list.append(weights)
for i in range(num_conv):
down_sample = True if i == 0 else False
with tf.variable_scope('conv3_%d' % (i + 1)):
conv3_x, conv3_x_w = residual_block(layers['conv2'], 32,
down_sample)
conv3, conv3_w = residual_block(conv3_x, 32, False)
layers['conv3_x'] = conv3_x
layers['conv3'] = conv3
for weights in conv3_x_w:
layer_list.append(weights)
for weights in conv3_w:
layer_list.append(weights)
for i in range(num_conv):
down_sample = True if i == 0 else False
with tf.variable_scope('conv4_%d' % (i + 1)):
conv4_x, conv4_x_w = residual_block(layers['conv3'], 64,
down_sample)
conv4, conv4_w = residual_block(conv4_x, 64, False)
layers['conv4_x'] = conv4_x
layers['conv4'] = conv4
for weights in conv4_x_w:
layer_list.append(weights)
for weights in conv4_w:
layer_list.append(weights)
return layers, layer_list
def resnet(inpt, n):
if n < 20 or (n - 20) % 12 != 0:
print("ResNet depth invalid.")
return
num_conv = int((n - 20) / 12 + 1)
layers = []
with tf.variable_scope('conv1'):
conv1 = conv_layer(inpt, [3, 3, 3, 16], 1)
layers.append(conv1)
# tf.summary.image("conv1", conv1, max_outputs=6)
for i in range(num_conv):
with tf.variable_scope('conv2_%d' % (i + 1)):
conv2_x = residual_block(layers[-1], 16, False)
conv2 = residual_block(conv2_x, 16, False)
layers.append(conv2_x)
layers.append(conv2)
assert conv2.get_shape().as_list()[1:] == [32, 32, 16]
# tf.summary.image("conv2", conv2, max_outputs=6)
for i in range(num_conv):
down_sample = True if i == 0 else False
with tf.variable_scope('conv3_%d' % (i + 1)):
conv3_x = residual_block(layers[-1], 32, down_sample)
conv3 = residual_block(conv3_x, 32, False)
layers.append(conv3_x)
layers.append(conv3)
assert conv3.get_shape().as_list()[1:] == [16, 16, 32]
# tf.summary.image("conv3", conv3, max_outputs=6)
for i in range(num_conv):
down_sample = True if i == 0 else False
with tf.variable_scope('conv4_%d' % (i + 1)):
conv4_x = residual_block(layers[-1], 64, down_sample)
conv4 = residual_block(conv4_x, 64, False)
layers.append(conv4_x)
layers.append(conv4)
assert conv4.get_shape().as_list()[1:] == [8, 8, 64]
with tf.variable_scope('fc'):
to_fc = tf.cond(tf.equal(seperate, tf.constant(False)),
lambda: layers[-1], lambda: data)
global_pool = tf.reduce_mean(to_fc, [1, 2])
assert global_pool.get_shape().as_list()[1:] == [64]
sm = softmax_layer(global_pool, [64, 20])
sm2 = softmax_layer(global_pool, [64, 100])
out = tf.cond(tf.equal(seperate, tf.constant(False)), lambda: sm,
lambda: sm2)
# out = softmax_layer(global_pool, [64, 20])
layers.append(out)
return layers[-1], layers[-2]
def resnet(inpt, n):
print(n)
n = int(n)
if n < 20 or (n - 20) % 12 != 0:
print("ResNet depth invalid.")
return
num_conv = int((n - 20) / 12 + 1)
layers = []
with tf.variable_scope('conv1'):
conv1 = conv_layer(inpt, [3, 3, 3, 16], 1)
layers.append(conv1)
for i in range(num_conv):
with tf.variable_scope('conv2_%d' % (i + 1)):
conv2_x = residual_block(layers[-1], 16, False)
conv2 = residual_block(conv2_x, 16, False)
layers.append(conv2_x)
layers.append(conv2)
# assert conv2.get_shape().as_list()[1:] == [32, 32, 16]
for i in range(num_conv):
down_sample = True if i == 0 else False
with tf.variable_scope('conv3_%d' % (i + 1)):
conv3_x = residual_block(layers[-1], 32, down_sample)
conv3 = residual_block(conv3_x, 32, False)
layers.append(conv3_x)
layers.append(conv3)
# assert conv3.get_shape().as_list()[1:] == [16, 16, 32]
for i in range(num_conv):
down_sample = True if i == 0 else False
with tf.variable_scope('conv4_%d' % (i + 1)):
conv4_x = residual_block(layers[-1], 64, down_sample)
conv4 = residual_block(conv4_x, 64, False)
layers.append(conv4_x)
layers.append(conv4)
# assert conv4.get_shape().as_list()[1:] == [8, 8, 64]
with tf.variable_scope('fc'):
global_pool = tf.reduce_mean(layers[-1], [1, 2])
# assert global_pool.get_shape().as_list()[1:] == [64]
out = softmax_layer(global_pool, [64, 100])
layers.append(out)
return layers[-1]
def resnet(inpt, n):
if n < 20 or (n - 20) % 12 != 0:
print "ResNet depth invalid."
return
num_conv = (n - 20) / 12 + 1
layers = []
with tf.variable_scope('conv1'):
conv1 = conv_layer(inpt, [3, 3, 3, 16], 1)
layers.append(conv1)
for i in range (num_conv):
with tf.variable_scope('conv2_%d' % (i+1)):
conv2_x = residual_block(layers[-1], 16, False)
conv2 = residual_block(conv2_x, 16, False)
layers.append(conv2_x)
layers.append(conv2)
assert conv2.get_shape().as_list()[1:] == [32, 32, 16]
for i in range (num_conv):
down_sample = True if i == 0 else False
with tf.variable_scope('conv3_%d' % (i+1)):
conv3_x = residual_block(layers[-1], 32, down_sample)
conv3 = residual_block(conv3_x, 32, False)
layers.append(conv3_x)
layers.append(conv3)
assert conv3.get_shape().as_list()[1:] == [16, 16, 32]
for i in range (num_conv):
down_sample = True if i == 0 else False
with tf.variable_scope('conv4_%d' % (i+1)):
conv4_x = residual_block(layers[-1], 64, down_sample)
conv4 = residual_block(conv4_x, 64, False)
layers.append(conv4_x)
layers.append(conv4)
assert conv4.get_shape().as_list()[1:] == [8, 8, 64]
with tf.variable_scope('fc'):
global_pool = tf.reduce_mean(layers[-1], [1, 2])
assert global_pool.get_shape().as_list()[1:] == [64]
out = softmax_layer(global_pool, [64, 10])
layers.append(out)
return layers[-1]
def resnet(inpt, n):
num_conv = 1
layers = []
with tf.variable_scope('conv1'):
conv1 = conv_layer(inpt, [3, 3, 3, 16], 1)
layers.append(conv1)
for i in range (num_conv):
with tf.variable_scope('conv2_%d' % (i+1)):
conv2_x = residual_block(layers[-1], 16, False)
conv2 = residual_block(conv2_x, 16, False)
layers.append(conv2_x)
layers.append(conv2)
for i in range (num_conv):
down_sample = True if i == 0 else False
with tf.variable_scope('conv3_%d' % (i+1)):
conv3_x = residual_block(layers[-1], 32, down_sample)
conv3 = residual_block(conv3_x, 32, False)
layers.append(conv3_x)
layers.append(conv3)
for i in range (num_conv):
down_sample = True if i == 0 else False
with tf.variable_scope('conv4_%d' % (i+1)):
conv4_x = residual_block(layers[-1], 64, down_sample)
conv4 = residual_block(conv4_x, 64, False)
layers.append(conv4_x)
layers.append(conv4)
with tf.variable_scope('fc'):
global_pool = tf.reduce_mean(layers[-1], [1, 2])
out = softmax_layer(global_pool, [64, 10])
layers.append(out)
return layers[-1]
def inference(inpt):
"""Build the CIFAR-10 model.
Args:
images: Images returned from distorted_inputs() or inputs().
Returns:
Logits.
"""
#Resnet architecture implementation from https://github.com/xuyuwei/resnet-tf
n = 44
n_dict = {20:1, 32:2, 44:3, 56:4}
# ResNet architectures used for CIFAR-10
if n < 20 or (n - 20) % 12 != 0:
print("ResNet depth invalid.")
return
num_conv = n_dict[n]
layers = []
with tf.variable_scope('conv1'):
conv1 = conv_layer(inpt, [3, 3, 3, 16], 1)
layers.append(conv1)
for i in range (num_conv):
with tf.variable_scope('conv2_%d' % (i+1)):
conv2_x = residual_block(layers[-1], 16, False)
conv2 = residual_block(conv2_x, 16, False)
layers.append(conv2_x)
layers.append(conv2)
assert conv2.get_shape().as_list()[1:] == [32, 32, 16]
for i in range (num_conv):
down_sample = True if i == 0 else False
with tf.variable_scope('conv3_%d' % (i+1)):
conv3_x = residual_block(layers[-1], 32, down_sample)
conv3 = residual_block(conv3_x, 32, False)
layers.append(conv3_x)
layers.append(conv3)
assert conv3.get_shape().as_list()[1:] == [16, 16, 32]
for i in range (num_conv):
down_sample = True if i == 0 else False
with tf.variable_scope('conv4_%d' % (i+1)):
conv4_x = residual_block(layers[-1], 64, down_sample)
conv4 = residual_block(conv4_x, 64, False)
layers.append(conv4_x)
layers.append(conv4)
assert conv4.get_shape().as_list()[1:] == [8, 8, 64]
with tf.variable_scope('fc'):
global_pool = tf.reduce_mean(layers[-1], [1, 2])
assert global_pool.get_shape().as_list()[1:] == [64]
out = softmax_layer(global_pool, [64, 10])
layers.append(out)
return layers[-1]
def resnet(inpt, n):
if n < 20 or (n - 20) % 12 != 0:
print "ResNet depth invalid."
return
num_conv = (n - 20) / 12 + 1
layers = []
with tf.variable_scope('conv1'):
conv1 = conv_layer(inpt, [3, 3, 3, 16], 1)
layers.append(conv1)
print("conv1 shape:")
print(conv1.get_shape())
for i in range(num_conv):
with tf.variable_scope('conv2_%d' % (i + 1)):
conv2_x = residual_block(layers[-1], 16, False)
conv2 = residual_block(conv2_x, 16, False)
layers.append(conv2_x)
layers.append(conv2)
print("conv2 shape:")
print(conv2.get_shape())
assert conv2.get_shape().as_list()[1:] == [448, 448, 16]
for i in range(num_conv):
down_sample = True if i == 0 else False
with tf.variable_scope('conv3_%d' % (i + 1)):
conv3_x = residual_block(layers[-1], 32, down_sample)
conv3 = residual_block(conv3_x, 32, False)
layers.append(conv3_x)
layers.append(conv3)
assert conv3.get_shape().as_list()[1:] == [224, 224, 32]
for i in range(num_conv):
down_sample = True if i == 0 else False
with tf.variable_scope('conv4_%d' % (i + 1)):
conv4_x = residual_block(layers[-1], 64, down_sample)
conv4 = residual_block(conv4_x, 64, False)
layers.append(conv4_x)
layers.append(conv4)
assert conv4.get_shape().as_list()[1:] == [112, 112, 64]
for i in range(num_conv):
down_sample = True if i == 0 else False
with tf.variable_scope('conv5_%d' % (i + 1)):
conv5_x = residual_block(layers[-1], 128, down_sample)
conv5 = residual_block(conv5_x, 128, False)
layers.append(conv5_x)
layers.append(conv5)
assert conv5.get_shape().as_list()[1:] == [56, 56, 128]
for i in range(num_conv):
down_sample = True if i == 0 else False
with tf.variable_scope('conv6_%d' % (i + 1)):
conv6_x = residual_block(layers[-1], 256, down_sample)
conv6 = residual_block(conv6_x, 256, False)
layers.append(conv6_x)
layers.append(conv6)
assert conv6.get_shape().as_list()[1:] == [28, 28, 256]
for i in range(num_conv):
down_sample = True if i == 0 else False
with tf.variable_scope('conv7_%d' % (i + 1)):
conv7_x = residual_block(layers[-1], 512, down_sample)
conv7 = residual_block(conv7_x, 512, False)
layers.append(conv7_x)
layers.append(conv7)
assert conv7.get_shape().as_list()[1:] == [14, 14, 512]
for i in range(num_conv):
down_sample = True if i == 0 else False
with tf.variable_scope('conv8_%d' % (i + 1)):
conv8_x = residual_block(layers[-1], 1024, down_sample)
conv8 = residual_block(conv8_x, 1024, False)
layers.append(conv8_x)
layers.append(conv8)
assert conv8.get_shape().as_list()[1:] == [7, 7, 1024]
return layers[-1]
def resnet(inpt, num_conv, batch_size, keep_prob):
layers = []
with tf.name_scope('conv1'):
conv1 = conv_layer(inpt, [3, 3, 3, 64], 1)
layers.append(conv1)
for i in range(num_conv):
with tf.name_scope('conv2_%d' % (i + 1)):
conv2_x = residual_block(layers[-1], 64, False)
conv2 = residual_block(conv2_x, 64, False)
layers.append(conv2_x)
layers.append(conv2)
assert conv2.get_shape().as_list()[1:] == [224, 224, 64]
for i in range(num_conv):
down_sample = True if i == 0 else False
with tf.name_scope('conv3_%d' % (i + 1)):
conv3_x = residual_block(layers[-1], 128, down_sample)
conv3 = residual_block(conv3_x, 128, False)
layers.append(conv3_x)
layers.append(conv3)
assert conv3.get_shape().as_list()[1:] == [112, 112, 128]
for i in range(num_conv):
down_sample = True if i == 0 else False
with tf.name_scope('conv4_%d' % (i + 1)):
conv4_x = residual_block(layers[-1], 256, down_sample)
conv4 = residual_block(conv4_x, 256, False)
layers.append(conv4_x)
layers.append(conv4)
assert conv4.get_shape().as_list()[1:] == [56, 56, 256]
for i in range(num_conv):
down_sample = True if i == 0 else False
with tf.name_scope('conv4_%d' % (i + 1)):
conv4_x = residual_block(layers[-1], 512, down_sample)
conv4 = residual_block(conv4_x, 512, False)
layers.append(conv4_x)
layers.append(conv4)
assert conv4.get_shape().as_list()[1:] == [28, 28, 512]
for i in range(num_conv):
down_sample = True if i == 0 else False
with tf.name_scope('conv4_%d' % (i + 1)):
conv4_x = residual_block(layers[-1], 512, down_sample)
conv4 = residual_block(conv4_x, 512, False)
layers.append(conv4_x)
layers.append(conv4)
assert conv4.get_shape().as_list()[1:] == [14, 14, 512]
for i in range(num_conv):
down_sample = True if i == 0 else False
with tf.name_scope('conv4_%d' % (i + 1)):
conv4_x = residual_block(layers[-1], 512, down_sample)
conv4 = residual_block(conv4_x, 512, False)
layers.append(conv4_x)
layers.append(conv4)
assert conv4.get_shape().as_list()[1:] == [7, 7, 512]
with tf.name_scope('fc'):
# global_pool = tf.reduce_mean(layers[-1], [1, 2])
global_conv = conv_layer(inpt, [7, 7, 512, 4096], 1)
print(global_conv.get_shape().as_list())
global_conv_flatten = tf.reshape(global_conv, [batch_size, -1])
fc1 = relu_layer(global_conv_flatten, (4096, 4096))
fc1 = tf.nn.dropout(fc1, keep_prob)
fc2 = relu_layer(fc1, (4096, 4096))
out = softmax_layer(fc2, [4096, 30])
layers.append(out)
return layers[-1]