def resnet(n=18, nr_classes=10):
input = Input(shape=(img_channels, img_rows, img_cols))
conv1 = conv_bn_relu(
input,
nb_filter=16,
nb_row=3,
nb_col=3,
W_regularizer=l2(weight_decay)) # Filters, filter_size
# Build residual blocks..
block_fn = _bottleneck
block1 = residual_block(conv1,
block_fn,
nb_filters=16,
repetations=n,
is_first_layer=True)
block2 = residual_block(block1, block_fn, nb_filters=32, repetations=n)
block3 = residual_block(block2,
block_fn,
nb_filters=64,
repetations=n,
subsample=True)
# Classifier block
pool2 = AveragePooling2D(pool_size=(8, 8))(block3)
flatten1 = Flatten()(pool2)
final = Dense(units=nr_classes,
kernel_initializer="he_normal",
activation="softmax",
kernel_regularizer=l2(weight_decay))(flatten1)
model = Model(inputs=input, outputs=final)
return model
def resnet_backbone(no_classes=3422,
no_channels=3,
start_neurons=32,
dropout_rate=0.1):
input_layer = layers.Input(name='input_image',
shape=(IMG_SIZE_CLASSIFY, IMG_SIZE_CLASSIFY,
no_channels),
dtype='float32')
for index, i in enumerate([1, 2, 2, 4, 8]):
if index == 0:
inner = input_layer
inner = layers.Conv2D(start_neurons * i, (3, 3),
activation=None,
padding="same")(inner)
inner = residual_block(inner, start_neurons * i)
inner = residual_block(inner, start_neurons * i, True)
if i <= 4:
inner = layers.MaxPooling2D((2, 2))(inner)
if dropout_rate:
inner = layers.Dropout(dropout_rate)(inner)
print(inner.get_shape())
inner = layers.Flatten()(inner)
inner = layers.Dense(no_classes, activation="softmax")(inner)
net = models.Model(inputs=[input_layer], outputs=inner)
return net
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 test_residual_block_downsample(self):
outputs = residual_block(filters=self.filters,
kernel_size=(3, 3),
downsample=True,
padding='same',
activation='relu')(self.inputs)
self.assertListEqual(outputs.shape.as_list(),
[None, self.height/2, self.width/2, self.filters])
def resnet():
input = Input(shape=(img_channels, img_rows, img_cols))
conv1 = conv_bn_relu(input, nb_filter=16, nb_row=3, nb_col=3, W_regularizer=l2(weight_decay))
# Build residual blocks..
block_fn = _bottleneck
block1 = residual_block(conv1, block_fn, nb_filters=16, repetations=18, is_first_layer=True)
block2 = residual_block(block1, block_fn, nb_filters=32, repetations=18)
block3 = residual_block(block2, block_fn, nb_filters=64, repetations=18, subsample=True)
# Classifier block
pool2 = AveragePooling2D(pool_size=(8, 8))(block3)
flatten1 = Flatten()(pool2)
final = Dense(output_dim=10, init="he_normal", activation="softmax", W_regularizer=l2(weight_decay))(flatten1)
model = Model(input=input, output=final)
return model
def resnet_blocks(x, ch, name, resnet_step=0): if resnet_step > 0: for i in range(resnet_step): x = residual_block(x, channels=ch, is_training=self.is_training, downsample=False, scope=name+"_residual_block_decode_%d" % i) x = batch_norm_resnet(x, is_training=self.is_training, scope = name+"_decode_0_batch_norm") # mark x = tf.nn.relu(x) return x
def resnet(inpt, n, is_training=False):
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_res(inpt, [3, 3, 3, 16], 1, is_training)
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,
is_training=is_training)
conv2 = residual_block(conv2_x, 16, False, is_training=is_training)
layers.append(conv2_x)
layers.append(conv2)
print("conv2 shape: {}".format(conv2.shape))
assert conv2.get_shape().as_list()[1:] == [75, 75, 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,
is_training=is_training)
conv3 = residual_block(conv3_x, 32, False, is_training=is_training)
layers.append(conv3_x)
layers.append(conv3)
assert conv3.get_shape().as_list()[1:] == [38, 38, 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,
is_training=is_training)
conv4 = residual_block(conv4_x, 64, False, is_training=is_training)
layers.append(conv4_x)
layers.append(conv4)
assert conv4.get_shape().as_list()[1:] == [19, 19, 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, 2])
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 = {}
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_unet(img_size=(512, 512),
no_channels=3,
start_neurons=32,
dropout_rate=0.25):
# inner
input_layer = layers.Input(name='the_input',
shape=(*img_size, no_channels), # noqa
dtype='float32')
# down 1
conv1 = layers.Conv2D(start_neurons * 1, (3, 3),
activation=None, padding="same")(input_layer)
conv1 = residual_block(conv1, start_neurons * 1)
conv1 = residual_block(conv1, start_neurons * 1, True)
pool1 = layers.MaxPooling2D((2, 2))(conv1)
pool1 = layers.Dropout(dropout_rate)(pool1)
# down 2
conv2 = layers.Conv2D(start_neurons * 2, (3, 3),
activation=None, padding="same")(pool1)
conv2 = residual_block(conv2, start_neurons * 2)
conv2 = residual_block(conv2, start_neurons * 2, True)
pool2 = layers.MaxPooling2D((2, 2))(conv2)
pool2 = layers.Dropout(dropout_rate)(pool2)
# down 3
conv3 = layers.Conv2D(start_neurons * 4, (3, 3),
activation=None, padding="same")(pool2)
conv3 = residual_block(conv3, start_neurons * 4)
conv3 = residual_block(conv3, start_neurons * 4, True)
pool3 = layers.MaxPooling2D((2, 2))(conv3)
pool3 = layers.Dropout(dropout_rate)(pool3)
# middle
middle = layers.Conv2D(start_neurons * 8, (3, 3),
activation=None, padding="same")(pool3)
middle = residual_block(middle, start_neurons * 8)
middle = residual_block(middle, start_neurons * 8, True)
# up 1
deconv3 = layers.Conv2DTranspose(
start_neurons * 4, (3, 3), strides=(2, 2), padding="same")(middle)
uconv3 = layers.concatenate([deconv3, conv3])
uconv3 = layers.Dropout(dropout_rate)(uconv3)
uconv3 = layers.Conv2D(start_neurons * 4, (3, 3),
activation=None, padding="same")(uconv3)
uconv3 = residual_block(uconv3, start_neurons * 4)
uconv3 = residual_block(uconv3, start_neurons * 4, True)
# up 2
deconv2 = layers.Conv2DTranspose(
start_neurons * 2, (3, 3), strides=(2, 2), padding="same")(uconv3)
uconv2 = layers.concatenate([deconv2, conv2])
uconv2 = layers.Dropout(dropout_rate)(uconv2)
uconv2 = layers.Conv2D(start_neurons * 2, (3, 3),
activation=None, padding="same")(uconv2)
uconv2 = residual_block(uconv2, start_neurons * 2)
uconv2 = residual_block(uconv2, start_neurons * 2, True)
# up 3
deconv1 = layers.Conv2DTranspose(
start_neurons * 1, (3, 3), strides=(2, 2), padding="same")(uconv2)
uconv1 = layers.concatenate([deconv1, conv1])
uconv1 = layers.Dropout(dropout_rate)(uconv1)
uconv1 = layers.Conv2D(start_neurons * 1, (3, 3),
activation=None, padding="same")(uconv1)
uconv1 = residual_block(uconv1, start_neurons * 1)
uconv1 = residual_block(uconv1, start_neurons * 1, True)
# output mask
output_layer = layers.Conv2D(
2, (1, 1), padding="same", activation=None)(uconv1)
# 2 classes: character mask & center point mask
output_layer = layers.Activation('sigmoid')(output_layer)
model = models.Model(inputs=[input_layer], outputs=output_layer)
return model
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]
