def generator(z):
with tf.variable_scope("generator", reuse=tf.AUTO_REUSE):
with tf.variable_scope("linear"):
linear = clayers.fully_connected(z, 1024 * 4 * 4)
with tf.variable_scope("conv1_transp"):
# Reshape as 4x4 images
conv1 = tf.reshape(linear, (-1, 4, 4, 1024))
conv1 = default_conv2d_transpose(conv1, 512)
conv1 = layers.batch_normalization(conv1)
conv1 = nn.relu(conv1)
with tf.variable_scope("conv2_transp"):
conv2 = default_conv2d_transpose(conv1, 256)
conv2 = layers.batch_normalization(conv2)
conv2 = nn.relu(conv2)
with tf.variable_scope("conv3_transp"):
conv3 = default_conv2d_transpose(conv2, 128)
conv3 = layers.batch_normalization(conv3)
conv3 = nn.relu(conv3)
with tf.variable_scope("conv4_transp"):
conv4 = default_conv2d_transpose(conv3, 3)
with tf.variable_scope("out"):
out = tf.tanh(conv4)
return out
def setUp(self):
super(AxiomsTest, self).setUp()
# Make a linear model for testing.
graph_lin = Graph()
with graph_lin.as_default():
x_lin = placeholder('float32', (None, self.input_size))
y_lin = x_lin @ self.model_lin_weights + self.model_lin_bias
self.model_lin = ModelWrapper(graph_lin, x_lin, y_lin)
# Make a deeper model for testing.
graph_deep = Graph()
with graph_deep.as_default():
x_deep = placeholder('float32', (None, self.input_size))
z1_deep = (x_deep @ self.model_deep_weights_1 +
self.model_deep_bias_1)
z2_deep = relu(z1_deep)
z3_deep = (z2_deep @ self.model_deep_weights_2 +
self.model_deep_bias_2)
z4_deep = relu(z3_deep)
y_deep = (z4_deep @ self.model_deep_weights_3 +
self.model_deep_bias_3)
self.model_deep = ModelWrapper(graph_deep, x_deep, y_deep,
dict(layer2=z2_deep, layer3=z3_deep))
self.layer2 = 'layer2'
self.layer3 = 'layer3'
def cnn_graph(x, keep_prob, size, captcha_list = CAPTCHA_LIST, captcha_len = CAPTCHA_LEN):
x_image = reshape(x, shape = [-1, size[0], size[1], 1])
w_conv1 = weight_variable([3, 3, 1, 32])
b_conv1 = bias_variable([32])
h_conv1 = relu(conv2d(x_image, w_conv1) + b_conv1)
h_pool1 = max_pool2d(h_conv1)
h_drop1 = dropout(h_pool1, rate = 1 - keep_prob)
w_conv2 = weight_variable([3, 3, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = relu(conv2d(h_drop1, w_conv2) + b_conv2)
h_pool2 = max_pool2d(h_conv2)
h_drop2 = dropout(h_pool2, rate = 1 - keep_prob)
w_conv3 = weight_variable([3, 3, 64, 64])
b_conv3 = bias_variable([64])
h_conv3 = relu(conv2d(h_drop2, w_conv3) + b_conv3)
h_pool3 = max_pool2d(h_conv3)
h_drop3 = dropout(h_pool3, rate = 1 - keep_prob)
image_height = int(h_drop3.shape[1])
image_width = int(h_drop3.shape[2])
w_fc = weight_variable([image_height * image_width * 64, 1024])
b_fc = bias_variable([1024])
h_drop3_re = reshape(h_drop3, [-1, image_height * image_width * 64])
h_fc = relu(matmul(h_drop3_re, w_fc) + b_fc)
h_drop_fc = dropout(h_fc, rate = 1 - keep_prob)
w_out = weight_variable([1024, len(captcha_list) * captcha_len])
b_out = bias_variable([len(captcha_list) * captcha_len])
y_conv = matmul(h_drop_fc, w_out) + b_out
return y_conv
def generator(z, reuse=tf.AUTO_REUSE):
with tf.variable_scope('gen', reuse=reuse):
with tf.variable_scope("linear"):
linear = clayers.fully_connected(z, 128 * 2 * 2)
with tf.variable_scope("conv1_transp"):
# Reshape as 4x4 images
conv1 = tf.reshape(linear, (-1, 2, 2, 128))
conv1 = default_conv2d_transpose(conv1, 64)
conv1 = nn.relu(conv1)
with tf.variable_scope("conv2_transp"):
conv2 = default_conv2d_transpose(conv1, 32)
conv2 = nn.relu(conv2)
with tf.variable_scope("conv3_transp"):
conv3 = default_conv2d_transpose(conv2, 16)
conv3 = nn.relu(conv3)
with tf.variable_scope("conv4_transp"):
conv4 = default_conv2d_transpose(conv3, 1)
with tf.variable_scope("out"):
out = tf.tanh(conv4)
return out
def call(self, inputs):
#Layer 1
Z_bottleneck = self.bottleneck(inputs)
Z_bottleneck = self.bn_bottleneck(Z_bottleneck)
Z_bottleneck = relu(Z_bottleneck)
Z_maxpool = self.max_pool(inputs)
#Layer 2
Z1 = self.conv_f1(Z_maxpool)
Z1 = self.bn_f1(Z1)
Z1 = relu(Z1)
Z2 = self.conv_f10(Z_bottleneck)
Z2 = self.bn_f10(Z2)
Z2 = relu(Z2)
Z3 = self.conv_f20(Z_bottleneck)
Z3 = self.bn_f20(Z3)
Z3 = relu(Z3)
Z4 = self.conv_f40(Z_bottleneck)
Z4 = self.bn_f40(Z4)
Z4 = relu(Z4)
#Layer 3
Z = Concatenate()([Z1, Z2, Z3, Z4])
return Z
def discriminator(x, alpha=0.01, reuse=tf.AUTO_REUSE):
with tf.variable_scope('dis', reuse=reuse):
with tf.variable_scope("conv1"):
conv1 = default_conv2d(x, 16)
conv1 = nn.relu(conv1)
with tf.variable_scope("conv2"):
conv2 = default_conv2d(conv1, 32)
conv2 = nn.relu(conv2)
with tf.variable_scope("conv3"):
conv3 = default_conv2d(conv2, 64)
conv3 = nn.relu(conv3)
with tf.variable_scope("conv4"):
conv4 = default_conv2d(conv3, 128)
conv4 = nn.relu(conv4)
with tf.variable_scope("linear"):
linear = clayers.flatten(conv4)
linear = clayers.fully_connected(linear, 1)
with tf.variable_scope("out"):
out = nn.sigmoid(linear)
return out
def __init__(self, sparse, guidance_map, params, reuse=False):
with tf.variable_scope("Local", reuse=reuse) as scope:
sparse = tf.reshape(sparse, [-1, 200, 200, 1])
guidance_map = tf.reshape(guidance_map, [-1, 200, 200, 1])
x = sparse + guidance_map
x = conv2d(x, 32, (3, 3), strides=(2, 2), padding='same')
x = relu(x)
x = conv2d(x, 64, (3, 3), strides=(2, 2), padding='same')
x = relu(x)
x = conv2d_transpose(x,
64, (5, 5),
strides=(2, 2),
padding='same',
use_bias=False)
x = batch_normalization(x)
x = relu(x)
x = conv2d_transpose(x,
2, (5, 5),
strides=(2, 2),
padding='same',
use_bias=False)
self.output = x
self.parameters = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope="Local")
def build(self, x, reuse=None):
""" TODO: define your model (2 conv layers and 2 fc layers?)
x: input image
logit: network output w/o softmax """
with tf.variable_scope('model', reuse=reuse):
W1 = tf.Variable(tf.random_normal([3, 3, 1, 32], stddev=0.01))
L1 = relu(conv2d(x, W1, strides=[1, 1, 1, 1], padding='SAME'))
L1 = max_pool(L1, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
W2 = tf.Variable(tf.random_normal([3, 3, 32, 64], stddev=0.01))
L2 = relu(conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME'))
L2 = max_pool(L2, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
L2_flat = tf.reshape(L2, [-1, 7 * 7 * 64])
W3 = tf.get_variable("W3", shape=[7 * 7 * 64, 10],
initializer=xavier_initializer())
b = tf.Variable(tf.random_normal([10]))
logit = tf.matmul(L2_flat, W3) + b
return logit
def __init__(self, images, sparse, params, reuse=False):
with tf.variable_scope("Global", reuse=reuse) as scope:
sparse = tf.reshape(sparse, [-1, 200, 200, 1])
images = tf.reshape(images, [-1, 200, 200, 3])
x = tf.concat([images, sparse], axis=3)
x = tf.cast(x, dtype=tf.float32)
x = conv2d(x, 32, (3, 3), strides=(2, 2), padding='same')
x = relu(x)
x = conv2d(x, 64, (3, 3), strides=(2, 2), padding='same')
x = relu(x)
x = conv2d_transpose(x,
64, (5, 5),
strides=(2, 2),
padding='same',
use_bias=False)
x = batch_normalization(x)
x = relu(x)
x = conv2d_transpose(x,
3, (5, 5),
strides=(2, 2),
padding='same',
use_bias=False)
self.output = x
self.parameters = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope="Global")
def call(self, x, mask=None):
x = self.lstm0(x, mask=mask, training=True)
x = relu(x)
x = self.lstm1(x, mask=mask, training=True)
x = relu(x)
x = self.dense(x)
return x
def __call__(self,
state,
inputs=None,
scope=None): # inputs are observations
"""
Basic HCNN with Architectural Teacher-Forcing (ATF): new_state = A * tanh( state - diff([id 0]*state,expand(inputs)) ).
: expand(.) zero pads vector to same size as state
state: dim [depth,1,n]
inputs: dim [depth,1,m]
output: dim [depth,1,m]
"""
# TODO: What is this? Write comment or remove.
if inputs is not None:
in_shape = inputs.get_shape()
in_len = len(in_shape)
if in_len == 3:
obs = inputs
if in_len <= 2:
obs = tf.expand_dims(inputs, 0)
if in_len == 1:
obs = tf.expand_dims(obs, 0)
if self._output_size is None:
self._output_size = in_shape[2].value
elif self._output_size != in_shape[2].value:
raise ValueError(
"output_size and input shape are inconsistent.")
if (self._output_size is None):
raise ValueError("Output_size is ill defined.")
if len(state.get_shape()) < 3:
raise ValueError("State should have 3 dimensions.")
with vs.variable_scope(
scope or type(self).__name__) as cell_scope: # "BasicHCNNCell"
# returns first output_size num elems from state as tensor
output = state[:, :, :self._output_size]
if inputs is not None:
# zero pads the difference between output and obs to be of the same size as state
padding = [[0, 0], [0, 0],
[0, self._num_units - self._output_size]]
tar = tf.pad((tf.subtract(output, obs)), padding)
# get new state.
new_state = _linear_cube(relu(state - tar),
self._num_units,
sparse_cube=self._sparse_cube,
scope=cell_scope)
else:
# When there is no teacher forcing, as with forecast.
new_state = _linear_cube(relu(state),
self._num_units,
sparse_cube=self._sparse_cube,
scope=cell_scope)
return new_state, output
def call(self, x, training=True):
y = nn.relu(self.max_pool(self.conv1(x)))
y = nn.relu(self.max_pool(self.dropout(self.conv2(y), training=training)))
# Flatten feature matrix
batch_size = y.shape[0]
y = tf.reshape(y, (batch_size, -1))
y = self.dense(y)
return y
def margin_loss(self, x, labels, size_average=True):
batch_size = x.size(0)
v_c = tf.math.sqrt(tf.reduce_sum(x**2, axis=2, keepdims=True))
left = tf.reshape(nn.relu(0.9 - v_c), (batch_size, -1))
right = tf.reshape(nn.relu(v_c - 0.1), (batch_size, -1))
loss = labels * left + 0.5 * (1.0 - labels) * right
loss = tf.metrics.mean(tf.reduce_sum(loss, axis=1))
return loss
def call(self, input_tensor):
x = nn.relu(input_tensor)
x = self.conv2a(x)
x = nn.relu(x)
x = self.conv2b(x)
x = nn.relu(x)
x = self.conv2c(x)
x += input_tensor
return x
def create_model(self,
model_input,
vocab_size,
l2_penalty=1e-8,
is_training=True,
input_size=1024 + 128,
**unused_params):
"""Creates a Multi Layered Perceptron model.
"""
# General transform layer
fc1 = slim.fully_connected(
model_input,
input_size,
activation_fn=None,
weights_regularizer=slim.l2_regularizer(l2_penalty),
scope='fc1')
bn1 = slim.batch_norm(fc1, is_training=is_training, scope='bn1')
relu1 = nn.relu(bn1, name='relu1')
# Coarse classification
coarse_scores = slim.fully_connected(
relu1,
25,
activation_fn=None,
weights_regularizer=slim.l2_regularizer(l2_penalty),
scope='coarse')
# Concatenate p(coarse) and prior features
concat = tf.concat([relu1, coarse_scores], -1, name='concat')
# Specific transform layer
fc2 = slim.fully_connected(
concat,
input_size + 25,
activation_fn=None,
weights_regularizer=slim.l2_regularizer(l2_penalty),
scope='fc2')
bn2 = slim.batch_norm(fc2, is_training=is_training, scope='bn2')
relu2 = nn.relu(bn2, name='relu2')
# Final classifier
classifier = slim.fully_connected(
relu2,
vocab_size,
activation_fn=None,
weights_regularizer=slim.l2_regularizer(l2_penalty),
scope='classifier')
final_probs = nn.sigmoid(classifier, name='final_probs')
coarse_probs = nn.sigmoid(coarse_scores, name='coarse_probs')
return {"predictions": final_probs, "coarse_predictions": coarse_probs}
def call(self, inputs):
residual = self.downsample(inputs)
x = self.conv1(inputs)
x = self.bn1(x)
x = nn.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x = tf.add(x, residual)
out = nn.relu(x)
return out
def call(self, inputs, training=False, mask=None):
x = self.conv1(inputs)
x = self.bn1(x)
x = relu(x)
x = self.pool1(x)
x = self.conv2(x)
x = self.bn2(x)
x = relu(x)
x = self.pool2(x)
x = flatten(x)
x = self.fc1(x)
x = relu(x)
x = self.fc2(x)
return x
def build_cnn(feat_dim=(1024, 14, 14),
res_block_dim=128,
num_res_blocks=0,
proj_dim=512,
pooling='maxpool2'):
C, H, W = feat_dim
layers = []
if num_res_blocks > 0:
layers.append(
tf.keras.layers.Conv2D(C,
res_block_dim,
kernel_size=(3, 3),
padding=1))
layers.append(nn.relu())
C = res_block_dim
for _ in range(num_res_blocks):
layers.append(ResidualBlock(C))
if proj_dim > 0:
layers.append(
tf.keras.layers.Conv2D(C, proj_dim, kernel_size=(1, 1), padding=0))
layers.append(tf.keras.layers.ReLU())
C = proj_dim
if pooling == 'maxpool2':
layers.append(tf.keras.layers.MaxPool2D(kernel_size=(2, 2), stride=2))
H, W = H // 2, W // 2
model = tf.keras.Sequential()
for layer in layers:
model.add(layer)
return model, (C, H, W)
def forward_propogation_sub_network(terminal,parameters,params):
"""
Forward progogation for each sub-network. Must indicate network layer id.
Args:
terminal: A dictionary with key X_(layer),Y_(layer), the input and output port for input and output
parameters: parameters: A dictionary with key in format W_(layer_id)_(order_id),b_(layer_id)_(order_id).
params: Network configuration of all layer
Returns:
result_dict: A diction with key Z1,Z2,...,Z4 as output of each layer.
"""
result_dict = {}
for lay in range(len(params)):
param = params[lay]
n = len(param) - 1
A = terminal["X"+str(lay+1)]
for i in range(1,n):
W = parameters[ "W"+str(lay+1)+'_'+str(i) ]
b = parameters[ "b"+str(lay+1)+'_'+str(i) ]
Z = tf.matmul(W,A) + b
A = relu(Z)
W = parameters[ "W"+str(n)+'_'+str(i) ]
b = parameters[ "b"+str(n)+'_'+str(i) ]
Z = tf.matmul(W,A) + b
cost = compute_cost(Z,terminal[Y+str(lay+1)])
result_dict[ "Z"+str(lay+1) ] = Z
result_dict[ "cost"+ +str(lay+1) ] = cost
return result_dict
def resBlock(x, num):
x = relu(x)
conv1 = tcl.conv2d(
x,
256,
3,
1,
activation_fn=tf.nn.relu,
normalizer_fn=tcl.batch_norm,
weights_initializer=tf.random_normal_initializer(stddev=0.02),
scope='g_resconv1_' + str(num))
print(conv1)
conv2 = tcl.conv2d(
conv1,
256,
3,
1,
activation_fn=tf.identity,
normalizer_fn=tcl.batch_norm,
weights_initializer=tf.random_normal_initializer(stddev=0.02),
scope='g_resconv2_' + str(num))
print(conv2)
output = tf.add(x, conv2)
print(output)
return output
def build_net(sess):
in_len = 32
in_dep = 1
x_hold = tf.placeholder(tf.float32,shape=[None,in_dep*in_len*in_len])
y_hold = tf.placeholder(tf.float32,shape=[None,2])
keep_prob = tf.placeholder(tf.float32)
xt = tf.reshape(x_hold,[-1,in_len,in_len,in_dep])
#Layer 1 - 5x5 convolution
w1 = tfac.weight([5,5,in_dep,4])
b1 = tfac.bias([4])
c1 = nn.relu(nn.conv2d(xt,w1,strides=[1,2,2,1],padding='VALID')+b1)
o1 = c1
#Layer 2 - 3x3 convolution
w2 = tfac.weight([3,3,4,16])
b2 = tfac.bias([16])
c2 = nn.relu(nn.conv2d(o1,w2,strides=[1,2,2,1],padding='VALID')+b2)
o2 = c2
#Layer 3 - 3x3 convolution
w3 = tfac.weight([3,3,16,32])
b3 = tfac.bias([32])
c3 = nn.relu(nn.conv2d(o2,w3,strides=[1,1,1,1],padding='VALID')+b3)
o3 = c3
dim = 32 * 4*4
#Fully connected layer - 600 units
of = tf.reshape(o3,[-1,dim])
w4 = tfac.weight([dim,600])
b4 = tfac.bias([600])
o4 = nn.relu(tf.matmul(of,w4)+b4)
o4 = nn.dropout(o4, keep_prob)
#Output softmax layer - 2 units
w5 = tfac.weight([600,2])
b5 = tfac.bias([2])
y = nn.softmax(tf.matmul(o4,w5)+b5)
sess.run(tf.initialize_all_variables())
return y,x_hold,y_hold,keep_prob
def setUp(self):
super(ModelWrapperTest, self).setUp()
graph = Graph()
with graph.as_default():
x = placeholder('float32', (None, 2))
z1 = relu(x @ self.layer1_weights + self.internal_bias)
z2 = relu(z1 @ self.layer2_weights + self.internal_bias)
y = z2 @ self.layer3_weights + self.bias
self.model = TensorflowModelWrapper(graph, x, y,
dict(x=x, z1=z1, z2=z2, logits=y))
self.layer0 = 'x'
self.layer1 = 'z1'
self.layer2 = z2.name
def ACT(inputs, act_fn):
if act_fn == 'relu':
act = relu(inputs)
elif act_fn == 'lrelu':
act = leaky_relu(inputs)
elif act_fn == 'sigmoid':
act = sigmoid(inputs)
return act
def ConvReluMaxPool(X, weights, bias):
conv = nn.conv2d(X, weights, strides=[1, 1, 1, 1], padding="VALID", data_format="NHWC")
conv_bias = nn.bias_add(conv, bias, data_format="NHWC")
relu = nn.relu(conv_bias)
maxpool = nn.max_pool2d(relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="VALID", data_format="NHWC")
return maxpool
def call(self, inputs, training=False):
x = self.layer1(inputs)
x = self.layer2(x, training=training)
x = relu(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.layer5(x)
return x
def net(X, is_training=False):
X = tf.reshape(X, shape=(-1, num_inputs))
H1 = tf.nn.relu(tf.matmul(X, W1) + b1)
if is_training: # 只在训练模型时使用丢弃法
H1 = dropout(H1, drop_prob1) # 在第一层全连接后添加丢弃层
H2 = nn.relu(tf.matmul(H1, W2) + b2)
if is_training:
H2 = dropout(H2, drop_prob2) # 在第二层全连接后添加丢弃层
return tf.math.softmax(tf.matmul(H2, W3) + b3)
def rnn(self):
"""RNN模型"""
def lstm_cell(): # lstm核
return BasicLSTMCell(self.config.hidden_size, state_is_tuple=True)
def gru_cell(): # gru核
return GRUCell(self.config.hidden_size)
def dropout(): # 在每一个rnn核后面加一个dropout层
if self.config.rnn == 'lstm':
cell = lstm_cell()
else:
cell = gru_cell()
return DropoutWrapper(
cell=cell, output_keep_prob=self.config.dropout_keep_prob)
# 词向量映射
with tf.device('/gpu:0'):
embedding = tf.get_variable(
'embedding',
[self.config.vocab_size, self.config.embedding_size])
embedding_inputs = tf.nn.embedding_lookup(embedding, self.input_x)
with tf.name_scope('rnn'):
# 多层rnn网络
cells = [dropout() for _ in range(self.config.num_layers)]
rnn_cell = MultiRNNCell(cells=cells, state_is_tuple=True)
_outputs, _ = tf.nn.dynamic_rnn(cell=rnn_cell,
inputs=embedding_inputs,
dtype=tf.float32)
last = _outputs[:, -1, :] # 取最后一个时序作为输出结果
with tf.name_scope('score'):
# 全连接层,后面连接dropout以及relu激活
fc = dense(last, self.config.hidden_size, name='fc1')
fc = tf.contrib.layers.dropout(fc, self.config.dropout_keep_prob)
fc = relu(fc)
# 分类器
self.logits = dense(fc, self.config.num_classes, name='fc2')
self.y_predict_class = tf.argmax(tf.nn.softmax(self.logits), 1)
with tf.name_scope('optimize'):
# 损失函数，交叉熵
cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(
logits=self.logits, labels=self.input_y)
self.loss = tf.reduce_mean(cross_entropy)
# 优化器
self.optimizer = tf.train.AdamOptimizer(
learning_rate=self.config.learning_rate).minimize(self.loss)
with tf.name_scope('accuracy'):
# 准确率
correct_pred = tf.equal(tf.argmax(self.input_y, 1),
self.y_predict_class)
self.accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
def relu(inputs):
"""
Relu activation
Parameters
----------
inputs: Input tensor
"""
return nn.relu(inputs)
def __init__(self, block, layers, modality='RGB',
shortcut_type='B', num_classes=400,dropout=0.5,ST_struc=('A','B','C')):
self.w_initer=tc.layers.xavier_initializer(tf.float32)
self.data_format='NCHW'
self.layer_data_format = 'channels_last' if data_format == 'NHWC' \
else 'channels_first'
self.is_training=True
self.inplanes = 64
self.input_channel = 3 if modality=='RGB' else 2 # 2 is for flow
self.ST_struc=ST_struc
self.conv1_custom = nn.Conv3d(self.input_channel, 64, kernel_size=(1,7,7), stride=(1,2,2),
padding=(0,3,3), bias=False)
self.conv1_custom = tl.Conv3D(filters=64, kernel_size=(1,7,7), strides=(1,2,2),
padding=self.layer_data_format, use_bias=False, kernel_initializer=self.w_initer)
self.depth_3d = sum(layers[:3])# C3D layers are only (res2,res3,res4), res5 is C2D
axis = 1 if self.data_format=="NCHW" else -1
self.bn1 = tl.BatchNormalization(axis=axis, scale=False fused=True)
# out = self.bn(in, training=True) False for eval
self.cnt = 0
self.relu = lambda input : nn.relu(input)
self.maxpool = tl.MaxPooling3D(pool_size=(2, 3, 3), strides=2, padding='valid',
data_format=self.layer_data_format) # pooling layer for conv1.
self.maxpool = tl.MaxPooling3D(pool_size=(2, 1, 1), strides=(2, 1, 1), padding='valid',
data_format=self.layer_data_format) # pooling layer for res2, 3, 4.
self.layer1 = self._make_layer(block, 64, layers[0], shortcut_type)
self.layer2 = self._make_layer(block, 128, layers[1], shortcut_type, stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], shortcut_type, stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], shortcut_type, stride=2)
self.avgpool = tl.AveragePooling2D(pool_size=5, strides=1,
data_format=self.layer_data_format) # pooling layer for res5.
self.dropout=tl.Dropout(dropout)
self.fc = tl.Dense(num_classes, use_bias=False, kernel_initializer=self.w_initer)
# self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv3d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm3d):
m.weight.data.fill_(1)
m.bias.data.zero_()
# some private attribute
self.input_size=(self.input_channel,16,160,160) # input of the network
self.input_mean = [0.485, 0.456, 0.406] if modality=='RGB' else [0.5]
self.input_std = [0.229, 0.224, 0.225] if modality=='RGB' else [np.mean([0.229, 0.224, 0.225])]
return
def call(self, inputs):
outputs = []
for i in range(self.num_outputs):
# First input
x = self.fuse_layers[i][0](inputs[0])
for j in range(1, self.num_inputs):
x = layers.add([x, self.fuse_layers[i][j](inputs[j])])
out = nn.relu(x)
outputs.append(out)
return outputs