def __call__(self, input_data, states):
"""
:param input_data: shape (batch, time, H, W, channel)
:param states: shape (batch, H, W, channel)
:return: outputs: shape(batch, time, H, W, channel)
"""
assert input_data is not None
assert states is not None
with tf.device(self.device):
outputs = []
for i in range(self.time_seq):
in_data = input_data[:,i-1,:,:,:]
i2h = conv2d(in_data, name="i2h", kshape=self.i2h_shape, dtype=self.dtype)
i2h = tf.split(i2h, 3, axis=3)
h2h = conv2d(states, name="h2h", kshape=self.h2h_shape, dtype=self.dtype)
h2h = tf.split(h2h, 3, axis=3)
reset_gate = tf.nn.sigmoid(i2h[0] + h2h[0], name="reset")
update_gate = tf.nn.sigmoid(i2h[1] + h2h[1], name="update")
new_mem = tf.nn.leaky_relu(i2h[2] + reset_gate * h2h[2], alpha=0.2, name="leaky")
next_h = update_gate * states + (1 - update_gate) * new_mem
states = next_h
outputs.append(next_h)
outputs = tf.stack(outputs, axis=1)
return outputs
def get_model(inputs, is_training, bn_decay=None):
stem = util.conv2d(inputs,
32, [3, 3],
scope='conv1',
stride=[2, 2],
padding='VALID',
use_xavier=True,
is_training=is_training,
bn_decay=bn_decacy)
stem = util.conv2d(stem,
32, [3, 3],
scope='conv2',
stride=[1, 1],
padding='VALID',
use_xavier=True,
is_training=is_training,
bn_decay=bn_decacy)
stem = util.conv2d(stem,
64, [3, 3],
scope='conv3',
stride=[1, 1],
padding='VALID',
use_xavier=True,
is_training=is_training,
bn_decay=bn_decacy)
def _build_network(self, name, conv):
if conv:
input_s = tf.placeholder(tf.float32,
[None, self.width, self.height, 1])
with tf.variable_scope(name):
conv1 = tf_utils.conv2d(input_s, 64, (3, 3), 1)
conv2 = tf_utils.conv2d(conv1, 32, (1, 1), 1)
conv3 = tf_utils.conv2d(conv2, 32, (1, 1), 1)
reward = tf_utils.conv2d(conv3, 1, (1, 1), 1)
theta = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope=name)
return input_s, tf.squeeze(tf.reshape(reward,
(-1, self.n_input))), theta
else:
input_s = tf.placeholder(tf.float32, [None, self.n_input])
with tf.variable_scope(name):
fc1 = tf_utils.fc(
input_s,
self.n_h1,
scope="fc1",
activation_fn=tf.nn.elu,
initializer=tf.contrib.layers.variance_scaling_initializer(
mode="FAN_IN"))
fc2 = tf_utils.fc(
fc1,
self.n_h2,
scope="fc2",
activation_fn=tf.nn.elu,
initializer=tf.contrib.layers.variance_scaling_initializer(
mode="FAN_IN"))
reward = tf_utils.fc(fc2, self.n_input, scope="reward")
theta = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope=name)
return input_s, tf.squeeze(reward), theta
def pc_encoder(point_cloud, nasmples, is_training, bn_decay=None):
nn_dis, idx_batch = tf_utils.get_knn(point_cloud, nasmples)
batch_size = point_cloud.get_shape()[0].value
num_point = point_cloud.get_shape()[1].value
point_dim = point_cloud.get_shape()[2].value
idx_batch = tf.cast(idx_batch, dtype=tf.int32)
latent_feature = {}
# con_point = tf.concat([point_cloud, cov_batch], axis=2)
# encoder_input = tf.expand_dims(con_point, -1) # (32 2048 3 1)
encoder_input = tf.expand_dims(point_cloud, -1) # (32 2048 3 1)
# (32, 2048, 1, 64)
net = tf_utils.conv2d(encoder_input,
64, [1, 3],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='mlp_1',
bn_decay=bn_decay)
# (32, 2048, 1, 64)
net = tf_utils.conv2d(net,
64, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='mlp_2',
bn_decay=bn_decay)
# (32, 2048, 1, 64)
net = tf_utils.conv2d(net,
64, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='mlp_3',
bn_decay=bn_decay)
net = tf_utils.conv2d(net,
128, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='mlp_4',
bn_decay=bn_decay)
net = tf_utils.conv2d(net,
1024, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='mlp_5',
bn_decay=bn_decay)
global_feat = tf_utils.max_pool2d(net, [num_point, 1],
padding='VALID',
scope='maxpool')
net = tf.reshape(global_feat, [batch_size, -1])
return net
def CNNEncoder(image, trainable=True):
x = image
with tf.variable_scope(
'CNNEncoder',
initializer=tf.contrib.layers.xavier_initializer(),
):
with tf.variable_scope('layer1'):
x = utils.conv2d(x,
name='conv1',
shape=[3, 3, 1, 64],
padding='SAME',
activation_func=tf.nn.relu,
trainable=trainable,
use_bn=True)
x = tf.nn.max_pool(x,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME')
# 14x14
with tf.variable_scope('layer2'):
x = utils.conv2d(x,
name='conv1',
shape=[3, 3, 64, 64],
padding='SAME',
activation_func=tf.nn.relu,
trainable=trainable,
use_bn=True)
x = tf.nn.max_pool(x,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME')
# 7x7
with tf.variable_scope('layer3'):
x = utils.conv2d(x,
name='conv1',
shape=[3, 3, 64, 64],
padding='SAME',
activation_func=tf.nn.relu,
trainable=trainable,
use_bn=True)
# x = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
# padding='SAME')
with tf.variable_scope('layer4'):
x = utils.conv2d(x,
name='conv1',
shape=[3, 3, 64, 64],
padding='SAME',
activation_func=tf.nn.relu,
trainable=trainable,
use_bn=True)
return x
def _build_network(self, name):
input_s = tf.placeholder(tf.float32, [None] + self.input_shape)
# input_s = tf.placeholder(tf.float32, [None]+self.input_shape+[1])
# with tf.variable_scope(name):
# fc1 = tf_utils.fc(input_s, self.n_h1, scope="fc1", activation_fn=tf.nn.elu,
# initializer=tf.contrib.layers.variance_scaling_initializer(mode="FAN_IN"))
# fc2 = tf_utils.fc(fc1, self.n_h2, scope="fc2", activation_fn=tf.nn.elu,
# initializer=tf.contrib.layers.variance_scaling_initializer(mode="FAN_IN"))
# reward = tf_utils.fc(fc2, 1, scope="reward")
with tf.variable_scope(name):
conv1 = tf_utils.conv2d(input_s, 8, [2, 2])
conv2 = tf_utils.conv2d(conv1, 16, [4, 4])
reward = tf_utils.conv2d(conv2, 1, [4, 4])
theta = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=name)
return input_s, reward, theta
def reduce35(net,activation_fn=tf.nn.relu,scope = None,reuse = None,is_training=False):
output = None
with tf.variable_scope(scope,'Reduction-A',[net],reuse=reuse):
with tf.variable_scope('branch_1'):
layer1_1 = util.max_pool2d(net,[3,3],scope = '6/1_MaxPool',stride = [2,2] , padding='VALID')
with tf.variable_scope('branch_2'):
layer2_1 = util.conv2d(net,384,[3,3],scope = '6/2_conv_3x3',stride = [2,2],padding='VALID',use_xavier = True,bn=True,is_training=is_training)
with tf.variable_scope('branch_3'):
layer3_1 = util.conv2d(net,256,[1,1],scope = '6/3_conv_1x1',stride = [1,1],padding='SAME',use_xavier = True,activation_fn = None,bn=True,is_training=is_training)
layer3_2 = util.conv2d(layer3_1,256,[3,3],scope = '6/3_conv_3x3_1',stride = [1,1],padding='SAME',use_xavier = True,bn=True,is_training=is_training)
layer3_3 = util.conv2d(layer3_2,384,[3,3],scope = '6/3_conv_3x3_2',stride = [2,2],padding='VALID',use_xavier = True,bn=True,is_training=is_training)
concat= util.filter_concat(3,[layer1_1,layer2_1,layer3_3])
output = concat
return output
def _setup_net(placeholder, layers, weights, mean_pixel):
"""
Returns the cnn built with given weights and normalized with mean_pixel
"""
net = {}
placeholder -= mean_pixel
for i, name in enumerate(layers):
kind = name[:4]
with tf.variable_scope(name):
if kind == 'conv':
kernels, bias = weights[i][0][0][0][0]
# matconvnet: [width, height, in_channels, out_channels]
# tensorflow: [height, width, in_channels, out_channels]
kernels = tf_utils.get_variable(
np.transpose(kernels, (1, 0, 2, 3)),
name=name + "_w")
bias = tf_utils.get_variable(
bias.reshape(-1),
name=name + "_b")
placeholder = tf_utils.conv2d(placeholder, kernels, bias)
elif kind == 'relu':
placeholder = tf.nn.relu(placeholder, name=name)
tf_utils.add_activation_summary(placeholder, collections=['train'])
elif kind == 'pool':
placeholder = tf_utils.max_pool_2x2(placeholder)
net[name] = placeholder
return net
def cls_model(seg_out, seg_out_former, is_training, bn_decay):
out_1 = tf_utils.conv2d(seg_out_former, kernel_shape=[1,1], strides=1, channel=32,
activation_fn=tf.nn.relu, scope='conv11')
out_2 = tf_utils.maxpool2d(seg_out, kernel_shape=[128,128], strides=1, padding='VALID')
out_3 = tf_utils.avgpool2d(seg_out, kernel_shape=[128,128], strides=1, padding='VALID')
out_4 = tf_utils.maxpool2d(out_1, kernel_shape=[128, 128], strides=1, padding='VALID')
out_5 = tf_utils.avgpool2d(out_1, kernel_shape=[128, 128], strides=1, padding='VALID')
out_6 = tf.concat([out_2, out_3, out_4, out_5], -1)
out_7 = tf_utils.conv2d(out_6, kernel_shape=[1,1], strides=1, channel=1,
bn=True, bn_decay=bn_decay, is_training=is_training,
activation_fn=tf.nn.sigmoid, scope='conv12')
return out_7
def FC(x,
units,
activations,
bn,
bn_decay,
is_training,
use_bias=True,
drop=None):
if isinstance(units, int):
units = [units]
activations = [activations]
elif isinstance(units, tuple):
units = list(units)
activations = list(activations)
assert type(units) == list
for num_unit, activation in zip(units, activations):
if drop is not None:
x = tf_utils.dropout(x, drop=drop, is_training=is_training)
x = tf_utils.conv2d(x,
output_dims=num_unit,
kernel_size=[1, 1],
stride=[1, 1],
padding='VALID',
use_bias=use_bias,
activation=activation,
bn=bn,
bn_decay=bn_decay,
is_training=is_training)
return x
def block8(net,is_training=False,activation_fn=tf.nn.relu,scope = None,reuse = None,scale = 0.2):
output = None
with tf.variable_scope(scope,'Inception-C',[net],reuse = reuse):
with tf.variable_scope('branch_1'):
layer1_1 = util.conv2d(net,192,[1,1],scope = '9/1_conv_1x1',stride = [1,1],padding='SAME',use_xavier = True,activation_fn = None,bn=True)
with tf.variable_scope('branch_2'):
layer2_1 = util.conv2d(net,192,[1,1],scope = '9/2_conv_1x1',stride = [1,1],padding='SAME',use_xavier = True,activation_fn = None,bn=True)
layer2_2 = util.conv2d(layer2_1,160,[1,7],scope = '9/2_conv_1x7',stride = [1,1],padding='SAME',use_xavier = True,activation_fn=activation_fn,bn = True)
layer2_3 = util.conv2d(layer2_2,192,[7,1],scope = '9/2_conv_7x1',stride = [1,1],padding='SAME',use_xavier = True,activation_fn=activation_fn,bn = True)
concat = util.filter_concat(3,[layer1_1,layer2_3])
concat = util.conv2d(concat,net.get_shape()[3].value,[1,1],scope = '9_concat',stride = [1,1],padding = 'SAME',use_xavier = True , activation_fn = None,bn=None,stddev = None)
concat = concat * scale
shortcut = net + concat
shortcut = activation_fn(shortcut)
output = shortcut
return output
def _build_network(self, name):
input_s = tf.placeholder(tf.float32, [None, self.n_input])
input_inv = tf.placeholder(tf.float32, [None, self.n_input])
img_in = tf.reshape(input_s, shape=[-1, 1, 4, 1])
img_inv = tf.reshape(input_inv, shape=[-1, 1, 4, 1])
with tf.variable_scope(name):
cnv1 = tf_utils.conv2d(img_in, 2, (2, 2))
# max_conv_p = tf_utils.max_pool(cnv1_p)
fltn_conv = tf_utils.flatten(cnv1)
fc1 = tf_utils.fc(
fltn_conv,
self.n_h2,
scope="fc1",
activation_fn=tf.nn.elu,
initializer=tf.contrib.layers.variance_scaling_initializer(
mode="FAN_IN"))
cnv1_inv = tf_utils.conv2d(img_inv, 2, (2, 2))
# max_conv_p = tf_utils.max_pool(cnv1_p)
fltn_conv_inv = tf_utils.flatten(cnv1_inv)
fc1_inv = tf_utils.fc(
fltn_conv_inv,
self.n_h2,
scope="fc1_inv",
activation_fn=tf.nn.elu,
initializer=tf.contrib.layers.variance_scaling_initializer(
mode="FAN_IN"))
subt = tf.subtract(fc1, fc1_inv)
# blah = tf.multiply(tf.divide(fc2, fc1_p), 0.35)
# comb = tf.concat([fc1, fc1_inv], 1)
fc_p1 = tf_utils.fc(
subt,
2 * self.n_h1,
scope="fc_p1",
activation_fn=tf.nn.elu,
initializer=tf.contrib.layers.variance_scaling_initializer(
mode="FAN_IN"))
# fc_p2 = tf_utils.fc(fc_p1, self.n_h2, scope="fc_p2", activation_fn=tf.nn.elu,
# initializer=tf.contrib.layers.variance_scaling_initializer(mode="FAN_IN"))
reward = tf_utils.fc(fc_p1, 1, scope="reward")
theta = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=name)
return input_s, input_inv, reward, theta
def RelationNetwork(encoder, hidden_size, trainable=True):
x = encoder
with tf.variable_scope('RelationNetwork') as scope:
with tf.variable_scope('layer1'):
x = utils.conv2d(x,
name='conv1',
shape=[3, 3, 128, 64],
padding='SAME',
activation_func=tf.nn.relu,
trainable=trainable,
use_bn=True)
x = tf.nn.max_pool(x,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME')
with tf.variable_scope('layer2'):
x = utils.conv2d(x,
name='conv1',
shape=[3, 3, 64, 64],
padding='SAME',
activation_func=tf.nn.relu,
trainable=trainable,
use_bn=True)
x = tf.nn.max_pool(x,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME')
with tf.variable_scope('fc1'):
x = utils.fc(x,
num_out=hidden_size,
name='fc1',
activation_func=tf.nn.relu)
with tf.variable_scope('fc2'):
x = utils.fc(x, num_out=1, name='fc2', activation_func=None)
return x
def define_convgru_graph_2(self):
with tf.name_scope("Graph"):
with tf.variable_scope("encoder") and tf.device('/device:GPU:0'):
self.in_data = tf.placeholder(self.dtype,
name="input",
shape=[None, self.time_seq, c.H, c.W, 1])
self.gt_data = tf.placeholder(self.dtype,
name="gt",
shape=[None, self.time_seq, c.H, c.W, 1])
conv_in = tf.reshape(self.in_data, shape=[-1, c.H, c.W, 1])
first_conv = conv2d(conv_in, name="conv1",
kshape=(7, 7, 1, c.NUM_FILTER), dtype=self.dtype)
with tf.variable_scope("Conv_Gru", reuse=tf.AUTO_REUSE):
gru = ConvGru(dtype=self.dtype)
gru_input = tf.reshape(first_conv, shape=[self.batch, self.time_seq, c.H, c.W, c.NUM_FILTER])
states = gru.init_state(shape=[self.batch, c.H, c.W, c.NUM_FILTER])
outputs = gru(gru_input, states)
with tf.variable_scope("decoder") and tf.device('/device:GPU:0'):
dec_in = tf.reshape(outputs, shape=[-1, c.H, c.W, c.NUM_FILTER])
# dec = deconv2d(dec_in, name="dec2_", kshape=(7, 7), n_outputs=1)
dec = conv2d(dec_in, name="conv2",
kshape=(7, 7, c.NUM_FILTER, 1), dtype=self.dtype)
# dec = tf.cast(dec, dtype=tf.float16)
out = tf.reshape(dec, shape=[-1, self.time_seq, c.H, c.W, 1])
print(out)
with tf.variable_scope("loss"):
self.result = out
if c.USE_BALANCED_LOSS:
self.loss = weighted_l2(out, self.gt_data)
else:
self.loss = tf.reduce_mean(tf.square(tf.subtract(out, self.gt_data)))
self.mse = tf.reduce_mean(tf.square(tf.subtract(out, self.gt_data)))
self.rmse = tf.sqrt(self.mse)
self.mae = tf.reduce_mean(tf.abs(tf.subtract(out, self.gt_data)))
self.optimizer = tf.train.AdamOptimizer(self._lr).minimize(self.loss)
def _build_network(self, name):
input_s = tf.placeholder(tf.float32, [None, self.n_input])
img_in = tf.reshape(input_s, shape=[-1, 1, 4, 1])
with tf.variable_scope(name):
cnv1 = tf_utils.conv2d(img_in, 2, (2,2))
fltn_conv = tf_utils.flatten(cnv1)
# fc1 = tf_utils.fc(input_s, self.n_h1, scope="fc1", activation_fn=tf.nn.elu,
# initializer=tf.contrib.layers.variance_scaling_initializer(mode="FAN_IN"))
fc2 = tf_utils.fc(fltn_conv, self.n_h2, scope="fc2", activation_fn=tf.nn.elu,
initializer=tf.contrib.layers.variance_scaling_initializer(mode="FAN_IN"))
reward = tf_utils.fc(fc2, 1, scope="reward")
theta = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=name)
return input_s, reward, theta
def seg_model(input, is_training, bn_decay):
extend_image = tf.expand_dims(input, axis=-1)
out_1 = tf_utils.conv2d(extend_image, kernel_shape=[11,11], strides=2, channel=32,
bn=True, bn_decay=bn_decay, is_training=is_training,
activation_fn=tf.nn.relu, scope='conv1')
out_2 = tf_utils.conv2d(out_1, kernel_shape=[11,11], strides=1, channel=32,
bn=True, bn_decay=bn_decay, is_training=is_training,
activation_fn=tf.nn.relu, scope='conv2')
out_3 = tf_utils.conv2d(out_2, kernel_shape=[11,11], strides=1, channel=32,
bn=True, bn_decay=bn_decay, is_training=is_training,
activation_fn=tf.nn.relu, scope='conv3')
out_4 = tf_utils.conv2d(out_3, kernel_shape=[7, 7], strides=2, channel=64,
bn=True, bn_decay=bn_decay, is_training=is_training,
activation_fn=tf.nn.relu, scope='conv4')
out_5 = tf_utils.conv2d(out_4, kernel_shape=[7, 7], strides=1, channel=64,
bn=True, bn_decay=bn_decay, is_training=is_training,
activation_fn=tf.nn.relu, scope='conv5')
out_6 = tf_utils.conv2d(out_5, kernel_shape=[7, 7], strides=1, channel=64,
bn=True, bn_decay=bn_decay, is_training=is_training,
activation_fn=tf.nn.relu, scope='conv6')
out_7 = tf_utils.conv2d(out_6, kernel_shape=[3, 3], strides=1, channel=128,
bn=True, bn_decay=bn_decay, is_training=is_training,
activation_fn=tf.nn.relu, scope='conv7')
out_8 = tf_utils.conv2d(out_7, kernel_shape=[3, 3], strides=1, channel=128,
bn=True, bn_decay=bn_decay, is_training=is_training,
activation_fn=tf.nn.relu, scope='conv8')
out_9 = tf_utils.conv2d(out_8, kernel_shape=[3, 3], strides=1, channel=128,
bn=True, bn_decay=bn_decay, is_training=is_training,
activation_fn=tf.nn.relu, scope='conv9')
seg_layer = tf_utils.conv2d(out_9, kernel_shape=[1, 1], strides=1, channel=1,
bn=True, bn_decay=bn_decay, is_training=is_training,
activation_fn=tf.nn.relu, scope='conv10')
return seg_layer, out_9
def stack_rnn_forecaster(self, block_state_list):
with tf.variable_scope("Forecaster"):
rnn_block_num = len(c.NUM_FILTER)
rnn_block_output = []
curr_inputs = None
for i in range(rnn_block_num - 1, -1, -1):
with tf.name_scope("Forecaster_rnn_block_" + str(i)):
with tf.name_scope("rnn_blocks_" + str(i)):
rnn_out, rnn_state = self.rnn_blocks[i].unroll(
length=self._seq,
inputs=curr_inputs,
begin_state=block_state_list[i],
)
rnn_block_output.append(rnn_out)
if i > 0:
print(rnn_out)
with tf.name_scope("up_sample_" + str(i)):
upsample = up_sampling(
rnn_out,
kshape=c.UPSAMPLE[i - 1][0],
stride=c.UPSAMPLE[i - 1][1],
num_filter=c.NUM_FILTER[i],
name="Up_sample_" + str(i))
curr_inputs = upsample
deconv1 = deconv2d_act(rnn_block_output[-1],
kernel=c.LAST_DECONV[1],
stride=c.LAST_DECONV[2],
num_filters=c.LAST_DECONV[0],
use_bias=False,
dtype=self._dtype,
name="last_conv")
with tf.name_scope('conv_final'):
conv_final = conv2d_act(deconv1,
kernel=3,
strides=1,
num_filters=8,
name="conv_final")
pred = conv2d(conv_final, kshape=(1, 1, 8, 1), name="out")
if self.mode == 'online':
pred = tf.reshape(pred,
shape=(self._batch, self._seq, c.PRED_H,
c.PRED_W, self._in_c))
else:
pred = tf.reshape(pred,
shape=(self._batch, self._seq, c.H_TRAIN,
c.W_TRAIN, self._in_c))
self.pred = pred
def build_model(net):
# Construct model..
X = tf.placeholder("float", [BATCH, T_in, IMG_H, IMG_W, IMG_CH])
Y = tf.placeholder("float", [BATCH, T_pred, IMG_H, IMG_W, IMG_CH])
# Flatten the images going in s.t. BATCH * T_in, height, width, ch..
X_flat = tf.reshape(X, [BATCH * T_in, IMG_H, IMG_W, IMG_CH])
conv1 = conv2d(X_flat, net.weights['wc1'], net.biases['bc1'], 2)
conv2 = conv2d(conv1, net.weights['wc2'], net.biases['bc2'], 2)
conv3 = conv2d(conv2, net.weights['wc3'], net.biases['bc3'], 2)
# Now we hyperflatten everything for the lstm: BATCH, T-in, everything.
res = tf.reshape(conv3, [BATCH, T_in, -1])
prediction = net.EncoderDecoder(res)
# Infer on BATCH * T_pred, everything.
fc_out = fc(prediction, net.weights['wfc1'], net.biases['bfc1'])
# Reshape to on BATCH, T_pred, IMG_H * IMG_W * IMG_CH.
fc_out = tf.reshape(fc_out, [BATCH, T_pred, IMG_H * IMG_W * IMG_CH])
sig_out = tf.sigmoid(fc_out)
# Calculate difference..
Y_flat = tf.reshape(Y, [BATCH, T_pred, IMG_H * IMG_W * IMG_CH])
diff = fc_out - Y_flat
# Compute loss...
vs = tf.trainable_variables()
lossL2 = tf.add_n([tf.nn.l2_loss(v) for v in vs
if 'bias' not in v.name ]) * 0.001
loss_op = tf.reduce_sum(tf.reduce_sum(diff * diff, axis=2), axis=1) + lossL2
loss_op = tf.reduce_mean(loss_op)
train_op = tf.train.AdamOptimizer(learning_rate=LR).minimize(loss_op)
return fc_out, sig_out, X, Y, loss_op, train_op
def _conv_layer(input_,
num_filters=32,
filter_size=3,
strides=1,
relu=True,
name=None):
with tf.compat.v1.variable_scope(name):
input_ = tf_utils.padding2d(input_,
p_h=int(filter_size / 2),
p_w=int(filter_size / 2),
pad_type='REFLECT')
input_ = tf_utils.conv2d(input_,
output_dim=num_filters,
k_h=filter_size,
k_w=filter_size,
d_h=strides,
d_w=strides,
padding='VALID')
input_ = tf_utils.instance_norm(input_)
if relu:
input_ = tf.nn.relu(input_)
return input_
def block35(net,is_training=False,activation_fn=tf.nn.relu,scope = None,reuse = None,scale=.2):
output = None
with tf.variable_scope(scope,'Inception-A',[net],reuse = reuse):
with tf.variable_scope('branch_1'):
layer1_1 = util.conv2d(net,32,[1,1],scope = '5/1_conv_1x1',stride = [1,1],padding='SAME',use_xavier = True,activation_fn = None,bn=True)
with tf.variable_scope('branch_2'):
layer2_1 = util.conv2d(net,32,[1,1],scope = '5/2_conv_1x1',stride = [1,1],padding='SAME',use_xavier = True,activation_fn = None,bn=True)
layer2_2 = util.conv2d(layer2_1,32,[3,3],scope = '5/2_conv_3x3',stride = [1,1],padding='SAME',use_xavier = True,activation_fn=activation_fn,bn=True)
with tf.variable_scope('branch_3'):
layer3_1 = util.conv2d(net,32,[1,1],scope = '5/3_conv_1x1',stride = [1,1],padding='SAME',use_xavier = True,activation_fn = None,bn=True)
layer3_2 = util.conv2d(layer3_1,48,[3,3],scope = '5/3_conv_3x3_1',stride = [1,1],padding='SAME',use_xavier = True,bn=True,activation_fn=activation_fn)
layer3_3 = util.conv2d(layer3_2,64,[3,3],scope = '5/3_conv_3x3_2',stride = [1,1],padding='SAME',use_xavier = True,bn=True,activation_fn=activation_fn)
concat1 = util.filter_concat(3,[layer1_1,layer2_2,layer3_3])
concat2 = util.conv2d(concat1,net.get_shape()[3].value,[1,1],scope = '5_concat',stride = [1,1],padding = 'SAME',use_xavier = True , activation_fn = None,bn=None,stddev = None)
concat2 = concat2 * scale
shortcut = net + concat2
shortcut = activation_fn(shortcut)
output = shortcut
return output
def builder_network(self, image):
x = image
with tf.variable_scope('conv1'):
x = tf_utils.conv2d(x, [3, 3, 3, 64],
name='conv1_1',
padding='SAME',
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation)
x = tf_utils.conv2d(x, [3, 3, 64, 64],
name='conv1_2',
padding='SAME',
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation)
x = tf.nn.max_pool(x,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='VALID')
with tf.variable_scope('conv2'):
x = tf_utils.conv2d(x, [3, 3, 64, 128],
name='conv2_1',
padding='SAME',
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation)
x = tf_utils.conv2d(x, [3, 3, 128, 128],
name='conv2_2',
padding='SAME',
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation)
x = tf.nn.max_pool(x,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='VALID')
with tf.variable_scope('conv3'):
x = tf_utils.conv2d(x, [3, 3, 128, 256],
name='conv3_1',
padding='SAME',
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation)
x = tf_utils.conv2d(x, [3, 3, 256, 256],
name='conv3_2',
padding='SAME',
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation)
x = tf_utils.conv2d(x, [3, 3, 256, 256],
name='conv3_3',
padding='SAME',
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation)
x = tf.nn.max_pool(x,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='VALID')
with tf.variable_scope('conv4'):
x = tf_utils.conv2d(x, [3, 3, 256, 512],
name='conv4_1',
padding='SAME',
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation)
x = tf_utils.conv2d(x, [3, 3, 512, 512],
name='conv4_2',
padding='SAME',
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation)
x = tf_utils.conv2d(x, [3, 3, 512, 512],
name='conv4_3',
padding='SAME',
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation)
x = tf.nn.max_pool(x,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='VALID')
with tf.variable_scope('conv5'):
x = tf_utils.conv2d(x, [3, 3, 512, 512],
name='conv5_1',
padding='SAME',
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation)
x = tf_utils.conv2d(x, [3, 3, 512, 512],
name='conv5_2',
padding='SAME',
use_bn=False,
trainable=self.trainable,
activation_func=self.activation)
x = tf_utils.conv2d(x, [3, 3, 512, 512],
name='conv5_3',
padding='SAME',
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation)
x = tf.nn.max_pool(x,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='VALID')
with tf.name_scope('fc6'):
x = tf_utils.conv2d(x, [7, 7, 512, 4096],
name='fc6',
padding='VALID',
use_bn=False,
trainable=self.trainable,
activation_func=self.activation)
x = self.activation(x)
with tf.name_scope('fc7'):
x = tf_utils.conv2d(x, [1, 1, 4096, 4096],
name='fc7',
padding='SAME',
use_bn=False,
trainable=self.trainable,
activation_func=self.activation)
x = self.activation(x)
with tf.name_scope('fc8'):
x = tf_utils.conv2d(x, [1, 1, 4096, self.num_classes],
name='fc8',
padding='SAME',
use_bn=False,
trainable=self.trainable,
activation_func=self.activation)
x = tf.squeeze(x, axis=[1, 2], name='squeezed')
return x
def get_model(imgs, is_training, weight_decay=0.0, bn_decay=None):
batch_size = imgs.get_shape()[0].value
im_dim = imgs.get_shape()[1].value
########
with tf.variable_scope('Encoding'):
# (batch_size, 64, 64, 64)
net = tf_utils.conv2d(imgs, 64, [7,7],
padding='SAME', stride=[2,2],
bn=True, is_training=is_training,
scope='conv1', bn_decay=bn_decay,
weight_decay=weight_decay, activation_fn=tf.nn.elu)
# (batch_size, 32, 32, 64)
net = tf_utils.conv2d(net, 64, [5,5],
padding='SAME', stride=[2,2],
bn=True, is_training=is_training,
scope='conv2', bn_decay=bn_decay,
weight_decay=weight_decay, activation_fn=tf.nn.elu)
# (batch_size, 16, 16, 128)
net = tf_utils.conv2d(net, 128, [5,5],
padding='SAME', stride=[2,2],
bn=True, is_training=is_training,
scope='conv3', bn_decay=bn_decay,
weight_decay=weight_decay, activation_fn=tf.nn.elu)
# (batch_size, 8, 8, 128)
net = tf_utils.conv2d(net, 128, [3,3],
padding='SAME', stride=[2,2],
bn=True, is_training=is_training,
scope='conv4', bn_decay=bn_decay,
weight_decay=weight_decay, activation_fn=tf.nn.elu)
# (batch_size, 4, 4, 256)
net = tf_utils.conv2d(net, 256, [3,3],
padding='SAME', stride=[2,2],
bn=True, is_training=is_training,
scope='conv5', bn_decay=bn_decay,
weight_decay=weight_decay, activation_fn=tf.nn.elu)
# (batch_size, 1, 1, 512)
net = tf_utils.conv2d(net, 512, [4,4],
padding='VALID', stride=[1,1],
bn=True, is_training=is_training,
scope='conv6', bn_decay=bn_decay,
weight_decay=weight_decay, activation_fn=tf.nn.elu)
########
with tf.variable_scope('Latent_variable'):
net = tf.reshape(net, [batch_size, 512])
net = tf_utils.fully_connected(net, 512, scope="fc1",
weight_decay=weight_decay, activation_fn=tf.nn.elu,
bn=True, bn_decay=bn_decay, is_training=is_training)
net = tf_utils.fully_connected(net, 128*4*4*4, scope="fc2",
weight_decay=weight_decay, activation_fn=tf.nn.elu,
bn=True, bn_decay=bn_decay, is_training=is_training)
net = tf.reshape(net, [batch_size, 4, 4, 4, 128])
########
with tf.variable_scope('Decoding'):
# (batch_size, 8, 8, 8, 64)
net = tf_utils.conv3d_transpose(net, 64, [3, 3, 3], scope="deconv1",
stride=[2, 2, 2], padding='SAME',
weight_decay=weight_decay, activation_fn=tf.nn.elu,
bn=True, bn_decay=bn_decay, is_training=is_training)
# (batch_size, 16, 16, 16, 32)
net = tf_utils.conv3d_transpose(net, 32, [3, 3, 3], scope="deconv2",
stride=[2, 2, 2], padding='SAME',
weight_decay=weight_decay, activation_fn=tf.nn.elu,
bn=True, bn_decay=bn_decay, is_training=is_training)
# (batch_size, 32, 32, 32, 32)
net = tf_utils.conv3d_transpose(net, 32, [3, 3, 3], scope="deconv3",
stride=[2, 2, 2], padding='SAME',
weight_decay=weight_decay, activation_fn=tf.nn.elu,
bn=True, bn_decay=bn_decay, is_training=is_training)
##################
## regressed color
##################
# (batch_size, 64, 64, 64, 24)
net_reg_clr = tf_utils.conv3d_transpose(net, 16, [3, 3, 3], scope="deconv_reg_clr1",
stride=[2, 2, 2], padding='SAME',
weight_decay=weight_decay, activation_fn=tf.nn.elu,
bn=True, bn_decay=bn_decay, is_training=is_training)
# (batch_size, 64, 64, 64, 3)
net_reg_clr = tf_utils.conv3d(net_reg_clr, 3, [3, 3, 3], scope="deconv_reg_clr2",
stride=[1, 1, 1], padding='SAME',
weight_decay=weight_decay, activation_fn=tf.sigmoid,
bn=True, bn_decay=bn_decay, is_training=is_training)
##############
### confidence
##############
net_conf = tf_utils.conv3d_transpose(net, 16, [3, 3, 3], scope="deconv_conf1",
stride=[2, 2, 2], padding='SAME',
weight_decay=weight_decay, activation_fn=tf.nn.elu,
bn=True, bn_decay=bn_decay, is_training=is_training)
# (batch_size, 64, 64, 64, 1)
net_conf = tf_utils.conv3d(net_conf, 1, [3, 3, 3], scope="conv_conf2",
stride=[1, 1, 1], padding='SAME',
weight_decay=weight_decay, activation_fn=tf.sigmoid,
bn=True, bn_decay=bn_decay, is_training=is_training)
##############
### flow color
##############
net_flow = tf_utils.conv3d_transpose(net, 2, [3, 3, 3], scope="deconv_flow",
stride=[2, 2, 2], padding='SAME',
weight_decay=weight_decay, activation_fn=tf.sigmoid,
bn=True, bn_decay=bn_decay, is_training=is_training)
# (batch_size, 64, 64, 64, 3)
net_flow_clr = tf_utils.Sampler(net_flow, imgs)
#################
### blended color
#################
net_blended_clr = net_reg_clr * net_conf + net_flow_clr * (1.0 - net_conf)
return net_reg_clr, net_conf, net_flow, net_blended_clr
"NHWC": [None, 28, 28, 1],
"NCHW": [None, 1, 28, 28],
}[DATA_FORMAT]
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, shape=init_shape)
y_ = tf.placeholder(tf.int64, shape=[None])
h = x
with tf.variable_scope("mlp",
initializer=tf.random_uniform_initializer(-0.05, 0.05)):
h = tfu.conv2d("conv1",
h,
num_filters=16,
filter_size=(5, 5),
# strides=(2, 2),
data_format=DATA_FORMAT)
h = tf.nn.relu(h)
h = tfu.max_pool(h, (2, 2), data_format=DATA_FORMAT)
h = tfu.conv2d("conv2",
h,
num_filters=32,
filter_size=(5, 5),
# strides=(2, 2),
data_format=DATA_FORMAT)
h = tf.nn.relu(h)
h = tfu.max_pool(h, (2, 2), data_format=DATA_FORMAT)
h = tfu.flatten(h, 2)
h = tfu.affine("fc1", h, 256)
h = tf.nn.relu(h)
def builder_network(self, image):
x = image
# x = tf_utils.conv2d(x, name='Conv2d_1a_7x7', shape=[7, 7, 3, 8], strides=[1, 2, 2, 1],
# padding='SAME',
# use_bn=self.use_bn,
# trainable=self.trainable,
# activation_func=self.activation_func)
x = tf_utils.separable_conv2d(x,
depthwise_filter=[7, 7, 3, 8],
pointwise_filter=[1, 1, 24, 64],
name='Conv2d_1a_7x7',
padding='SAME',
use_bn=False,
trainable=self.trainable,
activation_func=self.activation_func)
x = tf.nn.max_pool(x,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='MaxPool_2a_3x3')
x = tf_utils.conv2d(x,
name='Conv2d_2b_1x1',
shape=[1, 1, 64, 64],
strides=[1, 1, 1, 1],
padding='SAME',
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation_func)
x = tf_utils.conv2d(x,
name='Conv2d_2c_3x3',
shape=[3, 3, 64, 192],
strides=[1, 1, 1, 1],
padding='SAME',
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation_func)
x = tf.nn.max_pool(x,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='MaxPool_3a_3x3')
x = self.inception(x,
shapes=[64, 64, 64, 64, 96, 96, 32],
name='Mixed_3b')
x = self.inception(x,
shapes=[64, 64, 96, 64, 96, 96, 64],
name='Mixed_3c')
x = tf.nn.max_pool(x,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='MaxPool_4a_3x3')
dims = x.get_shape().as_list()[-1]
with tf.variable_scope('Mixed_4a'):
with tf.variable_scope('Branch_0'):
branch_0 = tf_utils.conv2d(
x,
name='Conv2d_0a_1x1',
shape=[1, 1, dims, 128],
strides=[1, 1, 1, 1],
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation_func)
branch_0 = tf_utils.conv2d(
branch_0,
name='Conv2d_1a_3x3',
shape=[3, 3, 128, 160],
strides=[1, 2, 2, 1],
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation_func)
print('branch_3:', branch_0)
with tf.variable_scope('Branch_1'):
branch_1 = tf_utils.conv2d(
x,
name='Conv2d_0a_1x1',
shape=[1, 1, dims, 64],
strides=[1, 1, 1, 1],
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation_func)
print('branch_3:', branch_1)
branch_1 = tf_utils.conv2d(
branch_1,
name='Conv2d_0b_3x3',
shape=[3, 3, 64, 96],
strides=[1, 1, 1, 1],
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation_func)
branch_1 = tf_utils.conv2d(
branch_1,
name='Conv2d_1a_3x3',
shape=[3, 3, 96, 96],
strides=[1, 2, 2, 1],
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation_func)
with tf.variable_scope('Branch_2'):
branch_2 = tf.nn.max_pool(x,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='MaxPool_1a_3x3')
# branch_2 = slim.max_pool2d(
# net, [3, 3], stride=2, scope='MaxPool_1a_3x3')
print('branch_2:', branch_2)
x = tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
x = self.inception(x,
shapes=[224, 64, 96, 96, 128, 128, 128],
name='Mixed_4b')
x = self.inception(x,
shapes=[192, 96, 128, 96, 128, 128, 128],
name='Mixed_4c')
x = self.inception(x,
shapes=[160, 128, 160, 128, 160, 160, 96],
name='Mixed_4d')
x = self.inception(x,
shapes=[96, 128, 192, 160, 192, 192, 96],
name='Mixed_4e')
# x = self.inception(x, shapes=[256, 160, 320, 32, 128, 128], name='Mixed_4f')
# x = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='MaxPool_5a_2x2')
dims = x.get_shape().as_list()[-1]
with tf.variable_scope('Mixed_5a'):
with tf.variable_scope('Branch_0'):
branch_0 = tf_utils.conv2d(
x,
name='Conv2d_0a_1x1',
shape=[1, 1, dims, 128],
strides=[1, 1, 1, 1],
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation_func)
branch_0 = tf_utils.conv2d(
branch_0,
name='Conv2d_1a_3x3',
shape=[3, 3, 128, 192],
strides=[1, 2, 2, 1],
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation_func)
print('branch_3:', branch_0)
with tf.variable_scope('Branch_1'):
branch_1 = tf_utils.conv2d(
x,
name='Conv2d_0a_1x1',
shape=[1, 1, dims, 192],
strides=[1, 1, 1, 1],
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation_func)
print('branch_3:', branch_1)
branch_1 = tf_utils.conv2d(
branch_1,
name='Conv2d_0b_3x3',
shape=[3, 3, 192, 256],
strides=[1, 1, 1, 1],
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation_func)
branch_1 = tf_utils.conv2d(
branch_1,
name='Conv2d_1a_3x3',
shape=[3, 3, 256, 256],
strides=[1, 2, 2, 1],
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation_func)
with tf.variable_scope('Branch_2'):
branch_2 = tf.nn.max_pool(x,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='MaxPool_1a_3x3')
# branch_2 = slim.max_pool2d(
# net, [3, 3], stride=2, scope='MaxPool_1a_3x3')
x = tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
x = self.inception(x,
shapes=[352, 192, 320, 160, 224, 224, 128],
name='Mixed_5b')
x = self.inception(x,
shapes=[352, 192, 320, 192, 224, 224, 128],
name='Mixed_5c')
print('x:', x)
with tf.variable_scope('Logits'):
x = tf.nn.avg_pool(x,
ksize=[1, 7, 7, 1],
strides=[1, 1, 1, 1],
padding='VALID')
x = tf_utils.conv2d(x,
shape=[1, 1, 1024, self.num_classes],
name='Conv2d_1c_1x1',
use_bn=False,
padding='SAME')
# pass
print('x:', x)
x = tf.squeeze(x, [1, 2], name='SpatialSqueeze')
print('x:', x)
return x
def stem(net,is_training=False,activation_fn=tf.nn.relu,scope = None,reuse = None,bn_decay=0.9):
output = None
with tf.variable_scope(scope,'Stem',[net],reuse = reuse):
stem = util.conv2d(net,32,[3,3],scope='1_conv2d_1',stride = [2,2],padding = 'VALID',use_xavier = True,is_training = is_training,bn=True)
stem = util.conv2d(stem,32,[3,3],scope='1_conv2d_2',stride = [1,1],padding = 'VALID',use_xavier = True,is_training = is_training,bn=True)
stem = util.conv2d(stem,64,[3,3],scope='1_conv2d_3',stride = [1,1],padding = 'SAME',use_xavier = True,is_training = is_training,bn=True)
with tf.variable_scope(scope,'Level-2',[stem],reuse=reuse):
layer2_1 = util.conv2d(stem,96,[3,3],scope = '2_Conv2d',stride = [2,2] , padding = 'VALID',use_xavier = True,is_training = is_training,bn=True)
layer2_2 = util.max_pool2d(stem,[3,3],scope = '2_MaxPool',stride = [2,2] , padding='VALID')
stem = util.filter_concat(3,[layer2_1,layer2_2])
with tf.variable_scope(scope,'Level-3',[stem],reuse=reuse):
with tf.variable_scope('branch_1'):
layer3_1_1 = util.conv2d(stem,64,[1,1],scope = '3/1_conv_1x1',stride = [1,1],padding='SAME',use_xavier = True,activation_fn = None,bn=True,is_training=is_training)
layer3_1_2 = util.conv2d(layer3_1_1,96,[3,3],scope = '3_conv_3x3',stride = [1,1],padding='VALID',use_xavier = True,bn=True,is_training=is_training)
with tf.variable_scope('branch_2'):
layer3_2_1 = util.conv2d(stem,64,[1,1],scope = '3/2-conv_1x1',stride = [1,1],padding = 'SAME',use_xavier = True,activation_fn = None,bn=True,is_training=is_training)
layer3_2_2 = util.conv2d(layer3_2_1,64,[7,1],scope = '3/2_conv_7x1',stride = [1,1],padding='SAME',use_xavier = True,bn=True,is_training=is_training)
layer3_2_3 = util.conv2d(layer3_2_2,64,[1,7],scope = '3/2_conv_1x7',stride = [1,1],padding='SAME',use_xavier = True,bn=True,is_training=is_training)
layer3_2_4 = util.conv2d(layer3_2_3,96,[3,3],scope = '3/2_conv_3x3',stride = [1,1],padding='VALID',use_xavier = True,bn=True,is_training=is_training)
stem=util.filter_concat(3,[layer3_1_2,layer3_2_4])
with tf.variable_scope('Level-4'):
layer4_1 = util.conv2d(stem,192,[3,3],scope = '4_Conv2d',stride = [2,2] , padding = 'VALID',use_xavier = True,bn=True,is_training = is_training)
layer4_2 = util.max_pool2d(stem,[3,3],scope = '4_MaxPool',stride = [2,2] , padding='VALID')
stem = util.filter_concat(3,[layer4_1,layer4_2])
output = stem
return output
def main():
"""
Runs a simple linear regression model on the mnist dataset.
"""
# Load the mnist dataset. Class stores the train, validation and testing sets as numpy arrays.
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
# Create a tensforlow session.
sess = tf.InteractiveSession()
# Create the computational graph. Start with creating placeholders for the input and output data.
# Input placeholder.
input_placeholder = tf.placeholder(tf.float32, shape=[None, 784])
# Output placeholder.
labeled_data = tf.placeholder(tf.float32, shape=[None, 10])
# Reshape input to a 4D tensor of [ -1 , width, height, channels]. -1 ensures the size remains consitent with
# the original size.
image_shape = [-1, 28, 28, 1]
input_image = tf.reshape(input_placeholder, image_shape)
# Create convolutional layers containing 2 convolutional layers and 1 fully connected layer.
# Layer 1 computes 32 features for each 5x5 patch.
conv1_weights = tf_utils.weight_variable([5, 5, 1, 32])
conv1_bias = tf_utils.bias_variable([32])
# Apply ReLU activation and max pool.
conv1_act = tf.nn.relu(
tf_utils.conv2d(input_image, conv1_weights) + conv1_bias)
conv1_pool = tf_utils.max_pool_2x2(conv1_act)
# Layer 2 computes 64 features of 5x5 patch.
conv2_weights = tf_utils.weight_variable([5, 5, 32, 64])
conv2_bias = tf_utils.bias_variable([64])
# Apply ReLU activation and max pool.
conv2_act = tf.nn.relu(
tf_utils.conv2d(conv1_pool, conv2_weights) + conv2_bias)
conv2_pool = tf_utils.max_pool_2x2(conv2_act)
# Add fully connected layers.
fc1_weights = tf_utils.weight_variable([7 * 7 * 64, 1024])
fc1_bias = tf_utils.bias_variable([1024])
# Apply Relu activation to flattened conv2d pool layer.
conv2_flat = tf.reshape(conv2_pool, [-1, 7 * 7 * 64])
fc1_act = tf.nn.relu(tf.matmul(conv2_flat, fc1_weights) + fc1_bias)
# Add dropout before the readout layer.
keep_prob = tf.placeholder(tf.float32)
dropout = tf.nn.dropout(fc1_act, keep_prob)
# Add the readout layer for the 10 classes.
readout_weights = tf_utils.weight_variable([1024, 10])
readout_bias = tf_utils.bias_variable([10])
readout_act = tf.matmul(dropout, readout_weights) + readout_bias
# Cross entropy loss between the output labels and the model.
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=labeled_data,
logits=readout_act))
# Define the training step with a learning rate for gradient descent and our cross entropy loss.
learning_rate = 1e-4
train_step = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy)
# Initialize all variables.
sess.run(tf.global_variables_initializer())
# Training model evaluation placeholders.
# Define a placeholder for comparing equality between output and labels.
predictions = tf.equal(tf.argmax(labeled_data, 1),
tf.argmax(readout_act, 1))
accuracy = tf.reduce_mean(tf.cast(predictions, tf.float32))
# Run the training for a n steps.
steps = 10000
batch_size = 50
for step in xrange(steps):
# Sample a batch from the mnist dataset.
batch = mnist.train.next_batch(batch_size)
# Create a dict of the data from the sampled batch and run one training step.
train_step.run(feed_dict={
input_placeholder: batch[0],
labeled_data: batch[1],
keep_prob: 0.5
})
# Print the training error after every 100 steps.
if step % 100 == 0:
train_accuracy = accuracy.eval(
feed_dict={
input_placeholder: batch[0],
labeled_data: batch[1],
keep_prob: 1.0
})
print "Step: ", step, " | Train Accuracy: ", train_accuracy
print "Accuracy: ", accuracy.eval(
feed_dict={
input_placeholder: mnist.test.images,
labeled_data: mnist.test.labels,
keep_prob: 1.0
})
def GEDDnet(face,
left_eye,
right_eye,
keep_prob,
is_train,
subj_id,
vgg_path,
num_subj=15,
rf=[[2, 2], [3, 3], [5, 5], [11, 11]],
num_face=[64, 128, 64, 64, 128, 256, 64],
r=[[2, 2], [3, 3], [4, 5], [5, 11]],
num_eye=[64, 128, 64, 64, 128, 256],
num_comb=[0, 256]):
num_comb[0] = num_face[-1] + 2 * num_eye[-1]
vgg = np.load(vgg_path)
with tf.variable_scope("transfer"):
W_conv1_1 = tf.Variable(vgg['conv1_1_W'])
b_conv1_1 = tf.Variable(vgg['conv1_1_b'])
W_conv1_2 = tf.Variable(vgg['conv1_2_W'])
b_conv1_2 = tf.Variable(vgg['conv1_2_b'])
W_conv2_1 = tf.Variable(vgg['conv2_1_W'])
b_conv2_1 = tf.Variable(vgg['conv2_1_b'])
W_conv2_2 = tf.Variable(vgg['conv2_2_W'])
b_conv2_2 = tf.Variable(vgg['conv2_2_b'])
del vgg
""" define network """
# face
face_h_conv1_1 = tf.nn.relu(conv2d(face, W_conv1_1) + b_conv1_1)
face_h_conv1_2 = tf.nn.relu(conv2d(face_h_conv1_1, W_conv1_2) + b_conv1_2)
face_h_pool1 = max_pool_2x2(face_h_conv1_2)
face_h_conv2_1 = tf.nn.relu(conv2d(face_h_pool1, W_conv2_1) + b_conv2_1)
face_h_conv2_2 = tf.nn.relu(conv2d(face_h_conv2_1, W_conv2_2) +
b_conv2_2) / 100.
with tf.variable_scope("face"):
face_W_conv2_3 = weight_variable([1, 1, num_face[1], num_face[2]],
std=0.125)
face_b_conv2_3 = bias_variable([num_face[2]], std=0.001)
face_W_conv3_1 = weight_variable([3, 3, num_face[2], num_face[3]],
std=0.06)
face_b_conv3_1 = bias_variable([num_face[3]], std=0.001)
face_W_conv3_2 = weight_variable([3, 3, num_face[3], num_face[3]],
std=0.06)
face_b_conv3_2 = bias_variable([num_face[3]], std=0.001)
face_W_conv4_1 = weight_variable([3, 3, num_face[3], num_face[4]],
std=0.08)
face_b_conv4_1 = bias_variable([num_face[4]], std=0.001)
face_W_conv4_2 = weight_variable([3, 3, num_face[4], num_face[4]],
std=0.07)
face_b_conv4_2 = bias_variable([num_face[4]], std=0.001)
face_W_fc1 = weight_variable([6 * 6 * num_face[4], num_face[5]],
std=0.035)
face_b_fc1 = bias_variable([num_face[5]], std=0.001)
face_W_fc2 = weight_variable([num_face[5], num_face[6]], std=0.1)
face_b_fc2 = bias_variable([num_face[6]], std=0.001)
face_h_conv2_3 = tf.nn.relu(
conv2d(face_h_conv2_2, face_W_conv2_3) + face_b_conv2_3)
face_h_conv2_3_norm = tf.layers.batch_normalization(face_h_conv2_3,
training=is_train,
scale=False,
renorm=True,
name="f_conv2_3")
face_h_conv3_1 = tf.nn.relu(
dilated2d(face_h_conv2_3_norm, face_W_conv3_1, rf[0]) +
face_b_conv3_1)
face_h_conv3_1_norm = tf.layers.batch_normalization(face_h_conv3_1,
training=is_train,
scale=False,
renorm=True,
name="f_conv3_1")
face_h_conv3_2 = tf.nn.relu(
dilated2d(face_h_conv3_1_norm, face_W_conv3_2, rf[1]) +
face_b_conv3_2)
face_h_conv3_2_norm = tf.layers.batch_normalization(face_h_conv3_2,
training=is_train,
scale=False,
renorm=True,
name="f_conv3_2")
face_h_conv4_1 = tf.nn.relu(
dilated2d(face_h_conv3_2_norm, face_W_conv4_1, rf[2]) +
face_b_conv4_1)
face_h_conv4_1_norm = tf.layers.batch_normalization(face_h_conv4_1,
training=is_train,
scale=False,
renorm=True,
name="f_conv4_1")
face_h_conv4_2 = tf.nn.relu(
dilated2d(face_h_conv4_1_norm, face_W_conv4_2, rf[3]) +
face_b_conv4_2)
face_h_conv4_2_norm = tf.layers.batch_normalization(face_h_conv4_2,
training=is_train,
scale=False,
renorm=True,
name="f_conv4_2")
face_h_pool4_flat = tf.reshape(face_h_conv4_2_norm,
[-1, 6 * 6 * num_face[4]])
face_h_fc1 = tf.nn.relu(
tf.matmul(face_h_pool4_flat, face_W_fc1) + face_b_fc1)
face_h_fc1_norm = tf.layers.batch_normalization(face_h_fc1,
training=is_train,
scale=False,
renorm=True,
name="f_fc1")
face_h_fc1_drop = tf.nn.dropout(face_h_fc1_norm, keep_prob)
face_h_fc2 = tf.nn.relu(
tf.matmul(face_h_fc1_drop, face_W_fc2) + face_b_fc2)
face_h_fc2_norm = tf.layers.batch_normalization(face_h_fc2,
training=is_train,
scale=False,
renorm=True,
name="f_fc2")
eye1_h_conv1_1 = tf.nn.relu(conv2d(left_eye, W_conv1_1) + b_conv1_1)
eye1_h_conv1_2 = tf.nn.relu(conv2d(eye1_h_conv1_1, W_conv1_2) + b_conv1_2)
eye1_h_pool1 = max_pool_2x2(eye1_h_conv1_2)
eye1_h_conv2_1 = tf.nn.relu(conv2d(eye1_h_pool1, W_conv2_1) + b_conv2_1)
eye1_h_conv2_2 = tf.nn.relu(conv2d(eye1_h_conv2_1, W_conv2_2) +
b_conv2_2) / 100.
eye2_h_conv1_1 = tf.nn.relu(conv2d(right_eye, W_conv1_1) + b_conv1_1)
eye2_h_conv1_2 = tf.nn.relu(conv2d(eye2_h_conv1_1, W_conv1_2) + b_conv1_2)
eye2_h_pool1 = max_pool_2x2(eye2_h_conv1_2)
eye2_h_conv2_1 = tf.nn.relu(conv2d(eye2_h_pool1, W_conv2_1) + b_conv2_1)
eye2_h_conv2_2 = tf.nn.relu(conv2d(eye2_h_conv2_1, W_conv2_2) +
b_conv2_2) / 100.
with tf.variable_scope("eye"):
# left eye
eye_W_conv2_3 = weight_variable([1, 1, num_eye[1], num_eye[2]],
std=0.125)
eye_b_conv2_3 = bias_variable([num_eye[2]], std=0.001)
eye_W_conv3_1 = weight_variable([3, 3, num_eye[2], num_eye[3]],
std=0.06)
eye_b_conv3_1 = bias_variable([num_eye[3]], std=0.001)
eye_W_conv3_2 = weight_variable([3, 3, num_eye[3], num_eye[3]],
std=0.06)
eye_b_conv3_2 = bias_variable([num_eye[3]], std=0.001)
eye_W_conv4_1 = weight_variable([3, 3, num_eye[3], num_eye[4]],
std=0.06)
eye_b_conv4_1 = bias_variable([num_eye[4]], std=0.001)
eye_W_conv4_2 = weight_variable([3, 3, num_eye[4], num_eye[4]],
std=0.04)
eye_b_conv4_2 = bias_variable([num_eye[4]], std=0.001)
eye1_W_fc1 = weight_variable([4 * 6 * num_eye[4], num_eye[5]],
std=0.026)
eye1_b_fc1 = bias_variable([num_eye[5]], std=0.001)
eye2_W_fc1 = weight_variable([4 * 6 * num_eye[4], num_eye[5]],
std=0.026)
eye2_b_fc1 = bias_variable([num_eye[5]], std=0.001)
eye1_h_conv2_3 = tf.nn.relu(
conv2d(eye1_h_conv2_2, eye_W_conv2_3) + eye_b_conv2_3)
eye1_h_conv2_3_norm = tf.layers.batch_normalization(eye1_h_conv2_3,
training=is_train,
scale=False,
renorm=True,
name="e_conv2_3")
eye1_h_conv3_1 = tf.nn.relu(
dilated2d(eye1_h_conv2_3_norm, eye_W_conv3_1, r[0]) +
eye_b_conv3_1)
eye1_h_conv3_1_norm = tf.layers.batch_normalization(eye1_h_conv3_1,
training=is_train,
scale=False,
renorm=True,
name="e_conv3_1")
eye1_h_conv3_2 = tf.nn.relu(
dilated2d(eye1_h_conv3_1_norm, eye_W_conv3_2, r[1]) +
eye_b_conv3_2)
eye1_h_conv3_2_norm = tf.layers.batch_normalization(eye1_h_conv3_2,
training=is_train,
scale=False,
renorm=True,
name="e_conv3_2")
eye1_h_conv4_1 = tf.nn.relu(
dilated2d(eye1_h_conv3_2_norm, eye_W_conv4_1, r[2]) +
eye_b_conv4_1)
eye1_h_conv4_1_norm = tf.layers.batch_normalization(eye1_h_conv4_1,
training=is_train,
scale=False,
renorm=True,
name="e_conv4_1")
eye1_h_conv4_2 = tf.nn.relu(
dilated2d(eye1_h_conv4_1_norm, eye_W_conv4_2, r[3]) +
eye_b_conv4_2)
eye1_h_conv4_2_norm = tf.layers.batch_normalization(eye1_h_conv4_2,
training=is_train,
scale=False,
renorm=True,
name="e_conv4_2")
eye1_h_pool4_flat = tf.reshape(eye1_h_conv4_2_norm,
[-1, 4 * 6 * num_eye[4]])
eye1_h_fc1 = tf.nn.relu(
tf.matmul(eye1_h_pool4_flat, eye1_W_fc1) + eye1_b_fc1)
eye1_h_fc1_norm = tf.layers.batch_normalization(eye1_h_fc1,
training=is_train,
scale=False,
renorm=True,
name="e1_fc1")
# right eye
eye2_h_conv2_3 = tf.nn.relu(
conv2d(eye2_h_conv2_2, eye_W_conv2_3) + eye_b_conv2_3)
eye2_h_conv2_3_norm = tf.layers.batch_normalization(eye2_h_conv2_3,
training=is_train,
scale=False,
renorm=True,
name="e_conv2_3",
reuse=True)
eye2_h_conv3_1 = tf.nn.relu(
dilated2d(eye2_h_conv2_3_norm, eye_W_conv3_1, r[0]) +
eye_b_conv3_1)
eye2_h_conv3_1_norm = tf.layers.batch_normalization(eye2_h_conv3_1,
training=is_train,
scale=False,
renorm=True,
name="e_conv3_1",
reuse=True)
eye2_h_conv3_2 = tf.nn.relu(
dilated2d(eye2_h_conv3_1_norm, eye_W_conv3_2, r[1]) +
eye_b_conv3_2)
eye2_h_conv3_2_norm = tf.layers.batch_normalization(eye2_h_conv3_2,
training=is_train,
scale=False,
renorm=True,
name="e_conv3_2",
reuse=True)
eye2_h_conv4_1 = tf.nn.relu(
dilated2d(eye2_h_conv3_2_norm, eye_W_conv4_1, r[2]) +
eye_b_conv4_1)
eye2_h_conv4_1_norm = tf.layers.batch_normalization(eye2_h_conv4_1,
training=is_train,
scale=False,
renorm=True,
name="e_conv4_1",
reuse=True)
eye2_h_conv4_2 = tf.nn.relu(
dilated2d(eye2_h_conv4_1_norm, eye_W_conv4_2, r[3]) +
eye_b_conv4_2)
eye2_h_conv4_2_norm = tf.layers.batch_normalization(eye2_h_conv4_2,
training=is_train,
scale=False,
renorm=True,
name="e_conv4_2",
reuse=True)
eye2_h_pool4_flat = tf.reshape(eye2_h_conv4_2_norm,
[-1, 4 * 6 * num_eye[4]])
eye2_h_fc1 = tf.nn.relu(
tf.matmul(eye2_h_pool4_flat, eye2_W_fc1) + eye2_b_fc1)
eye2_h_fc1_norm = tf.layers.batch_normalization(eye2_h_fc1,
training=is_train,
scale=False,
renorm=True,
name="e2_fc1")
# combine both eyes and face
with tf.variable_scope("combine"):
cls1_W_fc2 = weight_variable([num_comb[0], num_comb[1]], std=0.07)
cls1_b_fc2 = bias_variable([num_comb[1]], std=0.001)
cls1_W_fc3 = weight_variable([num_comb[1], 2], std=0.125)
cls1_b_fc3 = bias_variable([2], std=0.001)
cls1_h_fc1_norm = tf.concat(
[face_h_fc2_norm, eye1_h_fc1_norm, eye2_h_fc1_norm], axis=1)
cls1_h_fc1_drop = tf.nn.dropout(cls1_h_fc1_norm, keep_prob)
cls1_h_fc2 = tf.nn.relu(
tf.matmul(cls1_h_fc1_drop, cls1_W_fc2) + cls1_b_fc2)
cls1_h_fc2_norm = tf.layers.batch_normalization(cls1_h_fc2,
training=is_train,
scale=False,
renorm=True,
name="c_fc2")
cls1_h_fc2_drop = tf.nn.dropout(cls1_h_fc2_norm, keep_prob)
t_hat = tf.matmul(cls1_h_fc2_drop, cls1_W_fc3) + cls1_b_fc3
""" bias learning from subject id """
num_bias = (2 * num_subj, )
with tf.variable_scope("bias"):
bias_W_fc = weight_variable([num_bias[0], 2], std=0.125)
b_hat = tf.matmul(subj_id, bias_W_fc)
g_hat = t_hat + b_hat
l2_loss = (1e-2 * tf.nn.l2_loss(W_conv1_1) +
1e-2 * tf.nn.l2_loss(W_conv1_2) +
1e-2 * tf.nn.l2_loss(W_conv2_1) +
1e-2 * tf.nn.l2_loss(W_conv2_2) +
tf.nn.l2_loss(face_W_conv2_3) + tf.nn.l2_loss(face_W_conv3_1) +
tf.nn.l2_loss(face_W_conv3_2) + tf.nn.l2_loss(face_W_conv4_1) +
tf.nn.l2_loss(face_W_conv4_2) + tf.nn.l2_loss(face_W_fc1) +
tf.nn.l2_loss(face_W_fc2) + tf.nn.l2_loss(eye_W_conv2_3) +
tf.nn.l2_loss(eye_W_conv3_1) + tf.nn.l2_loss(eye_W_conv3_2) +
tf.nn.l2_loss(eye_W_conv4_1) + tf.nn.l2_loss(eye_W_conv4_2) +
tf.nn.l2_loss(eye1_W_fc1) + tf.nn.l2_loss(eye2_W_fc1) +
tf.nn.l2_loss(cls1_W_fc2) + tf.nn.l2_loss(cls1_W_fc3))
return g_hat, t_hat, bias_W_fc, l2_loss
def inception(self, x, shapes, name):
assert len(shapes) == 7
dims = x.get_shape().as_list()[-1]
with tf.variable_scope(name):
with tf.variable_scope('Branch_0'):
branch_0 = tf_utils.conv2d(
x,
name='Conv2d_0a_1x1',
shape=[1, 1, dims, shapes[0]],
strides=[1, 1, 1, 1],
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation_func)
# print('branch_3:', branch_0)
with tf.variable_scope('Branch_1'):
branch_1 = tf_utils.conv2d(
x,
name='Conv2d_0a_1x1',
shape=[1, 1, dims, shapes[1]],
strides=[1, 1, 1, 1],
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation_func)
# print('branch_3:', branch_1)
branch_1 = tf_utils.conv2d(
branch_1,
name='Conv2d_0b_3x3',
shape=[3, 3, shapes[1], shapes[2]],
strides=[1, 1, 1, 1],
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation_func)
# print('branch_3:', branch_1)
with tf.variable_scope('Branch_2'):
branch_2 = tf_utils.conv2d(
x,
name='Conv2d_0a_1x1',
shape=[1, 1, dims, shapes[3]],
strides=[1, 1, 1, 1],
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation_func)
# print('branch_3:', branch_2)
branch_2 = tf_utils.conv2d(
branch_2,
name='Conv2d_0b_3x3',
shape=[3, 3, shapes[3], shapes[4]],
strides=[1, 1, 1, 1],
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation_func)
branch_2 = tf_utils.conv2d(
branch_2,
name='Conv2d_0c_3x3',
shape=[3, 3, shapes[4], shapes[5]],
strides=[1, 1, 1, 1],
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation_func)
# print('branch_3:', branch_2)
with tf.variable_scope('Branch_3'):
branch_3 = tf.nn.avg_pool(x,
ksize=[1, 3, 3, 1],
strides=[1, 1, 1, 1],
padding='SAME',
name='AvgPool_0a_3x3')
# print('branch_3:', branch_3)
branch_3 = tf_utils.conv2d(
branch_3,
name='Conv2d_0b_1x1',
shape=[1, 1, dims, shapes[6]],
strides=[1, 1, 1, 1],
use_bn=self.use_bn,
trainable=self.trainable,
activation_func=self.activation_func)
# print('branch_3:', branch_3)
x = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
return x
def get_model(imgs, is_training, weight_decay=0.0, bn_decay=None):
"""
Args:
imgs: (batch_size, im_dim, im_dim, 3)
is_training: a boolean placeholder.
Return:
shape: (batch_size, vol_dim, vol_dim, vol_dim, 1)
"""
batch_size = imgs.get_shape()[0].value
im_dim = imgs.get_shape()[1].value
########
with tf.variable_scope('Encoding'):
# (batch_size, 64, 64, 64)
net = tf_utils.conv2d(imgs,
64, [7, 7],
padding='SAME',
stride=[2, 2],
bn=True,
is_training=is_training,
scope='conv1',
bn_decay=bn_decay,
weight_decay=weight_decay,
activation_fn=tf.nn.elu)
# (batch_size, 32, 32, 64)
net = tf_utils.conv2d(net,
64, [5, 5],
padding='SAME',
stride=[2, 2],
bn=True,
is_training=is_training,
scope='conv2',
bn_decay=bn_decay,
weight_decay=weight_decay,
activation_fn=tf.nn.elu)
# (batch_size, 16, 16, 128)
net = tf_utils.conv2d(net,
128, [5, 5],
padding='SAME',
stride=[2, 2],
bn=True,
is_training=is_training,
scope='conv3',
bn_decay=bn_decay,
weight_decay=weight_decay,
activation_fn=tf.nn.elu)
# (batch_size, 8, 8, 128)
net = tf_utils.conv2d(net,
128, [3, 3],
padding='SAME',
stride=[2, 2],
bn=True,
is_training=is_training,
scope='conv4',
bn_decay=bn_decay,
weight_decay=weight_decay,
activation_fn=tf.nn.elu)
# (batch_size, 4, 4, 256)
net = tf_utils.conv2d(net,
256, [3, 3],
padding='SAME',
stride=[2, 2],
bn=True,
is_training=is_training,
scope='conv5',
bn_decay=bn_decay,
weight_decay=weight_decay,
activation_fn=tf.nn.elu)
# (batch_size, 1, 1, 512)
net = tf_utils.conv2d(net,
512, [4, 4],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv6',
bn_decay=bn_decay,
weight_decay=weight_decay,
activation_fn=tf.nn.elu)
########
with tf.variable_scope('Latent_variable'):
net = tf.reshape(net, [batch_size, 512])
net = tf_utils.fully_connected(net,
512,
scope="fc1",
weight_decay=weight_decay,
activation_fn=tf.nn.elu,
bn=True,
bn_decay=bn_decay,
is_training=is_training)
net = tf_utils.fully_connected(net,
128 * 4 * 4 * 4,
scope="fc2",
weight_decay=weight_decay,
activation_fn=tf.nn.elu,
bn=True,
bn_decay=bn_decay,
is_training=is_training)
net = tf.reshape(net, [batch_size, 4, 4, 4, 128])
########
with tf.variable_scope('Decoding'):
# (batch_size, 8, 8, 8, 64)
net = tf_utils.conv3d_transpose(net,
64, [3, 3, 3],
scope="deconv1",
stride=[2, 2, 2],
padding='SAME',
weight_decay=weight_decay,
activation_fn=tf.nn.elu,
bn=True,
bn_decay=bn_decay,
is_training=is_training)
# (batch_size, 16, 16, 16, 32)
net = tf_utils.conv3d_transpose(net,
32, [3, 3, 3],
scope="deconv2",
stride=[2, 2, 2],
padding='SAME',
weight_decay=weight_decay,
activation_fn=tf.nn.elu,
bn=True,
bn_decay=bn_decay,
is_training=is_training)
# (batch_size, 32, 32, 32, 32)
net = tf_utils.conv3d_transpose(net,
32, [3, 3, 3],
scope="deconv3",
stride=[2, 2, 2],
padding='SAME',
weight_decay=weight_decay,
activation_fn=tf.nn.elu,
bn=True,
bn_decay=bn_decay,
is_training=is_training)
# (batch_size, 64, 64, 64, 16)
net = tf_utils.conv3d_transpose(net,
24, [3, 3, 3],
scope="deconv4",
stride=[2, 2, 2],
padding='SAME',
weight_decay=weight_decay,
activation_fn=tf.nn.elu,
bn=True,
bn_decay=bn_decay,
is_training=is_training)
# (batch_size, 64, 64, 64, 1)
net = tf_utils.conv3d(net,
1, [3, 3, 3],
scope="deconv5",
stride=[1, 1, 1],
padding='SAME',
weight_decay=weight_decay,
activation_fn=None,
bn=True,
bn_decay=bn_decay,
is_training=is_training)
return net
convs_comb = []
dw_h_convs_comb = OrderedDict()
for layer in range(0, layers):
stddev = np.sqrt(2 / (filter_size**2 * features))
if layer == 0:
w1 = weight_variable(
[filter_size, filter_size, n_channels // 3, features], stddev)
else:
w1 = weight_variable(
[filter_size, filter_size, features, features], stddev)
b1 = bias_variable([features])
conv1 = conv2d(in_node1, w1, keep_prob)
dw_h_convs1[layer] = tf.nn.relu(conv1 + b1)
conv2 = conv2d(in_node2, w1, keep_prob)
dw_h_convs2[layer] = tf.nn.relu(conv2 + b1)
conv3 = conv2d(in_node3, w1, keep_prob)
dw_h_convs3[layer] = tf.nn.relu(conv3 + b1)
weights.append(w1)
biases.append(b1)
convs1.append(conv1)
convs2.append(conv2)
convs3.append(conv3)
if layer < layers - 1:
for layer in range(0, layers):
features = 2**layer * features_root
stddev = np.sqrt(2 / (filter_size**2 * features))
if layer == 0:
w1 = weight_variable(
[filter_size, filter_size, n_channels, features], stddev)
else:
w1 = weight_variable(
[filter_size, filter_size, features // 2, features], stddev)
w2 = weight_variable([filter_size, filter_size, features, features],
stddev)
b1 = bias_variable([features])
b2 = bias_variable([features])
conv1 = conv2d(in_node, w1, keep_prob)
tmp_h_conv = tf.nn.relu(conv1 + b1)
conv2 = conv2d(tmp_h_conv, w2, keep_prob)
dw_h_convs[layer] = tf.nn.relu(conv2 + b2)
weights.append((w1, w2))
biases.append((b1, b2))
convs.append((conv1, conv2))
if layer < layers - 1:
pools[layer] = max_pool(dw_h_convs[layer], pool_size)
in_node = pools[layer]
in_node = dw_h_convs[layers - 1]
# up layers
