def __denseBlock(self,
x,
growth_rate=16,
num_layers=8,
kernel_size=[3, 3],
layer=0):
dense_block_output = x
for i in range(num_layers):
'''
In Paper <Densely Connected Convolutional Networks>
each composite function contains three consecutive operations:
batch normalization(BN), followed by a rectified linear unit (ReLU) and a 3*3 convolution (Conv).
'''
if self.is_bn:
x = tl.BatchNormLayer(x, name='denseblock%d/BN%d' % (layer, i))
x = ReluLayer(x, name='denseblock%d/relu%d' % (layer, i))
x = tl.Conv2d(x,
growth_rate,
kernel_size,
name='denseblock%d/conv%d' % (layer, i))
# concat the output of layer
dense_block_output = tl.ConcatLayer([dense_block_output, x],
concat_dim=3,
name='denseblock%d/concat%d' %
(layer, i))
x = dense_block_output
return dense_block_output
def build_model(self, n_dense_blocks=8, scale=8, subpixel=False):
print("Building DenseNet...")
norm_input = utils.normalize_color_tf(self.input)
norm_target = utils.normalize_color_tf(self.target)
x = tl.InputLayer(norm_input, name='input_layer')
'''
extract low level feature
In Paper <Densely Connected Convolutional Networks>,the filter size here is 7*7
and followed by a max pool layer
upscale_input = tl.Conv2d(x,self.feature_size, [7, 7], act = None, name = 'conv0')
upscale_input = tl.MaxPool2d(upscale_input, [3,3], [2,2], name = 'maxpool0')
'''
with tf.variable_scope("low_level_features"):
x = tl.Conv2d(x, 128, [3, 3], act=None, name='conv0')
conv1 = x
with tf.variable_scope("dense_blocks"):
for i in range(n_dense_blocks):
x = self.dense_block(x, 16, 8, (3, 3), layer=i)
x = tl.ConcatLayer([conv1, x],
concat_dim=3,
name='dense%d/concat_output' % i)
with tf.variable_scope("bottleneck_layer"):
'''
bottleneck layer
In Paper <Image Super-Resolution Using Dense Skip Connections>
The channel here is 256
'''
x = tl.Conv2d(x, 256, (1, 1), act=None, name='bottleneck')
with tf.variable_scope("upscale_module"):
'''
Paper <Densely Connected Convolutional Networks> using deconv layers to upscale the output
we provide two methods here: deconv, subpixel
'''
if subpixel:
x = utils.subpixel_upsample(x, 128, scale)
else:
x = utils.deconv_upsample(x, 128, (3, 3), scale)
with tf.variable_scope("reconstruction_layer"):
output = tl.Conv2d(x,
self.n_channels, (3, 3),
act=tf.nn.relu,
name='reconstruction')
self.output = tf.clip_by_value(output.outputs, 0.0, 1.0, name="output")
self.calculate_loss(norm_target, self.output)
conf = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
self.sess = tf.Session(config=conf)
self.saver = tf.train.Saver()
print("Done building!")
def dense_block(self,
x,
growth_rate=16,
n_conv=8,
kernel_size=(3, 3),
layer=0):
dense_block_output = x
for i in range(n_conv):
x = tl.BatchNormLayer(x, name='dense_%d/bn_%d' % (layer, i))
x = ReluLayer(x, name='dense_%d/relu_%d' % (layer, i))
x = tl.Conv2d(x,
growth_rate,
kernel_size,
name='dense_%d/conv_%d' % (layer, i))
# concat the output of layer
dense_block_output = tl.ConcatLayer([dense_block_output, x],
concat_dim=3,
name='dense_%d/concat_%d' %
(layer, i))
x = dense_block_output
return x
def buildModel(self):
print("Building Recurrent WaveletNet...")
# input layers
x = []
for i in range(self.num_layers):
x.append(tl.InputLayer(self.input, name='inputlayer%d' % (i + 1)))
'''
extract low level feature
In Paper <Densely Connected Convolutional Networks>,the filter size here is 7*7
and followed by a max pool layer
upscale_input = tl.Conv2d(x,self.feature_size, [7, 7], act = None, name = 'conv0')
upscale_input = tl.MaxPool2d(upscale_input, [3,3], [2,2], name = 'maxpool0')
'''
upscale_input = tl.Conv2d(x,
self.feature_size, [3, 3],
act=None,
name='conv0')
# dense-net
'''
using SRDenseNet_All model :
all levels of features(output of dense block) are combined
via skip connections as input for reconstructing the HR images
x
|\
| \
| dense blockl layer
| /
|/
x1
|
[x,x1] (concat)
'''
x = upscale_input
for i in range(self.dense_block):
# the output of dense blocl
x = self.__denseBlock(x,
self.growth_rate,
self.num_layers, [3, 3],
layer=i)
# concat
upscale_input = tl.ConcatLayer([upscale_input, x],
concat_dim=3,
name='denseblock%d/concat_output' %
(i))
'''
bottleneck layer
In Paper <Image Super-Resolution Using Dense Skip Connections>
The channel here is 256
'''
upscale_input = tl.Conv2d(upscale_input,
self.bottleneck_size, [1, 1],
act=None,
name='bottleneck')
'''
Paper <Densely Connected Convolutional Networks> using deconv layer to upscale the output
here provide two methods here: deconv, subpixel
'''
# subpixel to upscale
if self.is_subpixel:
upscale_output = tl.Conv2d(upscale_input,
self.bottleneck_size, [3, 3],
act=None,
name='s1/1')
upscale_output = tl.SubpixelConv2d(upscale_output,
scale=2,
act=tf.nn.relu,
name='pixelshufferx2/1')
upscale_output = tl.Conv2d(upscale_output,
self.bottleneck_size, [3, 3],
act=None,
name='s1/2')
upscale_output = tl.SubpixelConv2d(upscale_output,
scale=2,
act=tf.nn.relu,
name='pixelshufferx2/2')
if self.scale == 8:
upscale_output = tl.Conv2d(upscale_output,
self.bottleneck_size, [3, 3],
act=None,
name='s1/3')
upscale_output = tl.SubpixelConv2d(upscale_output,
scale=2,
act=tf.nn.relu,
name='pixelshufferx2/3')
# deconv to upscale
else:
# if scale is 8,using 3 deconv layers
# is scale is 4,using 2 deconv layers
width, height = int(upscale_input.outputs.shape[1]), int(
upscale_input.outputs.shape[2])
upscale_output, feature_size, width, height = self.__deconv(
upscale_input,
self.bottleneck_size,
width,
height,
name='deconv0')
upscale_output, feature_size, width, height = self.__deconv(
upscale_output, feature_size, width, height, name='deconv1')
if self.scale == 8:
upscale_output, feature_size, width, height = self.__deconv(
upscale_output,
feature_size,
width,
height,
name='deconv2')
# reconstruction layer
output = tl.Conv2d(upscale_output,
self.output_channels, [3, 3],
act=tf.nn.relu,
name='lastLayer')
self.output = output.outputs
self.cacuLoss(output)
# Tensorflow graph setup... session, saver, etc.
self.sess = tf.Session()
self.saver = tf.train.Saver()
print("Done building!")
def behaviour_net(self):
init = tf.truncated_normal_initializer(stddev=0.1)
zero_init = tf.zeros_initializer()
screen_input = layers.InputLayer(self.screen_in, name="screen_inputs")
screen_input_bn = layers.BatchNormLayer(screen_input, name="screen_bn")
conv_depth=64
conv1_scr = layers.Conv2d(screen_input_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init, b_init=init,
name="conv1_scr")
conv1_scr_bn = layers.BatchNormLayer(conv1_scr, name="screen_bn_1")
conv2_scr = layers.Conv2d(conv1_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init, b_init=init,
name="conv2_scr")
conv2_scr_bn = layers.BatchNormLayer(conv2_scr, name="screen_bn_2")
conv3_scr = layers.Conv2d(conv2_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init, b_init=init,
name="conv3_scr")
conv3_scr_bn = layers.BatchNormLayer(conv3_scr, name="screen_bn_3")
conv4_scr = layers.Conv2d(conv3_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init, b_init=init,
name="conv4_scr")
conv4_scr_bn = layers.BatchNormLayer(conv4_scr, name="screen_bn_4")
conv5_scr = layers.Conv2d(conv4_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init, b_init=init,
name="conv5_scr")
conv5_scr_bn = layers.BatchNormLayer(conv5_scr, name="screen_bn_5")
conv6_scr = layers.Conv2d(conv5_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init, b_init=init,
name="conv6_scr")
conv6_scr_bn = layers.BatchNormLayer(conv6_scr, name="screen_bn_6")
conv7_scr = layers.Conv2d(conv6_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init, b_init=init,
name="conv7_scr")
conv7_scr_bn = layers.BatchNormLayer(conv7_scr, name="screen_bn_7")
conv8_scr = layers.Conv2d(conv7_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init, b_init=init,
name="conv8_scr")
conv8_scr_bn = layers.BatchNormLayer(conv8_scr, name="screen_bn_8")
conv9_scr = layers.Conv2d(conv8_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init, b_init=init,
name="conv9_scr")
conv9_scr_bn = layers.BatchNormLayer(conv9_scr, name="screen_bn_9")
conv10_scr = layers.Conv2d(conv9_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init, b_init=init,
name="conv10_scr")
conv10_scr_bn = layers.BatchNormLayer(conv10_scr, name="screen_bn_10")
scr_GAP = layers.PoolLayer(conv10_scr_bn, ksize=[1, 84, 84, 1], padding="VALID", pool=tf.nn.avg_pool,
name="scr_GAP")
scr_info = layers.FlattenLayer(scr_GAP, name="scr_flattened")
minimap_input = layers.InputLayer(self.minimap_in, name="mini_in")
minimap_input_bn = layers.BatchNormLayer(minimap_input, name="minimap_bn")
conv1_mini = layers.Conv2d(minimap_input_bn, 32, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv1_mini")
conv1_mini_bn = layers.BatchNormLayer(conv1_mini, name="mini_bn_1")
conv2_mini = layers.Conv2d(conv1_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv2_mini")
conv2_mini_bn = layers.BatchNormLayer(conv2_mini, name="mini_bn_2")
conv3_mini = layers.Conv2d(conv2_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv3_mini")
conv3_mini_bn = layers.BatchNormLayer(conv3_mini, name="mini_bn_3")
conv4_mini = layers.Conv2d(conv3_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv4_mini")
conv4_mini_bn = layers.BatchNormLayer(conv4_mini, name="mini_bn_4")
conv5_mini = layers.Conv2d(conv4_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv5_mini")
conv5_mini_bn = layers.BatchNormLayer(conv5_mini, name="mini_bn_5")
conv6_mini = layers.Conv2d(conv5_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv6_mini")
conv6_mini_bn = layers.BatchNormLayer(conv6_mini, name="mini_bn_6")
conv7_mini = layers.Conv2d(conv6_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv7_mini")
conv7_mini_bn = layers.BatchNormLayer(conv7_mini, name="mini_bn_7")
conv8_mini = layers.Conv2d(conv7_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv8_mini")
conv8_mini_bn = layers.BatchNormLayer(conv8_mini, name="mini_bn_8")
conv9_mini = layers.Conv2d(conv8_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv9_mini")
conv9_mini_bn = layers.BatchNormLayer(conv9_mini, name="mini_bn_9")
conv10_mini = layers.Conv2d(conv9_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv10_mini")
conv10_mini_bn = layers.BatchNormLayer(conv10_mini, name="mini_bn_10")
mini_GAP = layers.PoolLayer(conv10_mini_bn, ksize=[1, 64, 64, 1], padding="VALID", pool=tf.nn.avg_pool,
name="mini_GAP")
mini_info = layers.FlattenLayer(mini_GAP, name="mini_flattened")
multi_select_in = layers.InputLayer(self.multi_select_in, name="multi_select")
select_info = layers.FlattenLayer(multi_select_in, name="select_flattened")
select_info_bn = layers.BatchNormLayer(select_info, name="select_bn")
info_combined = layers.ConcatLayer([scr_info, mini_info, select_info_bn], name="info_all")
dense1 = layers.DenseLayer(info_combined, n_units=1000, act=tf.nn.relu, W_init=init, b_init=init, name="Dense1")
dense1_bn = layers.BatchNormLayer(dense1, name="dense1_bn")
dense2 = layers.DenseLayer(dense1_bn, n_units=1000, act=tf.nn.relu, W_init=init, b_init=init,
name="Dense2")
dense2_bn = layers.BatchNormLayer(dense2, name="dense2_bn")
dense3 = layers.DenseLayer(dense2_bn, n_units=1000, act=tf.nn.relu, W_init=init, b_init=init,
name="Dense3")
dense3_bn = layers.BatchNormLayer(dense3, name="dense3_bn")
dense4 = layers.DenseLayer(dense3_bn, n_units=1000, act=tf.nn.relu, W_init=init, b_init=init,
name="Dense4")
dense4_bn = layers.BatchNormLayer(dense4, name="dense4_bn")
dense5 = layers.DenseLayer(dense4_bn, n_units=1000, act=tf.nn.relu, W_init=init, b_init=init,
name="Dense5")
dense5_bn = layers.BatchNormLayer(dense5, name="dense5_bn")
dense6 = layers.DenseLayer(dense5_bn, n_units=1000, act=tf.nn.relu, W_init=init, b_init=init,
name="Dense6")
dense6_bn = layers.BatchNormLayer(dense6, name="dense6_bn")
self.q=layers.DenseLayer(dense6_bn, n_units=7057, W_init=zero_init, b_init=zero_init,
name="q")
self.Q_behave=self.q.outputs
def target_net(self):
init = tf.truncated_normal_initializer(stddev=0.1)
zero_init = tf.zeros_initializer()
screen_input = layers.InputLayer(self.screen_in, name="screen_inputs_t")
screen_input_bn = layers.BatchNormLayer(screen_input, name="screen_bn_t")
conv_depth = 64
conv1_scr = layers.Conv2d(screen_input_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init,
name="conv1_scr_t")
conv1_scr_bn = layers.BatchNormLayer(conv1_scr, name="screen_bn_1_t")
conv2_scr = layers.Conv2d(conv1_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init,
name="conv2_scr_t")
conv2_scr_bn = layers.BatchNormLayer(conv2_scr, name="screen_bn_2_t")
conv3_scr = layers.Conv2d(conv2_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init,
name="conv3_scr_t")
conv3_scr_bn = layers.BatchNormLayer(conv3_scr, name="screen_bn_3_t")
conv4_scr = layers.Conv2d(conv3_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init,
name="conv4_scr_t")
conv4_scr_bn = layers.BatchNormLayer(conv4_scr, name="screen_bn_4_t")
conv5_scr = layers.Conv2d(conv4_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init,
name="conv5_scr_t")
conv5_scr_bn = layers.BatchNormLayer(conv5_scr, name="screen_bn_5_t")
conv6_scr = layers.Conv2d(conv5_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init,
name="conv6_scr_t")
conv6_scr_bn = layers.BatchNormLayer(conv6_scr, name="screen_bn_6_t")
conv7_scr = layers.Conv2d(conv6_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init,
name="conv7_scr_t")
conv7_scr_bn = layers.BatchNormLayer(conv7_scr, name="screen_bn_7_t")
conv8_scr = layers.Conv2d(conv7_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init,
name="conv8_scr_t")
conv8_scr_bn = layers.BatchNormLayer(conv8_scr, name="screen_bn_8_t")
conv9_scr = layers.Conv2d(conv8_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init,
name="conv9_scr_t")
conv9_scr_bn = layers.BatchNormLayer(conv9_scr, name="screen_bn_9_t")
conv10_scr = layers.Conv2d(conv9_scr_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init,
name="conv10_scr_t")
conv10_scr_bn = layers.BatchNormLayer(conv10_scr, name="screen_bn_10_t")
scr_GAP = layers.PoolLayer(conv10_scr_bn, ksize=[1, 84, 84, 1], padding="VALID", pool=tf.nn.avg_pool,
name="scr_GAP_t")
scr_info = layers.FlattenLayer(scr_GAP, name="scr_flattened_t")
minimap_input = layers.InputLayer(self.minimap_in, name="mini_in_t")
minimap_input_bn = layers.BatchNormLayer(minimap_input, name="minimap_bn_t")
conv1_mini = layers.Conv2d(minimap_input_bn, 32, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv1_mini_t")
conv1_mini_bn = layers.BatchNormLayer(conv1_mini, name="mini_bn_1_t")
conv2_mini = layers.Conv2d(conv1_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv2_mini_t")
conv2_mini_bn = layers.BatchNormLayer(conv2_mini, name="mini_bn_2_t")
conv3_mini = layers.Conv2d(conv2_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv3_mini_t")
conv3_mini_bn = layers.BatchNormLayer(conv3_mini, name="mini_bn_3_t")
conv4_mini = layers.Conv2d(conv3_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv4_mini_t")
conv4_mini_bn = layers.BatchNormLayer(conv4_mini, name="mini_bn_4_t")
conv5_mini = layers.Conv2d(conv4_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv5_mini_t")
conv5_mini_bn = layers.BatchNormLayer(conv5_mini, name="mini_bn_5_t")
conv6_mini = layers.Conv2d(conv5_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv6_mini_t")
conv6_mini_bn = layers.BatchNormLayer(conv6_mini, name="mini_bn_6_t")
conv7_mini = layers.Conv2d(conv6_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv7_mini_t")
conv7_mini_bn = layers.BatchNormLayer(conv7_mini, name="mini_bn_7_t")
conv8_mini = layers.Conv2d(conv7_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv8_mini_t")
conv8_mini_bn = layers.BatchNormLayer(conv8_mini, name="mini_bn_8_t")
conv9_mini = layers.Conv2d(conv8_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv9_mini_t")
conv9_mini_bn = layers.BatchNormLayer(conv9_mini, name="mini_bn_9_t")
conv10_mini = layers.Conv2d(conv9_mini_bn, conv_depth, (3, 3), act=tf.nn.relu, padding="SAME", W_init=init,
b_init=init, name="conv10_mini_t")
conv10_mini_bn = layers.BatchNormLayer(conv10_mini, name="mini_bn_10_t")
mini_GAP = layers.PoolLayer(conv10_mini_bn, ksize=[1, 64, 64, 1], padding="VALID", pool=tf.nn.avg_pool,
name="mini_GAP_t")
mini_info = layers.FlattenLayer(mini_GAP, name="mini_flattened_t")
multi_select_in = layers.InputLayer(self.multi_select_in, name="multi_select_t")
select_info = layers.FlattenLayer(multi_select_in, name="select_flattened_t")
select_info_bn = layers.BatchNormLayer(select_info, name="select_bn_t")
info_combined = layers.ConcatLayer([scr_info, mini_info, select_info_bn], name="info_all_t")
dense1 = layers.DenseLayer(info_combined, n_units=1000, act=tf.nn.relu, W_init=init, b_init=init,
name="Dense1_t")
dense1_bn = layers.BatchNormLayer(dense1, name="dense1_bn_t")
dense2 = layers.DenseLayer(dense1_bn, n_units=1000, act=tf.nn.relu, W_init=init, b_init=init,
name="Dense2_t")
dense2_bn = layers.BatchNormLayer(dense2, name="dense2_bn_t")
dense3 = layers.DenseLayer(dense2_bn, n_units=1000, act=tf.nn.relu, W_init=init, b_init=init,
name="Dense3_t")
dense3_bn = layers.BatchNormLayer(dense3, name="dense3_bn_t")
dense4 = layers.DenseLayer(dense3_bn, n_units=1000, act=tf.nn.relu, W_init=init, b_init=init,
name="Dense4_t")
dense4_bn = layers.BatchNormLayer(dense4, name="dense4_bn_t")
dense5 = layers.DenseLayer(dense4_bn, n_units=1000, act=tf.nn.relu, W_init=init, b_init=init,
name="Dense5_t")
dense5_bn = layers.BatchNormLayer(dense5, name="dense5_bn_t")
dense6 = layers.DenseLayer(dense5_bn, n_units=1000, act=tf.nn.relu, W_init=init, b_init=init,
name="Dense6_t")
dense6_bn = layers.BatchNormLayer(dense6, name="dense6_bn_t")
self.q_target = layers.DenseLayer(dense6_bn, n_units=7057, W_init=zero_init, b_init=zero_init,
name="q_t")
self.target_params = self.q_target.all_params
self.Q_target = self.q_target.outputs