def resnet_with_bottleneck(self,input,is_training,layer_from_2=[3,4,6,3],first_kernel=7,first_stride=2,first_pool=True,stride=2):
input_shape = input.get_shape().as_list()[1:]
conv=ops.conv2d(input,'initial_conv',[first_kernel,first_kernel,input_shape[2],64],[1,first_stride,first_stride,1])
if first_pool:
conv=ops.max_pool(conv, [1, 3, 3, 1], [1, 2, 2, 1])
for i in range(layer_from_2[0]):
conv=ops.residual_bottleneck_block(conv,'Block_1_'+str(i),is_training,256,kernel=3,first_block=True,stride=stride)
for i in range(layer_from_2[1]):
conv=ops.residual_bottleneck_block(conv,'Block_2_'+str(i),is_training,512,kernel=3,first_block=True,stride=stride)
for i in range(layer_from_2[2]):
conv=ops.residual_bottleneck_block(conv,'Block_3_'+str(i),is_training,1024,kernel=3,first_block=True,stride=stride)
for i in range(layer_from_2[3]):
conv=ops.residual_bottleneck_block(conv,'Block_4_'+str(i),is_training,2048,kernel=3,first_block=True,stride=stride)
with tf.variable_scope('unit'):
conv = ops.batch_normalization(conv,is_training)
conv = tf.nn.relu(conv)
conv = ops.global_avg_pool(conv)
conv =ops.flatten(conv)
with tf.variable_scope('logit'):
conv = ops.get_hidden_layer(conv,'output',self.no_of_classes,'none')
return conv
def build_model(input_img):
conv1 = conv(input_img,
7,
7,
64,
1,
1,
biased=False,
relu=False,
name='conv1')
bn_conv1 = batch_normalization(conv1,
is_training=is_training,
activation_fn=tf.nn.relu,
name='bn_conv1')
conv2 = conv(bn_conv1,
3,
3,
64,
1,
1,
biased=False,
relu=False,
name='conv2')
bn_conv2 = batch_normalization(conv2,
is_training=is_training,
activation_fn=tf.nn.relu,
name='bn_conv2')
pool1, pool1_indices = tf.nn.max_pool_with_argmax(bn_conv2,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool1')
print(pool1)
print('INd', pool1_indices)
conv3 = conv(pool1,
3,
3,
128,
1,
1,
biased=False,
relu=False,
name='conv3')
bn_conv3 = batch_normalization(conv3,
is_training=is_training,
activation_fn=tf.nn.relu,
name='bn_conv3')
conv4 = conv(bn_conv3,
3,
3,
128,
1,
1,
biased=False,
relu=False,
name='conv4')
bn_conv4 = batch_normalization(conv4,
is_training=is_training,
activation_fn=tf.nn.relu,
name='bn_conv4')
pool2, pool2_indices = tf.nn.max_pool_with_argmax(bn_conv4,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool2')
print(pool2)
print('INd', pool2_indices)
conv5 = conv(pool2,
3,
3,
256,
1,
1,
biased=False,
relu=False,
name='conv5')
bn_conv5 = batch_normalization(conv5,
is_training=is_training,
activation_fn=tf.nn.relu,
name='bn_conv5')
conv6 = conv(bn_conv5,
3,
3,
256,
1,
1,
biased=False,
relu=False,
name='conv6')
bn_conv6 = batch_normalization(conv6,
is_training=is_training,
activation_fn=tf.nn.relu,
name='bn_conv6')
conv7 = conv(bn_conv6,
3,
3,
256,
1,
1,
biased=False,
relu=False,
name='conv7')
bn_conv7 = batch_normalization(conv7,
is_training=is_training,
activation_fn=tf.nn.relu,
name='bn_conv7')
pool3, pool3_indices = tf.nn.max_pool_with_argmax(bn_conv7,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool3')
conv8 = conv(pool3,
3,
3,
512,
1,
1,
biased=False,
relu=False,
name='conv8')
bn_conv8 = batch_normalization(conv8,
is_training=is_training,
activation_fn=tf.nn.relu,
name='bn_conv8')
conv9 = conv(bn_conv8,
3,
3,
512,
1,
1,
biased=False,
relu=False,
name='conv9')
bn_conv9 = batch_normalization(conv9,
is_training=is_training,
activation_fn=tf.nn.relu,
name='bn_conv9')
conv10 = conv(bn_conv9,
3,
3,
512,
1,
1,
biased=False,
relu=False,
name='conv10')
bn_conv10 = batch_normalization(conv10,
is_training=is_training,
activation_fn=tf.nn.relu,
name='bn_conv10')
pool4, pool4_indices = tf.nn.max_pool_with_argmax(bn_conv10,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool4')
conv11 = conv(pool4,
3,
3,
512,
1,
1,
biased=False,
relu=False,
name='conv11')
bn_conv11 = batch_normalization(conv11,
is_training=is_training,
activation_fn=tf.nn.relu,
name='bn_conv11')
conv12 = conv(bn_conv11,
3,
3,
512,
1,
1,
biased=False,
relu=False,
name='conv12')
bn_conv12 = batch_normalization(conv12,
is_training=is_training,
activation_fn=tf.nn.relu,
name='bn_conv12')
conv13 = conv(bn_conv12,
3,
3,
512,
1,
1,
biased=False,
relu=False,
name='conv13')
bn_conv13 = batch_normalization(conv13,
is_training=is_training,
activation_fn=tf.nn.relu,
name='bn_conv13')
pool5, pool5_indices = tf.nn.max_pool_with_argmax(bn_conv13,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool5')
conv14 = conv(pool5,
3,
3,
4096,
1,
1,
biased=False,
relu=False,
name='conv14')
bn_conv14 = batch_normalization(conv14,
is_training=is_training,
activation_fn=tf.nn.relu,
name='bn_conv14')
deconv6 = conv(bn_conv14,
1,
1,
512,
1,
1,
biased=False,
relu=False,
name='conv15')
bn_deconv6 = batch_normalization(deconv6,
is_training=is_training,
activation_fn=tf.nn.relu,
name='bn_deconv6')
#print(bn_deconv6)
unpool_5 = unpool_with_argmax(bn_deconv6,
ind=pool5_indices,
name="unpool_5")
deconv5 = conv(unpool_5,
5,
5,
512,
1,
1,
biased=False,
relu=False,
name='deconv5')
bn_deconv5 = batch_normalization(deconv5,
is_training=is_training,
activation_fn=tf.nn.relu,
name='bn_deconv5')
#print(bn_deconv5)
unpool_4 = unpool_with_argmax(bn_deconv5,
ind=pool4_indices,
name="unpool_4")
deconv4 = conv(unpool_4,
5,
5,
256,
1,
1,
biased=False,
relu=False,
name='deconv4')
bn_deconv4 = batch_normalization(deconv4,
is_training=is_training,
activation_fn=tf.nn.relu,
name='bn_deconv4')
unpool_3 = unpool_with_argmax(bn_deconv4,
ind=pool3_indices,
name="unpool_3")
deconv3 = conv(unpool_3,
5,
5,
128,
1,
1,
biased=False,
relu=False,
name='deconv3')
bn_deconv3 = batch_normalization(deconv3,
is_training=is_training,
activation_fn=tf.nn.relu,
name='bn_decon3')
print(bn_deconv3)
unpool_2 = unpool_with_argmax(bn_deconv3,
ind=pool2_indices,
name="unpool_2")
deconv2 = conv(unpool_2,
5,
5,
64,
1,
1,
biased=False,
relu=False,
name='deconv2')
bn_deconv2 = batch_normalization(deconv2,
is_training=is_training,
activation_fn=tf.nn.relu,
name='bn_deconv2')
print(bn_deconv2)
unpool_1 = unpool_with_argmax(bn_deconv2,
ind=pool1_indices,
name="unpool_1")
deconv1 = conv(unpool_1,
5,
5,
32,
1,
1,
biased=False,
relu=False,
name='deconv1')
bn_deconv1 = batch_normalization(deconv1,
is_training=is_training,
activation_fn=tf.nn.relu,
name='bn_deconv1')
deconv0 = conv(bn_deconv1,
5,
5,
1,
1,
1,
biased=False,
relu=False,
name='deconv0')
# bn_deconv0=batch_normalization(deconv0,is_training=is_training, activation_fn=tf.nn.relu, name='bn_deconv0')
# pred = sigmoid(bn_deconv0,name='pred')
return deconv0
def inception_v2(self, input, is_training):
input_shape = input.get_shape().as_list()[1:]
conv = ops.conv2d(input,'conv1',kernel_size=[7, 7, input_shape[2], 64], strides=[1, 2, 2, 1])
conv = tf.nn.relu(conv)
conv = ops.max_pool(conv, size=[1, 3, 3, 1], strides=[1, 2, 2, 1])
conv = tf.nn.local_response_normalization(conv, depth_radius=2, alpha=2e-05, beta=0.75)
conv = ops.conv2d(conv,'conv2', kernel_size=[1, 1, 64, 64], strides=[1, 1, 1, 1], padding='VALID')
conv = tf.nn.relu(conv)
conv_shape = conv.get_shape().as_list()[1:]
conv = ops.conv2d(conv,'conv3', kernel_size=[3, 3, conv_shape[2], 192], strides=[1, 1, 1, 1])
conv = tf.nn.relu(conv)
conv = tf.nn.local_response_normalization(conv, depth_radius=2, alpha=2e-05, beta=0.75)
conv = ops.max_pool(conv, size=[1, 3, 3, 1], strides=[1, 2, 2, 1])
conv = ops.inception_v2_block(conv,'Block_1',is_training, out_channel={'1': 64, '3': 128, '5': 32},
reduced_out_channel={'3': 96, '5': 16, 'p': 32})
conv = ops.batch_normalization(conv, is_training)
conv = tf.nn.relu(conv)
conv = ops.inception_v2_block(conv,'Block_2', is_training, out_channel={'1': 128, '3': 192, '5': 96},
reduced_out_channel={'3': 128, '5': 32, 'p': 64})
conv = ops.batch_normalization(conv, is_training)
conv = tf.nn.relu(conv)
conv = ops.max_pool(conv, size=[1, 3, 3, 1], strides=[1, 2, 2, 1])
conv = ops.inception_v2_block(conv,'Block_3', is_training, out_channel={'1': 192, '3': 208, '5': 48},
reduced_out_channel={'3': 96, '5': 16, 'p': 64})
conv = ops.batch_normalization(conv, is_training)
conv = tf.nn.relu(conv)
conv = ops.inception_v2_block(conv,'Block_4', is_training, out_channel={'1': 160, '3': 224, '5': 64},
reduced_out_channel={'3': 112, '5': 24, 'p': 64})
conv = ops.batch_normalization(conv, is_training)
conv = tf.nn.relu(conv)
conv = ops.inception_v2_block(conv,'Block_5', is_training, out_channel={'1': 128, '3': 256, '5': 64},
reduced_out_channel={'3': 128, '5': 24, 'p': 64})
conv = ops.batch_normalization(conv, is_training)
conv = tf.nn.relu(conv)
conv = ops.inception_v2_block(conv,'Block_6', is_training, out_channel={'1': 112, '3': 228, '5': 64},
reduced_out_channel={'3': 144, '5': 32, 'p': 64})
conv = ops.batch_normalization(conv, is_training)
conv = tf.nn.relu(conv)
conv = ops.inception_v2_block(conv,'Block_7', is_training, out_channel={'1': 256, '3': 320, '5': 128},
reduced_out_channel={'3': 160, '5': 32, 'p': 128})
conv = ops.batch_normalization(conv, is_training)
conv = tf.nn.relu(conv)
conv = ops.max_pool(conv, size=[1, 3, 3, 1], strides=[1, 2, 2, 1])
conv = ops.inception_v2_block(conv,'Block_8', is_training, out_channel={'1': 256, '3': 320, '5': 128},
reduced_out_channel={'3': 160, '5': 32, 'p': 128})
conv = ops.batch_normalization(conv, is_training)
conv = tf.nn.relu(conv)
conv = ops.inception_v2_block(conv,'Block_9', is_training, out_channel={'1': 384, '3': 384, '5': 128},
reduced_out_channel={'3': 192, '5': 48, 'p': 128})
conv = ops.batch_normalization(conv, is_training)
conv = tf.nn.relu(conv)
conv = ops.global_avg_pool(conv)
conv = ops.flatten(conv)
conv = tf.nn.dropout(conv, 0.4)
conv = ops.get_hidden_layer(conv,'output_layer',1000, 'none', 'xavier')
return conv