def basic_detectModel(img, is_training, bn_decay, num_class): #512->4
with tf.variable_scope('conv_unit1_G'):
out = basic_tf.conv2d(img, 16, [3, 3], 'conv_11', [1, 1], 'SAME')
out = basic_tf.max_pool2d(out, [2, 2], 'maxpool_11', [2, 2], 'SAME')
out = basic_tf.conv2d(out, 32, [3, 3], 'conv_12', [1, 1], 'SAME')
out = basic_tf.max_pool2d(out, [2, 2], 'maxpool_12', [2, 2], 'SAME')
out = basic_tf.conv2d(out, 64, [3, 3], 'conv_13', [1, 1], 'SAME')
out = basic_tf.max_pool2d(out, [2, 2], 'maxpool_13', [2, 2], 'SAME')
out = basic_tf.conv2d(out, 128, [3, 3], 'conv_14', [1, 1], 'SAME')
out = basic_tf.max_pool2d(out, [2, 2], 'maxpool_14', [2, 2], 'SAME')
out = basic_tf.conv2d(out, 256, [3, 3], 'conv_15', [1, 1], 'SAME')
out = basic_tf.max_pool2d(out, [2, 2], 'maxpool_15', [2, 2], 'SAME')
out = basic_tf.conv2d(out, 512, [3, 3], 'conv_16', [1, 1], 'SAME')
out = basic_tf.max_pool2d(out, [2, 2], 'maxpool_16', [2, 2], 'SAME')
out = basic_tf.max_pool2d(out, [2, 2], 'maxpool_17', [2, 2], 'SAME')
with tf.variable_scope('conv_unit2'):
out1 = basic_tf.conv2d(out, 1024, [3, 3], 'conv_21', [1, 1], 'SAME')
out1 = basic_tf.conv2d(out1, 512, [1, 1], 'conv_22', [1, 1], 'SAME')
out1 = basic_tf.avg_pool2d(out1, [2, 2], 'pre_avepool', [2, 2], 'SAME')
with tf.variable_scope('fully_connected_unit_G'):
out2 = tf.reshape(out1, (int(out1._shape[0]), -1)) #b,8192
#out2 = basic_tf.fully_connected(out2,4096,'fc1')
#out2 = basic_tf.dropout(out2,is_training,'dp1',0.5)
out2 = basic_tf.fully_connected(out2, 1024, 'fc2')
out2 = basic_tf.dropout(out2, is_training, 'dp2', 0.5)
out2 = basic_tf.fully_connected(out2, 128, 'fc3')
out2 = basic_tf.dropout(out2, is_training, 'dp3', 0.5)
with tf.variable_scope('output_unit_G'):
pred = basic_tf.fully_connected(out2, (num_class + 4),
'fc4',
activation_fn=None)
return pred
def brute_classify(img, num_class, is_training, bn_decay): #512->4
with tf.variable_scope('conv_unit1'):
out = basic_tf.conv2d(img, 16, [3, 3], 'conv_11', [1, 1], 'SAME')
out = basic_tf.max_pool2d(out, [2, 2], 'maxpool_11', [2, 2], 'SAME')
out = basic_tf.conv2d(out, 32, [3, 3], 'conv_12', [1, 1], 'SAME')
out = basic_tf.max_pool2d(out, [2, 2], 'maxpool_12', [2, 2], 'SAME')
out = basic_tf.conv2d(out, 64, [3, 3], 'conv_13', [1, 1], 'SAME')
out = basic_tf.max_pool2d(out, [2, 2], 'maxpool_13', [2, 2], 'SAME')
out = basic_tf.conv2d(out, 128, [3, 3], 'conv_14', [1, 1], 'SAME')
out = basic_tf.max_pool2d(out, [2, 2], 'maxpool_14', [2, 2], 'SAME')
out = basic_tf.conv2d(out, 256, [3, 3], 'conv_15', [1, 1], 'SAME')
out = basic_tf.max_pool2d(out, [2, 2], 'maxpool_15', [2, 2], 'SAME')
out = basic_tf.conv2d(out, 512, [3, 3], 'conv_16', [1, 1], 'SAME')
out = basic_tf.max_pool2d(out, [2, 2], 'maxpool_16', [2, 2], 'SAME')
out = basic_tf.max_pool2d(out, [2, 2], 'maxpool_17', [2, 2], 'SAME')
with tf.variable_scope('conv_unit2'):
out1 = basic_tf.conv2d(out, 1024, [3, 3], 'conv_21', [1, 1], 'SAME')
out1 = basic_tf.conv2d(out1, 512, [1, 1], 'conv_22', [1, 1], 'SAME')
out1 = basic_tf.avg_pool2d(out1, [2, 2], 'pre_avepool', [2, 2], 'SAME')
with tf.variable_scope('fully_connected_unit'):
out2 = tf.reshape(out1, (int(out1._shape[0]), -1)) #b,4096
out2 = basic_tf.fully_connected(out2, 1024, 'fc1')
out2 = basic_tf.dropout(out2, is_training, 'dp1', 0.5)
out2 = basic_tf.fully_connected(out2, 128, 'fc2')
out2 = basic_tf.dropout(out2, is_training, 'dp2', 0.5)
pred = basic_tf.fully_connected(out2, num_class, 'fc3')
print(pred)
return pred
def output_down_size_unit(input, cout, is_training, bn_decay, scope, bn=True):
b = input.get_shape()[0].value
out = basic_tf.avg_pool2d(input, [3, 3],
scope='ave_pool_output',
stride=[1, 1],
padding='SAME')
out = basic_tf.conv2d(out,
cout, [1, 1],
padding='SAME',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv_output1',
bn_decay=bn_decay) #down_size
out = basic_tf.max_pool2d(out, [3, 3],
scope='max_pool_output',
stride=[2, 2],
padding='SAME')
out = tf.reshape(out, (b, -1)) #(-1,2048)
return out
def pointnet_AB_module(xyz,
points,
m,
r,
ns,
mlp,
mlp2,
group_all,
is_training,
bn_decay,
scope,
bn=True,
pooling='max',
tnet_spec=None,
knn=False,
use_xyz=True):
''' PointNet Set Abstraction (SA) Module
Input:
xyz: (b, n, 3) TF tensor
points: (b, n, c) TF tensor
m: int32 -- #points sampled in farthest point sampling
r: float32 -- search radius in local region
ns: int32 -- how many points in each local region
mlp: list of int32 -- output size for MLP on each point
mlp2: list of int32 -- output size for MLP on each region
group_all: bool -- group all points into one PC if set true, OVERRIDE
npoint, radius and nsample settings
use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features
Return:
new_xyz: (b,m, 3) TF tensor
new_points: (b,m, mlp[-1] or mlp2[-1]) TF tensor
idx: (b,m, ns) int32 -- indices for local regions
'''
with tf.variable_scope(scope) as sc:
#Sampling&Grouping
if group_all:
ns = xyz.get_shape()[1].value
new_xyz, new_points, idx, grouped_xyz = sample_and_group_all(
xyz, points, use_xyz)
else:
new_xyz, new_points, idx, grouped_xyz = sample_and_group(
m, r, ns, xyz, points, tnet_spec, knn,
use_xyz) #here we got the idx from sampling&grouping
print('convolution')
#convolutional layer mlp(handling the new_points we got)
for i, num_out_channel in enumerate(mlp):
print('conv', i)
new_points = basic_tf.conv2d(new_points,
num_out_channel, [1, 1],
padding='VALID',
stride=[1, 1],
bn=bn,
is_training=is_training,
scope='conv%d' % (i),
bn_decay=bn_decay)
#pooling
print('pooling')
if pooling == 'avg':
new_points = basic_tf.avg_pool2d(new_points, [1, ns],
stride=[1, 1],
padding='VALID',
scope='avgpool1')
elif pooling == 'weighted_avg':
with tf.variable_scope('weighted_avg1'):
dists = tf.norm(grouped_xyz, axis=-1, ord=2, keep_dims=True)
exp_dists = tf.exp(-dists * 5)
weights = exp_dists / tf.reduce_sum(
exp_dists, axis=2, keep_dims=True) # (b, m, ns, 1)
new_points *= weights # (b, m, ns, mlp[-1])
new_points = tf.reduce_sum(new_points, axis=2, keep_dims=True)
elif pooling == 'max':
new_points = tf.reduce_max(new_points, axis=[2], keep_dims=True)
elif pooling == 'min':
new_points = basic_tf.max_pool2d(-1 * new_points, [1, ns],
stride=[1, 1],
padding='VALID',
scope='minpool1')
elif pooling == 'max_and_avg':
avg_points = basic_tf.max_pool2d(new_points, [1, ns],
stride=[1, 1],
padding='VALID',
scope='maxpool1')
max_points = basic_tf.avg_pool2d(new_points, [1, ns],
stride=[1, 1],
padding='VALID',
scope='avgpool1')
new_points = tf.concat([avg_points, max_points], axis=-1)
#convolutional layer mlp2
if mlp2 is None: mlp2 = []
for i, num_out_channel in enumerate(mlp2):
new_points = basic_tf.conv2d(new_points,
num_out_channel, [1, 1],
padding='VALID',
stride=[1, 1],
bn=bn,
is_training=is_training,
scope='conv_post_%d' % (i),
bn_decay=bn_decay)
#prepare the result
new_points = tf.squeeze(new_points, [2]) # (b,m,mlp2[-1])
print('1 turn')
return new_xyz, new_points, idx
def color_net(rgb, is_training, bn_decay=None):
with tf.variable_scope('input_layer'):
h = rgb.get_shape()[1].value
w = rgb.get_shape()[2].value
b = rgb.get_shape()[0].value
og = [h, w]
end_data = {}
end_data['rgb_data'] = rgb
#end_data['srgb_data']=srgb #both of them are been normalized
out1 = basic_tf.conv2d(rgb, 96, [1, 1], 'input_conv', [1, 1], 'SAME')
out1 = basic_tf.max_pool2d(out1, [2, 2], 'input_pool', [1, 1], 'SAME')
with tf.variable_scope('intermidate_layer'):
for i, kernels in enumerate(list_of_kernel):
mlps = list_of_mlplist[i]
out1 = bm.ssc_color_info_abstraction(out1,
mlps,
is_training=is_training,
bn_decay=bn_decay,
scope='ssc_section_%d' % (i),
kernel_size=kernels,
bn=True)
if i == 0:
hyper_colume = out1
else:
hyper_colume = tf.concat([hyper_colume, out1], -1)
hyper_colume = basic_tf.avg_pool2d(hyper_colume, [2, 2],
'medium_avepool', [1, 1], 'SAME')
c = hyper_colume.get_shape()[-1].value
print(hyper_colume.shape)
hyper_colume = tf.reshape(hyper_colume, (b * h * w, c))
with tf.variable_scope('output_layer'):
out = basic_tf.fully_connected(hyper_colume,
256,
bn=True,
is_training=is_training,
scope='fc2',
bn_decay=bn_decay)
out = basic_tf.dropout(out,
keep_prob=0.5,
is_training=is_training,
scope='dp2')
out = basic_tf.fully_connected(out,
64,
bn=True,
is_training=is_training,
scope='fc3',
bn_decay=bn_decay)
out = basic_tf.dropout(out,
keep_prob=0.5,
is_training=is_training,
scope='dp3')
out = basic_tf.fully_connected(out,
3,
bn=True,
is_training=is_training,
scope='fc4',
bn_decay=bn_decay)
pred = tf.reshape(out, (b, h, w, 3))
return pred, end_data
