0.相关github网址
- 原版github代码-caffe实现
- tensorflow实现，相关版本较低，python2，本文根据此代码迁移到python3上面。
- pytorch实现，但将骨干模型从goglenet改成了resnet，实验效果得到提升，但没公布预训练权重
- 注意，有一个人体姿态检测的网络也叫做PoseNet，检索的时候注意不要弄混二者
- PoseNet论文总结
1.任务背景
- Image Matching Challenge 2023
- 大致任务：根据图片推算相机位姿，包括3*3的旋转矩阵和3维的位置矩阵
- 数据描述：train_labels.csv
- dataset：数据集名字
- scene：场景
- image_path：图像路径
- rotation_matrix：3*3的旋转矩阵
- translation_vector：3维的位置矩阵
结构如下：
2.四元数与旋转矩阵的转换
def matrix2quaternion(m):
w = ((np.trace(m) + 1) ** 0.5) / 2
x = (m[2][1] - m[1][2]) / (4 * w)
y = (m[0][2] - m[2][0]) / (4 * w)
z = (m[1][0] - m[0][1]) / (4 * w)
return w,x,y,z
def quaternion2matrix(q):
w,x,y,z = q
return np.array([[1-2*y*y-2*z*z, 2*x*y-2*z*w, 2*x*z+2*y*w],
[2*x*y+2*z*w, 1-2*x*x-2*z*z, 2*y*z-2*x*w],
[2*x*z-2*y*w, 2*y*z+2*x*w, 1-2*x*x-2*y*y]])
3.train_labels.csv文件处理
- 需要根据rotation_matrix的数据计算出对应的四元数并存储到新列rotation_matrix_quaternion中：（使用列表推导式和map实现）
def m(a):
a = a.split(';')
a = [float(i) for i in a]
A = np.array([[a[0],a[1],a[2]],
[a[3],a[4],a[5]],
[a[6],a[7],a[8]]])
return matrix2quaternion(A)
change_train_labels = 1
if change_train_labels:
train_labels = pd.read_csv('/kaggle/input/image-matching-challenge-2023/train/train_labels.csv')
train_labels['rotation_matrix_quaternion'] = [i for i in map(m,train_labels['rotation_matrix'])]
train_labels.to_csv('/kaggle/working/my_train_labels.csv')
- 从
'/kaggle/input/image-matching-challenge-2023/train/train_labels.csv'读入，写入'/kaggle/working/my_train_labels.csv'
4.网络模型相关
DEFAULT_PADDING = 'SAME'
def layer(op):
'''Decorator for composable network layers.'''
def layer_decorated(self, *args, **kwargs):
name = kwargs.setdefault('name', self.get_unique_name(op.__name__))
if len(self.terminals) == 0:
raise RuntimeError('No input variables found for layer %s.' % name)
elif len(self.terminals) == 1:
layer_input = self.terminals[0]
else:
layer_input = list(self.terminals)
layer_output = op(self, layer_input, *args, **kwargs)
self.layers[name] = layer_output
self.feed(layer_output)
return self
return layer_decorated
class Network(object):
def __init__(self, inputs, trainable=True):
self.inputs = inputs
self.terminals = []
self.layers = dict(inputs)
self.trainable = trainable
self.use_dropout = tf.placeholder_with_default(tf.constant(1.0),
shape=[],
name='use_dropout')
self.setup()
def setup(self):
'''Construct the network. '''
raise NotImplementedError('Must be implemented by the subclass.')
def load(self, data_path, session, ignore_missing=False):
'''Load network weights.
data_path: The path to the numpy-serialized network weights
session: The current TensorFlow session
ignore_missing: If true, serialized weights for missing layers are ignored.
'''
data_dict = np.load(data_path,allow_pickle=True,encoding="latin1").item()
for op_name in data_dict:
with tf.variable_scope(op_name, reuse=True):
for param_name, data in data_dict[op_name].items():
try:
var = tf.get_variable(param_name)
session.run(var.assign(data))
except ValueError:
if not ignore_missing:
raise
def feed(self, *args):
'''Set the input(s) for the next operation by replacing the terminal nodes.
The arguments can be either layer names or the actual layers.
'''
assert len(args) != 0
self.terminals = []
for fed_layer in args:
if isinstance(fed_layer, str):
try:
fed_layer = self.layers[fed_layer]
except KeyError:
raise KeyError('Unknown layer name fed: %s' % fed_layer)
self.terminals.append(fed_layer)
return self
def get_output(self):
'''Returns the current network output.'''
return self.terminals[-1]
def get_unique_name(self, prefix):
'''Returns an index-suffixed unique name for the given prefix.
This is used for auto-generating layer names based on the type-prefix.
'''
ident = sum(t.startswith(prefix) for t, _ in self.layers.items()) + 1
return '%s_%d' % (prefix, ident)
def make_var(self, name, shape):
'''Creates a new TensorFlow variable.'''
return tf.get_variable(name, shape, trainable=self.trainable)
def validate_padding(self, padding):
'''Verifies that the padding is one of the supported ones.'''
assert padding in ('SAME', 'VALID')
@layer
def conv(self,
input,
k_h,
k_w,
c_o,
s_h,
s_w,
name,
relu=True,
padding=DEFAULT_PADDING,
group=1,
biased=True):
self.validate_padding(padding)
c_i = input.get_shape()[-1]
assert c_i % group == 0
assert c_o % group == 0
convolve = lambda i, k: tf.nn.conv2d(i, k, [1, s_h, s_w, 1], padding=padding)
with tf.variable_scope(name) as scope:
kernel = self.make_var('weights', shape=[k_h, k_w, int(int(c_i) / group), c_o])
if group == 1:
output = convolve(input, kernel)
else:
input_groups = tf.split(3, group, input)
kernel_groups = tf.split(3, group, kernel)
output_groups = [convolve(i, k) for i, k in zip(input_groups, kernel_groups)]
output = tf.concat(3, output_groups)
if biased:
biases = self.make_var('biases', [c_o])
output = tf.nn.bias_add(output, biases)
if relu:
output = tf.nn.relu(output, name=scope.name)
return output
@layer
def relu(self, input, name):
return tf.nn.relu(input, name=name)
@layer
def max_pool(self, input, k_h, k_w, s_h, s_w, name, padding=DEFAULT_PADDING):
self.validate_padding(padding)
return tf.nn.max_pool(input,
ksize=[1, k_h, k_w, 1],
strides=[1, s_h, s_w, 1],
padding=padding,
name=name)
@layer
def avg_pool(self, input, k_h, k_w, s_h, s_w, name, padding=DEFAULT_PADDING):
self.validate_padding(padding)
return tf.nn.avg_pool(input,
ksize=[1, k_h, k_w, 1],
strides=[1, s_h, s_w, 1],
padding=padding,
name=name)
@layer
def lrn(self, input, radius, alpha, beta, name, bias=1.0):
return tf.nn.local_response_normalization(input,
depth_radius=radius,
alpha=alpha,
beta=beta,
bias=bias,
name=name)
@layer
def concat(self, inputs, axis, name):
return tf.concat(values=inputs, axis=axis, name=name)
@layer
def add(self, inputs, name):
return tf.add_n(inputs, name=name)
@layer
def fc(self, input, num_out, name, relu=True):
with tf.variable_scope(name) as scope:
input_shape = input.get_shape()
if input_shape.ndims == 4:
dim = 1
for d in input_shape[1:].as_list():
dim *= d
feed_in = tf.reshape(input, [-1, dim])
else:
feed_in, dim = (input, input_shape[-1].value)
weights = self.make_var('weights', shape=[dim, num_out])
biases = self.make_var('biases', [num_out])
op = tf.nn.relu_layer if relu else tf.nn.xw_plus_b
fc = op(feed_in, weights, biases, name=scope.name)
return fc
@layer
def softmax(self, input, name):
input_shape = map(lambda v: v.value, input.get_shape())
if len(input_shape) > 2:
if input_shape[1] == 1 and input_shape[2] == 1:
input = tf.squeeze(input, squeeze_dims=[1, 2])
else:
raise ValueError('Rank 2 tensor input expected for softmax!')
return tf.nn.softmax(input, name)
@layer
def batch_normalization(self, input, name, scale_offset=True, relu=False):
with tf.variable_scope(name) as scope:
shape = [input.get_shape()[-1]]
if scale_offset:
scale = self.make_var('scale', shape=shape)
offset = self.make_var('offset', shape=shape)
else:
scale, offset = (None, None)
output = tf.nn.batch_normalization(
input,
mean=self.make_var('mean', shape=shape),
variance=self.make_var('variance', shape=shape),
offset=offset,
scale=scale,
variance_epsilon=1e-5,
name=name)
if relu:
output = tf.nn.relu(output)
return output
@layer
def dropout(self, input, keep_prob, name):
keep = 1 - self.use_dropout + (self.use_dropout * keep_prob)
return tf.nn.dropout(input, keep, name=name)
class GoogLeNet(Network):
def setup(self):
(self.feed('data')
.conv(7, 7, 64, 2, 2, name='conv1')
.max_pool(3, 3, 2, 2, name='pool1')
.lrn(2, 2e-05, 0.75, name='norm1')
.conv(1, 1, 64, 1, 1, name='reduction2')
.conv(3, 3, 192, 1, 1, name='conv2')
.lrn(2, 2e-05, 0.75, name='norm2')
.max_pool(3, 3, 2, 2, name='pool2')
.conv(1, 1, 96, 1, 1, name='icp1_reduction1')
.conv(3, 3, 128, 1, 1, name='icp1_out1'))
(self.feed('pool2')
.conv(1, 1, 16, 1, 1, name='icp1_reduction2')
.conv(5, 5, 32, 1, 1, name='icp1_out2'))
(self.feed('pool2')
.max_pool(3, 3, 1, 1, name='icp1_pool')
.conv(1, 1, 32, 1, 1, name='icp1_out3'))
(self.feed('pool2')
.conv(1, 1, 64, 1, 1, name='icp1_out0'))
(self.feed('icp1_out0',
'icp1_out1',
'icp1_out2',
'icp1_out3')
.concat(3, name='icp2_in')
.conv(1, 1, 128, 1, 1, name='icp2_reduction1')
.conv(3, 3, 192, 1, 1, name='icp2_out1'))
(self.feed('icp2_in')
.conv(1, 1, 32, 1, 1, name='icp2_reduction2')
.conv(5, 5, 96, 1, 1, name='icp2_out2'))
(self.feed('icp2_in')
.max_pool(3, 3, 1, 1, name='icp2_pool')
.conv(1, 1, 64, 1, 1, name='icp2_out3'))
(self.feed('icp2_in')
.conv(1, 1, 128, 1, 1, name='icp2_out0'))
(self.feed('icp2_out0',
'icp2_out1',
'icp2_out2',
'icp2_out3')
.concat(3, name='icp2_out')
.max_pool(3, 3, 2, 2, name='icp3_in')
.conv(1, 1, 96, 1, 1, name='icp3_reduction1')
.conv(3, 3, 208, 1, 1, name='icp3_out1'))
(self.feed('icp3_in')
.conv(1, 1, 16, 1, 1, name='icp3_reduction2')
.conv(5, 5, 48, 1, 1, name='icp3_out2'))
(self.feed('icp3_in')
.max_pool(3, 3, 1, 1, name='icp3_pool')
.conv(1, 1, 64, 1, 1, name='icp3_out3'))
(self.feed('icp3_in')
.conv(1, 1, 192, 1, 1, name='icp3_out0'))
(self.feed('icp3_out0',
'icp3_out1',
'icp3_out2',
'icp3_out3')
.concat(3, name='icp3_out')
.avg_pool(5, 5, 3, 3, padding='VALID', name='cls1_pool')
.conv(1, 1, 128, 1, 1, name='cls1_reduction_pose')
.fc(1024, name='cls1_fc1_pose')
.fc(3, relu=False, name='cls1_fc_pose_xyz'))
(self.feed('cls1_fc1_pose')
.fc(4, relu=False, name='cls1_fc_pose_wpqr'))
(self.feed('icp3_out')
.conv(1, 1, 112, 1, 1, name='icp4_reduction1')
.conv(3, 3, 224, 1, 1, name='icp4_out1'))
(self.feed('icp3_out')
.conv(1, 1, 24, 1, 1, name='icp4_reduction2')
.conv(5, 5, 64, 1, 1, name='icp4_out2'))
(self.feed('icp3_out')
.max_pool(3, 3, 1, 1, name='icp4_pool')
.conv(1, 1, 64, 1, 1, name='icp4_out3'))
(self.feed('icp3_out')
.conv(1, 1, 160, 1, 1, name='icp4_out0'))
(self.feed('icp4_out0',
'icp4_out1',
'icp4_out2',
'icp4_out3')
.concat(3, name='icp4_out')
.conv(1, 1, 128, 1, 1, name='icp5_reduction1')
.conv(3, 3, 256, 1, 1, name='icp5_out1'))
(self.feed('icp4_out')
.conv(1, 1, 24, 1, 1, name='icp5_reduction2')
.conv(5, 5, 64, 1, 1, name='icp5_out2'))
(self.feed('icp4_out')
.max_pool(3, 3, 1, 1, name='icp5_pool')
.conv(1, 1, 64, 1, 1, name='icp5_out3'))
(self.feed('icp4_out')
.conv(1, 1, 128, 1, 1, name='icp5_out0'))
(self.feed('icp5_out0',
'icp5_out1',
'icp5_out2',
'icp5_out3')
.concat(3, name='icp5_out')
.conv(1, 1, 144, 1, 1, name='icp6_reduction1')
.conv(3, 3, 288, 1, 1, name='icp6_out1'))
(self.feed('icp5_out')
.conv(1, 1, 32, 1, 1, name='icp6_reduction2')
.conv(5, 5, 64, 1, 1, name='icp6_out2'))
(self.feed('icp5_out')
.max_pool(3, 3, 1, 1, name='icp6_pool')
.conv(1, 1, 64, 1, 1, name='icp6_out3'))
(self.feed('icp5_out')
.conv(1, 1, 112, 1, 1, name='icp6_out0'))
(self.feed('icp6_out0',
'icp6_out1',
'icp6_out2',
'icp6_out3')
.concat(3, name='icp6_out')
.avg_pool(5, 5, 3, 3, padding='VALID', name='cls2_pool')
.conv(1, 1, 128, 1, 1, name='cls2_reduction_pose')
.fc(1024, name='cls2_fc1')
.fc(3, relu=False, name='cls2_fc_pose_xyz'))
(self.feed('cls2_fc1')
.fc(4, relu=False, name='cls2_fc_pose_wpqr'))
(self.feed('icp6_out')
.conv(1, 1, 160, 1, 1, name='icp7_reduction1')
.conv(3, 3, 320, 1, 1, name='icp7_out1'))
(self.feed('icp6_out')
.conv(1, 1, 32, 1, 1, name='icp7_reduction2')
.conv(5, 5, 128, 1, 1, name='icp7_out2'))
(self.feed('icp6_out')
.max_pool(3, 3, 1, 1, name='icp7_pool')
.conv(1, 1, 128, 1, 1, name='icp7_out3'))
(self.feed('icp6_out')
.conv(1, 1, 256, 1, 1, name='icp7_out0'))
(self.feed('icp7_out0',
'icp7_out1',
'icp7_out2',
'icp7_out3')
.concat(3, name='icp7_out')
.max_pool(3, 3, 2, 2, name='icp8_in')
.conv(1, 1, 160, 1, 1, name='icp8_reduction1')
.conv(3, 3, 320, 1, 1, name='icp8_out1'))
(self.feed('icp8_in')
.conv(1, 1, 32, 1, 1, name='icp8_reduction2')
.conv(5, 5, 128, 1, 1, name='icp8_out2'))
(self.feed('icp8_in')
.max_pool(3, 3, 1, 1, name='icp8_pool')
.conv(1, 1, 128, 1, 1, name='icp8_out3'))
(self.feed('icp8_in')
.conv(1, 1, 256, 1, 1, name='icp8_out0'))
(self.feed('icp8_out0',
'icp8_out1',
'icp8_out2',
'icp8_out3')
.concat(3, name='icp8_out')
.conv(1, 1, 192, 1, 1, name='icp9_reduction1')
.conv(3, 3, 384, 1, 1, name='icp9_out1'))
(self.feed('icp8_out')
.conv(1, 1, 48, 1, 1, name='icp9_reduction2')
.conv(5, 5, 128, 1, 1, name='icp9_out2'))
(self.feed('icp8_out')
.max_pool(3, 3, 1, 1, name='icp9_pool')
.conv(1, 1, 128, 1, 1, name='icp9_out3'))
(self.feed('icp8_out')
.conv(1, 1, 384, 1, 1, name='icp9_out0'))
(self.feed('icp9_out0',
'icp9_out1',
'icp9_out2',
'icp9_out3')
.concat(3, name='icp9_out')
.avg_pool(7, 7, 1, 1, padding='VALID', name='cls3_pool')
.fc(2048, name='cls3_fc1_pose')
.fc(3, relu=False, name='cls3_fc_pose_xyz'))
(self.feed('cls3_fc1_pose')
.fc(4, relu=False, name='cls3_fc_pose_wpqr'))
5.图像预处理部分
- PoseNet的输入图像是224*224分辨率的，加上本任务对图像尺寸视角等敏感，不适合直接放缩，所以采用中心裁剪的办法，中心裁剪函数如下：
def centeredCrop(img, output_side_length):
height, width, depth = img.shape
new_height = output_side_length
new_width = output_side_length
if height > width:
new_height = output_side_length * height / width
else:
new_width = output_side_length * width / height
height_offset = (new_height - output_side_length) / 2
width_offset = (new_width - output_side_length) / 2
cropped_img = img[height_offset:height_offset + output_side_length,
width_offset:width_offset + output_side_length]
return cropped_img
- 预处理函数，在这个函数中会调用上面的中心裁剪函数，并对图像的每个通道进行归一化，并完成维度转换，方便送入PyTorch的网络：
def preprocess(images):
images_out = []
images_cropped = []
for i in tqdm(range(len(images)):
X = cv2.imread(images[i])
X = centeredCrop(X, 224)
images_cropped.append(X)
N = 0
mean = np.zeros((1, 3, 224, 224))
for X in tqdm(images_cropped):
X = np.transpose(X,(2,0,1))
mean[0][0] += X[0,:,:]
mean[0][1] += X[1,:,:]
mean[0][2] += X[2,:,:]
N += 1
mean[0] /= N
for X in tqdm(images_cropped):
X = np.transpose(X,(2,0,1))
X = X - mean
X = np.squeeze(X)
X = np.transpose(X, (1,2,0))
images_out.append(X)
return images_out
- 如果调试的时候，为了快速验证，可以不处理全部函数，比如
len(images)*0+2：
DEFAULT_PADDING = 'SAME'
def layer(op):
'''Decorator for composable network layers.'''
def layer_decorated(self, *args, **kwargs):
name = kwargs.setdefault('name', self.get_unique_name(op.__name__))
if len(self.terminals) == 0:
raise RuntimeError('No input variables found for layer %s.' % name)
elif len(self.terminals) == 1:
layer_input = self.terminals[0]
else:
layer_input = list(self.terminals)
layer_output = op(self, layer_input, *args, **kwargs)
self.layers[name] = layer_output
self.feed(layer_output)
return self
return layer_decorated
class Network(object):
def __init__(self, inputs, trainable=True):
self.inputs = inputs
self.terminals = []
self.layers = dict(inputs)
self.trainable = trainable
self.use_dropout = tf.placeholder_with_default(tf.constant(1.0),
shape=[],
name='use_dropout')
self.setup()
def setup(self):
'''Construct the network. '''
raise NotImplementedError('Must be implemented by the subclass.')
def load(self, data_path, session, ignore_missing=False):
'''Load network weights.
data_path: The path to the numpy-serialized network weights
session: The current TensorFlow session
ignore_missing: If true, serialized weights for missing layers are ignored.
'''
data_dict = np.load(data_path,allow_pickle=True,encoding="latin1").item()
for op_name in data_dict:
with tf.variable_scope(op_name, reuse=True):
for param_name, data in data_dict[op_name].items():
try:
var = tf.get_variable(param_name)
session.run(var.assign(data))
except ValueError:
if not ignore_missing:
raise
def feed(self, *args):
'''Set the input(s) for the next operation by replacing the terminal nodes.
The arguments can be either layer names or the actual layers.
'''
assert len(args) != 0
self.terminals = []
for fed_layer in args:
if isinstance(fed_layer, str):
try:
fed_layer = self.layers[fed_layer]
except KeyError:
raise KeyError('Unknown layer name fed: %s' % fed_layer)
self.terminals.append(fed_layer)
return self
def get_output(self):
'''Returns the current network output.'''
return self.terminals[-1]
def get_unique_name(self, prefix):
'''Returns an index-suffixed unique name for the given prefix.
This is used for auto-generating layer names based on the type-prefix.
'''
ident = sum(t.startswith(prefix) for t, _ in self.layers.items()) + 1
return '%s_%d' % (prefix, ident)
def make_var(self, name, shape):
'''Creates a new TensorFlow variable.'''
return tf.get_variable(name, shape, trainable=self.trainable)
def validate_padding(self, padding):
'''Verifies that the padding is one of the supported ones.'''
assert padding in ('SAME', 'VALID')
@layer
def conv(self,
input,
k_h,
k_w,
c_o,
s_h,
s_w,
name,
relu=True,
padding=DEFAULT_PADDING,
group=1,
biased=True):
self.validate_padding(padding)
c_i = input.get_shape()[-1]
assert c_i % group == 0
assert c_o % group == 0
convolve = lambda i, k: tf.nn.conv2d(i, k, [1, s_h, s_w, 1], padding=padding)
with tf.variable_scope(name) as scope:
kernel = self.make_var('weights', shape=[k_h, k_w, int(int(c_i) / group), c_o])
if group == 1:
output = convolve(input, kernel)
else:
input_groups = tf.split(3, group, input)
kernel_groups = tf.split(3, group, kernel)
output_groups = [convolve(i, k) for i, k in zip(input_groups, kernel_groups)]
output = tf.concat(3, output_groups)
if biased:
biases = self.make_var('biases', [c_o])
output = tf.nn.bias_add(output, biases)
if relu:
output = tf.nn.relu(output, name=scope.name)
return output
@layer
def relu(self, input, name):
return tf.nn.relu(input, name=name)
@layer
def max_pool(self, input, k_h, k_w, s_h, s_w, name, padding=DEFAULT_PADDING):
self.validate_padding(padding)
return tf.nn.max_pool(input,
ksize=[1, k_h, k_w, 1],
strides=[1, s_h, s_w, 1],
padding=padding,
name=name)
@layer
def avg_pool(self, input, k_h, k_w, s_h, s_w, name, padding=DEFAULT_PADDING):
self.validate_padding(padding)
return tf.nn.avg_pool(input,
ksize=[1, k_h, k_w, 1],
strides=[1, s_h, s_w, 1],
padding=padding,
name=name)
@layer
def lrn(self, input, radius, alpha, beta, name, bias=1.0):
return tf.nn.local_response_normalization(input,
depth_radius=radius,
alpha=alpha,
beta=beta,
bias=bias,
name=name)
@layer
def concat(self, inputs, axis, name):
return tf.concat(values=inputs, axis=axis, name=name)
@layer
def add(self, inputs, name):
return tf.add_n(inputs, name=name)
@layer
def fc(self, input, num_out, name, relu=True):
with tf.variable_scope(name) as scope:
input_shape = input.get_shape()
if input_shape.ndims == 4:
dim = 1
for d in input_shape[1:].as_list():
dim *= d
feed_in = tf.reshape(input, [-1, dim])
else:
feed_in, dim = (input, input_shape[-1].value)
weights = self.make_var('weights', shape=[dim, num_out])
biases = self.make_var('biases', [num_out])
op = tf.nn.relu_layer if relu else tf.nn.xw_plus_b
fc = op(feed_in, weights, biases, name=scope.name)
return fc
@layer
def softmax(self, input, name):
input_shape = map(lambda v: v.value, input.get_shape())
if len(input_shape) > 2:
if input_shape[1] == 1 and input_shape[2] == 1:
input = tf.squeeze(input, squeeze_dims=[1, 2])
else:
raise ValueError('Rank 2 tensor input expected for softmax!')
return tf.nn.softmax(input, name)
@layer
def batch_normalization(self, input, name, scale_offset=True, relu=False):
with tf.variable_scope(name) as scope:
shape = [input.get_shape()[-1]]
if scale_offset:
scale = self.make_var('scale', shape=shape)
offset = self.make_var('offset', shape=shape)
else:
scale, offset = (None, None)
output = tf.nn.batch_normalization(
input,
mean=self.make_var('mean', shape=shape),
variance=self.make_var('variance', shape=shape),
offset=offset,
scale=scale,
variance_epsilon=1e-5,
name=name)
if relu:
output = tf.nn.relu(output)
return output
@layer
def dropout(self, input, keep_prob, name):
keep = 1 - self.use_dropout + (self.use_dropout * keep_prob)
return tf.nn.dropout(input, keep, name=name)
def centeredCrop(img, output_side_length):
height, width, depth = img.shape
new_height = output_side_length
new_width = output_side_length
if height > width:
new_height = output_side_length * height / width
else:
new_width = output_side_length * width / height
height_offset = (new_height - output_side_length) / 2
width_offset = (new_width - output_side_length) / 2
cropped_img = img[height_offset:height_offset + output_side_length,
width_offset:width_offset + output_side_length]
return cropped_img
def preprocess(images):
images_out = []
images_cropped = []
for i in tqdm(range(len(images)*0+2)):
X = cv2.imread(images[i])
X = centeredCrop(X, 224)
images_cropped.append(X)
N = 0
mean = np.zeros((1, 3, 224, 224))
for X in tqdm(images_cropped):
X = np.transpose(X,(2,0,1))
mean[0][0] += X[0,:,:]
mean[0][1] += X[1,:,:]
mean[0][2] += X[2,:,:]
N += 1
mean[0] /= N
for X in tqdm(images_cropped):
X = np.transpose(X,(2,0,1))
X = X - mean
X = np.squeeze(X)
X = np.transpose(X, (1,2,0))
images_out.append(X)
return images_out
6.数据加载
- 基本配置设置（
my_train_labels是之前文件处理写入的路径）
batch_size = 75
max_iterations = 30000
my_train_labels = '/kaggle/working/my_train_labels.csv'
class datasource(object):
def __init__(self, images, poses):
self.images = images
self.poses = poses
def gen_data(source):
while True:
indices = list(range(len(source.images)))
random.shuffle(indices)
for i in indices:
image = source.images[i]
pose_x = source.poses[i][0:3]
pose_q = source.poses[i][3:7]
yield image, pose_x, pose_q
def gen_data_batch(source):
data_gen = gen_data(source)
while True:
image_batch = []
pose_x_batch = []
pose_q_batch = []
for _ in range(batch_size):
image, pose_x, pose_q = next(data_gen)
image_batch.append(image)
pose_x_batch.append(pose_x)
pose_q_batch.append(pose_q)
yield np.array(image_batch), np.array(pose_x_batch), np.array(pose_q_batch)
- 获取数据的最终函数：（中间的路径需要根据训练图像所处位置进行更改，按照任务背景的目录结构，是train_labels.csv所处文件夹）
def get_data():
poses = []
images = []
for i in pd.read_csv(my_train_labels).itertuples():
p0,p1,p2 = i[6].split(';')
p3,p4,p5,p6 = i[7].split('(')[1].split(')')[0].split(',')
p0 = float(p0)
p1 = float(p1)
p2 = float(p2)
p3 = float(p3)
p4 = float(p4)
p5 = float(p5)
p6 = float(p6)
poses.append((p0,p1,p2,p3,p4,p5,p6))
images.append('/kaggle/input/image-matching-challenge-2023/train/' + i[4])
images = preprocess(images)
return datasource(images, poses)
7.网络和数据容器的准备
images = tf.placeholder(tf.float32, [batch_size, 224, 224, 3])
poses_x = tf.placeholder(tf.float32, [batch_size, 3])
poses_q = tf.placeholder(tf.float32, [batch_size, 4])
datasource = get_data()
net = GoogLeNet({'data': images})
p1_x = net.layers['cls1_fc_pose_xyz']
p1_q = net.layers['cls1_fc_pose_wpqr']
p2_x = net.layers['cls2_fc_pose_xyz']
p2_q = net.layers['cls2_fc_pose_wpqr']
p3_x = net.layers['cls3_fc_pose_xyz']
p3_q = net.layers['cls3_fc_pose_wpqr']
l1_x = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p1_x, poses_x)))) * 0.3
l1_q = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p1_q, poses_q)))) * 150
l2_x = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p2_x, poses_x)))) * 0.3
l2_q = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p2_q, poses_q)))) * 150
l3_x = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p3_x, poses_x)))) * 1
l3_q = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p3_q, poses_q)))) * 500
loss = l1_x + l1_q + l2_x + l2_q + l3_x + l3_q
opt = tf.train.AdamOptimizer(learning_rate=0.0001, beta1=0.9, beta2=0.999, epsilon=0.00000001, use_locking=False, name='Adam').minimize(loss)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6833)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
outputFile = "PoseNet.ckpt"
- 这里运行第二遍会报错，因为网络在内存中已经构建起来了
8.开始训练
- 下面的代码cpu、gpu环境都可以，每20轮打印一下损失，每500轮保存一下权重
- 加载的预训练权重放在了
'/kaggle/input/tensorflow-posenet-master/tensorflow-posenet-master/posenet.npy'，是官方caffe权重转换过来的，下载链接
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
sess.run(init)
net.load('/kaggle/input/tensorflow-posenet-master/tensorflow-posenet-master/posenet.npy', sess)
data_gen = gen_data_batch(datasource)
for i in range(max_iterations):
np_images, np_poses_x, np_poses_q = next(data_gen)
feed = {images: np_images, poses_x: np_poses_x, poses_q: np_poses_q}
sess.run(opt, feed_dict=feed)
np_loss = sess.run(loss, feed_dict=feed)
if i % 20 == 0:
print("iteration: " + str(i) + "\n\t" + "Loss is: " + str(np_loss))
if i % 500 == 0:
saver.save(sess, outputFile)
print("Intermediate file saved at: " + outputFile)
saver.save(sess, outputFile)
print("Intermediate file saved at: " + outputFile)
- 效果如下：
## 9.完整代码如下
- 完整notebook下载链接
- tensorflow权重文件下载地址
- 完整代码如下：
import pandas as pd
import numpy as np
import tensorflow as tf
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
import random
import cv2
from tqdm import tqdm
change_train_labels = 1
def matrix2quaternion(m):
w = ((np.trace(m) + 1) ** 0.5) / 2
x = (m[2][1] - m[1][2]) / (4 * w)
y = (m[0][2] - m[2][0]) / (4 * w)
z = (m[1][0] - m[0][1]) / (4 * w)
return w,x,y,z
def quaternion2matrix(q):
w,x,y,z = q
return np.array([[1-2*y*y-2*z*z, 2*x*y-2*z*w, 2*x*z+2*y*w],
[2*x*y+2*z*w, 1-2*x*x-2*z*z, 2*y*z-2*x*w],
[2*x*z-2*y*w, 2*y*z+2*x*w, 1-2*x*x-2*y*y]])
def m(a):
a = a.split(';')
a = [float(i) for i in a]
A = np.array([[a[0],a[1],a[2]],
[a[3],a[4],a[5]],
[a[6],a[7],a[8]]])
return matrix2quaternion(A)
if change_train_labels:
train_labels = pd.read_csv('/kaggle/input/image-matching-challenge-2023/train/train_labels.csv')
train_labels['rotation_matrix_quaternion'] = [i for i in map(m,train_labels['rotation_matrix'])]
train_labels.to_csv('/kaggle/working/my_train_labels.csv')
DEFAULT_PADDING = 'SAME'
def layer(op):
'''Decorator for composable network layers.'''
def layer_decorated(self, *args, **kwargs):
name = kwargs.setdefault('name', self.get_unique_name(op.__name__))
if len(self.terminals) == 0:
raise RuntimeError('No input variables found for layer %s.' % name)
elif len(self.terminals) == 1:
layer_input = self.terminals[0]
else:
layer_input = list(self.terminals)
layer_output = op(self, layer_input, *args, **kwargs)
self.layers[name] = layer_output
self.feed(layer_output)
return self
return layer_decorated
class Network(object):
def __init__(self, inputs, trainable=True):
self.inputs = inputs
self.terminals = []
self.layers = dict(inputs)
self.trainable = trainable
self.use_dropout = tf.placeholder_with_default(tf.constant(1.0),
shape=[],
name='use_dropout')
self.setup()
def setup(self):
'''Construct the network. '''
raise NotImplementedError('Must be implemented by the subclass.')
def load(self, data_path, session, ignore_missing=False):
'''Load network weights.
data_path: The path to the numpy-serialized network weights
session: The current TensorFlow session
ignore_missing: If true, serialized weights for missing layers are ignored.
'''
data_dict = np.load(data_path,allow_pickle=True,encoding="latin1").item()
for op_name in data_dict:
with tf.variable_scope(op_name, reuse=True):
for param_name, data in data_dict[op_name].items():
try:
var = tf.get_variable(param_name)
session.run(var.assign(data))
except ValueError:
if not ignore_missing:
raise
def feed(self, *args):
'''Set the input(s) for the next operation by replacing the terminal nodes.
The arguments can be either layer names or the actual layers.
'''
assert len(args) != 0
self.terminals = []
for fed_layer in args:
if isinstance(fed_layer, str):
try:
fed_layer = self.layers[fed_layer]
except KeyError:
raise KeyError('Unknown layer name fed: %s' % fed_layer)
self.terminals.append(fed_layer)
return self
def get_output(self):
'''Returns the current network output.'''
return self.terminals[-1]
def get_unique_name(self, prefix):
'''Returns an index-suffixed unique name for the given prefix.
This is used for auto-generating layer names based on the type-prefix.
'''
ident = sum(t.startswith(prefix) for t, _ in self.layers.items()) + 1
return '%s_%d' % (prefix, ident)
def make_var(self, name, shape):
'''Creates a new TensorFlow variable.'''
return tf.get_variable(name, shape, trainable=self.trainable)
def validate_padding(self, padding):
'''Verifies that the padding is one of the supported ones.'''
assert padding in ('SAME', 'VALID')
@layer
def conv(self,
input,
k_h,
k_w,
c_o,
s_h,
s_w,
name,
relu=True,
padding=DEFAULT_PADDING,
group=1,
biased=True):
self.validate_padding(padding)
c_i = input.get_shape()[-1]
assert c_i % group == 0
assert c_o % group == 0
convolve = lambda i, k: tf.nn.conv2d(i, k, [1, s_h, s_w, 1], padding=padding)
with tf.variable_scope(name) as scope:
kernel = self.make_var('weights', shape=[k_h, k_w, int(int(c_i) / group), c_o])
if group == 1:
output = convolve(input, kernel)
else:
input_groups = tf.split(3, group, input)
kernel_groups = tf.split(3, group, kernel)
output_groups = [convolve(i, k) for i, k in zip(input_groups, kernel_groups)]
output = tf.concat(3, output_groups)
if biased:
biases = self.make_var('biases', [c_o])
output = tf.nn.bias_add(output, biases)
if relu:
output = tf.nn.relu(output, name=scope.name)
return output
@layer
def relu(self, input, name):
return tf.nn.relu(input, name=name)
@layer
def max_pool(self, input, k_h, k_w, s_h, s_w, name, padding=DEFAULT_PADDING):
self.validate_padding(padding)
return tf.nn.max_pool(input,
ksize=[1, k_h, k_w, 1],
strides=[1, s_h, s_w, 1],
padding=padding,
name=name)
@layer
def avg_pool(self, input, k_h, k_w, s_h, s_w, name, padding=DEFAULT_PADDING):
self.validate_padding(padding)
return tf.nn.avg_pool(input,
ksize=[1, k_h, k_w, 1],
strides=[1, s_h, s_w, 1],
padding=padding,
name=name)
@layer
def lrn(self, input, radius, alpha, beta, name, bias=1.0):
return tf.nn.local_response_normalization(input,
depth_radius=radius,
alpha=alpha,
beta=beta,
bias=bias,
name=name)
@layer
def concat(self, inputs, axis, name):
return tf.concat(values=inputs, axis=axis, name=name)
@layer
def add(self, inputs, name):
return tf.add_n(inputs, name=name)
@layer
def fc(self, input, num_out, name, relu=True):
with tf.variable_scope(name) as scope:
input_shape = input.get_shape()
if input_shape.ndims == 4:
dim = 1
for d in input_shape[1:].as_list():
dim *= d
feed_in = tf.reshape(input, [-1, dim])
else:
feed_in, dim = (input, input_shape[-1].value)
weights = self.make_var('weights', shape=[dim, num_out])
biases = self.make_var('biases', [num_out])
op = tf.nn.relu_layer if relu else tf.nn.xw_plus_b
fc = op(feed_in, weights, biases, name=scope.name)
return fc
@layer
def softmax(self, input, name):
input_shape = map(lambda v: v.value, input.get_shape())
if len(input_shape) > 2:
if input_shape[1] == 1 and input_shape[2] == 1:
input = tf.squeeze(input, squeeze_dims=[1, 2])
else:
raise ValueError('Rank 2 tensor input expected for softmax!')
return tf.nn.softmax(input, name)
@layer
def batch_normalization(self, input, name, scale_offset=True, relu=False):
with tf.variable_scope(name) as scope:
shape = [input.get_shape()[-1]]
if scale_offset:
scale = self.make_var('scale', shape=shape)
offset = self.make_var('offset', shape=shape)
else:
scale, offset = (None, None)
output = tf.nn.batch_normalization(
input,
mean=self.make_var('mean', shape=shape),
variance=self.make_var('variance', shape=shape),
offset=offset,
scale=scale,
variance_epsilon=1e-5,
name=name)
if relu:
output = tf.nn.relu(output)
return output
@layer
def dropout(self, input, keep_prob, name):
keep = 1 - self.use_dropout + (self.use_dropout * keep_prob)
return tf.nn.dropout(input, keep, name=name)
def centeredCrop(img, output_side_length):
height, width, depth = img.shape
new_height = output_side_length
new_width = output_side_length
if height > width:
new_height = output_side_length * height / width
else:
new_width = output_side_length * width / height
height_offset = (new_height - output_side_length) / 2
width_offset = (new_width - output_side_length) / 2
cropped_img = img[height_offset:height_offset + output_side_length,
width_offset:width_offset + output_side_length]
return cropped_img
def preprocess(images):
images_out = []
images_cropped = []
for i in tqdm(range(len(images))):
X = cv2.imread(images[i])
X = centeredCrop(X, 224)
images_cropped.append(X)
N = 0
mean = np.zeros((1, 3, 224, 224))
for X in tqdm(images_cropped):
X = np.transpose(X,(2,0,1))
mean[0][0] += X[0,:,:]
mean[0][1] += X[1,:,:]
mean[0][2] += X[2,:,:]
N += 1
mean[0] /= N
for X in tqdm(images_cropped):
X = np.transpose(X,(2,0,1))
X = X - mean
X = np.squeeze(X)
X = np.transpose(X, (1,2,0))
images_out.append(X)
return images_out
class GoogLeNet(Network):
def setup(self):
(self.feed('data')
.conv(7, 7, 64, 2, 2, name='conv1')
.max_pool(3, 3, 2, 2, name='pool1')
.lrn(2, 2e-05, 0.75, name='norm1')
.conv(1, 1, 64, 1, 1, name='reduction2')
.conv(3, 3, 192, 1, 1, name='conv2')
.lrn(2, 2e-05, 0.75, name='norm2')
.max_pool(3, 3, 2, 2, name='pool2')
.conv(1, 1, 96, 1, 1, name='icp1_reduction1')
.conv(3, 3, 128, 1, 1, name='icp1_out1'))
(self.feed('pool2')
.conv(1, 1, 16, 1, 1, name='icp1_reduction2')
.conv(5, 5, 32, 1, 1, name='icp1_out2'))
(self.feed('pool2')
.max_pool(3, 3, 1, 1, name='icp1_pool')
.conv(1, 1, 32, 1, 1, name='icp1_out3'))
(self.feed('pool2')
.conv(1, 1, 64, 1, 1, name='icp1_out0'))
(self.feed('icp1_out0',
'icp1_out1',
'icp1_out2',
'icp1_out3')
.concat(3, name='icp2_in')
.conv(1, 1, 128, 1, 1, name='icp2_reduction1')
.conv(3, 3, 192, 1, 1, name='icp2_out1'))
(self.feed('icp2_in')
.conv(1, 1, 32, 1, 1, name='icp2_reduction2')
.conv(5, 5, 96, 1, 1, name='icp2_out2'))
(self.feed('icp2_in')
.max_pool(3, 3, 1, 1, name='icp2_pool')
.conv(1, 1, 64, 1, 1, name='icp2_out3'))
(self.feed('icp2_in')
.conv(1, 1, 128, 1, 1, name='icp2_out0'))
(self.feed('icp2_out0',
'icp2_out1',
'icp2_out2',
'icp2_out3')
.concat(3, name='icp2_out')
.max_pool(3, 3, 2, 2, name='icp3_in')
.conv(1, 1, 96, 1, 1, name='icp3_reduction1')
.conv(3, 3, 208, 1, 1, name='icp3_out1'))
(self.feed('icp3_in')
.conv(1, 1, 16, 1, 1, name='icp3_reduction2')
.conv(5, 5, 48, 1, 1, name='icp3_out2'))
(self.feed('icp3_in')
.max_pool(3, 3, 1, 1, name='icp3_pool')
.conv(1, 1, 64, 1, 1, name='icp3_out3'))
(self.feed('icp3_in')
.conv(1, 1, 192, 1, 1, name='icp3_out0'))
(self.feed('icp3_out0',
'icp3_out1',
'icp3_out2',
'icp3_out3')
.concat(3, name='icp3_out')
.avg_pool(5, 5, 3, 3, padding='VALID', name='cls1_pool')
.conv(1, 1, 128, 1, 1, name='cls1_reduction_pose')
.fc(1024, name='cls1_fc1_pose')
.fc(3, relu=False, name='cls1_fc_pose_xyz'))
(self.feed('cls1_fc1_pose')
.fc(4, relu=False, name='cls1_fc_pose_wpqr'))
(self.feed('icp3_out')
.conv(1, 1, 112, 1, 1, name='icp4_reduction1')
.conv(3, 3, 224, 1, 1, name='icp4_out1'))
(self.feed('icp3_out')
.conv(1, 1, 24, 1, 1, name='icp4_reduction2')
.conv(5, 5, 64, 1, 1, name='icp4_out2'))
(self.feed('icp3_out')
.max_pool(3, 3, 1, 1, name='icp4_pool')
.conv(1, 1, 64, 1, 1, name='icp4_out3'))
(self.feed('icp3_out')
.conv(1, 1, 160, 1, 1, name='icp4_out0'))
(self.feed('icp4_out0',
'icp4_out1',
'icp4_out2',
'icp4_out3')
.concat(3, name='icp4_out')
.conv(1, 1, 128, 1, 1, name='icp5_reduction1')
.conv(3, 3, 256, 1, 1, name='icp5_out1'))
(self.feed('icp4_out')
.conv(1, 1, 24, 1, 1, name='icp5_reduction2')
.conv(5, 5, 64, 1, 1, name='icp5_out2'))
(self.feed('icp4_out')
.max_pool(3, 3, 1, 1, name='icp5_pool')
.conv(1, 1, 64, 1, 1, name='icp5_out3'))
(self.feed('icp4_out')
.conv(1, 1, 128, 1, 1, name='icp5_out0'))
(self.feed('icp5_out0',
'icp5_out1',
'icp5_out2',
'icp5_out3')
.concat(3, name='icp5_out')
.conv(1, 1, 144, 1, 1, name='icp6_reduction1')
.conv(3, 3, 288, 1, 1, name='icp6_out1'))
(self.feed('icp5_out')
.conv(1, 1, 32, 1, 1, name='icp6_reduction2')
.conv(5, 5, 64, 1, 1, name='icp6_out2'))
(self.feed('icp5_out')
.max_pool(3, 3, 1, 1, name='icp6_pool')
.conv(1, 1, 64, 1, 1, name='icp6_out3'))
(self.feed('icp5_out')
.conv(1, 1, 112, 1, 1, name='icp6_out0'))
(self.feed('icp6_out0',
'icp6_out1',
'icp6_out2',
'icp6_out3')
.concat(3, name='icp6_out')
.avg_pool(5, 5, 3, 3, padding='VALID', name='cls2_pool')
.conv(1, 1, 128, 1, 1, name='cls2_reduction_pose')
.fc(1024, name='cls2_fc1')
.fc(3, relu=False, name='cls2_fc_pose_xyz'))
(self.feed('cls2_fc1')
.fc(4, relu=False, name='cls2_fc_pose_wpqr'))
(self.feed('icp6_out')
.conv(1, 1, 160, 1, 1, name='icp7_reduction1')
.conv(3, 3, 320, 1, 1, name='icp7_out1'))
(self.feed('icp6_out')
.conv(1, 1, 32, 1, 1, name='icp7_reduction2')
.conv(5, 5, 128, 1, 1, name='icp7_out2'))
(self.feed('icp6_out')
.max_pool(3, 3, 1, 1, name='icp7_pool')
.conv(1, 1, 128, 1, 1, name='icp7_out3'))
(self.feed('icp6_out')
.conv(1, 1, 256, 1, 1, name='icp7_out0'))
(self.feed('icp7_out0',
'icp7_out1',
'icp7_out2',
'icp7_out3')
.concat(3, name='icp7_out')
.max_pool(3, 3, 2, 2, name='icp8_in')
.conv(1, 1, 160, 1, 1, name='icp8_reduction1')
.conv(3, 3, 320, 1, 1, name='icp8_out1'))
(self.feed('icp8_in')
.conv(1, 1, 32, 1, 1, name='icp8_reduction2')
.conv(5, 5, 128, 1, 1, name='icp8_out2'))
(self.feed('icp8_in')
.max_pool(3, 3, 1, 1, name='icp8_pool')
.conv(1, 1, 128, 1, 1, name='icp8_out3'))
(self.feed('icp8_in')
.conv(1, 1, 256, 1, 1, name='icp8_out0'))
(self.feed('icp8_out0',
'icp8_out1',
'icp8_out2',
'icp8_out3')
.concat(3, name='icp8_out')
.conv(1, 1, 192, 1, 1, name='icp9_reduction1')
.conv(3, 3, 384, 1, 1, name='icp9_out1'))
(self.feed('icp8_out')
.conv(1, 1, 48, 1, 1, name='icp9_reduction2')
.conv(5, 5, 128, 1, 1, name='icp9_out2'))
(self.feed('icp8_out')
.max_pool(3, 3, 1, 1, name='icp9_pool')
.conv(1, 1, 128, 1, 1, name='icp9_out3'))
(self.feed('icp8_out')
.conv(1, 1, 384, 1, 1, name='icp9_out0'))
(self.feed('icp9_out0',
'icp9_out1',
'icp9_out2',
'icp9_out3')
.concat(3, name='icp9_out')
.avg_pool(7, 7, 1, 1, padding='VALID', name='cls3_pool')
.fc(2048, name='cls3_fc1_pose')
.fc(3, relu=False, name='cls3_fc_pose_xyz'))
(self.feed('cls3_fc1_pose')
.fc(4, relu=False, name='cls3_fc_pose_wpqr'))
batch_size = 75
max_iterations = 30000
directory = 'path_to_datasets/KingsCollege/'
dataset = 'dataset_train.txt'
my_train_labels = '/kaggle/working/my_train_labels.csv'
class datasource(object):
def __init__(self, images, poses):
self.images = images
self.poses = poses
def centeredCrop(img, output_side_length):
height, width, depth = img.shape
new_height = output_side_length
new_width = output_side_length
if height > width:
new_height = output_side_length * height / width
else:
new_width = output_side_length * width / height
height_offset = (new_height - output_side_length) / 2
width_offset = (new_width - output_side_length) / 2
cropped_img = img[int(height_offset):int(height_offset + output_side_length),
int(width_offset):int(width_offset + output_side_length)]
return cropped_img
def gen_data(source):
while True:
indices = list(range(len(source.images)))
random.shuffle(indices)
for i in indices:
image = source.images[i]
pose_x = source.poses[i][0:3]
pose_q = source.poses[i][3:7]
yield image, pose_x, pose_q
def gen_data_batch(source):
data_gen = gen_data(source)
while True:
image_batch = []
pose_x_batch = []
pose_q_batch = []
for _ in range(batch_size):
image, pose_x, pose_q = next(data_gen)
image_batch.append(image)
pose_x_batch.append(pose_x)
pose_q_batch.append(pose_q)
yield np.array(image_batch), np.array(pose_x_batch), np.array(pose_q_batch)
def get_data():
poses = []
images = []
for i in pd.read_csv(my_train_labels).itertuples():
p0,p1,p2 = i[6].split(';')
p3,p4,p5,p6 = i[7].split('(')[1].split(')')[0].split(',')
p0 = float(p0)
p1 = float(p1)
p2 = float(p2)
p3 = float(p3)
p4 = float(p4)
p5 = float(p5)
p6 = float(p6)
poses.append((p0,p1,p2,p3,p4,p5,p6))
images.append('/kaggle/input/image-matching-challenge-2023/train/' + i[4])
images = preprocess(images)
return datasource(images, poses)
images = tf.placeholder(tf.float32, [batch_size, 224, 224, 3])
poses_x = tf.placeholder(tf.float32, [batch_size, 3])
poses_q = tf.placeholder(tf.float32, [batch_size, 4])
datasource = get_data()
net = GoogLeNet({'data': images})
p1_x = net.layers['cls1_fc_pose_xyz']
p1_q = net.layers['cls1_fc_pose_wpqr']
p2_x = net.layers['cls2_fc_pose_xyz']
p2_q = net.layers['cls2_fc_pose_wpqr']
p3_x = net.layers['cls3_fc_pose_xyz']
p3_q = net.layers['cls3_fc_pose_wpqr']
l1_x = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p1_x, poses_x)))) * 0.3
l1_q = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p1_q, poses_q)))) * 150
l2_x = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p2_x, poses_x)))) * 0.3
l2_q = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p2_q, poses_q)))) * 150
l3_x = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p3_x, poses_x)))) * 1
l3_q = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p3_q, poses_q)))) * 500
loss = l1_x + l1_q + l2_x + l2_q + l3_x + l3_q
opt = tf.train.AdamOptimizer(learning_rate=0.0001, beta1=0.9, beta2=0.999, epsilon=0.00000001, use_locking=False, name='Adam').minimize(loss)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6833)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
outputFile = "PoseNet.ckpt"
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
sess.run(init)
net.load('/kaggle/input/tensorflow-posenet-master/tensorflow-posenet-master/posenet.npy', sess)
data_gen = gen_data_batch(datasource)
for i in range(max_iterations):
np_images, np_poses_x, np_poses_q = next(data_gen)
feed = {images: np_images, poses_x: np_poses_x, poses_q: np_poses_q}
sess.run(opt, feed_dict=feed)
np_loss = sess.run(loss, feed_dict=feed)
if i % 20 == 0:
print("iteration: " + str(i) + "\n\t" + "Loss is: " + str(np_loss))
if i % 500 == 0:
saver.save(sess, outputFile)
print("Intermediate file saved at: " + outputFile)
saver.save(sess, outputFile)
print("Intermediate file saved at: " + outputFile)
9.环境版本
Package Version Editable project location
-------------------------------------- -------------------- -------------------------
absl-py 1.4.0
accelerate 0.12.0
access 1.1.9
affine 2.4.0
aiobotocore 2.5.0
aiofiles 22.1.0
aiohttp 3.8.4
aiohttp-cors 0.7.0
aioitertools 0.11.0
aiorwlock 1.3.0
aiosignal 1.3.1
aiosqlite 0.19.0
albumentations 1.3.0
alembic 1.11.1
altair 5.0.0
annoy 1.17.2
ansiwrap 0.8.4
anyio 3.6.2
apache-beam 2.46.0
aplus 0.11.0
appdirs 1.4.4
argon2-cffi 21.3.0
argon2-cffi-bindings 21.2.0
array-record 0.2.0
arrow 1.2.3
arviz 0.12.1
astroid 2.15.5
astropy 5.3
asttokens 2.2.1
astunparse 1.6.3
async-timeout 4.0.2
atpublic 3.1.1
attrs 23.1.0
audioread 3.0.0
autopep8 2.0.2
Babel 2.12.1
backcall 0.2.0
backoff 2.2.1
backports.functools-lru-cache 1.6.4
bayesian-optimization 1.4.3
bayespy 0.5.25
beatrix-jupyterlab 2023.58.190319
beautifulsoup4 4.12.2
bidict 0.22.1
biopython 1.81
blake3 0.2.1
bleach 6.0.0
blessed 1.20.0
blinker 1.6.2
blis 0.7.9
blosc2 2.0.0
bokeh 2.4.3
boltons 23.0.0
Boruta 0.3
boto3 1.26.100
botocore 1.29.76
bq-helper 0.4.1 /src/bq-helper
bqplot 0.12.39
branca 0.6.0
brewer2mpl 1.4.1
brotlipy 0.7.0
cached-property 1.5.2
cachetools 4.2.4
Cartopy 0.21.1
catalogue 2.0.8
catalyst 22.4
catboost 1.2
category-encoders 2.6.1
certifi 2023.5.7
cesium 0.12.1
cffi 1.15.1
cftime 1.6.2
charset-normalizer 2.1.1
chex 0.1.7
cleverhans 4.0.0
click 8.1.3
click-plugins 1.1.1
cligj 0.7.2
cloud-tpu-client 0.10
cloud-tpu-profiler 2.4.0
cloudpickle 2.2.1
cmaes 0.9.1
cmdstanpy 1.1.0
cmudict 1.0.13
colorama 0.4.6
colorcet 3.0.1
colorful 0.5.5
colorlog 6.7.0
colorlover 0.3.0
comm 0.1.3
commonmark 0.9.1
conda 23.3.1
conda-content-trust 0+unknown
conda-package-handling 2.0.2
conda_package_streaming 0.7.0
confection 0.0.4
contextily 1.3.0
contourpy 1.0.7
convertdate 2.4.0
crcmod 1.7
cryptography 40.0.2
cubinlinker 0.2.2
cuda-python 11.8.1
cudf 23.4.1
cufflinks 0.17.3
cuml 23.4.1
cupy 11.6.0
CVXcanon 0.1.2
cycler 0.11.0
cymem 2.0.7
cysignals 1.11.2
Cython 0.29.34
cytoolz 0.12.0
daal 2023.1.1
daal4py 2023.1.1
dask 2023.5.0
dask-cuda 23.4.0
dask-cudf 23.4.1
dataclasses 0.8
dataclasses-json 0.5.7
datasets 2.1.0
datashader 0.14.4
datashape 0.5.2
datatile 1.0.3
db-dtypes 1.1.1
deap 1.3.3
debugpy 1.6.7
decorator 5.1.1
defusedxml 0.7.1
Delorean 1.0.0
deprecat 2.1.1
Deprecated 1.2.13
deprecation 2.1.0
descartes 1.1.0
dill 0.3.6
dipy 1.7.0
distlib 0.3.6
distributed 2023.3.2.1
dm-tree 0.1.8
docker 6.1.1
docker-pycreds 0.4.0
docopt 0.6.2
docstring-parser 0.15
docstring-to-markdown 0.12
docutils 0.20.1
earthengine-api 0.1.354
easydict 1.10
easyocr 1.6.2
ecos 2.0.12
eli5 0.13.0
emoji 2.2.0
en-core-web-lg 3.5.0
en-core-web-sm 3.5.0
entrypoints 0.4
ephem 4.1.4
esda 2.4.3
essentia 2.1b6.dev1034
et-xmlfile 1.1.0
etils 1.2.0
executing 1.2.0
explainable-ai-sdk 1.3.3
fastai 2.7.12
fastapi 0.95.1
fastavro 1.7.4
fastcore 1.5.29
fastdownload 0.0.7
fasteners 0.18
fastjsonschema 2.16.3
fastprogress 1.0.3
fastrlock 0.8
fasttext 0.9.2
fbpca 1.0
feather-format 0.4.1
featuretools 1.26.0
filelock 3.12.0
Fiona 1.8.22
fire 0.5.0
fitter 1.5.2
flake8 6.0.0
flashtext 2.7
Flask 2.3.2
flatbuffers 23.3.3
flax 0.6.10
flit_core 3.8.0
folium 0.14.0
fonttools 4.39.3
fqdn 1.5.1
frozendict 2.3.8
frozenlist 1.3.3
fsspec 2023.5.0
funcy 2.0
fury 0.9.0
future 0.18.3
fuzzywuzzy 0.18.0
gast 0.4.0
gatspy 0.3
gcsfs 2023.5.0
gensim 4.3.1
geographiclib 2.0
Geohash 1.0
geojson 3.0.1
geopandas 0.13.0
geoplot 0.5.1
geopy 2.3.0
geoviews 1.9.6
ggplot 0.11.5
giddy 2.3.4
gitdb 4.0.10
GitPython 3.1.31
google-api-core 1.33.2
google-api-python-client 2.86.0
google-apitools 0.5.31
google-auth 2.17.3
google-auth-httplib2 0.1.0
google-auth-oauthlib 1.0.0
google-cloud-aiplatform 0.6.0a1
google-cloud-artifact-registry 1.8.1
google-cloud-automl 1.0.1
google-cloud-bigquery 2.34.4
google-cloud-bigtable 1.7.3
google-cloud-core 2.3.2
google-cloud-datastore 2.15.2
google-cloud-dlp 3.12.1
google-cloud-language 2.6.1
google-cloud-monitoring 2.14.2
google-cloud-pubsub 2.16.1
google-cloud-pubsublite 1.8.1
google-cloud-recommendations-ai 0.7.1
google-cloud-resource-manager 1.10.0
google-cloud-spanner 3.33.0
google-cloud-storage 1.44.0
google-cloud-translate 3.8.4
google-cloud-videointelligence 2.8.3
google-cloud-vision 2.8.0
google-crc32c 1.5.0
google-pasta 0.2.0
google-resumable-media 2.5.0
googleapis-common-protos 1.57.1
gplearn 0.4.2
gpustat 1.0.0
gpxpy 1.5.0
graphviz 0.20.1
greenlet 2.0.2
grpc-google-iam-v1 0.12.6
grpcio 1.51.1
grpcio-status 1.48.1
gviz-api 1.10.0
gym 0.26.2
gym-notices 0.0.8
Gymnasium 0.26.3
gymnasium-notices 0.0.1
h11 0.14.0
h2o 3.40.0.4
h5py 3.8.0
haversine 2.8.0
hdfs 2.7.0
hep-ml 0.7.2
hijri-converter 2.3.1
hmmlearn 0.3.0
holidays 0.24
holoviews 1.16.0
hpsklearn 0.1.0
html5lib 1.1
htmlmin 0.1.12
httplib2 0.21.0
httptools 0.5.0
huggingface-hub 0.14.1
humanize 4.6.0
hunspell 0.5.5
husl 4.0.3
hydra-slayer 0.4.1
hyperopt 0.2.7
hypertools 0.8.0
ibis-framework 5.1.0
idna 3.4
igraph 0.10.4
imagecodecs 2023.3.16
ImageHash 4.3.1
imageio 2.28.1
imbalanced-learn 0.10.1
imgaug 0.4.0
implicit 0.5.2
importlib-metadata 5.2.0
importlib-resources 5.12.0
inequality 1.0.0
ipydatawidgets 4.3.3
ipykernel 6.23.0
ipyleaflet 0.17.2
ipympl 0.7.0
ipython 8.13.2
ipython-genutils 0.2.0
ipython-sql 0.5.0
ipyvolume 0.6.1
ipyvue 1.9.0
ipyvuetify 1.8.10
ipywebrtc 0.6.0
ipywidgets 7.7.1
isoduration 20.11.0
isort 5.12.0
isoweek 1.3.3
itsdangerous 2.1.2
Janome 0.4.2
jaraco.classes 3.2.3
jax 0.4.10
jaxlib 0.4.7+cuda11.cudnn86
jedi 0.18.2
jeepney 0.8.0
jieba 0.42.1
Jinja2 3.1.2
jmespath 1.0.1
joblib 1.2.0
json5 0.9.11
jsonpatch 1.32
jsonpointer 2.0
jsonschema 4.17.3
jupyter_client 7.4.9
jupyter-console 6.6.3
jupyter_core 5.3.0
jupyter-events 0.6.3
jupyter-http-over-ws 0.0.8
jupyter-lsp 1.5.1
jupyter_server 2.5.0
jupyter_server_fileid 0.9.0
jupyter-server-mathjax 0.2.6
jupyter_server_proxy 4.0.0
jupyter_server_terminals 0.4.4
jupyter_server_ydoc 0.8.0
jupyter-ydoc 0.2.4
jupyterlab 3.6.3
jupyterlab-git 0.41.0
jupyterlab-lsp 4.1.0
jupyterlab-pygments 0.2.2
jupyterlab_server 2.22.1
jupyterlab-widgets 3.0.7
jupytext 1.14.5
kaggle 1.5.13
kaggle-environments 1.12.0
keras 2.12.0
keras-tuner 1.3.5
keyring 23.13.1
keyrings.google-artifactregistry-auth 1.1.2
kfp 1.8.21
kfp-pipeline-spec 0.1.16
kfp-server-api 1.8.5
kiwisolver 1.4.4
kmapper 2.0.1
kmodes 0.12.2
korean-lunar-calendar 0.3.1
kornia 0.6.12
kt-legacy 1.0.5
kubernetes 25.3.0
langcodes 3.3.0
langid 1.1.6
lazy_loader 0.2
lazy-object-proxy 1.9.0
learntools 0.3.4
leven 1.0.4
Levenshtein 0.21.0
libclang 16.0.0
libmambapy 1.4.2
libpysal 4.7.0
librosa 0.10.0.post2
lightgbm 3.3.2
lightning-utilities 0.8.0
lime 0.2.0.1
line-profiler 4.0.3
llvmlite 0.39.1
lml 0.1.0
locket 1.0.0
LunarCalendar 0.0.9
lxml 4.9.2
lz4 4.3.2
Mako 1.2.4
mamba 1.4.2
mapclassify 2.5.0
marisa-trie 0.8.0
Markdown 3.4.3
markdown-it-py 2.2.0
markovify 0.9.4
MarkupSafe 2.1.2
marshmallow 3.19.0
marshmallow-enum 1.5.1
matplotlib 3.6.3
matplotlib-inline 0.1.6
matplotlib-venn 0.11.9
mccabe 0.7.0
mdit-py-plugins 0.3.5
mdurl 0.1.2
memory-profiler 0.61.0
mercantile 1.2.1
mgwr 2.1.2
missingno 0.5.2
mistune 0.8.4
mizani 0.9.1
ml-dtypes 0.1.0
mlcrate 0.2.0
mlens 0.2.3
mlxtend 0.22.0
mmh3 4.0.0
mne 1.4.0
mnist 0.2.2
mock 5.0.2
momepy 0.6.0
more-itertools 9.1.0
mpld3 0.5.9
mpmath 1.3.0
msgpack 1.0.5
msgpack-numpy 0.4.8
multidict 6.0.4
multimethod 1.9.1
multipledispatch 0.6.0
multiprocess 0.70.14
munch 3.0.0
munkres 1.1.4
murmurhash 1.0.9
mypy-extensions 1.0.0
nb-conda 2.2.1
nb-conda-kernels 2.3.1
nbclassic 1.0.0
nbclient 0.5.13
nbconvert 6.4.5
nbdime 3.2.0
nbformat 5.8.0
nest-asyncio 1.5.6
netCDF4 1.6.3
networkx 3.1
nibabel 5.1.0
nilearn 0.10.1
ninja 1.11.1
nltk 3.2.4
nose 1.3.7
notebook 6.5.4
notebook-executor 0.2
notebook_shim 0.2.3
numba 0.56.4
numexpr 2.8.4
numpy 1.23.5
nvidia-ml-py 11.495.46
nvtx 0.2.5
oauth2client 4.1.3
oauthlib 3.2.2
objsize 0.6.1
odfpy 1.4.1
olefile 0.46
onnx 1.14.0
opencensus 0.11.2
opencensus-context 0.1.3
opencv-contrib-python 4.5.4.60
opencv-python 4.5.4.60
opencv-python-headless 4.5.4.60
openpyxl 3.1.2
openslide-python 1.2.0
opentelemetry-api 1.17.0
opentelemetry-exporter-otlp 1.17.0
opentelemetry-exporter-otlp-proto-grpc 1.17.0
opentelemetry-exporter-otlp-proto-http 1.17.0
opentelemetry-proto 1.17.0
opentelemetry-sdk 1.17.0
opentelemetry-semantic-conventions 0.38b0
opt-einsum 3.3.0
optax 0.1.5
optuna 3.1.1
orbax-checkpoint 0.2.3
orderedmultidict 1.0.1
orjson 3.8.12
ortools 9.4.1874
osmnx 1.1.1
overrides 6.5.0
packaging 21.3
pandas 1.5.3
pandas-datareader 0.10.0
pandas-profiling 3.6.6
pandas-summary 0.2.0
pandasql 0.7.3
pandocfilters 1.5.0
panel 0.14.4
papermill 2.4.0
param 1.13.0
parso 0.8.3
parsy 2.1
partd 1.4.0
path 16.6.0
path.py 12.5.0
pathos 0.3.0
pathtools 0.1.2
pathy 0.10.1
patsy 0.5.3
pdf2image 1.16.3
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