262 lines
10 KiB
Python
Executable File
262 lines
10 KiB
Python
Executable File
#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
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#
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#Licensed under the Apache License, Version 2.0 (the "License");
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#you may not use this file except in compliance with the License.
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#You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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#Unless required by applicable law or agreed to in writing, software
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#distributed under the License is distributed on an "AS IS" BASIS,
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#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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#See the License for the specific language governing permissions and
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#limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import math
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import paddle.fluid as fluid
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import paddle.fluid.layers as layers
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from paddle.fluid.param_attr import ParamAttr
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import numpy as np
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class LocalizationNetwork(object):
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def __init__(self, params):
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super(LocalizationNetwork, self).__init__()
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self.F = params['num_fiducial']
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self.loc_lr = params['loc_lr']
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self.model_name = params['model_name']
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def conv_bn_layer(self,
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input,
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num_filters,
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filter_size,
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stride=1,
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groups=1,
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act=None,
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name=None):
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conv = layers.conv2d(
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input=input,
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num_filters=num_filters,
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filter_size=filter_size,
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stride=stride,
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padding=(filter_size - 1) // 2,
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groups=groups,
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act=None,
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param_attr=ParamAttr(name=name + "_weights"),
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bias_attr=False)
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bn_name = "bn_" + name
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return layers.batch_norm(
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input=conv,
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act=act,
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param_attr=ParamAttr(name=bn_name + '_scale'),
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bias_attr=ParamAttr(bn_name + '_offset'),
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moving_mean_name=bn_name + '_mean',
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moving_variance_name=bn_name + '_variance')
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def get_initial_fiducials(self):
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""" see RARE paper Fig. 6 (a) """
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F = self.F
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ctrl_pts_x = np.linspace(-1.0, 1.0, int(F / 2))
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ctrl_pts_y_top = np.linspace(0.0, -1.0, num=int(F / 2))
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ctrl_pts_y_bottom = np.linspace(1.0, 0.0, num=int(F / 2))
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ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1)
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ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1)
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initial_bias = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], axis=0)
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return initial_bias
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def __call__(self, image):
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F = self.F
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loc_lr = self.loc_lr
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if self.model_name == "large":
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num_filters_list = [64, 128, 256, 512]
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fc_dim = 256
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else:
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num_filters_list = [16, 32, 64, 128]
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fc_dim = 64
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for fno in range(len(num_filters_list)):
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num_filters = num_filters_list[fno]
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name = "loc_conv%d" % fno
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if fno == 0:
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conv = self.conv_bn_layer(
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image, num_filters, 3, act='relu', name=name)
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else:
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conv = self.conv_bn_layer(
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pool, num_filters, 3, act='relu', name=name)
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if fno == len(num_filters_list) - 1:
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pool = layers.adaptive_pool2d(
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input=conv, pool_size=[1, 1], pool_type='avg')
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else:
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pool = layers.pool2d(
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input=conv,
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pool_size=2,
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pool_stride=2,
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pool_padding=0,
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pool_type='max')
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name = "loc_fc1"
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stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
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fc1 = layers.fc(input=pool,
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size=fc_dim,
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param_attr=fluid.param_attr.ParamAttr(
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learning_rate=loc_lr,
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initializer=fluid.initializer.Uniform(-stdv, stdv),
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name=name + "_w"),
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act='relu',
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name=name)
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initial_bias = self.get_initial_fiducials()
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initial_bias = initial_bias.reshape(-1)
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name = "loc_fc2"
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param_attr = fluid.param_attr.ParamAttr(
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learning_rate=loc_lr,
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initializer=fluid.initializer.NumpyArrayInitializer(
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np.zeros([fc_dim, F * 2])),
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name=name + "_w")
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bias_attr = fluid.param_attr.ParamAttr(
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learning_rate=loc_lr,
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initializer=fluid.initializer.NumpyArrayInitializer(initial_bias),
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name=name + "_b")
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fc2 = layers.fc(input=fc1,
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size=F * 2,
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param_attr=param_attr,
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bias_attr=bias_attr,
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name=name)
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batch_C_prime = layers.reshape(x=fc2, shape=[-1, F, 2], inplace=False)
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return batch_C_prime
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class GridGenerator(object):
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def __init__(self, params):
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super(GridGenerator, self).__init__()
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self.eps = 1e-6
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self.F = params['num_fiducial']
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def build_C(self):
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""" Return coordinates of fiducial points in I_r; C """
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F = self.F
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ctrl_pts_x = np.linspace(-1.0, 1.0, int(F / 2))
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ctrl_pts_y_top = -1 * np.ones(int(F / 2))
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ctrl_pts_y_bottom = np.ones(int(F / 2))
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ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1)
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ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1)
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C = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], axis=0)
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return C # F x 2
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def build_P(self, I_r_size):
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I_r_width, I_r_height = I_r_size
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I_r_grid_x = (np.arange(-I_r_width, I_r_width, 2) + 1.0)\
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/ I_r_width # self.I_r_width
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I_r_grid_y = (np.arange(-I_r_height, I_r_height, 2) + 1.0)\
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/ I_r_height # self.I_r_height
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# P: self.I_r_width x self.I_r_height x 2
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P = np.stack(np.meshgrid(I_r_grid_x, I_r_grid_y), axis=2)
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# n (= self.I_r_width x self.I_r_height) x 2
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return P.reshape([-1, 2])
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def build_inv_delta_C(self, C):
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""" Return inv_delta_C which is needed to calculate T """
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F = self.F
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hat_C = np.zeros((F, F), dtype=float) # F x F
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for i in range(0, F):
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for j in range(i, F):
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r = np.linalg.norm(C[i] - C[j])
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hat_C[i, j] = r
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hat_C[j, i] = r
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np.fill_diagonal(hat_C, 1)
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hat_C = (hat_C**2) * np.log(hat_C)
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# print(C.shape, hat_C.shape)
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delta_C = np.concatenate( # F+3 x F+3
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[
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np.concatenate(
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[np.ones((F, 1)), C, hat_C], axis=1), # F x F+3
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np.concatenate(
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[np.zeros((2, 3)), np.transpose(C)], axis=1), # 2 x F+3
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np.concatenate(
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[np.zeros((1, 3)), np.ones((1, F))], axis=1) # 1 x F+3
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],
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axis=0)
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inv_delta_C = np.linalg.inv(delta_C)
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return inv_delta_C # F+3 x F+3
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def build_P_hat(self, C, P):
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F = self.F
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eps = self.eps
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n = P.shape[0] # n (= self.I_r_width x self.I_r_height)
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#P_tile: n x 2 -> n x 1 x 2 -> n x F x 2
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P_tile = np.tile(np.expand_dims(P, axis=1), (1, F, 1))
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C_tile = np.expand_dims(C, axis=0) # 1 x F x 2
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P_diff = P_tile - C_tile # n x F x 2
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#rbf_norm: n x F
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rbf_norm = np.linalg.norm(P_diff, ord=2, axis=2, keepdims=False)
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#rbf: n x F
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rbf = np.multiply(np.square(rbf_norm), np.log(rbf_norm + eps))
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P_hat = np.concatenate([np.ones((n, 1)), P, rbf], axis=1)
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return P_hat # n x F+3
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def get_expand_tensor(self, batch_C_prime):
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name = "ex_fc"
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initializer = fluid.initializer.ConstantInitializer(value=0.0)
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param_attr = fluid.param_attr.ParamAttr(
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learning_rate=0.0, initializer=initializer, name=name + "_w")
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bias_attr = fluid.param_attr.ParamAttr(
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learning_rate=0.0, initializer=initializer, name=name + "_b")
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batch_C_ex_part_tensor = fluid.layers.fc(input=batch_C_prime,
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size=6,
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param_attr=param_attr,
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bias_attr=bias_attr,
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name=name)
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batch_C_ex_part_tensor = fluid.layers.reshape(
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x=batch_C_ex_part_tensor, shape=[-1, 3, 2])
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return batch_C_ex_part_tensor
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def __call__(self, batch_C_prime, I_r_size):
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C = self.build_C()
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P = self.build_P(I_r_size)
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inv_delta_C = self.build_inv_delta_C(C).astype('float32')
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P_hat = self.build_P_hat(C, P).astype('float32')
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inv_delta_C_tensor = layers.create_tensor(dtype='float32')
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layers.assign(inv_delta_C, inv_delta_C_tensor)
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inv_delta_C_tensor.stop_gradient = True
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P_hat_tensor = layers.create_tensor(dtype='float32')
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layers.assign(P_hat, P_hat_tensor)
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P_hat_tensor.stop_gradient = True
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batch_C_ex_part_tensor = self.get_expand_tensor(batch_C_prime)
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# batch_C_ex_part_tensor = create_tmp_var(
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# fluid.default_main_program(),
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# name='batch_C_ex_part_tensor',
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# dtype='float32', shape=[-1, 3, 2])
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# layers.py_func(func=get_batch_C_expand,
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# x=[batch_C_prime], out=[batch_C_ex_part_tensor])
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batch_C_ex_part_tensor.stop_gradient = True
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batch_C_prime_with_zeros = layers.concat(
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[batch_C_prime, batch_C_ex_part_tensor], axis=1)
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batch_T = layers.matmul(inv_delta_C_tensor, batch_C_prime_with_zeros)
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batch_P_prime = layers.matmul(P_hat_tensor, batch_T)
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return batch_P_prime
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class TPS(object):
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def __init__(self, params):
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super(TPS, self).__init__()
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self.loc_net = LocalizationNetwork(params)
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self.grid_generator = GridGenerator(params)
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def __call__(self, image):
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batch_C_prime = self.loc_net(image)
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I_r_size = [image.shape[3], image.shape[2]]
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batch_P_prime = self.grid_generator(batch_C_prime, I_r_size)
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batch_P_prime = layers.reshape(
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x=batch_P_prime, shape=[-1, image.shape[2], image.shape[3], 2])
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batch_I_r = layers.grid_sampler(x=image, grid=batch_P_prime)
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image.stop_gradient = False
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return batch_I_r
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