# imgloss + tmploss + featureloss
import numpy as np
height = 288
width = 512
batch_size = 10
initial_learning_rate = 2e-5
feature_mul = 1#10 * width
theta_mul = 400/2500#400
regu_mul = 30/2500#30
img_mul = 50#height * width * 1
temp_mul = 500#10#height * width * 10 
black_mul = 300000/2500#300000
id_mul = 10/2500#10
training_iter = 100000
step_size = 40000
train_data_size = 27000
test_data_size = 2500
crop_rate = 1
before_ch = 6
after_ch = 0
tot_ch = before_ch + after_ch + 1
test_batches = 10
random_crop_rate = 0.9
max_crop_rate = 0.6
disp_freq = 100
test_freq = 500
save_freq = 1000
no_theta_iter = 1000000
do_temp_loss_iter = 10000
do_theta_10_iter = -1
do_black_loss_iter = 1500
do_theta_only_iter = 600
tfrecord_item_num = 10
log_dir = 'log/v2_93/'
model_dir = 'models/v2_93/'
data_dir = 'data/'
rand_H_max = np.array([[1.1, 0.1, 0.5], [0.1, 1.1, 0.5], [0.1, 0.1, 1]])
rand_H_min = np.array([[0.9, -0.1, -0.5], [-0.1, 0.9, -0.5], [-0.1, -0.1, 1]])
max_matches = 3000
input_mask = True
do_crop_rate = 0.8
indices = [0, 1, 2, 4, 8, 16, 32]
rand_H_change_rate = 1
distortion_mul = 1#2500
consistency_mul = 20#50000
grid_large_h = 2
grid_large_w = 2
grid_h = 4
grid_w = 4
grid_theta_mul = 0
grid_max_move = 1
