def getW(lumi_len, K):
random = gaussian_random_matrix(lumi_len, K)
random_reshape = np.reshape(random, (lumi_len, K))
return random_reshape
MAX_ITR = 5000000
VALIDATE_ITR = 5
CHECK_QUALITY_ITR = 5000
SAVE_MODEL_ITR = 10000
LOG_ROOT = "logs/"
if __name__ == "__main__":
make_dir(LOG_ROOT)
visualize_init(UTILS_CONFIG_PATH)
data_pan = sys.argv[1]
pretrained_model = sys.argv[2]
########################################
######step1 parse config
########################################
train_configs = {}
train_configs["parameter_len"] = 7 #normal2 tangent1 axay2 pd1 ps1
train_configs["lumitexel_length"] = 24576
train_configs["measurements_length"] = 16
train_configs["learning_rate"] = 1e-4
train_configs["tamer_name"] = "tamer"
train_configs["logPath"] = LOG_ROOT + "logs_lumitexel_guesser_iso/"
make_dir(train_configs["logPath"])
train_configs[
"pretrained_projection_matrix_path"] = "G:/2019_jointly_capture_training/3_7/Julia_feature_extractor_865000/"
import sys
sys.path.append("../")
from lumitexel_related import visualize_init, shrink, expand_img, visualize_new, get_visualize_idxs
import numpy as np
import cv2
lumitexel_size = 24576
block_size = 64
UITLS_PATH = "../"
if __name__ == "__main__":
visualize_init(UITLS_PATH)
idxes = get_visualize_idxs() #[24576,2]
origin_lumitexel = np.array(range(lumitexel_size), np.int32) + 1
lumitexel_img = visualize_new(origin_lumitexel).astype(np.int32)
itr_count = 0
block_num = 64 // block_size
idx_collector = []
idx_invert_collector = np.zeros(lumitexel_size, np.int32)
for x in range(block_num * 4):
for y in range(block_num * 3):
real_x = x * block_size
real_y = y * block_size
hit_point = (idxes[:, 0] == real_x) & (idxes[:, 1] == real_y)
if np.count_nonzero(hit_point) != 0:
for i in range(block_size):