import os
import sys
import numpy as np
import tensorflow as tf
import tensors_saver
WEIGHTS_PATH = sys.argv[1]
tensors_saver.set_out_path(sys.argv[2])
BATCH = 64
def padd_ker(x0):
res = np.zeros((63, 63, 64, 64), dtype=np.float32)
for hIndex in range(0, 63, 2):
for wIndex in range(0, 63, 2):
for inCh in range(64):
for oCh in range(64):
prevHIndex = (int)(hIndex - ((int)(hIndex / 2)))
prevWIndex = (int)(wIndex - ((int)(wIndex / 2)))
res[hIndex, wIndex, inCh, oCh] = x0[inCh, prevHIndex,
prevWIndex, oCh]
return res
def im2col(y0):
res = np.zeros((64 * 63 * 63, 5 * 5 * 3), dtype=np.float32)
for b in range(3):
s[0] = s[1]
s[1] = s[2]
s[2] = s[3]
s[3] = s[3] ^ (s[3] >> np.uint64(19)) ^ t ^ (t >> np.uint64(8))
return s[3]
def next_f32():
x = np.float32(next_u64())
div = np.float32(0xFFFFFFFFFFFFFFFF)
return x / div
def np_f32(shape):
res = np.empty(shape).astype(np.float32)
res2 = res.reshape(res.size)
for i in range(res2.size):
res2[i] = next_f32()
return res
import tensors_saver
if __name__ == '__main__':
seed(234)
tensors_saver.set_out_path('./out.npz')
tensors_saver.add(np_f32((145, 18, 12, 34)))
