model.train = False
xp = cuda.cupy if args.gpu >= 0 else np
if not os.path.exists(args.out_dir):
os.makedirs(args.out_dir)
tile_conv1 = tile_W(model.conv1.W.data)
cv.imwrite('{}/conv1_W.png'.format(args.out_dir), tile_conv1)
o_side = args.ortho_original_side
l_side = args.label_original_side
om_side = args.ortho_side
lm_side = args.label_side
o_cur, o_txn, _ = get_cursor('data/mass_merged/lmdb/test_sat')
l_cur, l_txn, _ = get_cursor('data/mass_merged/lmdb/test_map')
i = 0
while True:
o_key, o_val = o_cur.item()
l_key, l_val = l_cur.item()
if o_key != l_key:
raise ValueError('Keys of ortho and label patches are different: '
'{} != {}'.format(o_key, l_key))
# prepare patch
o_patch = np.fromstring(o_val, dtype=np.uint8).reshape(
(o_side, o_side, 3))
l_patch = np.fromstring(l_val, dtype=np.uint8).reshape(
(l_side, l_side, 1))
model.train = False
xp = cuda.cupy if args.gpu >= 0 else np
if not os.path.exists(args.out_dir):
os.makedirs(args.out_dir)
tile_conv1 = tile_W(model.conv1.W.data)
cv.imwrite('{}/conv1_W.png'.format(args.out_dir), tile_conv1)
o_side = args.ortho_original_side
l_side = args.label_original_side
om_side = args.ortho_side
lm_side = args.label_side
o_cur, o_txn, _ = get_cursor('data/mass_merged/lmdb/test_sat')
l_cur, l_txn, _ = get_cursor('data/mass_merged/lmdb/test_map')
i = 0
while True:
o_key, o_val = o_cur.item()
l_key, l_val = l_cur.item()
if o_key != l_key:
raise ValueError(
'Keys of ortho and label patches are different: '
'{} != {}'.format(o_key, l_key))
# prepare patch
o_patch = np.fromstring(
o_val, dtype=np.uint8).reshape((o_side, o_side, 3))
l_patch = np.fromstring(
# -*- coding: utf-8 -*-
import argparse
import numpy as np
from train import get_cursor
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--db_fn', type=str)
parser.add_argument('--side', type=int, default=92)
args = parser.parse_args()
side = args.side
cur, txn, args.N = get_cursor(args.db_fn)
i = 0
norms = []
while True:
key, val = cur.item()
patch = np.fromstring(val, dtype=np.uint8).reshape((side, side, 3))
patch = patch.astype(np.float)
patch -= patch.reshape((-1, 3)).mean(axis=0)
patch /= patch.reshape((-1, 3)).std(axis=0)
norms.append(np.linalg.norm(patch))
ret = cur.next()
if not ret:
break
print(i)
i += 1