pickle.dump(colors, fp)
names = []
img = []
for image in listing:
x = []
X = LoadBatches.getImageArr(path1 + image, img_rows, img_cols, dp)
# print(X.shape)
X.reshape(img_rows, img_cols, img_channels)
pr = model.predict(np.array([X]))[0]
pr = np.array(pr)
pr = pr.reshape((img_rows, img_cols)) # .argmax(axis=2)
print(pr)
# break
seg_img = np.zeros((img_rows, img_cols, 1))
for i in range(img_rows):
for j in range(img_cols):
if pr[i][j] < 0.5:
seg_img[i][j] = 0
print('absent')
else:
seg_img[i][j] = 255
print('present')
seg_img = cv2.resize(seg_img, (101, 101))
cv2.imwrite(path2 + image, seg_img)
name = ''.join(list(image)[:-4])
img.append(seg_img)
names.append(name)
#break
make_submission(img, names, fast=False, path='result1.csv')
with open('/home/titanx/Desktop/Mainak/TGS SALT/dp', 'rb') as f:
dp = pickle.load(f)
names = []
img = []
for image in listing:
x = []
X = LoadBatches.getImageArr(path1 + image, img_rows, img_cols, dp)
# print(X.shape)
X.reshape(img_rows, img_cols, img_channels)
pr = model.predict(np.array([X]))[0]
pr = np.array(pr)
pr = pr.reshape((img_rows, img_cols)) # .argmax(axis=2)
print(pr)
seg_img = np.zeros((img_rows, img_cols, 1))
for i in range(img_rows):
for j in range(img_cols):
if pr[i][j] < 0.5:
seg_img[i][j] = 0
print('absent')
else:
seg_img[i][j] = 255
print('present')
seg_img = cv2.resize(seg_img, (101, 101))
cv2.imwrite(path2 + image, seg_img)
#break
name = ''.join(list(image)[:-4])
img.append(seg_img)
names.append(name)
#break
make_submission(img, names, fast=False, path='result4_selufull.csv')
collate_fn=tta_collate if args.use_tta else default_collate,
shuffle=False)
model = BaseModel(args)
model.init_model()
model.load_trained_model()
pred = model.eval(test_dataloader) # after softmax array
pickle.dump({'pred': pred}, open(pkl_name, 'wb'))
preds_test += pred
preds_test /= len(args.ensemble_exp)
# generate csv
print('generate csv ...')
for t in [0.29, 0.30, 0.31, 0.32, 0.33, 0.4, 0.45]:
make_submission((preds_test > t).astype(np.uint8),
test['names'],
path='{}_{:.2f}_submission.csv'.format(csv_name, t))
print('generate {}_submission.csv'.format(args.exp_name))
if args.vis:
print('vis mask ...')
OUT_1 = os.path.join('Visualize', args.exp_name + '_eval')
if not os.path.exists(OUT_1):
os.makedirs(OUT_1)
for i, d in enumerate(test['names']):
name = d
tmp = preds_test[i]
save_img(tmp > 0.5, os.path.join(OUT_1, name + '_pred.jpg'))
from rlen import make_submission
if __name__ == '__main__':
import pickle
pkl_path = 'train.pkl'
data = pickle.load(open(pkl_path, 'rb'))
masks, name = data['masks'], data['names']
make_submission(masks, name, fast=True, path='fast_submission.csv')
make_submission(masks, name, fast=False, path='slow_submission.csv')