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import os
from os import path, makedirs, listdir
import sys
import numpy as np
np.random.seed(1)
import random
random.seed(1)
import torch
from torch import nn
from torch.backends import cudnn
from torch.autograd import Variable
import pandas as pd
from tqdm import tqdm
import timeit
import cv2
from zoo.models import Res34_Unet_Double
from utils import *
cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False)
test_dir = 'test/images'
models_folder = 'weights'
if __name__ == '__main__':
t0 = timeit.default_timer()
seed = int(sys.argv[1])
# vis_dev = sys.argv[2]
# os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
# os.environ["CUDA_VISIBLE_DEVICES"] = vis_dev
pred_folder = 'res34cls2_{}_tuned'.format(seed)
makedirs(pred_folder, exist_ok=True)
# cudnn.benchmark = True
models = []
snap_to_load = 'res34_cls2_{}_tuned_best'.format(seed)
model = Res34_Unet_Double().cuda()
model = nn.DataParallel(model).cuda()
print("=> loading checkpoint '{}'".format(snap_to_load))
checkpoint = torch.load(path.join(models_folder, snap_to_load), map_location='cpu')
loaded_dict = checkpoint['state_dict']
sd = model.state_dict()
for k in model.state_dict():
if k in loaded_dict and sd[k].size() == loaded_dict[k].size():
sd[k] = loaded_dict[k]
loaded_dict = sd
model.load_state_dict(loaded_dict)
print("loaded checkpoint '{}' (epoch {}, best_score {})"
.format(snap_to_load, checkpoint['epoch'], checkpoint['best_score']))
model.eval()
models.append(model)
with torch.no_grad():
for f in tqdm(sorted(listdir(test_dir))):
if '_pre_' in f:
fn = path.join(test_dir, f)
img = cv2.imread(fn, cv2.IMREAD_COLOR)
img2 = cv2.imread(fn.replace('_pre_', '_post_'), cv2.IMREAD_COLOR)
img = np.concatenate([img, img2], axis=2)
img = preprocess_inputs(img)
inp = []
inp.append(img)
inp.append(img[::-1, ...])
inp.append(img[:, ::-1, ...])
inp.append(img[::-1, ::-1, ...])
inp = np.asarray(inp, dtype='float')
inp = torch.from_numpy(inp.transpose((0, 3, 1, 2))).float()
inp = Variable(inp).cuda()
pred = []
for model in models:
msk = model(inp)
msk = torch.sigmoid(msk)
msk = msk.cpu().numpy()
pred.append(msk[0, ...])
pred.append(msk[1, :, ::-1, :])
pred.append(msk[2, :, :, ::-1])
pred.append(msk[3, :, ::-1, ::-1])
pred_full = np.asarray(pred).mean(axis=0)
msk = pred_full * 255
msk = msk.astype('uint8').transpose(1, 2, 0)
cv2.imwrite(path.join(pred_folder, '{0}.png'.format(f.replace('.png', '_part1.png'))), msk[..., :3], [cv2.IMWRITE_PNG_COMPRESSION, 9])
cv2.imwrite(path.join(pred_folder, '{0}.png'.format(f.replace('.png', '_part2.png'))), msk[..., 2:], [cv2.IMWRITE_PNG_COMPRESSION, 9])
elapsed = timeit.default_timer() - t0
print('Time: {:.3f} min'.format(elapsed / 60)) |