predict34_loc.py

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_Loc

from utils import *

cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False)

test_dir = 'test/images'
pred_folder = 'pred34_loc'
models_folder = 'weights'

if __name__ == '__main__':
    t0 = timeit.default_timer()

    makedirs(pred_folder, exist_ok=True)
    
    os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
    # os.environ["CUDA_VISIBLE_DEVICES"] = sys.argv[1]

    # cudnn.benchmark = True

    models = []

    for seed in [0, 1, 2]:
        snap_to_load = 'res34_loc_{}_1_best'.format(seed)
        model = Res34_Unet_Loc().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)
                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[..., 0], [cv2.IMWRITE_PNG_COMPRESSION, 9])

    elapsed = timeit.default_timer() - t0
    print('Time: {:.3f} min'.format(elapsed / 60))