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/pre'
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]
                # print('loaded') --> for debugging to ensure model loads
        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 True:
                #import ipdb; ipdb.set_trace()
                fn = path.join(test_dir, f)
                #if '116' in fn:
                #    import ipdb; ipdb.set_trace()

                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}'.format(f.replace('.tif', '_part1.png'))), msk[..., 0], [cv2.IMWRITE_PNG_COMPRESSION, 9])

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