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import os |
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os.environ["MKL_NUM_THREADS"] = "1" |
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os.environ["NUMEXPR_NUM_THREADS"] = "1" |
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os.environ["OMP_NUM_THREADS"] = "1" |
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from os import path, makedirs |
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import sys |
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import numpy as np |
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np.random.seed(1) |
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import random |
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random.seed(1) |
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import torch |
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torch.set_num_threads(1) |
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from torch import nn |
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from torch.autograd import Variable |
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import timeit |
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import cv2 |
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os.environ["CUDA_VISIBLE_DEVICES"] = '' |
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from zoo.models import SeResNext50_Unet_Double |
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from utils import preprocess_inputs |
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cv2.setNumThreads(0) |
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cv2.ocl.setUseOpenCL(False) |
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models_folder = 'weights' |
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if __name__ == '__main__': |
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t0 = timeit.default_timer() |
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seed = int(sys.argv[1]) |
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pre_file = sys.argv[2] |
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post_file = sys.argv[3] |
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loc_pred_file = sys.argv[4] |
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cls_pred_file = sys.argv[5] |
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pred_folder = 'res50cls_{}_tuned'.format(seed) |
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makedirs(pred_folder, exist_ok=True) |
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models = [] |
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snap_to_load = 'res50_cls_cce_{}_tuned2_best'.format(seed) |
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model = SeResNext50_Unet_Double(pretrained=None) |
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model = nn.DataParallel(model) |
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print("=> loading checkpoint '{}'".format(snap_to_load)) |
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checkpoint = torch.load(path.join(models_folder, snap_to_load), map_location='cpu') |
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loaded_dict = checkpoint['state_dict'] |
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sd = model.state_dict() |
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for k in model.state_dict(): |
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if k in loaded_dict and sd[k].size() == loaded_dict[k].size(): |
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sd[k] = loaded_dict[k] |
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loaded_dict = sd |
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model.load_state_dict(loaded_dict) |
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print("loaded checkpoint '{}' (epoch {}, best_score {})" |
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.format(snap_to_load, checkpoint['epoch'], checkpoint['best_score'])) |
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model.eval() |
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models.append(model) |
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with torch.no_grad(): |
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img = cv2.imread(pre_file, cv2.IMREAD_COLOR) |
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img2 = cv2.imread(post_file, cv2.IMREAD_COLOR) |
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img = np.concatenate([img, img2], axis=2) |
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img = preprocess_inputs(img) |
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inp = [] |
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inp.append(img) |
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inp.append(img[::-1, ...]) |
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inp.append(img[:, ::-1, ...]) |
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inp.append(img[::-1, ::-1, ...]) |
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inp = np.asarray(inp, dtype='float') |
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inp = torch.from_numpy(inp.transpose((0, 3, 1, 2))).float() |
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inp = Variable(inp) |
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pred = [] |
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for model in models: |
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for j in range(2): |
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msk = model(inp[j*2:j*2+2]) |
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msk = torch.softmax(msk[:, :, ...], dim=1) |
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msk = msk.cpu().numpy() |
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msk[:, 0, ...] = 1 - msk[:, 0, ...] |
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if j == 0: |
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pred.append(msk[0, ...]) |
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pred.append(msk[1, :, ::-1, :]) |
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else: |
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pred.append(msk[0, :, :, ::-1]) |
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pred.append(msk[1, :, ::-1, ::-1]) |
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pred_full = np.asarray(pred).mean(axis=0) |
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msk = pred_full * 255 |
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msk = msk.astype('uint8').transpose(1, 2, 0) |
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f = os.path.basename(cls_pred_file) |
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cv2.imwrite(path.join(pred_folder, '{0}'.format(f + '_part1.png')), msk[..., :3], [cv2.IMWRITE_PNG_COMPRESSION, 9]) |
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cv2.imwrite(path.join(pred_folder, '{0}'.format(f + '_part2.png')), msk[..., 2:], [cv2.IMWRITE_PNG_COMPRESSION, 9]) |
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elapsed = timeit.default_timer() - t0 |
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print('Time: {:.3f} min'.format(elapsed / 60)) |
