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import argparse | ||
import os | ||
import os.path as osp | ||
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import numpy as np | ||
import torch | ||
import torch.nn as nn | ||
from PIL import Image | ||
from torch.nn import DataParallel | ||
from torch.nn import functional as F | ||
from torch.utils import data | ||
from torch.utils.data import DataLoader | ||
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import settings | ||
from network import EANet | ||
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logger = settings.logger | ||
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def ensure_dir(dir_path): | ||
if not osp.isdir (dir_path): | ||
os.makedirs(dir_path) | ||
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def fetch(image_path, label_path=None): | ||
with open(image_path, 'rb') as fp: | ||
image = Image.open(fp).convert('RGB') | ||
image = torch.FloatTensor(np.asarray(image)) / 255 | ||
image = (image - settings.MEAN) / settings.STD | ||
image = image.permute(2, 0, 1).unsqueeze(dim=0) | ||
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if label_path is not None: | ||
with open(label_path, 'rb') as fp: | ||
label = Image.open(fp).convert('P') | ||
label = torch.FloatTensor(np.asarray(label)) | ||
label = label.unsqueeze(dim=0).unsqueeze(dim=1) | ||
else: | ||
label = None | ||
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return image, label | ||
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def pad_inf(image, label=None): | ||
h, w = image.size()[-2:] | ||
stride = settings.STRIDE | ||
pad_h = (stride + 1 - h % stride) % stride | ||
pad_w = (stride + 1 - w % stride) % stride | ||
if pad_h > 0 or pad_w > 0: | ||
image = F.pad(image, (0, pad_w, 0, pad_h), mode='constant', value=0.) | ||
if label is not None: | ||
label = F.pad(label, (0, pad_w, 0, pad_h), mode='constant', | ||
value=settings.IGNORE_LABEL) | ||
return image, label | ||
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class BaseDataset(data.Dataset): | ||
def __init__(self, data_root, split): | ||
self.data_root = data_root | ||
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file_list = osp.join('datalist', split + '.txt') | ||
file_list = tuple(open(file_list, 'r')) | ||
file_list = [id_.rstrip() for id_ in file_list] | ||
self.files = file_list | ||
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def __len__(self): | ||
return len(self.files) | ||
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def __getitem__(self, idx): | ||
image_id = self.files[idx] | ||
return self._get_item(image_id) | ||
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def _get_item(self, idx): | ||
raise NotImplementedError | ||
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class TestDataset(BaseDataset): | ||
def __init__(self, data_root, split='test'): | ||
super(TestDataset, self).__init__(data_root, split) | ||
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def _get_item(self, image_id): | ||
image_path = osp.join(self.data_root, image_id + '.jpg') | ||
image, _ = fetch(image_path) | ||
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return image[0], image_id | ||
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class TestSession(object): | ||
def __init__(self, dt_split): | ||
self.log_dir = settings.LOG_DIR | ||
self.model_dir = settings.MODEL_DIR | ||
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self.net = EANet(settings.N_CLASSES, settings.N_LAYERS).cuda() | ||
self.net = DataParallel(self.net) | ||
dataset = TestDataset(data_root=settings.TEST_DATA_ROOT, split=dt_split) | ||
self.dataloader = DataLoader(dataset, batch_size=1, shuffle=False, | ||
num_workers=16, drop_last=False) | ||
self.hist = 0 | ||
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def load_checkpoints(self, name): | ||
ckp_path = name | ||
try: | ||
obj = torch.load(ckp_path, | ||
map_location=lambda storage, loc: storage.cuda()) | ||
logger.info('Load checkpoint %s.' % ckp_path) | ||
except FileNotFoundError: | ||
logger.info('No checkpoint %s!' % ckp_path) | ||
return | ||
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self.net.module.load_state_dict(obj['net']) | ||
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def inf_batch(self, image): | ||
image = image.cuda() | ||
with torch.no_grad(): | ||
logit = self.net(image) | ||
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return logit | ||
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def trans_scale(image, h, w): | ||
image = F.interpolate(image, size=(h, w), mode='bilinear', | ||
align_corners=True) | ||
return image | ||
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def get_palette(num_cls): | ||
n = num_cls | ||
palette = [0] * (n * 3) | ||
for j in range(0, n): | ||
lab = j | ||
palette[j * 3 + 0] = 0 | ||
palette[j * 3 + 1] = 0 | ||
palette[j * 3 + 2] = 0 | ||
i = 0 | ||
while lab: | ||
palette[j * 3 + 0] |= (((lab >> 0) & 1) << (7 - i)) | ||
palette[j * 3 + 1] |= (((lab >> 1) & 1) << (7 - i)) | ||
palette[j * 3 + 2] |= (((lab >> 2) & 1) << (7 - i)) | ||
i += 1 | ||
lab >>= 3 | ||
return palette | ||
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def test_main(ckp_name='final.pth'): | ||
sess = TestSession('test') | ||
sess.load_checkpoints(ckp_name) | ||
dt_iter = sess.dataloader | ||
sess.net.eval() | ||
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save_dir = osp.join(settings.TEST_SAVE_DIR, settings.EXP_NAME) | ||
logger.info('Set Test save dir, %s' % save_dir) | ||
ensure_dir(save_dir) | ||
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for _, [image, image_id] in enumerate(dt_iter): | ||
_, _, h, w = image.size() | ||
logits = np.zeros((1, settings.N_CLASSES,h, w), np.float32) | ||
logits = torch.Tensor (logits) | ||
test_scale = list (settings.TEST_SCALES) | ||
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# orig | ||
for scale in test_scale: | ||
scale_h = (int) (scale * h) | ||
scale_w = (int) (scale * w) | ||
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scale_image = trans_scale(image, scale_h, scale_w) | ||
scale_image, _ = pad_inf(scale_image) | ||
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scale_logit = sess.inf_batch(scale_image) | ||
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scale_logit = scale_logit[:, :, 0:scale_h, 0:scale_w] | ||
scale_logit = F.interpolate(scale_logit, size=[h, w], mode='bilinear', align_corners=True) | ||
logits += scale_logit.cpu() | ||
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# flip | ||
for scale in test_scale : | ||
scale_h = (int) (scale * h) | ||
scale_w = (int) (scale * w) | ||
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flip_image = torch.flip(image, [3]) | ||
flip_image = trans_scale(flip_image, scale_h, scale_w) | ||
flip_image, _ = pad_inf(flip_image) | ||
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flip_logit = sess.inf_batch(flip_image) | ||
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flip_logit = flip_logit[:, :, 0:scale_h, 0:scale_w] | ||
flip_logit = F.interpolate(flip_logit, size=[h, w], mode='bilinear', align_corners=True) | ||
logit = torch.flip(flip_logit, [3]) | ||
logits += logit.cpu() | ||
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pred = logits.max(dim=1)[1] | ||
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# save results | ||
pred_arr = np.array(pred.cpu().data) | ||
pred_image = Image.fromarray(np.uint8(pred_arr[0])) | ||
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palette = get_palette(256) | ||
pred_image.putpalette(palette) | ||
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name = image_id[0].split('.')[0] | ||
save_path = osp.join(save_dir, f'{name}.png') | ||
pred_image.save(save_path) | ||
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if __name__ == '__main__': | ||
load_dir = osp.join(settings.MODEL_DIR, settings.EXP_NAME) | ||
load_name = osp.join(load_dir, 'final.pth') | ||
test_main(load_name) |