| |
| |
|
|
| import re |
| import importlib |
| import torch |
| from argparse import Namespace |
| import numpy as np |
| from PIL import Image |
| import os |
| import argparse |
| import dill as pickle |
|
|
|
|
| def save_obj(obj, name): |
| with open(name, "wb") as f: |
| pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL) |
|
|
|
|
| def load_obj(name): |
| with open(name, "rb") as f: |
| return pickle.load(f) |
|
|
|
|
| def copyconf(default_opt, **kwargs): |
| conf = argparse.Namespace(**vars(default_opt)) |
| for key in kwargs: |
| print(key, kwargs[key]) |
| setattr(conf, key, kwargs[key]) |
| return conf |
|
|
|
|
| |
| |
| def tensor2im(image_tensor, imtype=np.uint8, normalize=True, tile=False): |
| if isinstance(image_tensor, list): |
| image_numpy = [] |
| for i in range(len(image_tensor)): |
| image_numpy.append(tensor2im(image_tensor[i], imtype, normalize)) |
| return image_numpy |
|
|
| if image_tensor.dim() == 4: |
| |
| images_np = [] |
| for b in range(image_tensor.size(0)): |
| one_image = image_tensor[b] |
| one_image_np = tensor2im(one_image) |
| images_np.append(one_image_np.reshape(1, *one_image_np.shape)) |
| images_np = np.concatenate(images_np, axis=0) |
|
|
| return images_np |
|
|
| if image_tensor.dim() == 2: |
| image_tensor = image_tensor.unsqueeze(0) |
| image_numpy = image_tensor.detach().cpu().float().numpy() |
| if normalize: |
| image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 |
| else: |
| image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0 |
| image_numpy = np.clip(image_numpy, 0, 255) |
| if image_numpy.shape[2] == 1: |
| image_numpy = image_numpy[:, :, 0] |
| return image_numpy.astype(imtype) |
|
|
|
|
| |
| def tensor2label(label_tensor, n_label, imtype=np.uint8, tile=False): |
| if label_tensor.dim() == 4: |
| |
| images_np = [] |
| for b in range(label_tensor.size(0)): |
| one_image = label_tensor[b] |
| one_image_np = tensor2label(one_image, n_label, imtype) |
| images_np.append(one_image_np.reshape(1, *one_image_np.shape)) |
| images_np = np.concatenate(images_np, axis=0) |
| |
| |
| |
| |
| |
| |
| return images_np |
|
|
| if label_tensor.dim() == 1: |
| return np.zeros((64, 64, 3), dtype=np.uint8) |
| if n_label == 0: |
| return tensor2im(label_tensor, imtype) |
| label_tensor = label_tensor.cpu().float() |
| if label_tensor.size()[0] > 1: |
| label_tensor = label_tensor.max(0, keepdim=True)[1] |
| label_tensor = Colorize(n_label)(label_tensor) |
| label_numpy = np.transpose(label_tensor.numpy(), (1, 2, 0)) |
| result = label_numpy.astype(imtype) |
| return result |
|
|
|
|
| def save_image(image_numpy, image_path, create_dir=False): |
| if create_dir: |
| os.makedirs(os.path.dirname(image_path), exist_ok=True) |
| if len(image_numpy.shape) == 2: |
| image_numpy = np.expand_dims(image_numpy, axis=2) |
| if image_numpy.shape[2] == 1: |
| image_numpy = np.repeat(image_numpy, 3, 2) |
| image_pil = Image.fromarray(image_numpy) |
|
|
| |
| image_pil.save(image_path.replace(".jpg", ".png")) |
|
|
|
|
| def mkdirs(paths): |
| if isinstance(paths, list) and not isinstance(paths, str): |
| for path in paths: |
| mkdir(path) |
| else: |
| mkdir(paths) |
|
|
|
|
| def mkdir(path): |
| if not os.path.exists(path): |
| os.makedirs(path) |
|
|
|
|
| def atoi(text): |
| return int(text) if text.isdigit() else text |
|
|
|
|
| def natural_keys(text): |
| """ |
| alist.sort(key=natural_keys) sorts in human order |
| http://nedbatchelder.com/blog/200712/human_sorting.html |
| (See Toothy's implementation in the comments) |
| """ |
| return [atoi(c) for c in re.split("(\d+)", text)] |
|
|
|
|
| def natural_sort(items): |
| items.sort(key=natural_keys) |
|
|
|
|
| def str2bool(v): |
| if v.lower() in ("yes", "true", "t", "y", "1"): |
| return True |
| elif v.lower() in ("no", "false", "f", "n", "0"): |
| return False |
| else: |
| raise argparse.ArgumentTypeError("Boolean value expected.") |
|
|
|
|
| def find_class_in_module(target_cls_name, module): |
| target_cls_name = target_cls_name.replace("_", "").lower() |
| clslib = importlib.import_module(module) |
| cls = None |
| for name, clsobj in clslib.__dict__.items(): |
| if name.lower() == target_cls_name: |
| cls = clsobj |
|
|
| if cls is None: |
| print( |
| "In %s, there should be a class whose name matches %s in lowercase without underscore(_)" |
| % (module, target_cls_name) |
| ) |
| exit(0) |
|
|
| return cls |
|
|
|
|
| def save_network(net, label, epoch, opt): |
| save_filename = "%s_net_%s.pth" % (epoch, label) |
| save_path = os.path.join(opt.checkpoints_dir, opt.name, save_filename) |
| torch.save(net.cpu().state_dict(), save_path) |
| if len(opt.gpu_ids) and torch.cuda.is_available(): |
| net.cuda() |
|
|
|
|
| def load_network(net, label, epoch, opt): |
| save_filename = "%s_net_%s.pth" % (epoch, label) |
| save_dir = os.path.join(opt.checkpoints_dir, opt.name) |
| save_path = os.path.join(save_dir, save_filename) |
| if os.path.exists(save_path): |
| weights = torch.load(save_path) |
| net.load_state_dict(weights) |
| return net |
|
|
|
|
| |
| |
| |
| |
| |
| def uint82bin(n, count=8): |
| """returns the binary of integer n, count refers to amount of bits""" |
| return "".join([str((n >> y) & 1) for y in range(count - 1, -1, -1)]) |
|
|
|
|
| class Colorize(object): |
| def __init__(self, n=35): |
| self.cmap = labelcolormap(n) |
| self.cmap = torch.from_numpy(self.cmap[:n]) |
|
|
| def __call__(self, gray_image): |
| size = gray_image.size() |
| color_image = torch.ByteTensor(3, size[1], size[2]).fill_(0) |
|
|
| for label in range(0, len(self.cmap)): |
| mask = (label == gray_image[0]).cpu() |
| color_image[0][mask] = self.cmap[label][0] |
| color_image[1][mask] = self.cmap[label][1] |
| color_image[2][mask] = self.cmap[label][2] |
|
|
| return color_image |
|
|
