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import os |
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import numpy as np |
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import torch |
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import json |
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from enum import Enum |
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class ConfigEncoder(json.JSONEncoder): |
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def default(self, o): |
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if isinstance(o, type): |
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return {'$class': o.__module__ + "." + o.__name__} |
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elif isinstance(o, Enum): |
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return { |
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'$enum': o.__module__ + "." + o.__class__.__name__ + '.' + o.name |
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} |
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elif callable(o): |
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return { |
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'$function': o.__module__ + "." + o.__name__ |
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} |
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return json.JSONEncoder.default(self, o) |
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def count_parameters(model, trainable=False): |
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if trainable: |
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return sum(p.numel() for p in model.parameters() if p.requires_grad) |
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return sum(p.numel() for p in model.parameters()) |
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def tensor2numpy(x): |
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return x.cpu().data.numpy() if x.is_cuda else x.data.numpy() |
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def target2onehot(targets, n_classes): |
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onehot = torch.zeros(targets.shape[0], n_classes).to(targets.device) |
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onehot.scatter_(dim=1, index=targets.long().view(-1, 1), value=1.0) |
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return onehot |
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def makedirs(path): |
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if not os.path.exists(path): |
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os.makedirs(path) |
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def accuracy(y_pred, y_true, nb_old, increment=10): |
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assert len(y_pred) == len(y_true), "Data length error." |
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all_acc = {} |
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all_acc["total"] = np.around( |
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(y_pred == y_true).sum() * 100 / len(y_true), decimals=2 |
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) |
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for class_id in range(0, np.max(y_true), increment): |
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idxes = np.where( |
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np.logical_and(y_true >= class_id, y_true < class_id + increment) |
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)[0] |
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label = "{}-{}".format( |
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str(class_id).rjust(2, "0"), str(class_id + increment - 1).rjust(2, "0") |
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) |
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all_acc[label] = np.around( |
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(y_pred[idxes] == y_true[idxes]).sum() * 100 / len(idxes), decimals=2 |
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) |
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idxes = np.where(y_true < nb_old)[0] |
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all_acc["old"] = ( |
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0 |
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if len(idxes) == 0 |
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else np.around( |
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(y_pred[idxes] == y_true[idxes]).sum() * 100 / len(idxes), decimals=2 |
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) |
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) |
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idxes = np.where(y_true >= nb_old)[0] |
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all_acc["new"] = np.around( |
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(y_pred[idxes] == y_true[idxes]).sum() * 100 / len(idxes), decimals=2 |
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) |
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return all_acc |
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def split_images_labels(imgs): |
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images = [] |
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labels = [] |
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for item in imgs: |
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images.append(item[0]) |
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labels.append(item[1]) |
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return np.array(images), np.array(labels) |
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def save_fc(args, model): |
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_path = os.path.join(args['logfilename'], "fc.pt") |
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if len(args['device']) > 1: |
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fc_weight = model._network.fc.weight.data |
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else: |
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fc_weight = model._network.fc.weight.data.cpu() |
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torch.save(fc_weight, _path) |
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_save_dir = os.path.join(f"./results/fc_weights/{args['prefix']}") |
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os.makedirs(_save_dir, exist_ok=True) |
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_save_path = os.path.join(_save_dir, f"{args['csv_name']}.csv") |
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with open(_save_path, "a+") as f: |
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f.write(f"{args['time_str']},{args['model_name']},{_path} \n") |
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def save_model(args, model): |
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_path = os.path.join(args['logfilename'], "model.pt") |
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if len(args['device']) > 1: |
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weight = model._network |
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else: |
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weight = model._network.cpu() |
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torch.save(weight, _path) |
