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