/
utils.py
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/
utils.py
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import torch
import numpy as np
from scipy import stats
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
def get_parameters(net, numpy=False):
# get weights from a torch model as a list of numpy arrays
parameter = torch.cat([i.data.reshape([-1]) for i in list(net.parameters())])
if numpy:
return parameter.cpu().numpy()
else:
return parameter
def set_parameters(net, parameters, device):
# load weights from a list of numpy arrays to a torch model
for i, (name, param) in enumerate(net.named_parameters()):
param.data = torch.Tensor(parameters[i]).to(device)
return net
def create_sequences(batch_size, dataset_size, epochs):
# create a sequence of data indices used for training
sequence = np.concatenate([np.random.default_rng().choice(dataset_size, size=dataset_size, replace=False)
for i in range(epochs)])
num_batch = int(len(sequence) // batch_size)
return np.reshape(sequence[:num_batch * batch_size], [num_batch, batch_size])
def consistent_type(model, architecture=None,
device=torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'), half=False):
# this function takes in directory to where model is saved, model weights as a list of numpy array,
# or a torch model and outputs model weights as a list of numpy array
if isinstance(model, str):
assert architecture is not None
state = torch.load(model)
net = architecture()
net.load_state_dict(state['net'])
weights = get_parameters(net)
elif isinstance(model, np.ndarray):
weights = torch.tensor(model)
elif not isinstance(model, torch.Tensor):
weights = get_parameters(model)
else:
weights = model
if half:
weights = weights.half()
return weights.to(device)
def parameter_distance(model1, model2, order=2, architecture=None, half=False):
# compute the difference between 2 checkpoints
weights1 = consistent_type(model1, architecture, half=half)
weights2 = consistent_type(model2, architecture, half=half)
if not isinstance(order, list):
orders = [order]
else:
orders = order
res_list = []
for o in orders:
if o == 'inf':
o = np.inf
if o == 'cos' or o == 'cosine':
res = (1 - torch.dot(weights1, weights2) /
(torch.norm(weights1) * torch.norm(weights1))).cpu().numpy()
else:
if o != np.inf:
try:
o = int(o)
except:
raise TypeError("input metric for distance is not understandable")
res = torch.norm(weights1 - weights2, p=o).cpu().numpy()
if isinstance(res, np.ndarray):
res = float(res)
res_list.append(res)
return res_list
def load_dataset(dataset, train, download=False):
try:
dataset_class = eval(f"torchvision.datasets.{dataset}")
except:
raise NotImplementedError(f"Dataset {dataset} is not implemented by pytorch.")
if dataset == "MNIST" or dataset == "FashionMNIST":
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))])
elif dataset == "CIFAR100":
if train:
transform = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(15),
transforms.ToTensor(),
transforms.Normalize((0.5070751592371323, 0.48654887331495095, 0.4409178433670343),
(0.2673342858792401, 0.2564384629170883, 0.27615047132568404))])
else:
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5070751592371323, 0.48654887331495095, 0.4409178433670343),
(0.2673342858792401, 0.2564384629170883, 0.27615047132568404))])
else:
if train:
transform = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, 4),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
else:
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
data = dataset_class(root='./data', train=train, download=download, transform=transform)
return data
def ks_test(reference, rvs):
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
with torch.no_grad():
ecdf = torch.arange(rvs.shape[0]).float() / torch.tensor(rvs.shape)
return torch.max(torch.abs(reference(torch.sort(rvs)[0]).to(device) - ecdf.to(device)))
def check_weights_initialization(param, method):
if method == 'default':
# kaimin uniform (default for weights of nn.Conv and nn.Linear)
fan = nn.init._calculate_correct_fan(param, 'fan_in')
gain = nn.init.calculate_gain('leaky_relu', np.sqrt(5))
std = gain / np.sqrt(fan)
bound = np.sqrt(3.0) * std
reference = torch.distributions.uniform.Uniform(-bound, bound).cdf
elif method == 'resnet_cifar':
# kaimin normal
fan = nn.init._calculate_correct_fan(param, 'fan_in')
gain = nn.init.calculate_gain('leaky_relu', 0)
std = gain / np.sqrt(fan)
reference = torch.distributions.normal.Normal(0, std).cdf
elif method == 'resnet':
# kaimin normal (default in conv layers of pytorch resnet)
fan = nn.init._calculate_correct_fan(param, 'fan_out')
gain = nn.init.calculate_gain('relu', 0)
std = gain / np.sqrt(fan)
reference = torch.distributions.normal.Normal(0, std).cdf
elif method == 'default_bias':
# default for bias of nn.Conv and nn.Linear
weight, param = param
fan_in, _ = nn.init._calculate_fan_in_and_fan_out(weight)
bound = 1 / np.sqrt(fan_in)
reference = torch.distributions.uniform.Uniform(-bound, bound).cdf
else:
raise NotImplementedError("Input initialization strategy is not implemented.")
param = param.reshape(-1)
ks_stats = ks_test(reference, param).cpu().item()
return stats.kstwo.sf(ks_stats, param.shape[0])
def check_weights_initialization_scipy(param, method):
if method == 'default':
# kaimin uniform (default for weights of nn.Conv and nn.Linear)
fan = nn.init._calculate_correct_fan(param, 'fan_in')
gain = nn.init.calculate_gain('leaky_relu', np.sqrt(5))
std = gain / np.sqrt(fan)
bound = np.sqrt(3.0) * std
reference = stats.uniform(loc=-bound, scale=bound * 2).cdf
elif method == 'resnet':
# kaimin normal (default in conv layers of pytorch resnet)
fan = nn.init._calculate_correct_fan(param, 'fan_out')
gain = nn.init.calculate_gain('relu', 0)
std = gain / np.sqrt(fan)
reference = stats.norm(loc=0, scale=std).cdf
elif method == 'default_bias':
# default for bias of nn.Conv and nn.Linear
weight, param = param
fan_in, _ = nn.init._calculate_fan_in_and_fan_out(weight)
bound = 1 / np.sqrt(fan_in)
reference = stats.uniform(loc=-bound, scale=bound * 2).cdf
else:
raise NotImplementedError("Input initialization strategy is not implemented.")
param = param.detach().numpy().reshape(-1)
return stats.kstest(param, reference)[1]