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utils.py
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utils.py
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import numpy as np
import torch.nn.functional as F
import time
import resnet_image as res_image
import resnet as res_cifar
import resnet_cifar as res_cifar_new
import torch
import random
import math
import torch.nn as nn
import torchvision
import os
import shutil
import global_var
import torch
import yaml
from numpy.testing import assert_array_almost_equal
smp = torch.nn.Softmax(dim=0)
smt = torch.nn.Softmax(dim=1)
# basic function#
def multiclass_noisify(y, P, random_state=0):
""" Flip classes according to transition probability matrix T.
It expects a number between 0 and the number of classes - 1.
"""
#print np.max(y), P.shape[0]
assert P.shape[0] == P.shape[1]
assert np.max(y) < P.shape[0]
# row stochastic matrix
assert_array_almost_equal(P.sum(axis=1), np.ones(P.shape[1]))
assert (P >= 0.0).all()
m = y.shape[0]
#print m
new_y = y.copy()
flipper = np.random.RandomState(random_state)
for idx in np.arange(m):
i = y[idx]
# draw a vector with only an 1
flipped = flipper.multinomial(1, P[i, :][0], 1)[0]
new_y[idx] = np.where(flipped == 1)[0]
return new_y
def distCosine(x, y):
"""
:param x: m x k array
:param y: n x k array
:return: m x n array
"""
xx = np.sum(x ** 2, axis=1) ** 0.5
x = x / xx[:, np.newaxis]
yy = np.sum(y ** 2, axis=1) ** 0.5
y = y / yy[:, np.newaxis]
dist = 1 - np.dot(x, y.transpose()) # 1 - cosine distance
return dist
def cosDistance(features):
# features: N*M matrix. N features, each features is M-dimension.
features = F.normalize(features, dim=1) # each feature's l2-norm should be 1
similarity_matrix = torch.matmul(features, features.T)
distance_matrix = 1.0 - similarity_matrix
return distance_matrix
def distEuclidean(x, y, squared=True):
"""Compute pairwise (squared) Euclidean distances.
"""
assert isinstance(x, np.ndarray) and x.ndim == 2
assert isinstance(y, np.ndarray) and y.ndim == 2
assert x.shape[1] == y.shape[1]
# x_square = np.sum(x*x, axis=1, keepdims=True)
x_square = np.expand_dims(np.einsum('ij,ij->i', x, x), axis=1)
if x is y:
y_square = x_square.T
else:
# y_square = np.sum(y*y, axis=1, keepdims=True).T
y_square = np.expand_dims(np.einsum('ij,ij->i', y, y), axis=0)
distances = np.dot(x, y.T)
# use inplace operation to accelerate
distances *= -2
distances += x_square
distances += y_square
# result maybe less than 0 due to floating point rounding errors.
np.maximum(distances, 0, distances)
if x is y:
# Ensure that distances between vectors and themselves are set to 0.0.
# This may not be the case due to floating point rounding errors.
distances.flat[::distances.shape[0] + 1] = 0.0
if not squared:
np.sqrt(distances, distances)
return distances
def check_T_torch(KINDS, clean_label, noisy_label):
T_real = np.zeros((KINDS,KINDS))
for i in range(clean_label.shape[0]):
T_real[clean_label[i]][noisy_label[i]] += 1
P_real = [sum(T_real[i])*1.0 for i in range(KINDS)] # random selection
for i in range(KINDS):
if P_real[i]>0:
T_real[i] /= P_real[i]
P_real = np.array(P_real)/sum(P_real)
print(f'Check: P = {P_real},\n T = \n{np.round(T_real,3)}')
return T_real, P_real
# def extract_sub_dataset_local_c100(origin_trans, center_idx = None, numLocal = 10000):
# feat_cord0 = origin_trans[center_idx]
# dist_all = torch.norm(feat_cord0.view(1,-1) - origin_trans, dim=1)
# dist_s, idx = torch.sort(dist_all)
# idx_sel = idx[:numLocal].detach().cpu().tolist()
# return idx_sel
def extract_sub_dataset_local(origin_trans, center_idx = None, numLocal = 250):
feat_cord0 = origin_trans[center_idx]
dist_all = torch.norm(feat_cord0.view(1,-1) - origin_trans, dim=1)
dist_s, idx = torch.sort(dist_all)
idx_sel = idx[:numLocal].detach().cpu().tolist()
return idx_sel
def extract_sub_dataset(sub_cluster_each, origin, sub_clean_dataset_name, sub_noisy_dataset_name = None):
for i in range(len(sub_cluster_each)): #KINDS
random.shuffle(origin[i])
origin[i] = origin[i][:sub_cluster_each[i]]
for ori in origin[i]:
ori['label'] = i
total_len = sum([len(a) for a in origin])
origin_trans = torch.zeros(total_len,origin[0][0]['feature'].shape[0])
origin_label = torch.zeros(total_len).long()
origin_index = torch.zeros(total_len).long()
cnt = 0
for item in origin:
for i in item:
origin_trans[cnt] = i['feature']
origin_label[cnt] = i['label']
origin_index[cnt] = i['index']
cnt += 1
torch.save({'feature': origin_trans, 'clean_label': origin_label, 'index': origin_index},f'{sub_clean_dataset_name}')
origin_dataset = torch.load(f'{sub_clean_dataset_name}')
origin_dataset['noisy_label'] = origin_dataset['clean_label'].clone()
torch.save(origin_dataset, f'{sub_noisy_dataset_name}')
def add_noise_dataset(KINDS, sub_clean_dataset_name, sub_noisy_dataset_name, cluster_cnt, sub_cluster_each, label_list, T):
origin_dataset = torch.load(f'{sub_noisy_dataset_name}')
T_real = np.zeros((KINDS,KINDS))
for i in range(sum(sub_cluster_each)):
origin_dataset['noisy_label'][i] = torch.tensor(np.random.choice(label_list,1,p=T[origin_dataset['clean_label'][i]])).long()
T_real[origin_dataset['clean_label'][i]][origin_dataset['noisy_label'][i]] += 1
P_real = [sum(T_real[i])*1.0 for i in range(KINDS)] # random selection
for i in range(KINDS):
if P_real[i]>0:
T_real[i] /= P_real[i]
P_real = np.array(P_real)/sum(P_real)
torch.save(origin_dataset, f'{sub_noisy_dataset_name}')
def add_noise_dataset_local(KINDS, sub_noisy_dataset_name, cluster_cnt, sub_cluster_each, label_list, T, idx_sel):
origin_dataset = torch.load(f'{sub_noisy_dataset_name}')
T_real = np.zeros((KINDS,KINDS))
for i in range(len(idx_sel)):
origin_dataset['noisy_label'][idx_sel[i]] = torch.tensor(np.random.choice(label_list,1,p=T[origin_dataset['clean_label'][idx_sel[i]]])).long()
T_real[origin_dataset['clean_label'][idx_sel[i]]][origin_dataset['noisy_label'][idx_sel[i]]] += 1
P_real = [sum(T_real[i])*1.0 for i in range(KINDS)] # random selection
for i in range(KINDS):
if P_real[i]>0:
T_real[i] /= P_real[i]
P_real = np.array(P_real)/sum(P_real)
torch.save(origin_dataset, f'{sub_noisy_dataset_name}')
return P_real, T_real
def get_feat_clusters(origin, sample):
final_feat = origin['feature'][sample]
noisy_label = origin['noisy_label'][sample]
return final_feat, noisy_label
def get_feat_clusters_local(subdataset_name, sample):
origin = torch.load(f'{subdataset_name}', map_location=torch.device('cpu'))
final_feat = origin['feature'][sample]
noisy_label = origin['noisy_label'][sample]
return 0, final_feat, noisy_label
def count_real(KINDS, T, P, mode, _device = 'cpu'):
# time1 = time.time()
P = P.reshape((KINDS, 1))
p_real = [[] for _ in range(3)]
p_real[0] = torch.mm(T.transpose(0, 1), P).transpose(0, 1)
# p_real[2] = torch.zeros((KINDS, KINDS, KINDS)).to(_device)
p_real[2] = torch.zeros((KINDS, KINDS, KINDS))
temp33 = torch.tensor([])
for i in range(KINDS):
Ti = torch.cat((T[:, i:], T[:, :i]), 1)
temp2 = torch.mm((T * Ti).transpose(0, 1), P)
p_real[1] = torch.cat([p_real[1], temp2], 1) if i != 0 else temp2
for j in range(KINDS):
Tj = torch.cat((T[:, j:], T[:, :j]), 1)
temp3 = torch.mm((T * Ti * Tj).transpose(0, 1), P)
temp33 = torch.cat([temp33, temp3], 1) if j != 0 else temp3
# adjust the order of the output (N*N*N), keeping consistent with p_estimate
t3 = []
for p3 in range(KINDS):
t3 = torch.cat((temp33[p3, KINDS - p3:], temp33[p3, :KINDS - p3]))
temp33[p3] = t3
if mode == -1:
for r in range(KINDS):
p_real[2][r][(i+r+KINDS)%KINDS] = temp33[r]
else:
p_real[2][mode][(i + mode + KINDS) % KINDS] = temp33[mode]
temp = [] # adjust the order of the output (N*N), keeping consistent with p_estimate
for p1 in range(KINDS):
temp = torch.cat((p_real[1][p1, KINDS-p1:], p_real[1][p1, :KINDS-p1]))
p_real[1][p1] = temp
return p_real
def build_T(cluster):
T = [[0 for _ in range(cluster)] for _ in range(cluster)]
for i in range(cluster):
rand_sum = 0
for j in range(cluster):
if i != j:
rand = round(random.uniform(0.01, 0.07), 3)
rand_sum += rand
T[i][j] = rand
T[i][i] = 1 - rand_sum
return T
def build_T_local(cluster, center_class):
T = [[0 for _ in range(cluster)] for _ in range(cluster)]
zero_class = np.random.choice(range(cluster),int(np.sqrt(cluster)), replace = False)
for i in range(cluster):
rand_sum = 0
for j in range(cluster):
if i != j:
rand = round(random.uniform(0.15, 0.25), 3) * (j in zero_class) if i == center_class else round(random.uniform(0.01, 0.07), 3)
rand_sum += rand
T[i][j] = rand
T[i][i] = 1 - rand_sum
# print(torch.tensor(T))
# exit()
return T
def check_T(KINDS, noisy_label, point_each_cluster):
temp_error_matrix = [[0 for _ in range(KINDS)] for _ in range(KINDS)]
cnt_point = 0
for cnt in range(len(point_each_cluster)):
for label in noisy_label[cnt_point : point_each_cluster[cnt]+cnt_point]:
temp_error_matrix[cnt][label] = temp_error_matrix[cnt][label] + 1
cnt_point += point_each_cluster[cnt]
for i in range(KINDS):
temp_sum = sum(temp_error_matrix[i])
for j in range(KINDS):
temp_error_matrix[i][j] = round(temp_error_matrix[i][j]/temp_sum, 3)
print(f'Check_Error_Rate = \n{np.array(temp_error_matrix)}')
def select_next_idx(selected_idx, idx_sel):
# selected_idx[idx_sel[:int(numLocal*0.7)]] = -1
selected_idx[idx_sel] = -1
if selected_idx[selected_idx > -1].size(0) > 0:
next_select_idx = random.choice(selected_idx[selected_idx > 0]) # select one from the remaining part
return next_select_idx, selected_idx
else:
return random.randint(0, 49999), selected_idx
def adjust_learning_rate(optimizer, epoch,alpha_plan):
for param_group in optimizer.param_groups:
param_group['lr']=alpha_plan[epoch]
# def accuracy(logit, target, topk=(1,)):
# """Computes the precision@k for the specified values of k"""
# output = F.softmax(logit, dim=1)
# maxk = max(topk)
# batch_size = target.size(0)
# _, pred = output.topk(maxk, 1, True, True)
# pred = pred.t()
# correct = pred.eq(target.view(1, -1).expand_as(pred))
# res = []
# for k in topk:
# correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
# res.append(correct_k.mul_(100.0 / batch_size))
# return res
def accuracy(output, target, topk=(1,)):
"""Computes the accuracy over the k top predictions for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
torch.save(state, filename)
if is_best:
shutil.copyfile(filename, 'model_best.pth.tar')
def save_config_file(model_checkpoints_folder, args):
if not os.path.exists(model_checkpoints_folder):
os.makedirs(model_checkpoints_folder)
with open(os.path.join(model_checkpoints_folder, 'config.yml'), 'w') as outfile:
yaml.dump(args, outfile, default_flow_style=False)
def set_device():
if torch.cuda.is_available():
_device = torch.device("cuda")
else:
_device = torch.device("cpu")
print(f'Current device is {_device}', flush=True)
return _device
def set_model_pre(config):
# use resnet50 for ImageNet pretrain (PyTorch official pre-trained model)
if config.pre_type == 'image':
model = res_image.resnet50(pretrained=True)
else:
RuntimeError('Undefined pretrained model.')
for param in model.parameters():
param.requires_grad = False
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, config.num_classes)
model.to(config.device)
return model
def set_model_train(config):
model = res_cifar_new.ResNet34(num_classes = config.num_classes)
model.to(config.device)
optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=0.0005)
return model, optimizer
def init_feature_set(config, model_pre, train_dataloader, rnd):
c1m_cluster_each = [0 for _ in range(config.num_classes)]
# save the 512-dim feature as a dataset
model_pre.eval()
record = [[] for _ in range(config.num_classes)]
for i_batch, (feature, label, index) in enumerate(train_dataloader):
feature = feature.to(config.device)
label = label.to(config.device)
extracted_feature, _ = model_pre(feature)
for i in range(extracted_feature.shape[0]):
record[label[i]].append({'feature': extracted_feature[i].detach().cpu(), 'index': index[i]})
path = f'./data/{config.pre_type}_{config.dataset}_{config.label_file_path[7:-3]}.pt'
return path, record, c1m_cluster_each
def build_dataset_informal(config, record, c1m_cluster_each):
# estimate T
original_clean_dataset_name = config.path
sub_clean_dataset_name = f'{config.path[:-3]}_clean.pt'
sub_noisy_dataset_name = f'{config.path[:-3]}_noisy.pt'
# if ~config.build_feat:
# return sub_clean_dataset_name, sub_noisy_dataset_name
# Build Dataset -----------------------------------------------
label_list = [i for i in range(config.num_classes)] # label-type: 0, 1, 2 ... 9
P = config.P
sub_cluster_each = [int(50000/config.num_classes)] * config.num_classes
extract_sub_dataset(sub_cluster_each, record, sub_clean_dataset_name,
sub_noisy_dataset_name)
if config.label_file_path is 'NA':
RuntimeError('Cannot load noisy labels.')
else:
# load noise file. re-format
origin_dataset = torch.load(f'{sub_noisy_dataset_name}')
noise_label = torch.load(config.label_file_path)
T_real = np.zeros((config.num_classes,config.num_classes))
for i in range(sum(sub_cluster_each)):
idx = origin_dataset['index'][i]
assert origin_dataset['clean_label'][i] == noise_label['clean_label_train'][idx]
origin_dataset['noisy_label'][i] = noise_label['noise_label_train'][idx].long()
T_real[origin_dataset['clean_label'][i]][origin_dataset['noisy_label'][i]] += 1
P_real = [sum(T_real[i])*1.0 for i in range(config.num_classes)] # random selection
for i in range(config.num_classes):
if P_real[i]>0:
T_real[i] /= P_real[i]
P_real = np.array(P_real)/sum(P_real)
config.T = T_real
torch.save(origin_dataset, f'{sub_noisy_dataset_name}')
return sub_clean_dataset_name, sub_noisy_dataset_name