Spaces:
Sleeping
Sleeping
File size: 5,215 Bytes
982865f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 |
import os
import sys
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed as dist
from collections import OrderedDict
import numpy as np
from sklearn.metrics import roc_auc_score, roc_curve
from scipy.optimize import brentq
from scipy.interpolate import interp1d
# Tracking the path to the definition of the model.
MODELS_PATH = {
"Recce": "model/network/Recce.py"
}
def exp_recons_loss(recons, x):
x, y = x
loss = torch.tensor(0., device=y.device)
real_index = torch.where(1 - y)[0]
for r in recons:
if real_index.numel() > 0:
real_x = torch.index_select(x, dim=0, index=real_index)
real_rec = torch.index_select(r, dim=0, index=real_index)
real_rec = F.interpolate(real_rec, size=x.shape[-2:], mode='bilinear', align_corners=True)
loss += torch.mean(torch.abs(real_rec - real_x))
return loss
def center_print(content, around='*', repeat_around=10):
num = repeat_around
s = around
print(num * s + ' %s ' % content + num * s)
def reduce_tensor(t):
rt = t.clone()
dist.all_reduce(rt)
rt /= float(dist.get_world_size())
return rt
def tensor2image(tensor):
image = tensor.permute([1, 2, 0]).cpu().detach().numpy()
return (image - np.min(image)) / (np.max(image) - np.min(image))
def state_dict(state_dict):
""" Remove 'module' keyword in state dictionary. """
weights = OrderedDict()
for k, v in state_dict.items():
weights.update({k.replace("module.", ""): v})
return weights
class Logger(object):
def __init__(self, filename):
self.terminal = sys.stdout
self.log = open(filename, "a")
def write(self, message):
self.terminal.write(message)
self.log.write(message)
self.log.flush()
def flush(self):
pass
class Timer(object):
"""The class for timer."""
def __init__(self):
self.o = time.time()
def measure(self, p=1):
x = (time.time() - self.o) / p
x = int(x)
if x >= 3600:
return '{:.1f}h'.format(x / 3600)
if x >= 60:
return '{}m'.format(round(x / 60))
return '{}s'.format(x)
class MLLoss(nn.Module):
def __init__(self):
super(MLLoss, self).__init__()
def forward(self, input, target, eps=1e-6):
# 0 - real; 1 - fake.
loss = torch.tensor(0., device=target.device)
batch_size = target.shape[0]
mat_1 = torch.hstack([target.unsqueeze(-1)] * batch_size)
mat_2 = torch.vstack([target] * batch_size)
diff_mat = torch.logical_xor(mat_1, mat_2).float()
or_mat = torch.logical_or(mat_1, mat_2)
eye = torch.eye(batch_size, device=target.device)
or_mat = torch.logical_or(or_mat, eye).float()
sim_mat = 1. - or_mat
for _ in input:
diff = torch.sum(_ * diff_mat, dim=[0, 1]) / (torch.sum(diff_mat, dim=[0, 1]) + eps)
sim = torch.sum(_ * sim_mat, dim=[0, 1]) / (torch.sum(sim_mat, dim=[0, 1]) + eps)
partial_loss = 1. - sim + diff
loss += max(partial_loss, torch.zeros_like(partial_loss))
return loss
class AccMeter(object):
def __init__(self):
self.nums = 0
self.acc = 0
def reset(self):
self.nums = 0
self.acc = 0
def update(self, pred, target, use_bce=False):
if use_bce:
pred = (pred >= 0.5).int()
else:
pred = pred.argmax(1)
self.nums += target.shape[0]
self.acc += torch.sum(pred == target)
def mean_acc(self):
return self.acc / self.nums
class AUCMeter(object):
def __init__(self):
self.score = None
self.true = None
def reset(self):
self.score = None
self.true = None
def update(self, score, true, use_bce=False):
if use_bce:
score = score.detach().cpu().numpy()
else:
score = torch.softmax(score.detach(), dim=-1)
score = torch.select(score, 1, 1).cpu().numpy()
true = true.flatten().cpu().numpy()
self.score = score if self.score is None else np.concatenate([self.score, score])
self.true = true if self.true is None else np.concatenate([self.true, true])
def mean_auc(self):
return roc_auc_score(self.true, self.score)
def curve(self, prefix):
fpr, tpr, thresholds = roc_curve(self.true, self.score, pos_label=1)
eer = brentq(lambda x: 1. - x - interp1d(fpr, tpr)(x), 0., 1.)
thresh = interp1d(fpr, thresholds)(eer)
print(f"# EER: {eer:.4f}(thresh: {thresh:.4f})")
torch.save([fpr, tpr, thresholds], os.path.join(prefix, "roc_curve.pickle"))
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
|