Spaces:
Running
Running
File size: 2,409 Bytes
404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af |
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 |
# Copyright 2019-present NAVER Corp.
# CC BY-NC-SA 3.0
# Available only for non-commercial use
import pdb
import numpy as np
import torch
import torch.nn as nn
class APLoss(nn.Module):
"""differentiable AP loss, through quantization.
Input: (N, M) values in [min, max]
label: (N, M) values in {0, 1}
Returns: list of query AP (for each n in {1..N})
Note: typically, you want to minimize 1 - mean(AP)
"""
def __init__(self, nq=25, min=0, max=1, euc=False):
nn.Module.__init__(self)
assert isinstance(nq, int) and 2 <= nq <= 100
self.nq = nq
self.min = min
self.max = max
self.euc = euc
gap = max - min
assert gap > 0
# init quantizer = non-learnable (fixed) convolution
self.quantizer = q = nn.Conv1d(1, 2 * nq, kernel_size=1, bias=True)
a = (nq - 1) / gap
# 1st half = lines passing to (min+x,1) and (min+x+1/a,0) with x = {nq-1..0}*gap/(nq-1)
q.weight.data[:nq] = -a
q.bias.data[:nq] = torch.from_numpy(
a * min + np.arange(nq, 0, -1)
) # b = 1 + a*(min+x)
# 2nd half = lines passing to (min+x,1) and (min+x-1/a,0) with x = {nq-1..0}*gap/(nq-1)
q.weight.data[nq:] = a
q.bias.data[nq:] = torch.from_numpy(
np.arange(2 - nq, 2, 1) - a * min
) # b = 1 - a*(min+x)
# first and last one are special: just horizontal straight line
q.weight.data[0] = q.weight.data[-1] = 0
q.bias.data[0] = q.bias.data[-1] = 1
def compute_AP(self, x, label):
N, M = x.shape
if self.euc: # euclidean distance in same range than similarities
x = 1 - torch.sqrt(2.001 - 2 * x)
# quantize all predictions
q = self.quantizer(x.unsqueeze(1))
q = torch.min(q[:, : self.nq], q[:, self.nq :]).clamp(min=0) # N x Q x M
nbs = q.sum(dim=-1) # number of samples N x Q = c
rec = (q * label.view(N, 1, M).float()).sum(
dim=-1
) # nb of correct samples = c+ N x Q
prec = rec.cumsum(dim=-1) / (1e-16 + nbs.cumsum(dim=-1)) # precision
rec /= rec.sum(dim=-1).unsqueeze(1) # norm in [0,1]
ap = (prec * rec).sum(dim=-1) # per-image AP
return ap
def forward(self, x, label):
assert x.shape == label.shape # N x M
return self.compute_AP(x, label)
|