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IQAmodel.py
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IQAmodel.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models
import numpy as np
def SPSP(x, P=1, method='avg'):
batch_size = x.size(0)
map_size = x.size()[-2:]
pool_features = []
for p in range(1, P+1):
pool_size = [np.int(d / p) for d in map_size]
if method == 'maxmin':
M = F.max_pool2d(x, pool_size)
m = -F.max_pool2d(-x, pool_size)
pool_features.append(torch.cat((M, m), 1).view(batch_size, -1)) # max & min pooling