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import torchvision
import torch.nn as nn
import pretrainedmodels
import torch.nn.functional as F
from constant import SCALE_FACTOR
import math
class Resnext(nn.Module):
def __init__(self, variant):
super(Resnext, self).__init__()
assert variant in ['resnext101_32x4d', 'resnext101_64x4d']
# load retrain model
model = pretrainedmodels.__dict__[variant](num_classes=1000, pretrained='imagenet')
self.features = model.features
num_ftrs = model.last_linear.in_features
self.classifier = nn.Sequential(
nn.Linear(num_ftrs, 14),
nn.Sigmoid()
)
# load other info
self.mean = model.mean
self.std = model.std
self.input_size = model.input_size[1] # assume every input is a square image
self.input_range = model.input_range
self.input_space = model.input_space
self.resize_size = int(math.floor(self.input_size / SCALE_FACTOR))
def forward(self, x):
x = self.features(x) #
s = x.size()[3] # 7 if input image is 224x224, 16 if input image is 512x512
x = F.avg_pool2d(x, kernel_size=(7, 7), stride=(1, 1)) # 1x1024x1x1
x = x.view(x.size(0), -1) # 1x1024
x = self.classifier(x) # 1x1000
return x
def extract(self, x):
return self.features(x)
def build(variant):
net = Resnext(variant).cuda()
return net
architect='resnext'
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