Spaces:
Sleeping
Sleeping
File size: 2,054 Bytes
ce91ea1 |
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 |
import torchvision
import torch.nn as nn
import pretrainedmodels
import torch.nn.functional as F
from collections import OrderedDict
from constant import SCALE_FACTOR
import math
class Resnet(nn.Module):
def __init__(self, variant):
super(Resnet, self).__init__()
assert variant in ['senet154', 'se_resnext101_32x4d', 'se_resnext50_32x4d', 'se_resnet152', 'se_resnet101', 'se_resnet50']
# load retrain model
model = pretrainedmodels.__dict__[variant](num_classes=1000, pretrained='imagenet')
self.features = nn.Sequential(OrderedDict([
('layer0', model.layer0),
('layer1', model.layer1),
('layer2', model.layer2),
('layer3', model.layer3),
('layer4', model.layer4)
]))
'''
Dropout
- For SENet154: 0.2
- For SE-ResNet models: None
- For SE-ResNeXt models: None
'''
self.dropout = model.dropout
num_ftrs = model.last_linear.in_features
self.classifier = nn.Sequential(
nn.Linear(num_ftrs, 14),
nn.Sigmoid()
)
# load other info
# 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) # 1x2048x7x7
s = x.size()[3] # 7 if input image is 224x224, 16 if input image is 512x512
x = F.avg_pool2d(x, kernel_size=s, stride=1) # 1x2048x1x1
x = x.view(x.size(0), -1) # 1x2048
x = self.classifier(x) # 1x1000
return x
def extract(self, x):
return self.features(x)
def build(variant):
net = Resnet(variant).cuda()
return net
architect='senet' |