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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
from pretrainedmodels.models.dpn import adaptive_avgmax_pool2d
class DPN(nn.Module):
def __init__(self, variant):
super(DPN, self).__init__()
assert variant in ['dpn68', 'dpn68b', 'dpn92', 'dpn98', 'dpn131', 'dpn107']
# load retrain model
model = pretrainedmodels.__dict__[variant](num_classes=1000, pretrained='imagenet')
self.features = model.features
num_ftrs = model.classifier.in_channels
self.classifier = nn.Sequential(
nn.Conv2d(num_ftrs, 14, kernel_size=1, bias=True), # something wrong here abt dimension
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) # 1x1024x7x7
if not self.training and self.test_time_tool:
x = F.avg_pool2d(x, kernel_size=7, stride=1)
x = self.classifier(x)
x = adaptive_avgmax_pool2d(out, pool_type='avgmax') # something wrong here abt dimension
else:
x = adaptive_avgmax_pool2d(x, pool_type='avg')
x = self.classifier(x)
return x
def extract(self, x):
return self.features(x)
def build(variant):
net = DPN(variant).cuda()
return net
architect='dpn'
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