Spaces:
Sleeping
Sleeping
File size: 2,615 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 62 63 64 65 66 67 68 69 70 71 72 73 |
import torchvision
import torch.nn as nn
import pretrainedmodels
import torch.nn.functional as F
from collections import OrderedDict
class Nasnet(nn.Module):
def __init__(self, variant):
super(Nasnet, self).__init__()
assert variant in ['nasnetalarge']
# load retrain model
self.model = pretrainedmodels.__dict__[variant](num_classes=1000, pretrained='imagenet')
# self.features = nn.Sequential(OrderedDict([
# ('conv0', model.conv0),
# ('cell_stem_0', model.cell_stem_0),
# ('cell_stem_1', model.cell_stem_1),
# ('cell_0', model.cell_0),
# ('cell_1', model.cell_1),
# ('cell_2', model.cell_2),
# ('cell_3', model.cell_3),
# ('cell_4', model.cell_4),
# ('cell_5', model.cell_5),
# ('reduction_cell_0', model.reduction_cell_0),
# ('cell_6', model.cell_6),
# ('cell_7', model.cell_7),
# ('cell_8', model.cell_8),
# ('cell_9', model.cell_9),
# ('cell_10', model.cell_10),
# ('cell_11', model.cell_11),
# ('reduction_cell_1', model.reduction_cell_1),
# ('cell_12', model.cell_6),
# ('cell_13', model.cell_7),
# ('cell_14', model.cell_8),
# ('cell_15', model.cell_9),
# ('cell_16', model.cell_10),
# ('cell_17', model.cell_11)
# ]))
num_ftrs = self.model.last_linear.in_features
self.model.last_linear = nn.Sequential(
nn.Linear(num_ftrs, 14),
nn.Sigmoid()
)
# load other info
# load other info
self.mean = self.model.mean
self.std = self.model.std
self.input_size = self.model.input_size[1] # assume every input is a square image
self.input_range = self.model.input_range
self.input_space = self.model.input_space
self.resize_size = 354 # as in pretrainmodels repo
def forward(self, x):
# x = self.features(x)
# x = F.avg_pool2d(x, kernel_size=11, stride=1, padding=0)
# x = x.view(x.size(0), -1)
# x = x.dropout(training=self.training)
# x = self.classifier(x) # 1x1000
# return x
return self.model.forward(x)
def extract(self, x):
# return self.features(x)
return self.model.features(x)
def build(variant):
net = Nasnet(variant).cuda()
return net
architect='nasnet' |