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jls_fcn.py
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jls_fcn.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd.variable import Variable
#from densenet import *
from torchvision.models import densenet169
import numpy as np
import sys
thismodule = sys.modules[__name__]
import pdb
class Pass(nn.Module):
def forward(self, x):
return x
def get_upsampling_weight(in_channels, out_channels, kernel_size):
"""Make a 2D bilinear kernel suitable for upsampling"""
factor = (kernel_size + 1) // 2
if kernel_size % 2 == 1:
center = factor - 1
else:
center = factor - 0.5
og = np.ogrid[:kernel_size, :kernel_size]
filt = (1 - abs(og[0] - center) / factor) * \
(1 - abs(og[1] - center) / factor)
weight = np.zeros((in_channels, out_channels, kernel_size, kernel_size),
dtype=np.float64)
weight[range(in_channels), range(out_channels), :, :] = filt
return torch.from_numpy(weight).float()
def weight_init(m):
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
m.weight.data.normal_(0.0, 0.02)
m.bias.data.fill_(0)
elif isinstance(m, nn.ConvTranspose2d) and m.in_channels == m.out_channels:
initial_weight = get_upsampling_weight(
m.in_channels, m.out_channels, m.kernel_size[0])
m.weight.data.copy_(initial_weight)
def fraze_bn(m):
if isinstance(m, nn.BatchNorm2d):
m.requires_grad=False
def proc_densenet(model):
def hook(module, input, output):
model.feats[output.device.index] += [output]
model.features.transition3[-2].register_forward_hook(hook)
model.features.transition2[-2].register_forward_hook(hook)
def remove_sequential(all_layers, network):
for layer in network.children():
if isinstance(layer, nn.Sequential): # if sequential layer, apply recursively to layers in sequential layer
remove_sequential(all_layers, layer)
if list(layer.children()) == []: # if leaf node, add it to list
all_layers.append(layer)
model.features.transition3[-1].kernel_size = 1
model.features.transition3[-1].stride = 1
all_layers = []
remove_sequential(all_layers, model.features.denseblock4)
for m in all_layers:
if isinstance(m, nn.Conv2d) and m.kernel_size==(3, 3):
m.dilation = (2, 2)
m.padding = (2, 2)
model.classifier = None
model.forward = model.features.forward
return model
def proc_vgg(model):
def hook(module, input, output):
model.feats[output.device.index] += [output]
model.features[3][-2].register_forward_hook(hook)
model.features[2][-2].register_forward_hook(hook)
model.classifier = None
return model
dim_dict = {
'densenet169': [64, 128, 256, 640, 1664],
'vgg16': [64, 128, 256, 512, 512],
'mobilenet2': [32, 24, 32, 64, 1280],
'resnet101': [64, 256, 512, 1024, 2048]
}
procs = {'densenet169': proc_densenet}
class JLSFCN(nn.Module):
def __init__(self, c_output=21, base='densenet169'):
super(JLSFCN, self).__init__()
dims = dim_dict[base][::-1]
self.pred_seg = nn.ModuleList([
nn.Conv2d(dims[0], c_output, 1),
nn.Conv2d(dims[1], c_output, 1),
nn.Conv2d(dims[2], c_output, 1)])
self.pred_sal = nn.Conv2d(dims[0], c_output, kernel_size=16)
self.upsample = nn.ModuleList([
#nn.ConvTranspose2d(c_output, c_output, kernel_size=4, stride=2, padding=1),
Pass(),
nn.ConvTranspose2d(c_output, c_output, kernel_size=4, stride=2, padding=1),
nn.ConvTranspose2d(c_output, c_output, kernel_size=16, stride=8, padding=4)])
self.apply(weight_init)
self.feature = getattr(thismodule, base)(pretrained=True)
self.feature.feats = {}
self.feature = procs[base](self.feature)
#self.apply(fraze_bn)
def forward(self, x, boxes=None, ids=None):
self.feature.feats[x.device.index] = []
feat32 = self.feature(x)
feat8, feat16 = self.feature.feats[x.device.index]
seg32 = self.pred_seg[0](feat32)
seg32x = seg32
seg32 = self.upsample[0](seg32)
seg16= self.pred_seg[1](feat16)
seg16 = seg16+seg32
seg16 = self.upsample[1](seg16)
seg8 = self.pred_seg[2](feat8)
seg =seg16+seg8
seg = self.upsample[2](seg)
sal = self.pred_sal(feat32)
sal = sal.mean(3,keepdim=True).mean(2, keepdim=True)
sal = torch.sigmoid(sal)
return seg, sal, seg32x
if __name__ == "__main__":
fcn = JLSFCN(base='densenet169').cuda()
x = torch.Tensor(2, 3, 256, 256).cuda()
sb = fcn(Variable(x))
pdb.set_trace()