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vgg_pt.py
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from collections import namedtuple
import random
import ssl
import torch.nn.functional as F
from torch.autograd import Variable
import torch
from torchvision import models
import numpy as np
ssl._create_default_https_context = ssl._create_unverified_context
use_random = True
class Vgg16_pt(torch.nn.Module):
def __init__(self, requires_grad=False):
super(Vgg16_pt, self).__init__()
vgg_pretrained_features = models.vgg16(pretrained=True).features
self.vgg_layers = vgg_pretrained_features
self.slice1 = torch.nn.Sequential()
self.slice2 = torch.nn.Sequential()
self.slice3 = torch.nn.Sequential()
self.slice4 = torch.nn.Sequential()
for x in range(1):
self.slice1.add_module(str(x), vgg_pretrained_features[x])
for x in range(1, 9):
self.slice2.add_module(str(x), vgg_pretrained_features[x])
for x in range(9, 16):
self.slice3.add_module(str(x), vgg_pretrained_features[x])
for x in range(16, 23):
self.slice4.add_module(str(x), vgg_pretrained_features[x])
if not requires_grad:
for param in self.parameters():
param.requires_grad = False
self.inds = range(11)
def forward_base(self,X,rand):
inds = self.inds
x = X
l2 = [X]
for i in range(30):
try:
x = self.vgg_layers[i].forward(x)#[:,:,1:-1,1:-1]
except:
pass
if i in [1,3,6,8,11,13,15,22,29]:
l2.append(x)
return l2
def forward(self, X, inds=[1,3,5,8,11], rand=True):
inds = self.inds
x = X
l2 = self.forward_base(X,rand)
out2 = l2
return out2
def forward_cat(self, X, r, inds=[1,3,5,8,11], rand=True,samps=100, forward_func=None):
if not forward_func:
forward_func = self.forward
x = X
out2 = forward_func(X,rand)
try:
r = r[:,:,0]
except:
pass
if r.max()<0.1:
region_mask = np.greater(r.flatten()+1.,0.5)
else:
region_mask = np.greater(r.flatten(),0.5)
xx,xy = np.meshgrid(np.array(range(x.size(2))), np.array(range(x.size(3))) )
xx = np.expand_dims(xx.flatten(),1)
xy = np.expand_dims(xy.flatten(),1)
xc = np.concatenate([xx,xy],1)
xc = xc[region_mask,:]
const2 = min(samps,xc.shape[0])
global use_random
if use_random:
np.random.shuffle(xc)
else:
xc = xc[::(xc.shape[0]//const2),:]
xx = xc[:const2,0]
yy = xc[:const2,1]
temp = X
temp_list = [ temp[:,:, xx[j], yy[j]].unsqueeze(2).unsqueeze(3) for j in range(const2)]
temp = torch.cat(temp_list,2)
l2 = []
for i in range(len(out2)):
temp = out2[i]
if i>0 and out2[i].size(2) < out2[i-1].size(2):
xx = xx/2.0
yy = yy/2.0
xx = np.clip(xx,0,temp.size(2)-1).astype(np.int32)
yy = np.clip(yy,0,temp.size(3)-1).astype(np.int32)
temp_list = [ temp[:,:, xx[j], yy[j]].unsqueeze(2).unsqueeze(3) for j in range(const2)]
temp = torch.cat(temp_list,2)
l2.append(temp.clone().detach())
out2 = [torch.cat([li.contiguous() for li in l2],1)]
return out2
def forward_diff(self, X, inds=[1,3,5,8,11], rand=True):
inds = self.inds
l2 = self.forward_base(X,inds,rand)
out2 = [l2[i].contiguous() for i in inds]
for i in range(len(out2)):
temp = out2[i]
temp2 = F.pad(temp,(2,2,0,0),value=1.)
temp3 = F.pad(temp,(0,0,2,2),value=1.)
out2[i] = torch.cat([temp,temp2[:,:,:,4:],temp2[:,:,:,:-4],temp3[:,:,4:,:],temp3[:,:,:-4,:]],1)
return out2