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import numpy as np
import os
import torch
import torch.nn.functional as F
from torch import nn
from torchvision.models import alexnet
import config as c
from freia_funcs import permute_layer, glow_coupling_layer, F_fully_connected, ReversibleGraphNet, OutputNode, \
InputNode, Node
WEIGHT_DIR = './weights'
MODEL_DIR = './models'
def nf_head(input_dim=c.n_feat):
nodes = list()
nodes.append(InputNode(input_dim, name='input'))
for k in range(c.n_coupling_blocks):
nodes.append(Node([nodes[-1].out0], permute_layer, {'seed': k}, name=F'permute_{k}'))
nodes.append(Node([nodes[-1].out0], glow_coupling_layer,
{'clamp': c.clamp_alpha, 'F_class': F_fully_connected,
'F_args': {'internal_size': c.fc_internal, 'dropout': c.dropout}},
name=F'fc_{k}'))
nodes.append(OutputNode([nodes[-1].out0], name='output'))
coder = ReversibleGraphNet(nodes)
return coder
class flow_model(nn.Module):
def __init__(self):
super(flow_model, self).__init__()
self.nf = nf_head(input_dim = 1024)
def forward(self, x):
z = self.nf(x)
return z
class flow_model_multi_fc(nn.Module):
def __init__(self):
super(flow_model_multi_fc, self).__init__()
self.fc1 = torch.nn.Linear(1024, 512)
self.relu = torch.nn.LeakyReLU(0.2)
self.fc2 = torch.nn.Linear(512, 256)
self.nf = nf_head(input_dim = 256)
def forward(self, x):
res_x = self.fc2(self.relu((self.fc1(x))))
z = self.nf(res_x)
return z
class DifferNet(nn.Module):
def __init__(self):
super(DifferNet, self).__init__()
self.feature_extractor = alexnet(pretrained=True)
self.nf = nf_head()
def forward(self, x):
y_cat = list()
for s in range(c.n_scales):
x_scaled = F.interpolate(x, size=c.img_size[0] // (2 ** s)) if s > 0 else x
feat_s = self.feature_extractor.features(x_scaled)
y_cat.append(torch.mean(feat_s, dim=(2, 3)))
y = torch.cat(y_cat, dim=1)
z = self.nf(y)
return z
def save_model(model, filename):
if not os.path.exists(MODEL_DIR):
os.makedirs(MODEL_DIR)
torch.save(model, os.path.join(MODEL_DIR, filename))
def load_model(filename):
path = os.path.join(MODEL_DIR, filename)
model = torch.load(path)
return model
def save_weights(model, filename):
if not os.path.exists(WEIGHT_DIR):
os.makedirs(WEIGHT_DIR)
torch.save(model.state_dict(), os.path.join(WEIGHT_DIR, filename))
def load_weights(model, filename):
path = os.path.join(WEIGHT_DIR, filename)
model.load_state_dict(torch.load(path))
return model
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