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JaeseokNet.py
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JaeseokNet.py
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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
@author: Nino Cauli
"""
from __future__ import print_function, division
import torch
import torch.nn as nn
from torchvision import models
class JaeseokNetPretrained(torch.nn.Module):
def __init__(self, D_latent = 4096, D_outputs = (3 + 3 * 3) * 5):
super(JaeseokNetPretrained, self).__init__()
self.D_features = 256 * 6 * 6
alexNet = models.alexnet(pretrained=True)
self.features = alexNet.features
self.latent1 = nn.Sequential(
nn.Linear(self.D_features, D_latent),
nn.ReLU(inplace=True),
)
self.fc = nn.Linear(D_latent, D_outputs)
def forward(self, img1):
features1 = self.features(img1)
features1 = features1.view(features1.size(0), self.D_features)
lat1 = self.latent1(features1)
output = self.fc(lat1)
return output
class JaeseokNet(torch.nn.Module):
def __init__(self, D_latent = 4096, D_outputs = (3 + 6) * 5):
super(JaeseokNet, self).__init__()
self.D_features = 256 * 6 * 9
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(64, 192, kernel_size=5, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(192, 384, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(384, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
)
self.latent1 = nn.Sequential(
nn.Linear(self.D_features, D_latent),
nn.ReLU(inplace=True),
nn.Dropout(p=0.5),
)
self.fc = nn.Linear(D_latent, D_outputs)
def forward(self, img1):
features1 = self.features(img1)
features1 = features1.view(features1.size(0), self.D_features)
lat1 = self.latent1(features1)
output = self.fc(lat1)
return output