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regress.py
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from __future__ import print_function
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
#import h5py
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
import numpy as np
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
#in_channel, out_channel, kernel_size, stride, padding=0 (default)
self.conv1 = nn.Conv2d(1, 16, 5, 1)
self.bn1 = nn.BatchNorm2d(16)
self.conv2 = nn.Conv2d(16, 16, 5, 1)
self.bn2 = nn.BatchNorm2d(16)
self.conv3 = nn.Conv2d(16, 64, 2, 1)
self.bn3 = nn.BatchNorm2d(64)
self.conv4 = nn.Conv2d(64, 128, 2, 1)
self.bn4 = nn.BatchNorm2d(128)
self.dropout1 = nn.Dropout2d(0.3)
self.dropout2 = nn.Dropout2d(0.3)
self.dropout3 = nn.Dropout2d(0.3)
self.fc1 = nn.Linear(4608, 512)
self.fc2 = nn.Linear(512, 128)
self.fc3 = nn.Linear(128, 32)
self.fc4 = nn.Linear(32, 1)
def forward(self, x):
h = F.relu(self.conv1(x))
h = F.max_pool2d(h, kernel_size=2)
h = self.bn1(h)
h = F.relu(self.conv2(h))
h = F.max_pool2d(h, kernel_size=2)
h = self.bn2(h)
h = F.relu(self.conv3(h))
h = F.max_pool2d(h, kernel_size=2)
h = self.bn3(h)
h = F.relu(self.conv4(h))
h = F.max_pool2d(h, kernel_size=2)
h = self.bn4(h)
h = torch.flatten(h, start_dim=1) #flatten input of [bs, c, w, h], so from dim=1
h = F.relu(self.fc1(h))
h = self.dropout1(h)
h = F.relu(self.fc2(h))
h = self.dropout2(h)
h = F.relu(self.fc3(h))
h = self.dropout3(h)
h = F.relu(self.fc4(h))
h = h.squeeze() #squeezing to reduce dims from (64,1) to (64,) to match target
output = h
return output
#joshua's model
class Net2(nn.Module):
def __init__(self):
super(Net2, self).__init__()
#in_channel, out_channel, kernel_size, stride, padding=0 (default)
self.conv1 = nn.Conv2d(1, 16, 7, 1)
self.bn1 = nn.BatchNorm2d(16)
self.conv2 = nn.Conv2d(16, 16, 5, 1)
self.bn2 = nn.BatchNorm2d(16)
self.dropout1 = nn.Dropout2d(0.3)
self.dropout2 = nn.Dropout2d(0.3)
self.dropout3 = nn.Dropout2d(0.3)
self.fc1 = nn.Linear(12544, 512)
self.fc2 = nn.Linear(512, 1)
def forward(self, x):
h = F.relu(self.conv1(x))
h = F.max_pool2d(h, kernel_size=2)
h = self.bn1(h)
h = F.relu(self.conv2(h))
h = F.max_pool2d(h, kernel_size=2)
h = self.bn2(h)
h = torch.flatten(h, start_dim=1) #flatten input of [bs, c, w, h], so from dim=1
h = F.relu(self.fc1(h))
h = self.dropout1(h)
h = F.relu(self.fc2(h))
h = h.squeeze() #squeezing to reduce dims from (64,1) to (64,) to match target
output = h
return output
#latest model based on fixed-flip data
class Net3(nn.Module):
def __init__(self):
super(Net3, self).__init__()
#in_channel, out_channel, kernel_size, stride, padding=0 (default)
self.conv1 = nn.Conv2d(1, 16, 9, 1)
self.bn1 = nn.BatchNorm2d(16)
self.conv2 = nn.Conv2d(16, 32, 9, 1)
self.bn2 = nn.BatchNorm2d(32)
self.dropout1 = nn.Dropout2d(0.3)
self.dropout2 = nn.Dropout2d(0.3)
self.dropout3 = nn.Dropout2d(0.3)
self.fc1 = nn.Linear(21632, 512)
self.fc2 = nn.Linear(512, 1)
def forward(self, x):
h = F.relu(self.conv1(x))
h = F.max_pool2d(h, kernel_size=2)
h = self.bn1(h)
h = F.relu(self.conv2(h))
h = F.max_pool2d(h, kernel_size=2)
h = self.bn2(h)
h = torch.flatten(h, start_dim=1) #flatten input of [bs, c, w, h], so from dim=1
h = F.relu(self.fc1(h))
h = self.dropout1(h)
h = F.relu(self.fc2(h))
h = h.squeeze() #squeezing to reduce dims from (64,1) to (64,) to match target
output = h
return output
class JF_Net(nn.Module):
def __init__(self):
super(JF_Net, self).__init__()
#in_channel, out_channel, kernel_size, stride, padding=0 (default)
self.conv1 = nn.Conv2d(1, 16, 9, 1)
self.bn1 = nn.BatchNorm2d(16)
self.conv2 = nn.Conv2d(16, 32, 9, 1)
self.bn2 = nn.BatchNorm2d(32)
self.dropout1 = nn.Dropout2d(0.3)
self.fc1 = nn.Linear(21632, 1024)
self.fc4 = nn.Linear(1024, 2)
def forward(self, x):
h = F.relu(self.conv1(x))
h = F.max_pool2d(h, kernel_size=2)
h = self.bn1(h)
h = F.relu(self.conv2(h))
h = F.max_pool2d(h, kernel_size=2)
h = self.bn2(h)
h = torch.flatten(h, start_dim=1) #flatten input of [bs, c, w, h], so from dim=1
h = F.relu(self.fc1(h))
h = self.dropout1(h)
h = F.relu(self.fc4(h))
h = h.squeeze() #squeezing to reduce dims from (64,1) to (64,) to match target
output = h
return output
def test(x):
device = torch.device("cuda")
model = Net()
#if load using state_dict, everythin becomes 1
model = torch.load('regressor.pt')
model = Net().to(device)
model.eval()
J = model(x)
#test without network
# J = x.mean(dim=(2,3))
# J.squeeze(1)
return J