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08_ae.py
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08_ae.py
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#%%
import terrain_set
from torch.utils.data import DataLoader
import numpy as np
import pandas as pd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch
torch.manual_seed(1)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#%%
n=128
ts = terrain_set.TerrainSet('data/USGS_1M_10_x43y465_OR_RogueSiskiyouNF_2019_B19.tif',
size=n, stride=8, local_norm=True, square_output=True)
t,v = torch.utils.data.random_split(ts, [0.9, 0.1])
train = DataLoader(t, batch_size=256, shuffle=True,
num_workers=2, pin_memory=True, persistent_workers=True, prefetch_factor=4)
val = DataLoader(v, batch_size=256, shuffle=True,
num_workers=2, pin_memory=True, persistent_workers=True, prefetch_factor=4)
#%%
class View(nn.Module):
def __init__(self, dim, shape):
super(View, self).__init__()
self.dim = dim
self.shape = shape
def forward(self, input):
new_shape = list(input.shape)[:self.dim] + list(self.shape) + list(input.shape)[self.dim+1:]
return input.view(*new_shape)
# https://github.com/pytorch/pytorch/issues/49538
nn.Unflatten = View
ch = 16
conv1 = nn.Sequential(
nn.Conv2d(1, ch, 3, padding=1),
nn.BatchNorm2d(ch),
nn.ReLU(inplace=True),
nn.MaxPool2d(2),
nn.Conv2d(ch, ch*2, 3, padding=1),
nn.BatchNorm2d(ch*2),
nn.ReLU(inplace=True),
nn.MaxPool2d(2),
nn.Conv2d(ch*2, ch*4, 3, padding=1),
nn.BatchNorm2d(ch*4),
nn.ReLU(inplace=True),
nn.MaxPool2d(2),
nn.Conv2d(ch*4, ch*8, 3, padding=1),
nn.BatchNorm2d(ch*8),
nn.ReLU(inplace=True),
nn.MaxPool2d(2),
nn.Conv2d(ch*8, ch*16, 3, padding=1),
nn.BatchNorm2d(ch*16),
nn.ReLU(inplace=True),
nn.MaxPool2d(2),
nn.Conv2d(ch*16, ch*32, 3, padding=1),
nn.BatchNorm2d(ch*32),
nn.ReLU(inplace=True),
nn.MaxPool2d(2),
nn.Flatten(),
nn.Linear(2*2*(ch*32), 256),
nn.ReLU(True),
nn.Linear(256, 2*2*(ch*32)),
nn.ReLU(True),
nn.Unflatten(1, (ch*32, 2, 2)),
nn.ConvTranspose2d(ch*32, ch*16, 3, stride=2, padding=1, output_padding=1),
nn.BatchNorm2d(ch*16),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(ch*16, ch*8, 3, stride=2, padding=1, output_padding=1),
nn.BatchNorm2d(ch*8),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(ch*8, ch*4, 3, stride=2, padding=1, output_padding=1),
nn.BatchNorm2d(ch*4),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(ch*4, ch*2, 3, stride=2, padding=1, output_padding=1),
nn.BatchNorm2d(ch*2),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(ch*2, ch, 3, stride=2, padding=1, output_padding=1),
nn.BatchNorm2d(ch),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(ch, 1, 3, stride=2, padding=1, output_padding=1),
)
conv1(torch.Tensor([ts[0][1], ts[1][1]]).unsqueeze(1)).shape
#%%
net = conv1.to(device)
opt = optim.Adam(net.parameters())
lossfn = nn.MSELoss()
#lossfn = nn.L1Loss()
min_val_loss = 9999999999.0
early_stop_counter = 0
for epoch in range(999): # loop over the dataset multiple times
running_loss = 0.0
net.train()
for i, data in enumerate(train, 0):
inputs, targets = data
# zero the parameter gradients
opt.zero_grad()
# forward + backward + optimize
outputs = net(targets.unsqueeze(1).to(device))
loss = lossfn(outputs, targets.unsqueeze(1).to(device))
loss.backward()
opt.step()
# print statistics
running_loss += loss.item()
if i % 10 == 9:
print("train: %.2f" % (running_loss/10.0))
running_loss = 0.0
running_loss = 0.0
net.eval()
with torch.no_grad():
for i,data in enumerate(val, 0):
inputs, targets = data
outputs = net(targets.unsqueeze(1).to(device))
loss = lossfn(outputs, targets.unsqueeze(1).to(device))
running_loss += loss.item()
vl = running_loss/len(val)
print("val: %.2f" % (vl))
if vl<min_val_loss:
min_val_loss = vl
early_stop_counter = 0
print('saving...')
torch.save(net, 'models/08')
else:
early_stop_counter += 1
if early_stop_counter>=3:
break