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import torch
import torch.nn as nn
import numpy as np
from torchvision import transforms, datsets
from torch.utils.data.sampler import SubsetRandomSampler
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# This is a VGG-16 architecture (16 layers) - just for reference or if you want to build on top of it
class VGG16(nn.Module):
def __init__(self, num_classes = 2):
# Has two classes, crosswalk or background.
self.layer1 = nn.Sequential(
nn.Conv2d(3, 342, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU()
),
self.layer2 = nn.Sequential(
nn.Conv2d(3, 342, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2)
),
self.layer3 = nn.Sequential(
nn.Conv2d(3, 342, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU()
),
self.layer4 = nn.Sequential(
nn.Conv2d(3, 342, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU()
),
self.layer5 = nn.Sequential(
nn.Conv2d(3, 342, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU()
),
self.layer6 = nn.Sequential(
nn.Conv2d(3, 342, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU()
)
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