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211 lines
6.5 KiB
211 lines
6.5 KiB
import torch
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import numpy as np
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import cv2, os, sys
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import pandas as pd
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from torch.utils.data import Dataset
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from matplotlib import pyplot as plt
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from torch.utils.data import ConcatDataset, DataLoader, Subset
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import torch.nn as nn
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import torchvision.transforms as transforms
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from torchvision.datasets import DatasetFolder
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from PIL import Image
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import torchvision.models
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import BinaryNetpytorch.models as models
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from BinaryNetpytorch.models.binarized_modules import BinarizeLinear,BinarizeConv2d
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batch_size = 32
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num_epoch = 10
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train_tfm = transforms.Compose([
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# transforms.RandomHorizontalFlip(),
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# transforms.RandomResizedCrop((40,30)),
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transforms.Grayscale(),
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transforms.Resize((40, 30)),
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transforms.ToTensor(),
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#transforms.RandomResizedCrop((40,30)),
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#transforms.TenCrop((40,30)),
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# transforms.Normalize(0.5,0.5),
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])
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test_tfm = transforms.Compose([
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transforms.Grayscale(),
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transforms.Resize((40, 30)),
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transforms.ToTensor()
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])
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def Binaryconv3x3(in_planes, out_planes, stride=1):
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"3x3 convolution with padding"
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return BinarizeConv2d(in_planes, out_planes, kernel_size=3, stride=stride,
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padding=1, bias=False)
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def conv3x3(in_planes, out_planes, stride=1):
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"3x3 convolution with padding"
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return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
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padding=1, bias=False)
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class BasicBlock(nn.Module):
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expansion = 1
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def __init__(self, inplanes, planes, stride=1, downsample=None,do_bntan=True):
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super(BasicBlock, self).__init__()
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self.conv1 = Binaryconv3x3(inplanes, planes, stride)
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self.bn1 = nn.BatchNorm2d(planes)
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self.tanh1 = nn.Hardtanh(inplace=True)
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self.conv2 = Binaryconv3x3(planes, planes)
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self.tanh2 = nn.Hardtanh(inplace=True)
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self.bn2 = nn.BatchNorm2d(planes)
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self.downsample = downsample
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self.do_bntan=do_bntan
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self.stride = stride
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def forward(self, x):
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residual = x.clone()
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.tanh1(out)
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out = self.conv2(out)
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if self.downsample is not None:
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if residual.data.max()>1:
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import pdb; pdb.set_trace()
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residual = self.downsample(residual)
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out += residual
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if self.do_bntan:
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out = self.bn2(out)
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out = self.tanh2(out)
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return out
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class ResNet(nn.Module):
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def __init__(self):
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super(ResNet, self).__init__()
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def _make_layer(self, block, planes, blocks, stride=1,do_bntan=True):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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BinarizeConv2d(self.inplanes, planes * block.expansion,
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kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample))
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self.inplanes = planes * block.expansion
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for i in range(1, blocks-1):
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layers.append(block(self.inplanes, planes))
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layers.append(block(self.inplanes, planes,do_bntan=do_bntan))
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return nn.Sequential(*layers)
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def forward(self, x):
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x = self.conv1(x)
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x = self.maxpool(x)
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x = self.bn1(x)
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x = self.tanh1(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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x = self.avgpool(x)
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x = x.view(x.size(0), -1)
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x = self.bn2(x)
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x = self.tanh2(x)
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x = self.fc(x)
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x = self.bn3(x)
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x = self.logsoftmax(x)
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return x
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class ResNet_cifar10(ResNet):
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def __init__(self, num_classes=3,
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block=BasicBlock, depth=18):
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super(ResNet_cifar10, self).__init__()
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self.inflate = 5
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self.inplanes = 16*self.inflate
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n = int((depth - 2) / 6)
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self.conv1 = BinarizeConv2d(1, 16*self.inflate, kernel_size=3, stride=1, padding=1,
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bias=False)
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self.maxpool = lambda x: x
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self.bn1 = nn.BatchNorm2d(16*self.inflate)
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self.tanh1 = nn.Hardtanh(inplace=True)
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self.tanh2 = nn.Hardtanh(inplace=True)
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self.layer1 = self._make_layer(block, 16*self.inflate, n)
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self.layer2 = self._make_layer(block, 32*self.inflate, n, stride=2)
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self.layer3 = self._make_layer(block, 64*self.inflate, n, stride=2,do_bntan=False)
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self.layer4 = lambda x: x
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self.avgpool = nn.AvgPool2d(8)
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self.bn2 = nn.BatchNorm1d(64*self.inflate)
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self.bn3 = nn.BatchNorm1d(3)
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self.logsoftmax = nn.LogSoftmax()
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self.fc = BinarizeLinear(64*self.inflate, 3)
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def main():
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train_set = DatasetFolder("pose_data/training/labeled", loader=lambda x: Image.open(x), extensions="bmp", transform=train_tfm)
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test_set = DatasetFolder("pose_data/testing", loader=lambda x: Image.open(x), extensions="bmp", transform=test_tfm)
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train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)
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test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=True)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = ResNet_cifar10(num_classes=3,block=BasicBlock,depth=18)
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model = model.to(device)
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print(model)
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optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
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criterion = nn.CrossEntropyLoss()
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for epoch in range(num_epoch):
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running_loss = 0.0
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total = 0
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correct = 0
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for i, data in enumerate(train_loader):
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inputs, labels = data
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inputs = inputs.to(device)
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labels = labels.to(device)
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optimizer.zero_grad()
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outputs = model(inputs)
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loss = criterion(outputs, labels)
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loss.backward()
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optimizer.step()
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running_loss += loss.item()
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total += labels.size(0)
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_,predicted = torch.max(outputs.data,1)
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#print(predicted)
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#print("label",labels)
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correct += (predicted == labels).sum().item()
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train_acc = correct / total
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print(f"[ Train | {epoch + 1:03d}/{num_epoch:03d} ] loss = {running_loss:.5f}, acc = {train_acc:.5f}")
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model.eval()
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with torch.no_grad():
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correct = 0
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total = 0
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for i, data in enumerate(test_loader):
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inputs, labels = data
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inputs = inputs.to(device)
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labels = labels.to(device)
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outputs = model(inputs)
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_,predicted = torch.max(outputs.data,1)
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total += labels.size(0)
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correct += (predicted == labels).sum().item()
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#print(predicted)
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#print("labels:",labels)
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print('Test Accuracy:{} %'.format((correct / total) * 100))
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if __name__ == '__main__':
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main()
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