You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
285 lines
9.5 KiB
285 lines
9.5 KiB
import torch
|
|
import numpy as np
|
|
import cv2, os, sys
|
|
import pandas as pd
|
|
from torch.utils.data import Dataset
|
|
from matplotlib import pyplot as plt
|
|
from torch.utils.data import ConcatDataset, DataLoader, Subset
|
|
import torch.nn as nn
|
|
import torchvision.transforms as transforms
|
|
from torchvision.datasets import DatasetFolder
|
|
from PIL import Image
|
|
import torchvision.models
|
|
import BinaryNetpytorch.models as models
|
|
from BinaryNetpytorch.models.binarized_modules import BinarizeLinear,BinarizeConv2d
|
|
import progressbar
|
|
import seaborn as sns
|
|
|
|
batch_size = 32
|
|
num_epoch = 60
|
|
|
|
torch.cuda.set_device(1)
|
|
|
|
train_tfm = transforms.Compose([
|
|
# transforms.RandomHorizontalFlip(),
|
|
# transforms.RandomResizedCrop((40,30)),
|
|
transforms.Grayscale(),
|
|
transforms.Resize((68, 68)),
|
|
transforms.ToTensor(),
|
|
#transforms.RandomResizedCrop((40,30)),
|
|
#transforms.TenCrop((40,30)),
|
|
# transforms.Normalize(0.5,0.5),
|
|
])
|
|
test_tfm = transforms.Compose([
|
|
transforms.Grayscale(),
|
|
transforms.Resize((68, 68)),
|
|
transforms.ToTensor()
|
|
])
|
|
|
|
def Quantize(img):
|
|
scaler = torch.div(img, 0.0078125, rounding_mode="floor")
|
|
scaler_t1 = scaler * 0.0078125
|
|
scaler_t2 = (scaler + 1) * 0.0078125
|
|
img = torch.where(abs(img - scaler_t1) < abs(img -scaler_t2), scaler_t1 , scaler_t2)
|
|
return img
|
|
# bar = progressbar.ProgressBar(maxval=img.size(0)*img.size(2)*img.size(3), \
|
|
# widgets=[progressbar.Bar('=', '[', ']'), ' ', progressbar.Percentage()])
|
|
# bar.start()
|
|
# for p in range(img.size(0)):
|
|
# for i in range(img.size(2)):
|
|
# for j in range(img.size(3)):
|
|
|
|
# scaler = int(img[p][0][i][j] / 0.0078125)
|
|
# t1 = scaler * 0.0078125
|
|
# t2 = (scaler + 1) * 0.0078125
|
|
# if(abs(img[p][0][i][j] - t1) < abs(img[p][0][i][j] - t2)):
|
|
# img[p][0][i][j] = t1
|
|
# else:
|
|
# img[p][0][i][j] = t2
|
|
# bar.finish()
|
|
# return img
|
|
def Binaryconv3x3(in_planes, out_planes, stride=1):
|
|
"3x3 convolution with padding"
|
|
return BinarizeConv2d(in_planes, out_planes, kernel_size=3, stride=stride,
|
|
padding=1, bias=False)
|
|
|
|
def conv3x3(in_planes, out_planes, stride=1):
|
|
"3x3 convolution with padding"
|
|
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
|
|
padding=1, bias=False)
|
|
class BasicBlock(nn.Module):
|
|
expansion = 1
|
|
|
|
def __init__(self, inplanes, planes, stride=1, downsample=None,do_bntan=True):
|
|
super(BasicBlock, self).__init__()
|
|
|
|
self.conv1 = Binaryconv3x3(inplanes, planes, stride)
|
|
self.bn1 = nn.BatchNorm2d(planes)
|
|
self.tanh1 = nn.Hardtanh(inplace=True)
|
|
self.conv2 = Binaryconv3x3(planes, planes)
|
|
self.tanh2 = nn.Hardtanh(inplace=True)
|
|
self.bn2 = nn.BatchNorm2d(planes)
|
|
|
|
self.downsample = downsample
|
|
self.do_bntan=do_bntan
|
|
self.stride = stride
|
|
|
|
def forward(self, x):
|
|
|
|
residual = x.clone()
|
|
x = Quantize(x)
|
|
out = self.conv1(x)
|
|
out = self.bn1(out)
|
|
out = self.tanh1(out)
|
|
out = Quantize(out)
|
|
out = self.conv2(out)
|
|
|
|
|
|
if self.downsample is not None:
|
|
if residual.data.max()>1:
|
|
import pdb; pdb.set_trace()
|
|
residual = self.downsample(residual)
|
|
|
|
out += residual
|
|
if self.do_bntan:
|
|
out = self.bn2(out)
|
|
out = self.tanh2(out)
|
|
|
|
return out
|
|
class ResNet(nn.Module):
|
|
|
|
def __init__(self):
|
|
super(ResNet, self).__init__()
|
|
|
|
def _make_layer(self, block, planes, blocks, stride=1,do_bntan=True):
|
|
downsample = None
|
|
if stride != 1 or self.inplanes != planes * block.expansion:
|
|
downsample = nn.Sequential(
|
|
BinarizeConv2d(self.inplanes, planes * block.expansion,
|
|
kernel_size=1, stride=stride, bias=False),
|
|
nn.BatchNorm2d(planes * block.expansion),
|
|
)
|
|
|
|
layers = []
|
|
layers.append(block(self.inplanes, planes, stride, downsample))
|
|
self.inplanes = planes * block.expansion
|
|
for i in range(1, blocks-1):
|
|
layers.append(block(self.inplanes, planes))
|
|
layers.append(block(self.inplanes, planes,do_bntan=do_bntan))
|
|
return nn.Sequential(*layers)
|
|
|
|
def forward(self, x):
|
|
x = Quantize(x)
|
|
|
|
x = self.conv1(x)
|
|
x = self.maxpool(x)
|
|
x = self.bn1(x)
|
|
x = self.tanh1(x)
|
|
x = self.layer1(x)
|
|
x = self.layer2(x)
|
|
x = self.layer3(x)
|
|
x = self.layer4(x)
|
|
|
|
x = self.avgpool(x)
|
|
x = x.view(x.size(0), -1)
|
|
x = self.bn2(x)
|
|
x = self.tanh2(x)
|
|
#print(x.size())
|
|
x = x.view(32,1280,1,1)
|
|
x = self.fc(x)
|
|
x = x.view(x.size(0), -1)
|
|
x = self.bn3(x)
|
|
x = self.logsoftmax(x)
|
|
|
|
return x
|
|
class ResNet_cifar10(ResNet):
|
|
|
|
def __init__(self, num_classes=8,
|
|
block=BasicBlock, depth=18):
|
|
super(ResNet_cifar10, self).__init__()
|
|
self.inflate = 5
|
|
self.inplanes = 16*self.inflate
|
|
n = int((depth - 2) / 6)
|
|
self.conv1 = BinarizeConv2d(1, 16*self.inflate, kernel_size=3, stride=1, padding=1,
|
|
bias=False)
|
|
self.maxpool = lambda x: x
|
|
self.bn1 = nn.BatchNorm2d(16*self.inflate)
|
|
self.tanh1 = nn.Hardtanh(inplace=True)
|
|
self.tanh2 = nn.Hardtanh(inplace=True)
|
|
self.layer1 = self._make_layer(block, 16*self.inflate, n)
|
|
self.layer2 = self._make_layer(block, 32*self.inflate, n, stride=2)
|
|
self.layer3 = self._make_layer(block, 64*self.inflate, n, stride=2,do_bntan=False)
|
|
self.layer4 = lambda x: x
|
|
self.avgpool = nn.AvgPool2d(8)
|
|
self.bn2 = nn.BatchNorm1d(256*self.inflate)
|
|
self.bn3 = nn.BatchNorm1d(8)
|
|
self.logsoftmax = nn.LogSoftmax()
|
|
#self.fc = BinarizeLinear(256*self.inflate, 8)
|
|
self.fc = BinarizeConv2d(256*self.inflate, 8, kernel_size=1)
|
|
def main():
|
|
train_set = DatasetFolder("pose_data2/train", loader=lambda x: Image.open(x), extensions="bmp", transform=train_tfm)
|
|
test_set = DatasetFolder("pose_data2/test", loader=lambda x: Image.open(x), extensions="bmp", transform=test_tfm)
|
|
|
|
train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)
|
|
test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=True)
|
|
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
|
model = ResNet_cifar10(num_classes=8,block=BasicBlock,depth=18)
|
|
model = model.to(device)
|
|
print(model)
|
|
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
|
|
criterion = nn.CrossEntropyLoss()
|
|
|
|
model_path = "model.ckpt"
|
|
|
|
for epoch in range(num_epoch):
|
|
running_loss = 0.0
|
|
total = 0
|
|
correct = 0
|
|
for i, data in enumerate(train_loader):
|
|
inputs, labels = data
|
|
inputs = inputs.to(device)
|
|
labels = labels.to(device)
|
|
optimizer.zero_grad()
|
|
|
|
outputs = model(inputs)
|
|
|
|
loss = criterion(outputs, labels)
|
|
loss.backward()
|
|
|
|
optimizer.step()
|
|
|
|
running_loss += loss.item()
|
|
total += labels.size(0)
|
|
_,predicted = torch.max(outputs.data,1)
|
|
#print(predicted)
|
|
#print("label",labels)
|
|
correct += (predicted == labels).sum().item()
|
|
train_acc = correct / total
|
|
|
|
print(f"[ Train | {epoch + 1:03d}/{num_epoch:03d} ] loss = {running_loss:.5f}, acc = {train_acc:.5f}")
|
|
|
|
torch.save(model.state_dict(), model_path)
|
|
|
|
model = ResNet_cifar10(num_classes=8,block=BasicBlock,depth=18)
|
|
model = model.to(device)
|
|
model.load_state_dict(torch.load(model_path))
|
|
model.eval()
|
|
|
|
with torch.no_grad():
|
|
correct = 0
|
|
total = 0
|
|
|
|
correct_2 = 0
|
|
stat = np.zeros((8,8))
|
|
for i, data in enumerate(test_loader):
|
|
inputs, labels = data
|
|
|
|
inputs = inputs.to(device)
|
|
labels = labels.to(device)
|
|
|
|
outputs = model(inputs)
|
|
_,predicted = torch.max(outputs.data,1)
|
|
total += labels.size(0)
|
|
correct += (predicted == labels).sum().item()
|
|
|
|
for b in range(batch_size):
|
|
if predicted[b] == 0 or predicted[b] == 1 or predicted[b] == 2 or predicted[b] == 3:
|
|
if labels[b] == 0 or labels[b] == 1 or labels[b] == 2 or labels[b] == 3:
|
|
correct_2 += 1
|
|
else:
|
|
if labels[b] == 4 or labels[b] == 5 or labels[b] == 6 or labels[b] == 7:
|
|
correct_2 += 1
|
|
|
|
|
|
|
|
for k in range(batch_size):
|
|
if predicted[k] != labels[k]:
|
|
img = inputs[k].mul(255).byte()
|
|
img = img.cpu().numpy().squeeze(0)
|
|
img = np.moveaxis(img, 0, -1)
|
|
|
|
predict = predicted[k].cpu().numpy()
|
|
label = labels[k].cpu().numpy()
|
|
path = "test_result/predict:"+str(predict)+"_labels:"+str(label)+".jpg"
|
|
stat[int(label)][int(predict)] += 1
|
|
|
|
|
|
|
|
cv2.imwrite(path,img)
|
|
print(stat)
|
|
ax = sns.heatmap(stat, linewidth=0.5)
|
|
plt.xlabel('Prediction')
|
|
plt.ylabel('Label')
|
|
plt.savefig('heatmap.jpg')
|
|
#print(predicted)
|
|
#print("labels:",labels)
|
|
print('Test_2clasee Accuracy:{} %'.format((correct_2 / total) * 100))
|
|
print('Test Accuracy:{} %'.format((correct / total) * 100))
|
|
|
|
|
|
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|