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validation.py
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validation.py
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from sklearn.metrics import confusion_matrix
import model as md
import torch
from pathlib import Path
import os
from torchvision import transforms
import dataloader
from torch.utils.data import Dataset, DataLoader
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.c= 8
self.bs = 4
self.conv1 = nn.Conv3d(64,self.c, kernel_size=(3,3,1),stride=1,padding=1,bias=False)
self.conv2 = nn.Conv3d(self.c,self.c*2,kernel_size=(3,3,1),stride=1,padding=1,bias=False)
self.conv3 = nn.Conv3d(self.c*2,self.c*4,kernel_size=(3,3,1),stride=1,padding=1,bias=False)
self.bn1 = nn.BatchNorm3d(self.c, affine = False)
self.bn2 = nn.BatchNorm3d(self.c*2, affine = False)
self.bn3 = nn.BatchNorm3d(self.c*4, affine = False)
self.dropout1 = nn.Dropout3d(0.25)
self.fc1 = nn.Linear((self.bs*self.c*64*64*1), 256)
self.fc2 = nn.Linear(256,64)
self.fc3 = nn.Linear(64,2)
# self.softmax = nn.LogSoftmax(dim=1)
# self.softmax = nn.Softmax(dim=1)
# x represents our data
def forward(self, x):
# Pass data through conv1
# print('-------1',x.shape)
#conv1
x = self.conv1(x)
x = F.relu(x)
x = F.max_pool3d(x, 2)
x = self.bn1(x)
# print('-------after conv1+maxpool+bn1',x.shape)
# conv2
x = self.conv2(x)
x = F.relu(x)
x = F.max_pool3d(x, 2)
x = self.bn2(x)
# print('-------after conv2+maxpool+bn2',x.shape)
# conv3
x = self.conv3(x)
x = F.relu(x)
x = F.max_pool3d(x, 2)
x = self.bn3(x)
# print('-------after conv3+maxpooling+bn3',x.shape)
# # Pass data through dropout1
# print('***************************',x.shape)
conv_out = x.view(-1, self.bs*self.c*64*64*1)
# # Pass data through fc1
# print('-------after x.view',conv_out.shape)
x = self.fc1(conv_out)
# print('-------after fc1',x.shape)
x = F.relu(x)
# x = self.dropout1(x)
x = self.fc2(x)
x = F.relu(x)
x = self.fc3(x)
# print('-------after fc2',x.shape)
# Apply softmax to x
# output= self.softmax(x)
# print('-------fo',output.shape,output)
return x
losses = []
pred = []
real =[]
valid_losses = []
corrects = 0
training_loss = 0.0
valid_loss = 0.0
valid_correct = 0
root = Path(os.getcwd())
criterion = nn.CrossEntropyLoss()
csv_file = root/'newwww.csv'
print('csv file:',csv_file,'\n','root:',root,'\n')
image_dir = root/'covid_safavi/sample/4173146/lung_white'
device = "cuda:0" if torch.cuda.is_available() else "cpu"
transform_img = transforms.Compose([
transforms.ToTensor()
])
dset = dataloader.covid_ct(root, csv_file)
train_set, val_set = torch.utils.data.random_split(dset, [250, 47])
print(len(train_set),len(val_set))
train_loader = DataLoader(dataset = train_set, batch_size = 4, shuffle=True, num_workers=0,drop_last=True)
valid_loader = DataLoader(dataset = val_set, batch_size = 1, shuffle=False, num_workers=0,drop_last=True)
Model = Net()
print(Model)
Model = torch.load('epoch_35')
Model.to(device ='cuda:0')
Model.eval() # Optional when not using Model Specific layer
for i, (data, target,features) in enumerate(valid_loader):
with torch.no_grad():
inputs, labels,f = data,target,features
inputs = inputs.to(device)
labels = labels.to(device)
inputs = Variable(inputs.view(1,64,512,512,1)).type(torch.FloatTensor).cuda()
targets = Variable(labels).type(torch.LongTensor).cuda()
# optimizer.zero_grad()
print(inputs.shape,"inputs")
outputs = Model(inputs.cuda())
print("outputs: ",torch.argmax(outputs,dim=1) ,"targets: ",targets.cuda())
pred.append(torch.argmax(outputs,dim=1).cpu().numpy()[0])
real.append(targets.cpu().numpy()[0])
print("pred",pred,"\nreal",real)
valid_loss += criterion(outputs,targets)
valid_correct += (torch.argmax(outputs,dim=1) == targets).sum().item()
valid_losses += [valid_loss.item()]
print('Validation Accuracy in epoch: %.2f, Valid Loss: %.4f'
%((100 * (valid_correct / len(val_set))),valid_loss/len(val_set)))
confusion_matrix(real, pred)