def test_predict(model,dataloader_test,use_cuda):
if use_cuda:
model = model.cuda()
precision = Precision()
recall = Recall()
f1 = Fbeta(beta=1.0, average=True, precision=precision, recall=recall)
for i,(img, label) in enumerate(dataloader_test):
img, labels = Variable(img),Variable(label)
if use_cuda:
img = img.cuda()
label = label.cuda()
pred = model(img)
_,my_label = torch.max(label, dim=1)
precision.update((pred, my_label))
recall.update((pred, my_label))
f1.update((pred, my_label))
precision.compute()
recall.compute()
print("\tF1 Score: {:0.2f}".format(f1.compute()*100))
def train_predict(dataloader_train,dataloader_val,model,epochs,learning_rate,use_cuda):
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
if use_cuda:
model = model.cuda()
model = model.train()
start.record()
train_loss_list=[]
val_loss_list=[]
train_f1=[]
val_f1=[]
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
precision = Precision()
recall = Recall()
f1 = Fbeta(beta=1.0, average=True, precision=precision, recall=recall)
for epoch in range(epochs):
print("Epoch: {}".format(epoch+1))
for i,(img, label) in enumerate(dataloader_train):
img, label = Variable(img),Variable(label)
if use_cuda:
img = img.cuda()
label = label.cuda()
optimizer.zero_grad()
pred = model.forward(img)
_,my_label = torch.max(label, dim=1)
loss = loss_fn(pred,my_label)
if i == len(dataloader_train)-1:
train_loss_list.append(loss.item())
loss.backward()
optimizer.step()
precision.update((pred, my_label))
recall.update((pred, my_label))
f1.update((pred, my_label))
print("\tTrain loss: {:0.2f}".format(train_loss_list[-1]))
precision.compute()
recall.compute()
train_f1.append(f1.compute()*100)
print("\tTrain F1 Score: {:0.2f}%".format(train_f1[-1]))
precision = Precision()
recall = Recall()
f1 = Fbeta(beta=1.0, average=True, precision=precision, recall=recall)
with torch.no_grad():
for i,(img, label) in enumerate(dataloader_val):
img, labels = Variable(img),Variable(label)
if use_cuda:
img = img.cuda()
label = label.cuda()
pred = model(img)
_,my_label = torch.max(label, dim=1)
loss = loss_fn(pred,my_label)
if i == len(dataloader_val)-1:
val_loss_list.append(loss.item())
precision.update((pred, my_label))
recall.update((pred, my_label))
f1.update((pred, my_label))
print("\n\tVal loss: {:0.2f}".format(val_loss_list[-1]))
precision.compute()
recall.compute()
val_f1.append(f1.compute()*100)
print("\tVal F1 Score: {:0.2f}%".format(val_f1[-1]))
end.record()
torch.cuda.synchronize()
time = start.elapsed_time(end)
return (train_loss_list,val_loss_list,train_f1,val_f1,time,model)