def save_acc(self, dataloader, x_value, split="train"):
"""
Calculate the accuracy on the testset given with DataLoader object dataloader
Arguments: dataloader - DataLoader object containing the dataset
x_value - corresponding x_value to be written to list
split - train or test
"""
accuracy = evaluate(self, dataloader, use_gpu=self.use_gpu)
if split == "train":
self.list_train_acc.append([x_value, accuracy])
else:
self.list_test_acc.append([x_value, accuracy])
return None
tqdm.write("CUDA is available: " + str(use_gpu))
# Model creation and training
model = CNN_STN(43, use_gpu=use_gpu)
#model.use_gpu=use_gpu
logger = Logger()
model.databatch = next(iter(dataloader_train))["tensor"].cuda()
train(model,
dataloader_train,
n_epochs=30,
checkpoint_name="test",
use_gpu=use_gpu,
stn=True,
dataloader_test=dataloader_test,
logger=logger)
print("Train accuracy: " + str(evaluate(model, dataloader_train)))
print("Test accuracy: " + str(evaluate(model, dataloader_test)))
# Plot the results of STN side-by-side
tensor = model.databatch.cpu()
original = convert_image_np(
torchvision.utils.make_grid(tensor.narrow(1, 0, 3)).cpu())
fig = plt.figure(figsize=(8, 5), dpi=100)
fig.add_subplot(2, 3, 1)
plt.imshow(original)
plt.xticks([]), plt.yticks([])
plt.title('Dataset Images', fontsize=9)
epoch_list = [6, 9, 12, 18, 29]
Images.append(temp)
#Load our pretrained CNN-Model
model, logger, _ = import_classifier()
# Construct dataset and dataloader with Images
evaluationset = Evalset(Images)
dataloader = DataLoader(evaluationset, batch_size=32, shuffle=True)
use_gpu = torch.cuda.is_available()
if not use_gpu:
print("Warning! No CUDA available. This is untested!")
if use_gpu:
model.cuda()
# Calculate the accuracy of the pretrained model on the data from the SVM
accuracy = evaluate(model, dataloader, use_gpu=use_gpu)
# Visualize the STN on sample images of SVM output
if use_gpu:
model.databatch = next(iter(dataloader))["tensor"].cuda()
else:
model.databatch = next(iter(dataloader))["tensor"].cuda()
visualize_stn(model, filename="./Classifier/Plots/stn_gtsdb_output.pdf")
print(
"For the GTSDB dataset (full) we obtain a total accuracy of the STN+CNN classifier of : "
+ str(accuracy))