def example2():
cm = ConfigManager('testset')
imgs = DataLoader.get_images_objects(cm.get_dataset_path(),
'processed_x.pt',
'processed_y.pt',
to_tensor=True)
print(type(imgs))
dm = DatasetsManager(cm, imgs)
n_output = 2
net = ConvNet(n_output)
optimizer = optim.Adam(net.parameters(), lr=1e-3)
loss_function = nn.MSELoss()
EPOCHS = 10
BATCH_SIZE = 128
print('Start training')
for epoch in range(EPOCHS):
for k in tqdm(range(0, len(dm.train), BATCH_SIZE)):
batch_x = torch.cat(dm.train.get_x(start=k, end=k + BATCH_SIZE),
dim=0)
batch_y = torch.Tensor(dm.train.get_y(start=k, end=k + BATCH_SIZE))
print(type(batch_x))
net.zero_grad()
out = net(batch_x)
loss = loss_function(out, batch_y)
loss.backward()
optimizer.step()
print(f'Epoch: {epoch}. Loss: {loss}')
correct = 0
total = 0
# with torch.no_grad():
# for k in tqdm(range(len(x_test))):
# real_class = torch.argmax(y_test[k])
# net_out = net(x_test[k].view(-1, 1, IMG_SIZE, IMG_SIZE))[0] # returns list
# predicted_class = torch.argmax(net_out)
# if predicted_class == real_class:
# correct += 1
# total += 1
print('Accuracy: ', round(correct / total, 3))
torch.save(net, 'data/cnn_cats_dogs_model.pt')
def example1():
""" Train convnet and then save the model """
DATASETS_DICT = './data'
IMG_SIZE = CONFIG['img_size']
# x_train = DataLoader.load(os.path.join(DATASETS_DICT, 'x_train_cats_dogs.npy'))
# y_train = DataLoader.load(os.path.join(DATASETS_DICT, 'y_train_cats_dogs.npy'))
# x_train = DataLoader.load(os.path.join(DATASETS_DICT, 'x_cats_dogs_skimage.npy'))
# y_train = DataLoader.load(os.path.join(DATASETS_DICT, 'y_cats_dogs_skimage.npy'))
# x_train = DataLoader.load(os.path.join(DATASETS_DICT, 'x_rps_skimage.npy'))
# y_train = DataLoader.load(os.path.join(DATASETS_DICT, 'y_rps_skimage.npy'))
x_train = DataLoader.load_npy(CONFIG['data']['x_path'])
y_train = DataLoader.load_npy(CONFIG['data']['y_path'])
x_train = torch.Tensor(x_train).view(-1, IMG_SIZE, IMG_SIZE)
y_train = torch.Tensor(y_train)
N_TRAIN = CONFIG['n_train']
N_EVAL = CONFIG['n_eval']
N_TEST = CONFIG['n_test']
if N_TRAIN + N_EVAL + N_TEST > len(x_train):
raise Exception('Not enough data!')
# resnet50 works with 224, 244 input size
n_output = 2
net = ConvNet(n_output)
optimizer = optim.Adam(net.parameters(), lr=1e-3)
loss_function = nn.MSELoss()
# split data
x_eval = x_train[:N_EVAL]
y_eval = y_train[:N_EVAL]
x_test = x_train[N_EVAL:N_EVAL + N_TEST]
y_test = y_train[N_EVAL:N_EVAL + N_TEST]
x_train = x_train[N_EVAL + N_TEST:N_EVAL + N_TEST + N_TRAIN]
y_oracle = y_train[N_EVAL + N_TEST:N_EVAL + N_TEST + N_TRAIN]
# show_grid_imgs(x_train[:16], y_oracle[:16], (4, 4))
EPOCHS = 10
BATCH_SIZE = 128
print('Start training')
for epoch in range(EPOCHS):
for k in tqdm(range(0, len(x_train), BATCH_SIZE)):
batch_x = x_train[k:k + BATCH_SIZE].view(-1, 1, IMG_SIZE, IMG_SIZE)
batch_y = y_oracle[k:k + BATCH_SIZE]
net.zero_grad()
out = net(batch_x)
loss = loss_function(out, batch_y)
loss.backward()
optimizer.step()
print(f'Epoch: {epoch}. Loss: {loss}')
correct = 0
total = 0
with torch.no_grad():
for k in tqdm(range(len(x_test))):
real_class = torch.argmax(y_test[k])
net_out = net(x_test[k].view(-1, 1, IMG_SIZE,
IMG_SIZE))[0] # returns list
predicted_class = torch.argmax(net_out)
if predicted_class == real_class:
correct += 1
total += 1
print('Accuracy: ', round(correct / total, 3))
torch.save(net, f'{DATASETS_DICT}/cnn_rps_model.pt')
