def test_2layer_net():
params = init_toy_model()
X, y = init_toy_data()
Y_enc = ut.encode_labels(y)
# Make the net
layer_1 = layers.Linear(*params['W1'].T.shape,
reg='frob',
reg_param=0.05,
init_vals=(params['W1'].T, params['b1'].ravel()))
act_1 = layers.Relu()
layer_2 = layers.Linear(*params['W2'].T.shape,
reg='frob',
reg_param=0.05,
init_vals=(params['W2'].T, params['b2'].ravel()))
net_2 = nn.Network([layer_1, act_1, layer_2], ls.CrossEntropy(),
optim.SGD(lr=1e-5))
scores = net_2.forward(X)
correct_scores = np.asarray([[-1.07260209, 0.05083871, -0.87253915],
[-2.02778743, -0.10832494, -1.52641362],
[-0.74225908, 0.15259725, -0.39578548],
[-0.38172726, 0.10835902, -0.17328274],
[-0.64417314, -0.18886813, -0.41106892]])
diff = np.sum(np.abs(scores - correct_scores))
assert (np.isclose(diff, 0.0, atol=1e-6))
loss = net_2.loss(X, Y_enc)
correct_loss = 1.071696123862817
assert (np.isclose(loss, correct_loss, atol=1e-8))
def test_sample(filename):
with open(filename, 'r') as o:
lr = float(o.readline().strip())
p, q, r = list(map(int, o.readline().split()))
W = np.zeros([p, q])
b = np.zeros([r])
grads_flat = np.zeros([p*q + r])
W_ans = np.zeros([p, q])
b_ans = np.zeros([r])
for i in range(p):
line = list(map(float, o.readline().split()))
W[i, :] = line
line = list(map(float, o.readline().split()))
b[:] = line
line = list(map(float, o.readline().split()))
grads_flat[:] = line
for i in range(p):
line = list(map(float, o.readline().split()))
W_ans[i, :] = line
line = list(map(float, o.readline().split()))
b_ans[:] = line
model = TestModel(W, b)
optimizer = optim.SGD(model, lr)
optimizer.update(grads_flat)
self.assertTrue(np.isclose(model.params()[0], W_ans).all(), filename)
self.assertTrue(np.isclose(model.params()[1], b_ans).all(), filename)
def main(args):
train_cfg = config_from_json(args.train_cfg)
model_cfg = config_from_json(args.model_cfg)
model_cfg.block_size = model_cfg.max_len // model_cfg.n_blocks
set_seeds(train_cfg.seed)
if model_cfg.projection not in ["dense", "cnn"]:
if args.max_len == 0:
model_cfg.reduced_max_len = model_cfg.max_len
else:
model_cfg.reduced_max_len = args.max_len
if args.reduce_block_size:
assert model_cfg.reduced_max_len % model_cfg.n_blocks == 0, "Reduced len cannot be divided by n_blocks"
model_cfg.block_size = model_cfg.reduced_max_len // model_cfg.n_blocks
else:
assert model_cfg.reduced_max_len % model_cfg.block_size == 0, "Reduced len cannot be divided by initial block_size"
model_cfg.n_blocks = model_cfg.reduced_max_len // model_cfg.block_size
print("max_len:", model_cfg.reduced_max_len, "block_size:", model_cfg.block_size, "n_blocks:", model_cfg.n_blocks)
else:
if args.max_len != 0:
warnings.warn("Projection is incompatible with a reduced max len, using default max_len")
print("Loading dataset")
(data, labels), criterion = get_data_and_optimizer_from_dataset(args.data_file, train_cfg.task)
loader = GlueDataset(data, labels, train_cfg, model_cfg)
model = BertInnerForSequenceClassification(model_cfg, loader.get_n_labels(), criterion)
if train_cfg.optimizer == "lamb":
if train_cfg.opt_level != "" and train_cfg.opt_level is not None:
optimizer = apex.optimizers.FusedLAMB(model.parameters(), **train_cfg.optimizer_parameters)
else:
optimizer = torch_optimizer.Lamb(model.parameters(), **train_cfg.optimizer_parameters)
elif train_cfg.optimizer == "radam":
optimizer = torch_optimizer.RAdam(model.parameters(), **train_cfg.optimizer_parameters)
elif train_cfg.optimizer == "sgd":
optimizer = optim.SGD(model.parameters(), **train_cfg.optimizer_parameters)
else:
optimizer = optim4GPU(train_cfg, model)
trainer = GlueTrainer(loader, model, optimizer, args.save_dir, get_device(), train_cfg.parallel)
if args.load_model != "":
print("Loading checkpoint")
trainer.load_model(args.load_model, args.load_dataset_state)
if not args.eval:
trainer.train(train_cfg)
else:
trainer.eval(train_cfg)
def train_framework(train_set,
val_set,
test_set,
epochs=1000,
mini_batch_size=50,
lr=0.001):
# Turn autograd off
torch.set_grad_enabled(False)
# Net definition
model = nn.Sequential(nn.Linear(2, 25, activation="relu"), F.ReLU(),
nn.Linear(25, 25, activation="relu"), F.ReLU(),
nn.Linear(25, 25, activation="relu"), F.ReLU(),
nn.Linear(25, 2, activation="relu"))
# Training params
opt = optim.SGD(lr, model)
criterion = losses.LossMSE()
# Train
start_time = time.perf_counter()
history = train_model(model,
train_set[0],
train_set[1],
val_set[0],
val_set[1],
criterion,
opt,
epochs,
mini_batch_size,
pytorch=False,
verbose=True)
end_time = time.perf_counter()
# Compute final accuracies
train_acc = compute_accuracy(model,
train_set[0],
train_set[1],
pytorch=False)
test_acc = compute_accuracy(model, test_set[0], test_set[1], pytorch=False)
print("\tTraining time : %s s" % (end_time - start_time))
print("\tAccuracy : train_acc = %s \t test_acc = %s" %
(train_acc, test_acc))
return history, end_time - start_time, (train_acc, test_acc)
def test_2layer_grad():
params = init_toy_model()
X, y = init_toy_data()
Y_enc = ut.encode_labels(y)
# Make the net
layer_1 = layers.Linear(*params['W1'].T.shape,
reg='frob',
reg_param=0.05,
init_vals=(params['W1'].T, params['b1'].ravel()))
act_1 = layers.Relu()
layer_2 = layers.Linear(*params['W2'].T.shape,
reg='frob',
reg_param=0.05,
init_vals=(params['W2'].T, params['b2'].ravel()))
net_2 = nn.Network([layer_1, act_1, layer_2], ls.CrossEntropy(),
optim.SGD(lr=1e-5))
loss = net_2.loss(X, Y_enc)
net_2.backward()
def f_change_param(param_name, U):
if param_name == 3:
net_2.layers[0].params['b'] = U
if param_name == 2:
net_2.layers[0].params['W'] = U
if param_name == 1:
net_2.layers[2].params['b'] = U
if param_name == 0:
net_2.layers[2].params['W'] = U
return net_2.loss(X, Y_enc)
rel_errs = np.empty(4)
for param_name in range(4):
f = lambda U: f_change_param(param_name, U)
if param_name == 3:
pass_pars = net_2.layers[0].params['b']
if param_name == 2:
pass_pars = net_2.layers[0].params['W']
if param_name == 1:
pass_pars = net_2.layers[2].params['b']
if param_name == 0:
pass_pars = net_2.layers[2].params['W']
param_grad_num = dutil.grad_check(f, pass_pars, epsilon=1e-5)
rel_errs[param_name] = ut.rel_error(param_grad_num,
net_2.grads[param_name])
assert (np.allclose(rel_errs, np.zeros(4), atol=1e-7))
X_train, Y_train = generate_linear(n=100)
X_test, Y_test = generate_linear(n=100)
elif args.dataset == 'xor':
X_train, Y_train = generate_XOR_easy()
X_test, Y_test = generate_XOR_easy()
else:
raise RuntimeError('Dataset Not Found')
net = Net()
if args.criterion == 'mse':
criterion = nn.MSE()
elif args.criterion == 'crossentropy':
criterion = nn.CrossEntropy()
else:
raise RuntimeError('Criterion Not Found')
if args.optimizer == 'sgd':
optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=args.momentum)
elif args.optimizer == 'adagrad':
optimizer = optim.Adagrad(net.parameters(), lr=args.lr)
else:
raise RuntimeError('Optimizer Not Found')
model = Model(net, criterion, optimizer)
train_history = model.train(X_train, Y_train, epochs=args.epochs)
test_history = model.test(X_test, Y_test)
show_history(train_history)
show_result(X_test, Y_test, test_history['predict'])
def run():
with open(args.cfg_path) as f:
cfg = json.load(f)
os.environ["CUDA_VISIBLE_DEVICES"] = args.device_ids
num_GPU = len(args.device_ids.split(','))
batch_size_train = cfg['train_batch_size'] * num_GPU
batch_size_valid = cfg['test_batch_size'] * num_GPU
num_workers = args.num_workers * num_GPU
data_path = cfg['data_path_40']
if cfg['image_size'] % cfg['patch_size'] != 0:
raise Exception('Image size / patch size != 0 : {} / {}'.format(
cfg['image_size'], cfg['patch_size']))
patch_per_side = cfg['image_size'] // cfg['patch_size']
grid_size = patch_per_side * patch_per_side
model = MODELS[cfg['model']](num_classes=1,
num_nodes=grid_size,
use_crf=cfg['use_crf'])
if args.resume:
model = load_checkpoint(args, model)
model = DataParallel(model, device_ids=None)
model = model.to(device)
loss_fn = nn.BCEWithLogitsLoss().to(device)
optimizer = optim.SGD(model.parameters(),
lr=cfg['lr'],
momentum=cfg['momentum'],
weight_decay=1e-4,
l2_reg=False)
summary_train = {
'epoch': 0,
'step': 0,
'fp': 0,
'tp': 0,
'Neg': 0,
'Pos': 0
}
summary_valid = {'loss': float('inf'), 'step': 0, 'acc': 0}
summary_writer = SummaryWriter(log_path)
loss_valid_best = float('inf')
tumor_all = []
paracancerous_all = []
for epoch in range(args.start_epoch, args.end_epoch):
dataset_train = GridImageDataset(data_path,
cfg['json_path_train'],
cfg['image_size'],
cfg['patch_size'],
cfg['crop_size'],
rand_list=[])
dataloader_train = DataLoader(dataset_train,
batch_size=batch_size_train,
num_workers=num_workers,
drop_last=True,
shuffle=True)
dataset_valid = GridImageDataset(data_path,
cfg['json_path_valid'],
cfg['image_size'],
cfg['patch_size'],
cfg['crop_size'],
way="valid")
dataloader_valid = DataLoader(dataset_valid,
batch_size=batch_size_valid,
num_workers=num_workers,
drop_last=True,
shuffle=True)
summary_train = train_epoch(epoch, summary_train, cfg, model, loss_fn,
optimizer, dataloader_train)
torch.save(
{
'epoch': summary_train['epoch'],
'step': summary_train['step'],
'state_dict': model.module.state_dict()
}, (ckpt_path_save + '/' + str(epoch) + '.ckpt'))
summary_writer.add_scalar('train/loss', summary_train['loss'], epoch)
summary_writer.add_scalar('train/acc', summary_train['acc'], epoch)
summary_writer.add_scalar('learning_rate', summary_train['lr'], epoch)
summary_writer.add_scalar('train/Precision',
summary_train['Precision'], epoch)
summary_writer.add_scalar('train/Recall', summary_train['Recall'],
epoch)
summary_writer.add_scalar('train/F1', summary_train['F1'], epoch)
if epoch % 2 == 0:
summary_valid = valid_epoch(summary_valid, summary_writer, epoch,
model, loss_fn, dataloader_valid)
summary_writer.add_scalar('valid/loss', summary_valid['loss'],
epoch)
summary_writer.add_scalar('valid/acc', summary_valid['acc'], epoch)
summary_writer.add_scalar('valid/Precision',
summary_valid['Precision'], epoch)
summary_writer.add_scalar('valid/Recall', summary_valid['Recall'],
epoch)
summary_writer.add_scalar('valid/F1', summary_valid['F1'], epoch)
# summary_writer.add_scalar('learning_rate', lr, epoch)
if summary_valid['loss'] < loss_valid_best:
loss_valid_best = summary_valid['loss']
torch.save(
{
'epoch': summary_train['epoch'],
'step': summary_train['step'],
'state_dict': model.module.state_dict()
}, os.path.join(ckpt_path_save, 'best.ckpt'))
summary_writer.close()
print("Generating dataset...")
nPoints = 1000
train, train_label = generate_disk_dataset(nPoints)
test, test_label = generate_disk_dataset(nPoints)
# Select model
print("Building the model...")
model = m.Sequential(m.Linear(2, 25), m.ReLU(), m.Linear(25, 25), m.ReLU(),
m.Linear(25, 25), m.ReLU(), m.Linear(25, 2))
#model = m.Sequential(m.Linear(2,25), m.ReLU(), m.Linear(25,2, bias = False))
#model = m.Sequential(m.Linear(2,128), m.ReLU(), m.Linear(128,2))
#model = m.Sequential(m.Linear(2,2))
# #Select optimizer
# optim = o.GradientDescent(0.04)
optim = o.SGD(10, 0.01)
# optim = o.SGDWithRepetition(153,0.05)
# #Select loss
loss = l.MSE()
# loss = l.CrossEntropyLoss()
# #Train the model and plot the train loss evolution
print("Training the model...")
v = optim.train_model(model, train, train_label, loss, n_epochs=100)
file = open('train_loss.out', 'w')
json.dump(v, file)
file.close()
fig = plt.figure()
plt.plot(
