def main(argv=None):
print("Running on {}".format(device))
parser = argparse.ArgumentParser(
description="Train a transformer for a copy task"
)
add_optimizer_arguments(parser)
add_transformer_arguments(parser)
add_auxiliary_arguments(parser)
args = parser.parse_args(argv)
print("args:\n-----\n", args)
data_points = []
data_points_acc = []
n_of_each_model = 10
n_trials = 8
for model_type in [ 'transformer','lstm','rnn',]: #add back transformers and rnn
for max_trained_depth in range(1, 12):
for ii in range(n_of_each_model):
print(f'dep{max_trained_depth}_ii_{ii}')
if model_type == "transformer":
d_model = 16
model = SequencePredictorRecurrentTransformer(
d_model=d_model, n_classes=5,
sequence_length=args.sequence_length,
attention_type=args.attention_type,
n_layers=args.n_layers,
n_heads=args.n_heads,
d_query=d_model, # used to be d_query
dropout=args.dropout,
softmax_temp=None,
attention_dropout=args.attention_dropout,
)
else:
d_model = 8
model = SequencePredictorRNN(
d_model=d_model, n_classes=5,
n_layers=args.n_layers,
dropout=args.dropout,
rnn_type=model_type
)
print(f"Created model:\n{model}")
model.to(device)
model.load_state_dict(torch.load(f"models_from_colab/agreement_models/model_{model_type}_depth_{max_trained_depth}_num_{ii}.zip", map_location=device)['model_state'])
for test_depth in range(1, 21): # was 1, 32
stack_size = test_depth # Change this value to test longer / shorter sequences
n_correct = 0
for i_trial in range(n_trials):
x, y, m = SubjectVerbAgreement.get_seq(stack_size)
model.eval()
yhat = model(x.unsqueeze(1))
loss, acc = loss_fn(y.unsqueeze(1), yhat, m.unsqueeze(1))
n_correct += acc
data_points.append({'model_type': model_type, 'max_trained_depth': max_trained_depth,
'test_depth': test_depth, 'accuracy': n_correct / n_trials})
print("data points")
print(data_points)
with open("data_points_pr_acc_r.txt", "wb") as fp:
pickle.dump(data_points, fp)
"""
def main(argv=None):
print("Running on {}".format(device))
parser = argparse.ArgumentParser(
description="Train a transformer for a copy task"
)
add_optimizer_arguments(parser)
add_transformer_arguments(parser)
add_auxiliary_arguments(parser)
args = parser.parse_args(argv)
print("args:\n-----\n", args)
if args.model_type == "transformer":
model = SequencePredictorRecurrentTransformer(
d_model=args.d_model, n_classes=5,
sequence_length=args.sequence_length,
attention_type=args.attention_type,
n_layers=args.n_layers,
n_heads=args.n_heads,
d_query=args.d_model, # used to be d_query
dropout=args.dropout,
softmax_temp=None,
attention_dropout=args.attention_dropout,
)
else:
model = SequencePredictorRNN(
d_model=args.d_model, n_classes=5,
n_layers=args.n_layers,
dropout=args.dropout,
rnn_type=args.model_type
)
print(f"Created model:\n{model}")
model.to(device)
print("Number of epochs model was trained on: ",torch.load(args.continue_from, map_location=device)['epoch'])
model.load_state_dict(torch.load(args.continue_from, map_location=device)['model_state'])
def format_preds(x, y, preds, mask):
n = len(x)
n_dig = math.floor(math.log10(n)) + 1
nums = []
for p_dig in range(n_dig):
nums.append( "# |" + "".join([str((i//10**p_dig)%10) for i in range(n)]) + "\n")
nums = "".join(nums[::-1])
xs = "x |" + "".join([str(int(v)) for v in x]) + "\n"
ys = "y |" + "".join([elt if mask[i] == 1 else '?' for i, elt in enumerate([str(int(v)) for v in y])]) + "\n"
yh = "yh|" + "".join([elt if mask[i] == 1 else '?' for i, elt in enumerate([str(int(v)) for v in preds])]) + "\n"
return nums + xs + ys + yh
acc_list = []
max_acc = None
for stack_size in range(1, 64):
x, y, m = SubjectVerbAgreement.get_seq(stack_size)
# print(x.shape, y.shape, m.shape)
model.eval()
yhat = model(x.unsqueeze(1))
hdn = model.hidden_state # batch x seq x hdn
loss, acc = loss_fn(y.unsqueeze(1), yhat, m.unsqueeze(1))
acc_list.append((stack_size, acc))
if acc == 1:
max_acc = stack_size
print("Highest perfect score at depth:", max_acc)
plot_hidden_state_2d(np.array(acc_list), pca=False)
stack_size = 7 # Change this value to test longer / shorter sequences
x, y, m = SubjectVerbAgreement.get_seq(stack_size)
model.eval()
yhat = model(x.unsqueeze(1))
hdn = model.hidden_state # batch x seq x hdn
loss, acc = loss_fn(y.unsqueeze(1), yhat, m.unsqueeze(1))
print("Model loss: ", loss)
print("Model accuracy: ", acc)
print(format_preds(x, y, torch.argmax(yhat, dim=2)[0], m))
plot_hidden_state_2d(hdn[0].detach().cpu().numpy(), pca=True)
"""
def main(argv=None):
# Choose a device and move everything there
print("Running on {}".format(device))
parser = argparse.ArgumentParser(
description="Train a transformer for an agreement task"
)
add_optimizer_arguments(parser)
add_transformer_arguments(parser)
add_auxiliary_arguments(parser)
args = parser.parse_args(argv)
print("args:\n-----\n", args)
# Make the dataset and the model
for model_type in ['rnn', 'lstm', 'transformer']:
for max_depth in range(1, 12):
ii = 0
while ii < 10:
print(max_depth, ii)
# skip existing models
if os.path.isfile("/content/drive/My Drive/final_project_material/agreement_models/model_" + model_type + "_depth_" + str(max_depth) + "_num_" + str(ii)):
ii += 1
continue
train_set = SubjectVerbAgreement(2*max_depth+1, max_depth=max_depth)
test_set = SubjectVerbAgreement(2*max_depth+1, max_depth=max_depth)
train_loader = DataLoader(
train_set,
batch_size=batch_size,
pin_memory=device=="cuda"
)
test_loader = DataLoader(
test_set,
batch_size=batch_size,
pin_memory=device=="cuda"
)
if model_type == "transformer":
d_model = 16
model = SequencePredictorRecurrentTransformer(
d_model=d_model, n_classes=5,
sequence_length=args.sequence_length,
attention_type=args.attention_type,
n_layers=args.n_layers,
n_heads=args.n_heads,
d_query=d_model, # used to be d_query
dropout=args.dropout,
softmax_temp=None,
attention_dropout=args.attention_dropout,
)
else:
d_model=8
model = SequencePredictorRNN(
d_model=d_model, n_classes=5,
n_layers=args.n_layers,
dropout=args.dropout,
rnn_type=model_type
)
print(f"Created model:\n{model}")
model.to(device)
# Start training
optimizer = get_optimizer(model.parameters(), args)
start_epoch = 1
if args.continue_from:
start_epoch = load_model(
args.continue_from,
model,
optimizer,
device
)
lr_schedule = torch.optim.lr_scheduler.LambdaLR(
optimizer,
lambda e: 1. if e < args.reduce_lr_at else 0.1
)
for e in range(start_epoch, args.epochs+1):
print('Epoch:', e)
print('Training...')
train(model, optimizer, train_loader, device)
print('Evaluating...')
acc = evaluate(model, test_loader, device, return_accuracy=True)
lr_schedule.step()
if e == 100:
break
if acc >= 0.95:
save_model("/content/drive/My Drive/final_project_material/agreement_models/model_" + model_type + "_depth_" + str(max_depth) + "_num_" + str(ii),
model, optimizer, e)
ii += 1
break
def main(argv=None):
parser = argparse.ArgumentParser(
description="Train a transformer to generate images")
add_transformer_arguments(parser)
add_optimizer_arguments(parser)
add_dataset_arguments(parser)
parser.add_argument("--mixtures",
type=int,
default=10,
help="How many logistics to use to model the output")
parser.add_argument("--iterations",
type=int,
default=100,
help="How many iterations to train for")
parser.add_argument("--batch_size",
type=int,
default=4,
help="How many samples to use together")
parser.add_argument("--save_to",
default=None,
help="Set a file to save the models to.")
parser.add_argument("--continue_from",
default=None,
help="Load the model from a file")
parser.add_argument("--save_frequency",
default=3000,
type=int,
help="Save every that many steps")
parser.add_argument(
"--evaluate_frequency",
default=3000,
type=int,
help="Evaluate on the test set after that many iterations")
parser.add_argument(
"--yield_frequency",
default=10**9,
type=int,
help="Stop after that many iterations so that other jobs can run")
args = parser.parse_args(argv)
print_transformer_arguments(args)
# Make the dataset and the model
train_set, test_set = get_dataset(args)
model = ImageGenerator(args.d_query * args.n_heads,
train_set.sequence_length,
args.mixtures,
attention_type=args.attention_type,
n_layers=args.n_layers,
n_heads=args.n_heads,
d_query=args.d_query,
dropout=args.dropout,
softmax_temp=None,
attention_dropout=args.attention_dropout,
bits=args.bits,
rounds=args.rounds,
chunk_size=args.chunk_size,
masked=True)
# Choose a device and move everything there
device = "cuda" if torch.cuda.is_available() else "cpu"
print("Running on {}".format(device))
model.to(device)
# Start training
train_loader = DataLoader(train_set,
batch_size=args.batch_size,
pin_memory=device == "cuda")
test_loader = DataLoader(test_set,
batch_size=args.batch_size,
pin_memory=device == "cuda")
optimizer = get_optimizer(model.parameters(), args)
iteration = 0
if args.continue_from:
iteration = load_model(args.continue_from, model, optimizer, device)
optimizer.set_lr(args.lr)
callbacks = callback_chain(
saver(args.save_frequency, args.save_to, model, optimizer),
evaluator(args.evaluate_frequency, model, test_loader, device),
stopper(args.yield_frequency))
yielded = train(model, optimizer, train_loader, iteration, args.iterations,
callbacks, device)
# Non-zero exit code to notify the process watcher that we yielded
if yielded:
sys.exit(1)
def main(argv=None):
parser = argparse.ArgumentParser(
description="Train a transformer for a copy task")
add_optimizer_arguments(parser)
add_transformer_arguments(parser)
parser.add_argument("--sequence_length",
type=int,
default=128,
help="Set the maximum sequence length")
parser.add_argument("--n_classes",
type=int,
default=10,
help="Set the number of classes")
parser.add_argument("--epochs",
type=int,
default=100,
help="How many epochs to train for")
parser.add_argument("--batch_size",
type=int,
default=64,
help="How many samples to use together")
parser.add_argument("--reduce_lr_at",
type=int,
default=30,
help="At this epoch divide the lr by 10")
parser.add_argument("--save_to",
default=None,
help="Set a file to save the models to.")
parser.add_argument("--continue_from",
default=None,
help="Load the model from a file")
parser.add_argument("--save_frequency",
default=1,
type=int,
help="Save every that many epochs")
args = parser.parse_args(argv)
print_transformer_arguments(args)
# Make the dataset and the model
train_set = CopyTask(args.sequence_length, args.n_classes)
test_set = CopyTask(args.sequence_length, args.n_classes)
model = SequencePredictor(args.d_query * args.n_heads,
args.sequence_length,
args.n_classes,
attention_type=args.attention_type,
n_layers=args.n_layers,
n_heads=args.n_heads,
d_query=args.d_query,
dropout=args.dropout,
softmax_temp=None,
attention_dropout=args.attention_dropout,
bits=args.bits,
rounds=args.rounds,
chunk_size=args.chunk_size,
masked=args.masked)
# Choose a device and move everything there
device = "cuda" if torch.cuda.is_available() else "cpu"
print("Running on {}".format(device))
model.to(device)
# Start training
train_loader = DataLoader(train_set,
batch_size=args.batch_size,
pin_memory=device == "cuda")
test_loader = DataLoader(test_set,
batch_size=args.batch_size,
pin_memory=device == "cuda")
optimizer = get_optimizer(model.parameters(), args)
start_epoch = 1
if args.continue_from:
start_epoch = load_model(args.continue_from, model, optimizer, device)
lr_schedule = torch.optim.lr_scheduler.LambdaLR(
optimizer, lambda e: 1. if e < args.reduce_lr_at else 0.1)
for e in range(start_epoch, args.epochs + 1):
train(model, optimizer, train_loader, device)
evaluate(model, test_loader, device)
if (e % args.save_frequency) == 0 and args.save_to:
save_model(args.save_to, model, optimizer, e)
lr_schedule.step()
def main(argv=None):
print("Running on {}".format(device))
parser = argparse.ArgumentParser(
description="Train a transformer for a copy task")
add_optimizer_arguments(parser)
add_transformer_arguments(parser)
add_auxiliary_arguments(parser)
args = parser.parse_args(argv)
print("args:\n-----\n", args)
data_points = []
for model_type in ['rnn', 'lstm', 'transformer']:
for max_trained_depth in range(1, 12):
for test_depth in range(1, 21):
for ii in range(10):
if model_type == "transformer":
model = SequencePredictorRecurrentTransformer(
d_model=16,
n_classes=5,
sequence_length=args.sequence_length,
attention_type=args.attention_type,
n_layers=args.n_layers,
n_heads=args.n_heads,
d_query=8, # used to be d_query
dropout=args.dropout,
softmax_temp=None,
attention_dropout=args.attention_dropout,
)
else:
model = SequencePredictorRNN(
d_model=8 if model_type == 'lstm' else 8,
n_classes=5,
n_layers=args.n_layers,
dropout=args.dropout,
rnn_type=model_type)
print(f"Created model:\n{model}")
model.to(device)
model_name = "models_from_colab/agreement_models/model_" + model_type + "_depth_" + str(
max_trained_depth) + "_num_" + str(ii) + ".zip"
model.load_state_dict(
torch.load(model_name,
map_location=device)['model_state'])
stack_size = test_depth
x, y, m = SubjectVerbAgreement.get_seq(stack_size)
model.eval()
yhat = model(x.unsqueeze(1))
hdn = model.hidden_state # batch x seq x hdn
loss, acc = loss_fn(y.unsqueeze(1), yhat, m.unsqueeze(1))
data_points.append({
'model_type': model_type,
'max_trained_depth': max_trained_depth,
'test_depth': test_depth,
'accuracy': acc
})
print("data points:")
print(data_points)
with open("data_points_sva.txt", "wb") as fp:
pickle.dump(data_points, fp)
"""
def main(argv=None):
# Choose a device and move everything there
print("Running on {}".format(device))
parser = argparse.ArgumentParser(
description="Train a transformer for a copy task")
add_optimizer_arguments(parser)
add_transformer_arguments(parser)
add_auxiliary_arguments(parser)
args = parser.parse_args(argv)
print("args:\n-----\n", args)
# Make the dataset and the model
max_depth = 12
train_set = CountTaskWithEOS(max_depth * 2 + 1, max_depth=max_depth)
test_set = CountTaskWithEOS(max_depth * 2 + 1, max_depth=max_depth)
train_loader = DataLoader(train_set,
batch_size=args.batch_size,
pin_memory=device == "cuda")
test_loader = DataLoader(test_set,
batch_size=args.batch_size,
pin_memory=device == "cuda")
if args.model_type == "transformer":
model = SequencePredictorRecurrentTransformer(
d_model=args.d_model,
n_classes=args.n_classes,
sequence_length=args.sequence_length,
attention_type=args.attention_type,
n_layers=args.n_layers,
n_heads=args.n_heads,
d_query=args.d_model, # used to be d_query
dropout=args.dropout,
softmax_temp=None,
attention_dropout=args.attention_dropout,
)
else:
model = SequencePredictorRNN(d_model=args.d_model,
n_classes=args.n_classes,
n_layers=args.n_layers,
dropout=args.dropout,
rnn_type=args.model_type)
print(f"Created model:\n{model}")
model.to(device)
# Start training
optimizer = get_optimizer(model.parameters(), args)
start_epoch = 1
if args.continue_from:
start_epoch = load_model(args.continue_from, model, optimizer, device)
lr_schedule = torch.optim.lr_scheduler.LambdaLR(
optimizer, lambda e: 1. if e < args.reduce_lr_at else 0.1)
for e in range(start_epoch, args.epochs + 1):
train(model, optimizer, train_loader, device)
print('Epoch:', e)
evaluate(model, test_loader, device)
if (e % args.save_frequency) == 0 and args.save_to:
save_model(args.save_to, model, optimizer, e)
lr_schedule.step()
if args.plot_hidden:
x, y, m = test_set.__next__() # x is 1d of length sequence_len
x.to(device)
y.to(device)
m.to(device)
model.eval()
yhat = model(x.unsqueeze(1).to(device))
hdn = model.hidden_state # batch x seq x hdn
max_len = 10
print("Plotting on: ", x[:max_len])
plot_hidden_state_2d(hdn[0, :max_len, :].detach().cpu().numpy(),
pca=True)
def main(argv):
parser = argparse.ArgumentParser(
description="Generate an image from a pretrained model")
add_dataset_arguments(parser)
add_transformer_arguments(parser)
parser.add_argument(
"model",
help="The path to the model (give '-' for random intialization)")
parser.add_argument("--mixtures",
type=int,
default=10,
help="How many logistics to use to model the output")
parser.add_argument("--plot",
action="store_true",
help="Plot the generated image")
parser.add_argument("--save_image", help="Path to save an image to")
parser.add_argument("--image_shape",
type=lambda x: tuple(int(xi) for xi in x.split(",")),
default=(28, 28),
help="Reshape the prediction to plot it")
parser.add_argument("--index",
type=index_type,
default=[0],
help="Choose the index from the dataset")
parser.add_argument("--offset",
type=int,
default=300,
help="Choose the offset in the image")
parser.add_argument("--training_set",
action="store_true",
help="Predict from the training set")
parser.add_argument("--load_pytorch",
action="store_true",
help="Load old pytorch model")
parser.add_argument("--force_cpu",
action="store_true",
help="Set the device to cpu")
parser.add_argument("--recurrent",
action="store_true",
help="Use a recurrent model for inference")
args = parser.parse_args(argv)
print_transformer_arguments(args)
# Choose a device to run on
device = ("cuda"
if torch.cuda.is_available() and not args.force_cpu else "cpu")
# Get the dataset and load the model
train_set, test_set = get_dataset(args)
if args.recurrent:
model = RecurrentGenerator(args.d_query * args.n_heads,
train_set.sequence_length,
args.mixtures,
attention_type=args.attention_type,
n_layers=args.n_layers,
n_heads=args.n_heads,
d_query=args.d_query,
dropout=args.dropout,
softmax_temp=None,
attention_dropout=args.attention_dropout,
bits=args.bits,
rounds=args.rounds,
chunk_size=args.chunk_size,
masked=True)
else:
model = ImageGenerator(args.d_query * args.n_heads,
train_set.sequence_length,
args.mixtures,
attention_type=args.attention_type,
n_layers=args.n_layers,
n_heads=args.n_heads,
d_query=args.d_query,
dropout=args.dropout,
softmax_temp=None,
attention_dropout=args.attention_dropout,
bits=args.bits,
rounds=args.rounds,
chunk_size=args.chunk_size,
masked=True)
# Gather the images
images = collect_batch(train_set if args.training_set else test_set,
args.index, device)
# Load the model
if args.model != "-":
if args.load_pytorch:
load_model_pytorch(args.model, model, None, device)
else:
load_model(args.model, model, None, device)
model.to(device)
model.eval()
# Do the predictions
if args.recurrent:
timer = Timer()
pred_images = predict_with_recurrent(model, images, args.offset)
print("Elapsed time:", timer.measure())
else:
timer = Timer()
pred_images = predict(model, images, args.offset)
print("Elapsed time:", timer.measure())
# Plot or save the images
if args.plot:
print(pred_images)
pred_images = pred_images.cpu()
images = images.cpu()
plt.figure()
plt.imshow(pred_images[0].reshape(*args.image_shape))
plt.figure()
plt.imshow(np.hstack([images[0], 0]).reshape(*args.image_shape))
plt.show()
if args.save_image:
pred_images = pred_images.cpu()
images = images.cpu()
for i in range(len(images)):
imwrite(args.save_image.format("pred", i),
pred_images[i].reshape(*args.image_shape))
imwrite(args.save_image.format("real", i),
np.hstack([images[i], 0]).reshape(*args.image_shape))