def test_state_metrics():
y_pred = torch.randint(0, 2, size=(15, 10, 4)).float()
y = torch.randint(0, 2, size=(15, 10, 4)).long()
def update_fn(engine, batch):
y_pred, y = batch
return y_pred, y
evaluator = Engine(update_fn)
precision = Precision(average=False)
recall = Recall(average=False)
F1 = precision * recall * 2 / (precision + recall + 1e-20)
F1 = MetricsLambda(lambda t: torch.mean(t).item(), F1)
precision.attach(evaluator, "precision")
recall.attach(evaluator, "recall")
F1.attach(evaluator, "f1")
def data(y_pred, y):
for i in range(y_pred.shape[0]):
yield (y_pred[i], y[i])
d = data(y_pred, y)
state = evaluator.run(d, max_epochs=1)
assert set(state.metrics.keys()) == set(["precision", "recall", "f1"])
def create_eval_engine(model, device):
process_function = get_process_function(model, device)
eval_engine = Engine(process_function)
accuracy = Accuracy()
accuracy.attach(eval_engine, "accuracy")
recall = Recall(average=False)
recall.attach(eval_engine, "recall")
precision = Precision(average=False)
precision.attach(eval_engine, "precision")
f1 = (precision * recall * 2 / (precision + recall))
f1.attach(eval_engine, "f1")
f2 = (precision * recall * 5 / ((4 * precision) + recall))
f2.attach(eval_engine, "f2")
def Fbeta(r, p, beta):
return torch.mean(
(1 + beta**2) * p * r / (beta**2 * p + r + 1e-20)).item()
avg_f1 = MetricsLambda(Fbeta, recall, precision, 1)
avg_f1.attach(eval_engine, "average f1")
avg_f2 = MetricsLambda(Fbeta, recall, precision, 2)
avg_f2.attach(eval_engine, "average f2")
avg_recall = Recall(average=True)
avg_recall.attach(eval_engine, "average recall")
avg_precision = Precision(average=True)
avg_precision.attach(eval_engine, "average precision")
return eval_engine
def _test(average, n_epochs):
n_iters = 60
s = 16
n_classes = 7
offset = n_iters * s
y_true = torch.randint(0, n_classes, size=(offset * idist.get_world_size(),)).to(device)
y_preds = torch.rand(offset * idist.get_world_size(), n_classes).to(device)
def update(engine, i):
return (
y_preds[i * s + rank * offset : (i + 1) * s + rank * offset, :],
y_true[i * s + rank * offset : (i + 1) * s + rank * offset],
)
engine = Engine(update)
re = Recall(average=average)
re.attach(engine, "re")
data = list(range(n_iters))
engine.run(data=data, max_epochs=n_epochs)
assert "re" in engine.state.metrics
res = engine.state.metrics["re"]
if isinstance(res, torch.Tensor):
assert res.device.type == "cpu"
res = res.cpu().numpy()
true_res = recall_score(
y_true.cpu().numpy(), torch.argmax(y_preds, dim=1).cpu().numpy(), average="macro" if average else None
)
assert pytest.approx(res) == true_res
def test_integration():
np.random.seed(1)
n_iters = 10
batch_size = 10
n_classes = 10
y_true = np.arange(0, n_iters * batch_size) % n_classes
y_pred = 0.2 * np.random.rand(n_iters * batch_size, n_classes)
for i in range(n_iters * batch_size):
if np.random.rand() > 0.4:
y_pred[i, y_true[i]] = 1.0
else:
j = np.random.randint(0, n_classes)
y_pred[i, j] = 0.7
y_true_batch_values = iter(y_true.reshape(n_iters, batch_size))
y_pred_batch_values = iter(y_pred.reshape(n_iters, batch_size, n_classes))
def update_fn(engine, batch):
y_true_batch = next(y_true_batch_values)
y_pred_batch = next(y_pred_batch_values)
return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch)
evaluator = Engine(update_fn)
precision = Precision(average=False)
recall = Recall(average=False)
def Fbeta(r, p, beta):
return torch.mean((1 + beta**2) * p * r / (beta**2 * p + r)).item()
F1 = MetricsLambda(Fbeta, recall, precision, 1)
precision.attach(evaluator, "precision")
recall.attach(evaluator, "recall")
F1.attach(evaluator, "f1")
data = list(range(n_iters))
state = evaluator.run(data, max_epochs=1)
precision_true = precision_score(y_true,
np.argmax(y_pred, axis=-1),
average=None)
recall_true = recall_score(y_true,
np.argmax(y_pred, axis=-1),
average=None)
f1_true = f1_score(y_true, np.argmax(y_pred, axis=-1), average='macro')
precision = state.metrics['precision'].numpy()
recall = state.metrics['recall'].numpy()
assert precision_true == approx(precision), "{} vs {}".format(
precision_true, precision)
assert recall_true == approx(recall), "{} vs {}".format(
recall_true, recall)
assert f1_true == approx(state.metrics['f1']), "{} vs {}".format(
f1_true, state.metrics['f1'])
def test_integration():
np.random.seed(1)
n_iters = 10
batch_size = 10
n_classes = 10
y_true = np.arange(0, n_iters * batch_size, dtype="int64") % n_classes
y_pred = 0.2 * np.random.rand(n_iters * batch_size, n_classes)
for i in range(n_iters * batch_size):
if np.random.rand() > 0.4:
y_pred[i, y_true[i]] = 1.0
else:
j = np.random.randint(0, n_classes)
y_pred[i, j] = 0.7
y_true_batch_values = iter(y_true.reshape(n_iters, batch_size))
y_pred_batch_values = iter(y_pred.reshape(n_iters, batch_size, n_classes))
def update_fn(engine, batch):
y_true_batch = next(y_true_batch_values)
y_pred_batch = next(y_pred_batch_values)
return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch)
evaluator = Engine(update_fn)
precision = Precision(average=False)
recall = Recall(average=False)
F1 = precision * recall * 2 / (precision + recall)
precision.attach(evaluator, "precision")
recall.attach(evaluator, "recall")
F1.attach(evaluator, "f1")
data = list(range(n_iters))
state = evaluator.run(data, max_epochs=1)
precision_true = precision_score(y_true,
np.argmax(y_pred, axis=-1),
average=None)
recall_true = recall_score(y_true,
np.argmax(y_pred, axis=-1),
average=None)
f1_true = f1_score(y_true, np.argmax(y_pred, axis=-1), average=None)
precision = state.metrics["precision"].numpy()
recall = state.metrics["recall"].numpy()
f1 = state.metrics["f1"].numpy()
assert precision_true == approx(precision), "{} vs {}".format(
precision_true, precision)
assert recall_true == approx(recall), "{} vs {}".format(
recall_true, recall)
assert f1_true == approx(f1), "{} vs {}".format(f1_true, f1)
def _test(average, n_epochs, metric_device):
n_iters = 60
s = 16
n_classes = 7
offset = n_iters * s
y_true = torch.randint(0,
2,
size=(offset * idist.get_world_size(),
n_classes, 6, 8)).to(device)
y_preds = torch.randint(0,
2,
size=(offset * idist.get_world_size(),
n_classes, 6, 8)).to(device)
def update(engine, i):
return (
y_preds[i * s + rank * offset:(i + 1) * s + rank * offset,
...],
y_true[i * s + rank * offset:(i + 1) * s + rank * offset, ...],
)
engine = Engine(update)
re = Recall(average=average, is_multilabel=True, device=metric_device)
re.attach(engine, "re")
assert re._updated is False
data = list(range(n_iters))
engine.run(data=data, max_epochs=n_epochs)
assert "re" in engine.state.metrics
assert re._updated is True
res = engine.state.metrics["re"]
res2 = re.compute()
if isinstance(res, torch.Tensor):
res = res.cpu().numpy()
res2 = res2.cpu().numpy()
assert (res == res2).all()
else:
assert res == res2
np_y_preds = to_numpy_multilabel(y_preds)
np_y_true = to_numpy_multilabel(y_true)
assert re._type == "multilabel"
res = res if average else res.mean().item()
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UndefinedMetricWarning)
assert recall_score(np_y_true, np_y_preds,
average="samples") == pytest.approx(res)
def create_evaluator(model, criterion, cfg):
def _validation_step(_, batch):
model.eval()
with torch.no_grad():
x, y = batch_to_tensor(batch, cfg)
x, y = x.to(cfg.device), y.to(cfg.device)
y_pred, hidden = model(x)
loss = criterion(y_pred, y)
if cfg.multi_label:
y_pred = (y_pred > 0).float()
return y_pred, y, loss, hidden
evaluator = Engine(_validation_step)
accuracy = Accuracy(lambda x: x[0:2], is_multilabel=cfg.multi_label)
accuracy.attach(evaluator, "acc")
precision = Precision(lambda x: x[0:2],
average=False,
is_multilabel=cfg.multi_label)
precision.attach(evaluator, 'precision')
MetricsLambda(lambda t: torch.mean(t).item(),
precision).attach(evaluator, "MP")
recall = Recall(lambda x: x[0:2],
average=False,
is_multilabel=cfg.multi_label)
recall.attach(evaluator, 'recall')
MetricsLambda(lambda t: torch.mean(t).item(),
recall).attach(evaluator, "MR")
F1 = 2. * precision * recall / (precision + recall + 1e-20)
f1 = MetricsLambda(lambda t: torch.mean(t).item(), F1)
f1.attach(evaluator, "F1")
Average(lambda x: x[2]).attach(evaluator, 'loss')
return evaluator
def _test(average, n_epochs):
n_iters = 60
s = 16
n_classes = 7
offset = n_iters * s
y_true = torch.randint(0, 2, size=(offset * dist.get_world_size(), n_classes, 6, 8)).to(device)
y_preds = torch.randint(0, 2, size=(offset * dist.get_world_size(), n_classes, 6, 8)).to(device)
def update(engine, i):
return y_preds[i * s + rank * offset:(i + 1) * s + rank * offset, ...], \
y_true[i * s + rank * offset:(i + 1) * s + rank * offset, ...]
engine = Engine(update)
re = Recall(average=average, is_multilabel=True, device=device)
re.attach(engine, "re")
data = list(range(n_iters))
engine.run(data=data, max_epochs=n_epochs)
assert "re" in engine.state.metrics
res = engine.state.metrics['re']
res2 = re.compute()
if isinstance(res, torch.Tensor):
res = res.cpu().numpy()
res2 = res2.cpu().numpy()
assert (res == res2).all()
else:
assert res == res2
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UndefinedMetricWarning)
true_res = recall_score(to_numpy_multilabel(y_true),
to_numpy_multilabel(y_preds),
average='samples' if average else None)
assert pytest.approx(res) == true_res
def create_evaluator(model, cfg):
def _validation_step(_, batch):
model.eval()
with torch.no_grad():
x_char, x_type, y_word, y_syllable = batch_to_tensor(batch, cfg)
x_char, x_type, y_word, y_syllable = (t.to(
cfg.device) for t in [x_char, x_type, y_word, y_syllable])
logits_word, logits_syllable = model(x_char, x_type)
loss, word_loss, syllable_loss, align_loss = model.joint_loss(
logits_word, y_word, logits_syllable, y_syllable)
return ((logits_word > 0.5).long(), y_word,
(logits_syllable > 0.5).long(), y_syllable, loss,
word_loss, syllable_loss, align_loss)
evaluator = Engine(_validation_step)
w_loss = Accuracy(lambda x: x[0:2])
w_loss.attach(evaluator, 'w_acc')
s_acc = Accuracy(lambda x: x[2:4])
s_acc.attach(evaluator, 's_acc')
Average(lambda x: x[4]).attach(evaluator, 'loss')
Average(lambda x: x[5]).attach(evaluator, 'w_loss')
Average(lambda x: x[6]).attach(evaluator, 's_loss')
Average(lambda x: x[7]).attach(evaluator, 'a_loss')
accuracy = Accuracy(lambda x: x[0:2])
accuracy.attach(evaluator, "acc")
w_precision = Precision(lambda x: x[0:2])
w_precision.attach(evaluator, 'WP')
MetricsLambda(lambda t: torch.mean(t).item(),
w_precision).attach(evaluator, "WMP")
s_precision = Precision(lambda x: x[2:4])
s_precision.attach(evaluator, 'SP')
MetricsLambda(lambda t: torch.mean(t).item(),
s_precision).attach(evaluator, "SMP")
w_recall = Recall(lambda x: x[0:2])
w_recall.attach(evaluator, 'WR')
MetricsLambda(lambda t: torch.mean(t).item(),
w_recall).attach(evaluator, "WMR")
s_recall = Recall(lambda x: x[2:4])
s_recall.attach(evaluator, 'SR')
MetricsLambda(lambda t: torch.mean(t).item(),
s_recall).attach(evaluator, "SMR")
w_f1 = 2. * w_precision * w_recall / (w_precision + w_recall + 1e-20)
w_f1 = MetricsLambda(lambda t: torch.mean(t).item(), w_f1)
w_f1.attach(evaluator, "WF1")
s_f1 = 2. * s_precision * s_recall / (s_precision + s_recall + 1e-20)
s_f1 = MetricsLambda(lambda t: torch.mean(t).item(), s_f1)
s_f1.attach(evaluator, "SF1")
return evaluator
def train(name, load, lrate, weight_decay, workers, smooth, device, validation,
ground_truth):
if not name:
name = '{}_{}'.format(lrate, weight_decay)
click.echo('model output name: {}'.format(name))
torch.set_num_threads(1)
train_set = BaselineSet(glob.glob('{}/**/*.seeds.png'.format(ground_truth),
recursive=True),
smooth=smooth)
train_data_loader = DataLoader(dataset=train_set,
num_workers=workers,
batch_size=1,
shuffle=True,
pin_memory=True)
val_set = BaselineSet(glob.glob('{}/**/*.seeds.png'.format(validation),
recursive=True),
smooth=smooth)
val_data_loader = DataLoader(dataset=val_set,
num_workers=workers,
batch_size=1,
pin_memory=True)
click.echo('loading network')
model = ResUNet(refine_encoder=False).to(device)
if load:
click.echo('loading weights')
model = torch.load(load, map_location=device)
criterion = nn.BCEWithLogitsLoss()
opti = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()),
lr=lrate,
weight_decay=weight_decay)
def score_function(engine):
val_loss = engine.state.metrics['loss']
return -val_loss
def output_preprocess(output):
o, target = output
o = torch.sigmoid(o)
o = denoising_hysteresis_thresh(o.detach().squeeze().cpu().numpy(),
0.8, 0.9, 2.5)
return torch.from_numpy(o.astype('f')).unsqueeze(0).unsqueeze(0).to(
device), target.double().to(device)
trainer = create_supervised_trainer(model,
opti,
criterion,
device=device,
non_blocking=True)
accuracy = Accuracy(output_transform=output_preprocess)
precision = Precision(output_transform=output_preprocess)
recall = Recall(output_transform=output_preprocess)
loss = Loss(criterion)
precision = Precision(average=False)
recall = Recall(average=False)
f1 = (precision * recall * 2 / (precision + recall)).mean()
evaluator = create_supervised_evaluator(model,
device=device,
non_blocking=True)
accuracy.attach(evaluator, 'accuracy')
precision.attach(evaluator, 'precision')
recall.attach(evaluator, 'recall')
loss.attach(evaluator, 'loss')
f1.attach(evaluator, 'f1')
ckpt_handler = ModelCheckpoint('.',
name,
save_interval=1,
n_saved=10,
require_empty=False)
RunningAverage(output_transform=lambda x: x).attach(trainer, 'loss')
progress_bar = ProgressBar(persist=True)
progress_bar.attach(trainer, ['loss'])
trainer.add_event_handler(event_name=Events.EPOCH_COMPLETED,
handler=ckpt_handler,
to_save={'net': model})
trainer.add_event_handler(event_name=Events.ITERATION_COMPLETED,
handler=TerminateOnNan())
@trainer.on(Events.EPOCH_COMPLETED)
def log_validation_results(engine):
evaluator.run(val_data_loader)
metrics = evaluator.state.metrics
progress_bar.log_message(
'eval results - epoch {} loss: {:.4f} f1: {:.4f}, accuracy: {:.4f} recall: {:.4f} precision {:.4f}'
.format(engine.state.epoch, metrics['loss'], metrics['f1'],
metrics['accuracy'], metrics['recall'],
metrics['precision']))
trainer.run(train_data_loader, max_epochs=1000)
def create_zero_shot_eval_engine(self, model, zero_shot_label,
model_mapping, label_mapping,
is_test_multilabel, cpu):
# Iterate through all labels in both the train and test sets to see which labels correspond to the zero shot label (the unifying label)
model_target_int = [
int for label, int in model_mapping.items()
if zero_shot_label in label.lower()
]
label_target_int = [
int for label, int in label_mapping.items()
if zero_shot_label in label.lower()
]
# There should only be one unifying label in each dataset (Possible TODO: Allow multiple labels to map to one unifying label)
assert len(
model_target_int
) == 1, f"Ambiguous or empty model label list when trying to map {zero_shot_label} to {model_target_int}"
assert len(
label_target_int
) == 1, f"Ambiguous or empty gold label list when trying to map {zero_shot_label} to {label_target_int}"
model_target_int = model_target_int[0]
label_target_int = label_target_int[0]
def process_function(engine, batch):
X, y = batch
if cpu:
pred = model(X.cpu())
gold = y.cpu()
else:
pred = model(X.cuda())
gold = y.cuda()
# Get the softmax of the raw model output (logits)
pred = torch.softmax(pred, dim=1)
# Get the probability that the prediction is the target class
pred_in_class_prob = pred[:, [model_target_int]]
# Get all the probabilities of all the other classes outside the target class by finding the complement of the in class probability
pred_out_class_prob = 1 - pred_in_class_prob
# Create a combined tensor which acts as a set of probabilities for in vs out of the zero-shot target class.
# In this, 0 is out of class, whilst 1 is in class, so the combined tensor has the out of class probabilities in the 0th column and the in-class probs in the 1st column.
pred = torch.cat((pred_out_class_prob, pred_in_class_prob), dim=1)
if is_test_multilabel:
# If test task is multilabel, get the values from the appropriate column of the truth labels
gold = gold[:, label_target_int]
else:
# To correspond to the above contructed tensor, we set the golds as 1 (I.e. True) if the gold label is the zero-shot label, and 0 (False) if not.
gold = (gold == label_target_int).long()
return pred, gold
eval_engine = Engine(process_function)
really_small_number = 1e-10
accuracy = Accuracy()
accuracy.attach(eval_engine, "accuracy")
recall = Recall()
recall.attach(eval_engine, "recall")
precision = Precision()
precision.attach(eval_engine, "precision")
f1 = (precision * recall * 2 /
(precision + recall + really_small_number))
f1.attach(eval_engine, "f1")
f2 = (precision * recall * 5 /
((4 * precision) + recall + really_small_number))
f2.attach(eval_engine, "f2")
avg_recall = Recall(average=True)
avg_recall.attach(eval_engine, "average recall")
avg_precision = Precision(average=True)
avg_precision.attach(eval_engine, "average precision")
avg_f1 = (avg_precision * avg_recall * 2 /
(avg_precision + avg_recall + really_small_number))
avg_f1.attach(eval_engine, "average f1")
avg_f2 = (avg_precision * avg_recall * 5 /
((4 * avg_precision) + avg_recall + really_small_number))
avg_f2.attach(eval_engine, "average f2")
return eval_engine