def _test_tf(avg, beta, act, pred, threshold):
act = tf.constant(act, tf.float32)
pred = tf.constant(pred, tf.float32)
fbeta = FBetaScore(3, avg, beta, threshold)
fbeta.update_state(act, pred)
return fbeta.result().numpy()
def _test_tf(self, avg, beta, act, pred, threshold):
act = tf.constant(act, tf.float32)
pred = tf.constant(pred, tf.float32)
fbeta = FBetaScore(3, avg, beta, threshold)
self.evaluate(tf.compat.v1.variables_initializer(fbeta.variables))
self.evaluate(fbeta.update_state(act, pred))
return self.evaluate(fbeta.result())
def test_config(self):
fbeta_obj = FBetaScore(num_classes=3, beta=0.5, average=None)
self.assertEqual(fbeta_obj.beta, 0.5)
self.assertEqual(fbeta_obj.average, None)
self.assertEqual(fbeta_obj.num_classes, 3)
self.assertEqual(fbeta_obj.dtype, tf.float32)
# Check save and restore config
fbeta_obj2 = FBetaScore.from_config(fbeta_obj.get_config())
self.assertEqual(fbeta_obj2.beta, 0.5)
self.assertEqual(fbeta_obj2.average, None)
self.assertEqual(fbeta_obj2.num_classes, 3)
self.assertEqual(fbeta_obj2.dtype, tf.float32)
def test_eq(self):
f1 = F1Score(3)
fbeta = FBetaScore(3, beta=1.0)
self.evaluate(tf.compat.v1.variables_initializer(f1.variables))
self.evaluate(tf.compat.v1.variables_initializer(fbeta.variables))
preds = [[0.9, 0.1, 0], [0.2, 0.6, 0.2], [0, 0, 1], [0.4, 0.3, 0.3],
[0, 0.9, 0.1], [0, 0, 1]]
actuals = [[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 0, 0], [1, 0, 0],
[0, 0, 1]]
self.evaluate(fbeta.update_state(actuals, preds))
self.evaluate(f1.update_state(actuals, preds))
self.assertAllClose(
self.evaluate(fbeta.result()), self.evaluate(f1.result()))
def test_config_fbeta():
fbeta_obj = FBetaScore(num_classes=3, beta=0.5, threshold=0.3, average=None)
assert fbeta_obj.beta == 0.5
assert fbeta_obj.average is None
assert fbeta_obj.threshold == 0.3
assert fbeta_obj.num_classes == 3
assert fbeta_obj.dtype == tf.float32
# Check save and restore config
fbeta_obj2 = FBetaScore.from_config(fbeta_obj.get_config())
assert fbeta_obj2.beta == 0.5
assert fbeta_obj2.average is None
assert fbeta_obj2.threshold == 0.3
assert fbeta_obj2.num_classes == 3
assert fbeta_obj2.dtype == tf.float32
def initialize_vars(self, beta_val, average):
# initialize variables
fbeta_obj = FBetaScore(num_classes=3, beta=beta_val, average=average)
self.evaluate(tf.compat.v1.variables_initializer(fbeta_obj.variables))
return fbeta_obj
def test_eq():
f1 = F1Score(3)
fbeta = FBetaScore(3, beta=1.0)
preds = [
[0.9, 0.1, 0],
[0.2, 0.6, 0.2],
[0, 0, 1],
[0.4, 0.3, 0.3],
[0, 0.9, 0.1],
[0, 0, 1],
]
actuals = [[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 0, 0], [1, 0, 0], [0, 0, 1]]
fbeta.update_state(actuals, preds)
f1.update_state(actuals, preds)
np.testing.assert_allclose(fbeta.result().numpy(), f1.result().numpy())
def test_keras_model(self):
model = tf.keras.Sequential()
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(1, activation='softmax'))
fb = FBetaScore(1, 'macro')
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['acc', fb])
# data preparation
data = np.random.random((10, 3))
labels = np.random.random((10, 1))
labels = np.where(labels > 0.5, 1, 0)
model.fit(data, labels, epochs=1, batch_size=32, verbose=0)
def test_keras_model(self):
fbeta = FBetaScore(5, "micro", 1.0)
utils._get_model(fbeta, 5)
# Load the model
# model = ResNet(pretrain_dataset=pretrain_dataset, pooling="max", task=task)
model = func_resnet(pretrain_dataset=pretrain_dataset,
pooling="max",
task=task)
if task == "orig_labels":
n_outputs = 17
elif task == "deforestation":
n_outputs = 1
else:
raise Exception(
f'ERROR: Unrecognized task "{task}". Please select one of "orig_labels" or "deforestation".'
)
model_metrics = [
"accuracy",
FBetaScore(num_classes=n_outputs, average="macro", beta=2.0),
]
wandb.init(
project="fsdl_deforestation_detection",
entity="fsdl-andre-karthik",
tags="mvp",
reinit=True,
config={
**vars(args),
**dict(current_task=task)
},
)
# The epoch on which to start the full model training
initial_epoch = 0
if args.pretrained or count > 0:
# Train initially the final layer
