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from caffe2.python import workspace, crf, brew
from caffe2.python.model_helper import ModelHelper
import numpy as np
from scipy.special import logsumexp
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
from hypothesis import given, settings
class TestCRFOp(hu.HypothesisTestCase):
@given(num_tags=st.integers(2, 4),
num_words=st.integers(2, 15))
@settings(deadline=10000)
def test_crf_with_loss_op(self, num_tags, num_words):
model = ModelHelper(name='external')
embeddings_dim = 200
embeddings = np.random.randn(num_words, embeddings_dim).astype(np.float32)
transitions = np.random.uniform(
low=-1, high=1, size=(num_tags + 2, num_tags + 2)
).astype(np.float32)
labels = np.random.randint(num_tags, size=(num_words)).astype(np.int64)
embeddings_blob, labels_blob, transitions_blob = (
model.net.AddExternalInputs(
'embeddings_blob',
'labels_blob',
'crf_transitions')
)
workspace.FeedBlob(str(embeddings_blob), embeddings)
workspace.FeedBlob(str(labels_blob), labels)
workspace.FeedBlob(str(transitions_blob), transitions)
predictions_blob = brew.fc(
model,
embeddings_blob, "fc_0",
embeddings_dim, num_tags,
('UniformFill', {'min': -1.0}, {'max': 1.0}),
('UniformFill', {'min': -1.0}, {'max': 1.0})
)
crf_layer = crf.CRFWithLoss(model, num_tags, transitions_blob)
crf_loss = crf_layer.crf_loss(predictions_blob, labels_blob)
model.net.AddGradientOperators([crf_loss])
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
loss = workspace.FetchBlob(str(crf_loss))
predictions = workspace.FetchBlob(str(predictions_blob))
np.testing.assert_allclose(
loss,
self._compute_loss_manual(
predictions, num_tags, labels, transitions
),
atol=0.001,
rtol=0.001,
err_msg='CRF LOSS is not matching the reference'
)
@given(num_tags=st.integers(1, 4),
num_words=st.integers(2, 4))
@settings(deadline=10000)
def test_crf_gradient(self, num_tags, num_words):
base_model = ModelHelper(name='base_model')
transitions = np.random.randn(
num_tags + 2, num_tags + 2
).astype(np.float32)
predictions = np.random.randn(num_words, 1, num_tags + 2).astype(np.float32)
initial = np.random.randn(1, num_tags + 2).astype(np.float32)
predictions_blob, transitions_blob, initial_blob = (
base_model.net.AddExternalInputs(
'predictions_blob', 'crf_transitions', 'inital_blob'
)
)
workspace.FeedBlob(str(predictions_blob), predictions)
workspace.FeedBlob(str(transitions_blob), transitions)
workspace.FeedBlob(str(initial_blob), initial)
crf_layer = crf.CRFWithLoss(base_model, num_tags, transitions_blob)
crf_layer.build_crf_net(
predictions_blob, initial_blob, transitions_blob
)
op = base_model.net._net.op[-1]
workspace.RunNetOnce(base_model.param_init_net)
gradients_to_check = (
index for (index, input_name) in enumerate(op.input)
if input_name != "crf_net/zero_segment_id"
)
inputs = [workspace.FetchBlob(name) for name in op.input]
for param in gradients_to_check:
self.assertGradientChecks(
device_option=hu.cpu_do,
op=op,
inputs=inputs,
outputs_to_check=param,
outputs_with_grads=[1],
threshold=0.05,
stepsize=0.001,
)
def _compute_loss_manual(self, predictions, num_tags, labels, transitions):
low_score = -1000
b_s = np.array(
[[low_score] * num_tags + [0, low_score]]
).astype(np.float32)
e_s = np.array(
[[low_score] * num_tags + [low_score, 0]]
).astype(np.float32)
predictions = np.concatenate(
[predictions, low_score * np.ones((predictions.shape[0], 2))],
axis=1
)
predictions = np.concatenate(
[b_s, predictions, e_s],
axis=0
)
b_id = np.array([num_tags], dtype=np.int32)
e_id = np.array([num_tags + 1], dtype=np.int32)
labels = np.concatenate(
[b_id, labels, e_id],
axis=0
)
curr_state = predictions[0]
input_states = predictions[1:]
for input_state in input_states:
prev = np.expand_dims(curr_state, axis=1)
curr_input = np.expand_dims(input_state, axis=0)
curr_state = logsumexp(prev + curr_input + transitions, axis=0)
total_score = logsumexp(curr_state, axis=0)
# Compute best path score
unary_scores = sum(w[labels[i]] for i, w in enumerate(predictions))
binary_scores = sum(
transitions[a][b] for a, b in zip(labels[:-1], labels[1:])
)
loss = total_score - (binary_scores + unary_scores)
return loss
|
