official/vision/modeling/layers/edgetpu_test.py
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"""Tests EdgeTPU oriented layers and tools."""
from typing import Optional
from absl.testing import parameterized
import numpy as np
import tensorflow as tf, tf_keras
from official.vision.modeling.layers import edgetpu
def random_boxes(shape):
a = tf.random.uniform(shape=shape+[2])
b = tf.random.uniform(shape=shape+[2])
l = tf.minimum(a, b)
u = tf.maximum(a, b)
return tf.concat([l, u], axis=-1)
def _maximum_activation_size(model):
max_size = 0
for layer in model.layers:
outputs = layer.output
if not isinstance(outputs, list):
outputs = [outputs]
for output in outputs:
if hasattr(output, 'shape'):
size = np.prod(output.shape)
max_size = max(max_size, size)
return max_size
def _deviation_and_margin(reference, valid, optimized):
"""Returns deviation and margin between two batched sets of indices."""
deviation_rate = 0
min_union = reference.shape[1] + optimized.shape[1]
runs = reference.shape[0]
for run in range(runs):
reference_slice = {*reference[run, :valid[run]].numpy().tolist()}
optimized_slice = {*optimized[run].numpy().astype(int).tolist()} - {-1}
union_size = len(optimized_slice | reference_slice)
symdiff_size = len(optimized_slice ^ reference_slice)
deviation_rate += symdiff_size / union_size
min_union = min(min_union, union_size)
deviation_rate = deviation_rate / runs
# six sigma estimate via LLN theorem
margin = 6 * (deviation_rate / np.sqrt(runs) + 1 / (runs * min_union))
return deviation_rate, margin
class NonMaxSuppressionTest(parameterized.TestCase, tf.test.TestCase):
def setUp(self):
super().setUp()
tf.random.set_seed(42)
def test_refinement_sample(self):
"""Tests difference in NMS behaviours.
Runs on four boxes with following IOU table (only neighbours will qualify
as similar boxes)
box | 0 | 1 | 2 | 3
--- | ---- | ---- | ---- | ----
0 | 1 | 7/13 | 1/4 | 1/19
1 | 7/13 | 1 | 7/13 | 1/4
2 | 1/4 | 7/13 | 1 | 7/13
3 | 1/19 | 1/4 | 7/13 | 1
So 0 is best box, it eliminates 1, next is box 2 which is eleminated by 1
if it is allowed (depending on number of refinements).
"""
boxes: tf.Tensor = tf.constant(
[
# y1, x1, y2, x2
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.3, 1.0, 1.3],
[0.0, 0.6, 1.0, 1.6],
[0.0, 0.9, 1.0, 1.9],
],
dtype=tf.float32)
scores: tf.Tensor = tf.constant([
1.0,
0.9,
0.8,
0.7,
], dtype=tf.float32)
self.assertAllEqual(
edgetpu.non_max_suppression_padded(boxes, scores, 4, refinements=0),
tf.constant([0.0, -1.0, -1.0, -1.0], dtype=tf.float32))
self.assertAllEqual(
edgetpu.non_max_suppression_padded(boxes, scores, 4, refinements=1),
tf.constant([0.0, 2.0, -1.0, -1.0], dtype=tf.float32))
@parameterized.parameters((16, 8, 200, [0.009, 0.004, 0.004]),
(31, 17, 100, [0.013, 0.004, 0.004]),
(71, 41, 100, [0.045, 0.003, 0.002]),
(150, 100, 100, [0.129, 0.010, 0.001]),
(300, 300, 100, [0.116, 0.016, 0.002]),
(600, 600, 50, [0.176, 0.032, 0.003]))
def test_reference_match(self, n, top, runs, max_devs):
"""Compares that new optimized method is close to reference method.
Runs two algorithms with same sets of input boxes and scores, and measures
deviation between returned sets of prunned boxes.
Read more about test results at ./g3doc/non_max_suppression.md
(*) Avoid flakiness with safe boundary (go/python-tips/048): deviation
between two sets is a positive number, which may vary from test to test.
Doing multiple runs expected to reduce average deviation variation following
LLN theorem. Therefore by having first test run we know upper deviation
bound which algorithm would not exceed until broken (in any feasible amount
of time in the future). Use of this safe boundary makes test non-flaky.
Args:
n: number of boxes and scores on input of the algorithm.
top: limit of output boxes count.
runs: for the statistical testing number of runs to performs to avoid
tests flakiness.
max_devs: series of mean limits on deviation between optimized and
reference algorithms with different number of refinements. (Indexes of
elements correspond to number of refinements) Please use margin based
values proposed by failed test to avoid flaky testing.
"""
boxes = random_boxes([runs, n])
scores = tf.random.uniform(shape=[runs, n])
reference, valid = tf.image.non_max_suppression_padded(
boxes, scores, top, pad_to_max_output_size=True)
for refinements, max_deviation in enumerate(max_devs):
optimized = edgetpu.non_max_suppression_padded(
boxes, scores, top, refinements=refinements)
deviation, margin = _deviation_and_margin(reference, valid, optimized)
self.assertLess(
deviation,
max_deviation,
msg='Deviation rate between optimized and reference implementations is '
'higher than expected. If you are tuning the test, recommended safe '
'deviation rate is '
f'{deviation} + {margin} = {deviation + margin}')
@parameterized.parameters(([16], 8), ([91, 150], 100), ([20, 20, 200], 10))
def test_sharded_match(self, shape: list[int], top: int):
boxes = random_boxes(shape)
scores = tf.random.uniform(shape=shape)
optimized = edgetpu.non_max_suppression_padded(boxes, scores, top)
reference = edgetpu._non_max_suppression_as_is(boxes, scores, top)
self.assertAllEqual(optimized, reference)
_sharded_nms = edgetpu.non_max_suppression_padded
_stright_nms = edgetpu._non_max_suppression_as_is
@parameterized.parameters(([16], 8, _sharded_nms, True),
([16], 8, _stright_nms, True),
([91, 150], 100, _sharded_nms, True),
([91, 150], 100, _stright_nms, False),
([20, 20, 200], 10, _sharded_nms, True),
([20, 20, 200], 10, _stright_nms, False))
def test_sharded_size(self, shape: list[int], top: int, algorithm,
fits_as_is: bool):
scores = tf_keras.Input(shape=shape, batch_size=1)
boxes = tf_keras.Input(shape=shape + [4], batch_size=1)
optimized = algorithm(boxes, scores, top)
model = tf_keras.Model(inputs=[boxes, scores], outputs=optimized)
max_size = _maximum_activation_size(model)
if fits_as_is:
# Sharding done or not needed.
self.assertLessEqual(max_size, edgetpu._RECOMMENDED_NMS_MEMORY)
else:
# Sharding needed.
self.assertGreater(max_size, edgetpu._RECOMMENDED_NMS_MEMORY)
def test_shard_tensors(self):
a: tf.Tensor = tf.constant([[0, 1, 2, 3, 4]])
b: tf.Tensor = tf.constant([[
[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24],
]])
for i, (a_i, b_i) in enumerate(edgetpu.shard_tensors(1, 3, (a, b))):
self.assertAllEqual(a_i, a[:, i * 3:i * 3 + 3])
self.assertAllEqual(b_i, b[:, i * 3:i * 3 + 3, :])
def test_top_k_sharded_fusion_arguments_validation(self):
# Input scores is not pair of aggregation and shard.
self.assertRaises(ValueError, edgetpu.concat_and_top_k, 100,
tf.zeros(shape=[1000]))
# Input other values is not pairs of aggregation and shard.
self.assertRaises(TypeError, edgetpu.concat_and_top_k, 100,
(None, tf.zeros(shape=[1000])), None,
tf.zeros(shape=[1000]))
# Insufficient rank to do top_k
self.assertRaises(IndexError, edgetpu.concat_and_top_k, 100,
(None, tf.constant(1.)))
@parameterized.parameters(0, 1, 2)
def test_top_k_sharded_fusion_vs_top_k_unsharded(self, axis: int):
r"""Tests `horizontal` sharding using shard_tensors and concat_and_top_k.
Will generate and test graph (on diagram 4 shards, in test 6 shards):
Input
-----
|
+-------+--------------------------------------------
| Split |----------------------- \
+-------+--- \ |
| \ | |
+-------+ +--------+ +-------+ +--------+ +-------+ +--------+ +-------+
| top k |-| concat |-| top k |-| concat |-| top k |-| concat |-| top k |
+-------+ +--------+ +-------+ +--------+ +-------+ +--------+ +-------+
|
Output
------
Args:
axis: test top_k axis (tensor rank will be axis + 1)
"""
sample: tf.Tensor = tf.random.uniform(
shape=axis * [1] + [10000], dtype=tf.float32)
top_1000_direct: tf.Tensor = tf.math.top_k(sample, 1000).values
top_1000_sharded: Optional[tf.Tensor] = None
for (piece,) in edgetpu.shard_tensors(axis, 1500, (sample,)):
(top_1000_sharded,) = edgetpu.concat_and_top_k(
1000, (top_1000_sharded, piece))
self.assertAllEqual(top_1000_direct, top_1000_sharded)
if __name__ == '__main__':
tf.test.main()