def test_very_big_output(self):
"""
This test checks whether the layer can handle a corner case
where the number of output pixels is very large, possibly larger
than the number of available GPU threads
"""
pooled_w, pooled_h = 7, 7
input_w, input_h = 72, 240
n_channels = 512
n_batches = 2
x_input = np.ones(shape=(n_batches, input_w, input_h, n_channels))
n_rois = 5000
rois_input = np.ones(shape=(n_rois, 5))
input = tf.placeholder(tf.float32, shape=[n_batches, input_w, input_h, n_channels])
single_roi_dimension = 5
rois = tf.placeholder(tf.int32, shape=[n_rois, single_roi_dimension])
y = roi_pooling(input, rois, pool_height=pooled_w, pool_width=pooled_h)
with tf.Session('') as sess:
y_output = sess.run(y, feed_dict={input: x_input, rois: rois_input})
self.assertTrue(np.all(y_output == 1))
def test_roi_pooling_grad(self):
# TODO(maciek): corner cases
input_value = [[
[[1], [2], [4], [4]],
[[3], [4], [1], [2]],
[[6], [2], [1], [7.0]],
[[1], [3], [2], [8]]
]]
input_value = np.asarray(input_value, dtype='float32')
rois_value = [
[0, 0, 0, 1, 1],
[0, 1, 1, 2, 2],
[0, 2, 2, 3, 3],
[0, 0, 0, 2, 2],
[0, 0, 0, 3, 3]
]
rois_value = np.asarray(rois_value, dtype='int32')
with tf.Session(''):
# NOTE(maciek): looks like we have to use consts here, based on tensorflow/python/ops/nn_test.py
input_const = tf.constant(input_value, tf.float32)
rois_const = tf.constant(rois_value, tf.int32)
y = roi_pooling(input_const, rois_const, pool_height=2, pool_width=2)
mean = tf.reduce_mean(y)
numerical_grad_error_1 = tf.test.compute_gradient_error(
[input_const], [input_value.shape], y, [5, 2, 2, 1])
numerical_grad_error_2 = tf.test.compute_gradient_error(
[input_const], [input_value.shape], mean, [])
self.assertLess(numerical_grad_error_1, 1e-4)
self.assertLess(numerical_grad_error_2, 1e-4)
def test_roi_pooling_grad(self):
# TODO(maciek): corner cases
input_value = [[
[[1], [2], [4], [4]],
[[3], [4], [1], [2]],
[[6], [2], [1], [7.0]],
[[1], [3], [2], [8]]
]]
input_value = np.asarray(input_value, dtype='float32')
rois_value = [
[0, 0, 0, 1, 1],
[0, 1, 1, 2, 2],
[0, 2, 2, 3, 3],
[0, 0, 0, 2, 2],
[0, 0, 0, 3, 3]
]
rois_value = np.asarray(rois_value, dtype='int32')
with tf.Session(''):
# NOTE(maciek): looks like we have to use consts here, based on tensorflow/python/ops/nn_test.py
input_const = tf.constant(input_value, tf.float32)
rois_const = tf.constant(rois_value, tf.int32)
y = roi_pooling(input_const, rois_const, pool_height=2, pool_width=2)
mean = tf.reduce_mean(y)
numerical_grad_error_1 = tf.test.compute_gradient_error(
[input_const], [input_value.shape], y, [5, 2, 2, 1])
numerical_grad_error_2 = tf.test.compute_gradient_error(
[input_const], [input_value.shape], mean, [])
self.assertLess(numerical_grad_error_1, 1e-4)
self.assertLess(numerical_grad_error_2, 1e-4)
def test_very_big_output(self):
"""
This test checks whether the layer can handle a corner case
where the number of output pixels is very large, possibly larger
than the number of available GPU threads
"""
pooled_w, pooled_h = 7,7
input_w, input_h = 72, 240
n_channels = 512
n_batches = 2
x_input = np.ones(shape=(n_batches, input_w, input_h, n_channels))
n_rois = 5000
rois_input = np.ones(shape=(n_rois, 5))
input = tf.placeholder(tf.float32, shape=[n_batches, input_w, input_h, n_channels])
single_roi_dimension = 5
rois = tf.placeholder(tf.int32, shape=[n_rois, single_roi_dimension])
y = roi_pooling(input, rois, pool_height=pooled_w, pool_width=pooled_h)
with tf.Session('') as sess:
y_output = sess.run(y, feed_dict={input: x_input, rois: rois_input})
self.assertTrue(np.all(y_output == 1))
def test_shape_inference_2(self):
pooled_w, pooled_h = 3, 4
input_w, input_h = 200, 300
n_channels = 3
n_batches = None
input = tf.placeholder(tf.float32, shape=[n_batches, input_w, input_h, n_channels])
n_rois = None
single_roi_dimension = 5
rois = tf.placeholder(tf.int32, shape=[n_rois, single_roi_dimension])
y = roi_pooling(input, rois, pool_height=pooled_w, pool_width=pooled_h)
self.assertEqual(y.get_shape().ndims, 4)
self.assertIs(y.get_shape()[0].value, n_rois)
self.assertIs(y.get_shape()[1].value, n_channels)
self.assertIs(y.get_shape()[2].value, pooled_h)
self.assertIs(y.get_shape()[3].value, pooled_w)
def test_shape_inference_2(self):
pooled_w, pooled_h = 3, 4
input_w, input_h = 200, 300
n_channels = 3
n_batches = None
input = tf.placeholder(tf.float32, shape=[n_batches, input_w, input_h, n_channels])
n_rois = None
single_roi_dimension = 5
rois = tf.placeholder(tf.int32, shape=[n_rois, single_roi_dimension])
y = roi_pooling(input, rois, pool_height=pooled_w, pool_width=pooled_h)
self.assertEqual(y.get_shape().ndims, 4)
self.assertIs(y.get_shape()[0].value, n_rois)
self.assertIs(y.get_shape()[1].value, n_channels)
self.assertIs(y.get_shape()[2].value, pooled_h)
self.assertIs(y.get_shape()[3].value, pooled_w)