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from caffe2.python import core, workspace
from hypothesis import given
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
import copy
class RoIAlignRotatedOp(hu.HypothesisTestCase):
def bbox_xywh_to_xyxy(self, boxes):
"""
Convert from [center_x center_y w h] format to [x1 y1 x2 y2].
"""
w, h = boxes[:, 2], boxes[:, 3]
boxes[:, 0] -= w / 2.0 # x1 = center_x - width/2
boxes[:, 1] -= h / 2.0 # y1 = center_y - height/2
boxes[:, 2] = boxes[:, 0] + w # x2 = x1 + width
boxes[:, 3] = boxes[:, 1] + h # y2 = y1 + height
return boxes
@given(
H=st.integers(min_value=50, max_value=100),
W=st.integers(min_value=50, max_value=100),
C=st.integers(min_value=1, max_value=3),
num_rois=st.integers(min_value=0, max_value=10),
pooled_size=st.sampled_from([7, 14]),
**hu.gcs
)
def test_horizontal_rois(self, H, W, C, num_rois, pooled_size, gc, dc):
"""
Test that results match with RoIAlign when angle=0.
"""
X = np.random.randn(1, C, H, W).astype(np.float32)
R = np.zeros((num_rois, 6)).astype(np.float32)
angle = 0.0
for i in range(num_rois):
x = np.random.uniform(1, W - 1)
y = np.random.uniform(1, H - 1)
w = np.random.uniform(1, min(x, W - x))
h = np.random.uniform(1, min(y, H - y))
R[i] = [0, x, y, w, h, angle]
op = core.CreateOperator(
"RoIAlignRotated",
["X", "R"],
["Y"],
pooled_h=pooled_size,
pooled_w=pooled_size,
sampling_ratio=0,
)
def roialign_ref(X, R):
# Remove angle and convert from [center_x center_y w h]
# to [x1 y1 x2 y2] format.
R_ref = copy.deepcopy(R[:, 0:5])
R_ref[:, 1:5] = self.bbox_xywh_to_xyxy(R_ref[:, 1:5])
ref_op = core.CreateOperator(
"RoIAlign",
["X_ref", "R_ref"],
["Y_ref"],
pooled_h=pooled_size,
pooled_w=pooled_size,
sampling_ratio=0,
)
workspace.FeedBlob("X_ref", X)
workspace.FeedBlob("R_ref", R_ref)
workspace.RunOperatorOnce(ref_op)
return [workspace.FetchBlob("Y_ref")]
self.assertReferenceChecks(
device_option=gc, op=op, inputs=[X, R], reference=roialign_ref
)
if core.IsGPUDeviceType(gc.device_type):
self.assertGradientChecks(gc, op, [X, R], 0, [0])
@given(
H=st.integers(min_value=50, max_value=100),
W=st.integers(min_value=50, max_value=100),
C=st.integers(min_value=1, max_value=3),
num_rois=st.integers(min_value=0, max_value=10),
pooled_size=st.sampled_from([7, 14]),
angle=st.sampled_from([-270, -180, -90, 90, 180, 270]),
**hu.gcs
)
def test_simple_rotations(
self, H, W, C, num_rois, pooled_size, angle, gc, dc
):
"""
Test with right-angled rotations that don't need interpolation.
"""
X = np.random.randn(1, C, H, W).astype(np.float32)
R = np.zeros((num_rois, 6)).astype(np.float32)
for i in range(num_rois):
x = np.random.uniform(1, W - 1)
y = np.random.uniform(1, H - 1)
w = np.random.uniform(1, min(x, W - x, y, H - y))
h = np.random.uniform(1, min(x, W - x, y, H - y))
R[i] = [0, x, y, w, h, angle]
op = core.CreateOperator(
"RoIAlignRotated",
["X", "R"],
["Y"],
pooled_h=pooled_size,
pooled_w=pooled_size,
sampling_ratio=0,
)
def roialign_rot90(m, k=1, axes=(0,1)):
axes = tuple(axes)
if len(axes) != 2:
raise ValueError("len(axes) must be 2.")
m = np.asanyarray(m)
if axes[0] == axes[1] or np.absolute(axes[0] - axes[1]) == m.ndim:
raise ValueError("Axes must be different.")
if (axes[0] >= m.ndim or axes[0] < -m.ndim or
axes[1] >= m.ndim or axes[1] < -m.ndim):
raise ValueError(
"Axes={} out of range for array of ndim={}.".format(axes, m.ndim))
k %= 4
if k == 0:
return m[:]
if k == 2:
return roialign_flip(roialign_flip(m, axes[0]), axes[1])
axes_list = np.arange(0, m.ndim)
(axes_list[axes[0]], axes_list[axes[1]]) = (axes_list[axes[1]],
axes_list[axes[0]])
if k == 1:
return np.transpose(roialign_flip(m,axes[1]), axes_list)
else:
# k == 3
return roialign_flip(np.transpose(m, axes_list), axes[1])
def roialign_flip(m, axis):
if not hasattr(m, 'ndim'):
m = np.asarray(m)
indexer = [slice(None)] * m.ndim
try:
indexer[axis] = slice(None, None, -1)
except IndexError:
raise ValueError("axis=%i is invalid for the %i-dimensional input array"
% (axis, m.ndim))
return m[tuple(indexer)]
def roialign_ref(X, R):
# `angle` denotes counter-clockwise rotation. Rotate the input
# feature map in the opposite (clockwise) direction and perform
# standard RoIAlign. We assume all RoIs have the same angle.
#
# Also note that we need to have our own version of np.rot90,
# since axes isn't an argument until 1.12.0 and doesn't exist
# on all tested platforms.
norm_angle = (angle + 360) % 360
X_ref = roialign_rot90(X, k=-norm_angle / 90, axes=(2, 3))
# Rotate RoIs clockwise wrt the center of the input feature
# map to make them horizontal and convert from
# [center_x center_y w h] to [x1 y1 x2 y2] format.
roi_x, roi_y = R[:, 1], R[:, 2]
if norm_angle == 90:
new_roi_x = H - roi_y - 1
new_roi_y = roi_x
elif norm_angle == 180:
new_roi_x = W - roi_x - 1
new_roi_y = H - roi_y - 1
elif norm_angle == 270:
new_roi_x = roi_y
new_roi_y = W - roi_x - 1
else:
raise NotImplementedError
R_ref = copy.deepcopy(R[:, 0:5])
R_ref[:, 1], R_ref[:, 2] = new_roi_x, new_roi_y
R_ref[:, 1:5] = self.bbox_xywh_to_xyxy(R_ref[:, 1:5])
ref_op = core.CreateOperator(
"RoIAlign",
["X_ref", "R_ref"],
["Y_ref"],
pooled_h=pooled_size,
pooled_w=pooled_size,
sampling_ratio=0,
)
workspace.FeedBlob("X_ref", X_ref)
workspace.FeedBlob("R_ref", R_ref)
workspace.RunOperatorOnce(ref_op)
return [workspace.FetchBlob("Y_ref")]
self.assertReferenceChecks(
device_option=gc, op=op, inputs=[X, R], reference=roialign_ref
)
if core.IsGPUDeviceType(gc.device_type):
self.assertGradientChecks(gc, op, [X, R], 0, [0])
if __name__ == '__main__':
import unittest
unittest.main()
|
