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from caffe2.python import core
from hypothesis import given, settings
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
class RMACRegionsOpTest(hu.HypothesisTestCase):
@given(
n=st.integers(500, 500),
h=st.integers(1, 10),
w=st.integers(1, 10),
scales=st.integers(1, 3),
**hu.gcs
)
@settings(deadline=10000)
def test(self, n, h, w, scales, gc, dc):
X = np.random.rand(n, 64, h, w).astype(np.float32)
overlap = 0.4
def ref_op(X):
N, H, W = X.shape[0], X.shape[2], X.shape[3]
# Possible regions for the long dimension
steps = np.array((2, 3, 4, 5, 6, 7), dtype=np.float32)
minW = np.minimum(H, W)
# steps(idx) regions for long dimension
b = (np.maximum(H, W) - minW) / (steps - 1)
idx = np.argmin(
np.abs(((minW**2 - minW * b) / minW**2) - overlap)) + 1
# Region overplus per dimension
Wd = 0
Hd = 0
if H < W:
Wd = idx
elif H > W:
Hd = idx
regions_xywh = []
for l in range(1, scales + 1):
wl = np.floor(2 * minW / (l + 1))
# Center coordinates
if l + Wd - 1 > 0:
b = (W - wl) / (l + Wd - 1)
else:
b = 0
cenW = np.floor(b * np.arange(l - 1 + Wd + 1))
# Center coordinates
if l + Hd - 1 > 0:
b = (H - wl) / (l + Hd - 1)
else:
b = 0
cenH = np.floor(b * np.arange(l - 1 + Hd + 1))
for i_ in cenW:
for j_ in cenH:
regions_xywh.append([i_, j_, wl, wl])
# Round the regions. Careful with the borders!
for i in range(len(regions_xywh)):
for j in range(4):
regions_xywh[i][j] = int(round(regions_xywh[i][j]))
if regions_xywh[i][0] + regions_xywh[i][2] > W:
regions_xywh[i][0] -= (
(regions_xywh[i][0] + regions_xywh[i][2]) - W
)
if regions_xywh[i][1] + regions_xywh[i][3] > H:
regions_xywh[i][1] -= (
(regions_xywh[i][1] + regions_xywh[i][3]) - H
)
# Filter out 0-sized regions
regions_xywh = [r for r in regions_xywh if r[2] * r[3] > 0]
# Convert to ROIPoolOp format: (batch_index x1 y1 x2 y2)
regions = [
[i, x, y, x + w - 1, y + h - 1]
for i in np.arange(N) for x, y, w, h in regions_xywh
]
return (np.array(regions).astype(np.float32), )
op = core.CreateOperator(
'RMACRegions',
['X'],
['RMAC_REGIONS'],
scales=scales,
overlap=overlap,
)
# Check against numpy reference
self.assertReferenceChecks(gc, op, [X], ref_op)
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