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from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
from hypothesis import given, settings
import hypothesis.strategies as st
import numpy as np
import unittest
class TestPiecewiseLinearTransform(serial.SerializedTestCase):
def constrain(self, v, min_val, max_val):
def constrain_internal(x):
return min(max(x, min_val), max_val)
return np.array([constrain_internal(x) for x in v])
def transform(self, x, bounds, slopes, intercepts):
n = len(slopes)
x_ = self.constrain(x, bounds[0], bounds[-1])
index = np.minimum(
np.maximum(
np.searchsorted(bounds, x_) - 1,
0
),
n - 1
)
y = slopes[index] * x_ + intercepts[index]
return y
@given(n=st.integers(1, 100), **hu.gcs_cpu_only)
@settings(deadline=10000)
def test_multi_predictions_params_from_arg(self, n, gc, dc):
slopes = np.random.uniform(-1, 1, (2, n)).astype(np.float32)
intercepts = np.random.uniform(-1, 1, (2, n)).astype(np.float32)
bounds = np.random.uniform(0.1, 0.9,
(2, n + 1)).astype(np.float32)
bounds.sort()
X = np.random.uniform(0, 1, (n, 2)).astype(np.float32)
op = core.CreateOperator(
"PiecewiseLinearTransform", ["X"], ["Y"],
bounds=bounds.flatten().tolist(),
slopes=slopes.flatten().tolist(),
intercepts=intercepts.flatten().tolist(),
)
def piecewise(x, *args, **kw):
x_0 = self.transform(
x[:, 0], bounds[0, :], slopes[0, :], intercepts[0, :])
x_1 = self.transform(
x[:, 1], bounds[1, :], slopes[1, :], intercepts[1, :])
return [np.vstack((x_0, x_1)).transpose()]
self.assertReferenceChecks(gc, op, [X], piecewise)
self.assertDeviceChecks(dc, op, [X], [0])
@given(n=st.integers(1, 100), **hu.gcs_cpu_only)
@settings(deadline=10000)
def test_binary_predictions_params_from_arg(self, n, gc, dc):
slopes = np.random.uniform(-1, 1, size=n).astype(np.float32)
intercepts = np.random.uniform(-1, 1, size=n).astype(np.float32)
bounds = np.random.uniform(0.1, 0.9, n + 1).astype(np.float32)
bounds.sort()
X = np.random.uniform(0, 1, (n, 2)).astype(np.float32)
X[:, 0] = 1 - X[:, 1]
op = core.CreateOperator(
"PiecewiseLinearTransform", ["X"], ["Y"],
bounds=bounds.flatten().tolist(),
slopes=slopes.flatten().tolist(),
intercepts=intercepts.flatten().tolist(),
pieces=n,
binary=True,
)
def piecewise(x):
x_ = self.transform(x[:, 1], bounds, slopes, intercepts)
return [np.vstack((1 - x_, x_)).transpose()]
self.assertReferenceChecks(gc, op, [X], piecewise)
self.assertDeviceChecks(dc, op, [X], [0])
@given(n=st.integers(1, 100), **hu.gcs_cpu_only)
@settings(deadline=10000)
def test_multi_predictions_params_from_input(self, n, gc, dc):
slopes = np.random.uniform(-1, 1, (2, n)).astype(np.float32)
intercepts = np.random.uniform(-1, 1, (2, n)).astype(np.float32)
bounds = np.random.uniform(0.1, 0.9,
(2, n + 1)).astype(np.float32)
bounds.sort()
X = np.random.uniform(0, 1, (n, 2)).astype(np.float32)
op = core.CreateOperator(
"PiecewiseLinearTransform",
["X", "bounds", "slopes", "intercepts"],
["Y"],
)
def piecewise(x, bounds, slopes, intercepts):
x_0 = self.transform(
x[:, 0], bounds[0, :], slopes[0, :], intercepts[0, :])
x_1 = self.transform(
x[:, 1], bounds[1, :], slopes[1, :], intercepts[1, :])
return [np.vstack((x_0, x_1)).transpose()]
self.assertReferenceChecks(
gc, op, [X, bounds, slopes, intercepts], piecewise)
self.assertDeviceChecks(dc, op, [X, bounds, slopes, intercepts], [0])
@given(n=st.integers(1, 100), **hu.gcs_cpu_only)
@settings(deadline=10000)
def test_binary_predictions_params_from_input(self, n, gc, dc):
slopes = np.random.uniform(-1, 1, size=n).astype(np.float32)
intercepts = np.random.uniform(-1, 1, size=n).astype(np.float32)
bounds = np.random.uniform(0.1, 0.9, n + 1).astype(np.float32)
bounds.sort()
X = np.random.uniform(0, 1, (n, 2)).astype(np.float32)
X[:, 0] = 1 - X[:, 1]
op = core.CreateOperator(
"PiecewiseLinearTransform",
["X", "bounds", "slopes", "intercepts"],
["Y"],
binary=True,
)
def piecewise(x, bounds, slopes, intercepts):
x_ = self.transform(x[:, 1], bounds, slopes, intercepts)
return [np.vstack((1 - x_, x_)).transpose()]
self.assertReferenceChecks(
gc, op, [X, bounds, slopes, intercepts], piecewise)
self.assertDeviceChecks(dc, op, [X, bounds, slopes, intercepts], [0])
@given(n=st.integers(1, 100), **hu.gcs_cpu_only)
@settings(deadline=10000)
def test_1D_predictions_params_from_input(self, n, gc, dc):
slopes = np.random.uniform(-1, 1, size=n).astype(np.float32)
intercepts = np.random.uniform(-1, 1, size=n).astype(np.float32)
bounds = np.random.uniform(0.1, 0.9, n + 1).astype(np.float32)
bounds.sort()
X = np.random.uniform(0, 1, size=n).astype(np.float32)
op = core.CreateOperator(
"PiecewiseLinearTransform",
["X", "bounds", "slopes", "intercepts"],
["Y"],
binary=True,
)
def piecewise(x, bounds, slopes, intercepts):
x_ = self.transform(x, bounds, slopes, intercepts)
return [x_]
self.assertReferenceChecks(
gc, op, [X, bounds, slopes, intercepts], piecewise)
self.assertDeviceChecks(dc, op, [X, bounds, slopes, intercepts], [0])
if __name__ == "__main__":
unittest.main()
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