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Use np.ravel instead of np.copy

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This should decrease memory usage and
increase speed without affecting anything else
This commit is contained in:
Ömer Özak
2015-05-25 18:21:53 -04:00
parent a65b8af4bf
commit b455d851b1
2 changed files with 23 additions and 9 deletions
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skimage/graph/_mcp.pyx
+6 -1
View File
@@ -36,6 +36,7 @@ THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import cython
import numpy as np
import heap
import warnings
cimport numpy as cnp
cimport heap
@@ -299,7 +300,11 @@ cdef class MCP:
# but this is my code and I like fortran-style! Also, it's
# faster when working with image arrays, which are often
# already fortran-strided.)
self.flat_costs = costs.astype(FLOAT_D).flatten('F')
try:
self.flat_costs = costs.astype(FLOAT_D, copy=False).ravel('F')
except TypeError:
self.flat_costs = costs.astype(FLOAT_D).flatten('F')
warnings.warn('Upgrading NumPy should decrease memory usage and increase speed.', Warning)
size = self.flat_costs.shape[0]
self.flat_cumulative_costs = np.empty(size, dtype=FLOAT_D)
self.dim = len(costs.shape)
skimage/graph/tests/test_mcp.py
+17 -8
View File
@@ -4,15 +4,18 @@ from numpy.testing import (assert_array_equal,
)
import skimage.graph.mcp as mcp
from skimage._shared._warnings import expected_warnings
np.random.seed(0)
a = np.ones((8, 8), dtype=np.float32)
a[1:-1, 1] = 0
a[1, 1:-1] = 0
warning_optional = r'|\A\Z'
def test_basic():
m = mcp.MCP(a, fully_connected=True)
with expected_warnings(['Upgrading NumPy' + warning_optional]):
m = mcp.MCP(a, fully_connected=True)
costs, traceback = m.find_costs([(1, 6)])
return_path = m.traceback((7, 2))
assert_array_equal(costs,
@@ -53,12 +56,14 @@ def test_neg_inf():
(6, 1)]
test_neg = np.where(a == 1, -1, 0)
test_inf = np.where(a == 1, np.inf, 0)
m = mcp.MCP(test_neg, fully_connected=True)
with expected_warnings(['Upgrading NumPy' + warning_optional]):
m = mcp.MCP(test_neg, fully_connected=True)
costs, traceback = m.find_costs([(1, 6)])
return_path = m.traceback((6, 1))
assert_array_equal(costs, expected_costs)
assert_array_equal(return_path, expected_path)
m = mcp.MCP(test_inf, fully_connected=True)
with expected_warnings(['Upgrading NumPy' + warning_optional]):
m = mcp.MCP(test_inf, fully_connected=True)
costs, traceback = m.find_costs([(1, 6)])
return_path = m.traceback((6, 1))
assert_array_equal(costs, expected_costs)
@@ -66,8 +71,9 @@ def test_neg_inf():
def test_route():
return_path, cost = mcp.route_through_array(a, (1, 6), (7, 2),
geometric=True)
with expected_warnings(['Upgrading NumPy' + warning_optional]):
return_path, cost = mcp.route_through_array(a, (1, 6), (7, 2),
geometric=True)
assert_almost_equal(cost, np.sqrt(2) / 2)
assert_array_equal(return_path,
[(1, 6),
@@ -84,7 +90,8 @@ def test_route():
def test_no_diagonal():
m = mcp.MCP(a, fully_connected=False)
with expected_warnings(['Upgrading NumPy' + warning_optional]):
m = mcp.MCP(a, fully_connected=False)
costs, traceback = m.find_costs([(1, 6)])
return_path = m.traceback((7, 2))
assert_array_equal(costs,
@@ -114,7 +121,8 @@ def test_no_diagonal():
def test_offsets():
offsets = [(1, i) for i in range(10)] + [(1, -i) for i in range(1, 10)]
m = mcp.MCP(a, offsets=offsets)
with expected_warnings(['Upgrading NumPy' + warning_optional]):
m = mcp.MCP(a, offsets=offsets)
costs, traceback = m.find_costs([(1, 6)])
assert_array_equal(traceback,
[[-2, -2, -2, -2, -2, -2, -2, -2],
@@ -139,7 +147,8 @@ def _test_random(shape):
(np.random.rand(len(shape)) * shape).astype(int)]
ends = [(np.random.rand(len(shape)) * shape).astype(int)
for i in range(4)]
m = mcp.MCP(a, fully_connected=True)
with expected_warnings(['Upgrading NumPy' + warning_optional]):
m = mcp.MCP(a, fully_connected=True)
costs, offsets = m.find_costs(starts)
for point in [(np.random.rand(len(shape)) * shape).astype(int)
for i in range(4)]: