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test_conv2d.py
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test_conv2d.py
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import unittest
import numpy as np
import dezero.layers as L
import dezero.functions as F
from dezero.utils import gradient_check, array_equal
import chainer.functions as CF
class TestConv2d_simple(unittest.TestCase):
def test_forward1(self):
n, c, h, w = 1, 5, 15, 15
o, k, s, p = 8, (3, 3), (1, 1), (1, 1)
x = np.random.randn(n, c, h, w).astype('f')
W = np.random.randn(o, c, k[0], k[1]).astype('f')
b = None
y = F.conv2d_simple(x, W, b, s, p)
expected = CF.convolution_2d(x, W, b, s, p)
self.assertTrue(array_equal(expected.data, y.data))
def test_forward2(self):
n, c, h, w = 1, 5, 15, 15
o, k, s, p = 8, (3, 3), (3, 1), (2, 1)
x = np.random.randn(n, c, h, w).astype('f')
W = np.random.randn(o, c, k[0], k[1]).astype('f')
b = None
y = F.conv2d_simple(x, W, b, s, p)
expected = CF.convolution_2d(x, W, b, s, p)
self.assertTrue(array_equal(expected.data, y.data))
def test_forward3(self):
n, c, h, w = 1, 5, 20, 15
o, k, s, p = 3, (5, 3), 1, 3
x = np.random.randn(n, c, h, w).astype('f')
W = np.random.randn(o, c, k[0], k[1]).astype('f')
b = None
y = F.conv2d_simple(x, W, b, s, p)
expected = CF.convolution_2d(x, W, b, s, p)
self.assertTrue(array_equal(expected.data, y.data))
def test_forward4(self):
n, c, h, w = 1, 5, 20, 15
o, k, s, p = 3, (5, 3), 1, 3
x = np.random.randn(n, c, h, w).astype('f')
W = np.random.randn(o, c, k[0], k[1]).astype('f')
b = np.random.randn(o).astype('f')
y = F.conv2d_simple(x, W, b, s, p)
expected = CF.convolution_2d(x, W, b, s, p)
self.assertTrue(array_equal(expected.data, y.data))
def test_backward1(self):
n, c, h, w = 1, 5, 20, 15
o, k, s, p = 3, (5, 3), 1, 3
x = np.random.randn(n, c, h, w)
W = np.random.randn(o, c, k[0], k[1])
b = np.random.randn(o)
f = lambda x: F.conv2d_simple(x, W, b, s, p)
self.assertTrue(gradient_check(f, x))
def test_backward2(self):
n, c, h, w = 1, 5, 20, 15
o, k, s, p = 3, (5, 3), 1, 3
x = np.random.randn(n, c, h, w)
W = np.random.randn(o, c, k[0], k[1])
b = np.random.randn(o)
f = lambda b: F.conv2d_simple(x, W, b, s, p)
self.assertTrue(gradient_check(f, b))
def test_backward3(self):
n, c, h, w = 1, 5, 20, 15
o, k, s, p = 3, (5, 3), 1, 3
x = np.random.randn(n, c, h, w)
W = np.random.randn(o, c, k[0], k[1])
b = np.random.randn(o)
f = lambda W: F.conv2d_simple(x, W, b, s, p)
self.assertTrue(gradient_check(f, W))
class TestConv2d(unittest.TestCase):
def test_forward1(self):
n, c, h, w = 1, 5, 15, 15
o, k, s, p = 8, (3, 3), (1, 1), (1, 1)
x = np.random.randn(n, c, h, w).astype('f')
W = np.random.randn(o, c, k[0], k[1]).astype('f')
b = None
y = F.conv2d(x, W, b, s, p)
expected = CF.convolution_2d(x, W, b, s, p)
self.assertTrue(array_equal(expected.data, y.data))
def test_forward2(self):
n, c, h, w = 1, 5, 15, 15
o, k, s, p = 8, (3, 3), (3, 1), (2, 1)
x = np.random.randn(n, c, h, w).astype('f')
W = np.random.randn(o, c, k[0], k[1]).astype('f')
b = None
y = F.conv2d(x, W, b, s, p)
expected = CF.convolution_2d(x, W, b, s, p)
self.assertTrue(array_equal(expected.data, y.data))
def test_forward3(self):
n, c, h, w = 1, 5, 20, 15
o, k, s, p = 3, (5, 3), 1, 3
x = np.random.randn(n, c, h, w).astype('f')
W = np.random.randn(o, c, k[0], k[1]).astype('f')
b = None
y = F.conv2d(x, W, b, s, p)
expected = CF.convolution_2d(x, W, b, s, p)
self.assertTrue(array_equal(expected.data, y.data))
def test_forward4(self):
n, c, h, w = 1, 5, 20, 15
o, k, s, p = 3, (5, 3), 1, 3
x = np.random.randn(n, c, h, w).astype('f')
W = np.random.randn(o, c, k[0], k[1]).astype('f')
b = np.random.randn(o).astype('f')
y = F.conv2d(x, W, b, s, p)
expected = CF.convolution_2d(x, W, b, s, p)
self.assertTrue(array_equal(expected.data, y.data))
def test_backward1(self):
n, c, h, w = 1, 5, 20, 15
o, k, s, p = 3, (5, 3), 1, 3
x = np.random.randn(n, c, h, w)
W = np.random.randn(o, c, k[0], k[1])
b = np.random.randn(o)
f = lambda x: F.conv2d(x, W, b, s, p)
self.assertTrue(gradient_check(f, x))
def test_backward2(self):
n, c, h, w = 1, 5, 20, 15
o, k, s, p = 3, (5, 3), 1, 3
x = np.random.randn(n, c, h, w)
W = np.random.randn(o, c, k[0], k[1])
b = np.random.randn(o)
f = lambda b: F.conv2d(x, W, b, s, p)
self.assertTrue(gradient_check(f, b))
def test_backward3(self):
n, c, h, w = 1, 5, 20, 15
o, k, s, p = 3, (5, 3), 1, 3
x = np.random.randn(n, c, h, w)
W = np.random.randn(o, c, k[0], k[1])
b = np.random.randn(o)
f = lambda W: F.conv2d(x, W, b, s, p)
self.assertTrue(gradient_check(f, W))