def forward(self, x):
if self.W.data is None:
self.in_channels = x.shape[1]
xp = cuda.get_array_module(x)
self._init_W(xp)
y = F.conv2d_simple(x, self.W, self.b, self.stride, self.pad)
return y
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(np.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(np.array_equal(expected.data, y.data))
def simple_conv(self):
N, C, H, W = 1, 5, 15, 15
OC, (KH, KW) = 8, (3, 3)
x = Variable(np.random.randn(N, C, H, W))
W = np.random.randn(OC, C, KH, KW)
y = F.conv2d_simple(x, W, b=None, stride=1, pad=1)
y.backward()
print(y.shape)
print(x.grad.shape)
if '__file__' in globals():
import os, sys
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
import numpy as np
from dezero import Variable
import dezero.functions as F
# im2col
x1 = np.random.rand(1, 3, 7, 7)
col1 = F.im2col(x1, kernel_size=5, stride=1, pad=0, to_matrix=True)
print(col1.shape) # (9, 75)
x2 = np.random.rand(10, 3, 7, 7) # 10個のデータ
kernel_size = (5, 5)
stride = (1, 1)
pad = (0, 0)
col2 = F.im2col(x2, kernel_size, stride, pad, to_matrix=True)
print(col2.shape) # (90, 75)
# conv2d
N, C, H, W = 1, 5, 15, 15
OC, (KH, KW) = 8, (3, 3)
x = Variable(np.random.randn(N, C, H, W))
W = np.random.randn(OC, C, KH, KW)
y = F.conv2d_simple(x, W, b=None, stride=1, pad=1)
y.backward()
print(y.shape) # (1, 8, 15, 15)
print(x.grad.shape) # (1, 5, 15, 15)