def test_performance():
batch_size = 64
params = HyperParameters_4_2(0.1,
1,
batch_size,
net_type=NetType.MultipleClassifier,
init_method=InitialMethod.Xavier)
stride = 1
padding = 1
fh = 3
fw = 3
input_channel = 3
output_channel = 4
iw = 28
ih = 28
# 64 个 3 x 28 x 28 的图像输入（模拟 mnist）
x = np.random.randn(batch_size, input_channel, iw, ih)
c1 = ConvLayer((input_channel, iw, ih), (output_channel, fh, fw),
(stride, padding), params)
c1.initialize("test", "test", False)
# dry run
for i in range(5):
f1 = c1.forward_numba(x)
delta_in = np.ones((f1.shape))
b1, dw1, db1 = c1.backward_numba(delta_in, 1)
# run
s1 = time.time()
for i in range(100):
f1 = c1.forward_numba(x)
b1, dw1, db1 = c1.backward_numba(delta_in, 1)
e1 = time.time()
print("method numba:", e1 - s1)
# dry run
for i in range(5):
f2 = c1.forward_img2col(x)
b2, dw2, db2 = c1.backward_col2img(delta_in, 1)
# run
s2 = time.time()
for i in range(100):
f2 = c1.forward_img2col(x)
b2, dw2, db2 = c1.backward_col2img(delta_in, 1)
e2 = time.time()
print("method img2col:", e2 - s2)
print("compare correctness of method 1 and method 2:")
print("forward:", np.allclose(f1, f2, atol=1e-7))
print("backward:", np.allclose(b1, b2, atol=1e-7))
print("dW:", np.allclose(dw1, dw2, atol=1e-7))
print("dB:", np.allclose(db1, db2, atol=1e-7))
def test_4d_im2col():
batch_size = 2
stride = 1
padding = 0
fh = 2
fw = 2
input_channel = 3
output_channel = 2
iw = 3
ih = 3
x = np.random.randn(batch_size, input_channel, iw, ih)
params = HyperParameters_4_2(
0.1, 1, batch_size,
net_type=NetType.MultipleClassifier,
init_method=InitialMethod.Xavier)
c1 = ConvLayer((input_channel,iw,ih), (output_channel,fh,fw), (stride, padding), params)
c1.initialize("test", "test", False)
f1 = c1.forward_numba(x)
f2 = c1.forward_img2col(x)
print("correctness:", np.allclose(f1, f2, atol=1e-7))