def test_2d_conv():
batch_size = 1
stride = 1
padding = 0
fh = 2
fw = 2
input_channel = 1
output_channel = 1
iw = 3
ih = 3
(output_height, output_width) = calculate_output_size(ih, iw, fh, fw, padding, stride)
wb = ConvWeightsBias(output_channel, input_channel, fh, fw, InitialMethod.MSRA, OptimizerName.SGD, 0.1)
wb.Initialize("test", "test", True)
wb.W = np.array([3,2,1,0]).reshape(1,1,2,2)
wb.B = np.array([0])
x = np.array(range(9)).reshape(1,1,3,3)
output1 = jit_conv_4d(x, wb.W, wb.B, output_height, output_width, stride)
print("input=\n", x)
print("weights=\n", wb.W)
print("output=\n", output1)
col = img2col(x, 2, 2, 1, 0)
w = wb.W.reshape(4, 1)
output2 = np.dot(col, w)
print("input=\n", col)
print("weights=\n", w)
print("output2=\n", output2)
def understand_4d_im2col():
batch_size = 2
stride = 1
padding = 0
fh = 2
fw = 2
input_channel = 3
output_channel = 2
iw = 3
ih = 3
(output_height, output_width) = calculate_output_size(ih, iw, fh, fw, padding, stride)
wb = ConvWeightsBias(output_channel, input_channel, fh, fw, InitialMethod.MSRA, OptimizerName.SGD, 0.1)
wb.Initialize("test", "test", True)
wb.W = np.array(range(output_channel * input_channel * fh * fw)).reshape(output_channel, input_channel, fh, fw)
wb.B = np.array([0])
x = np.array(range(input_channel * iw * ih * batch_size)).reshape(batch_size, input_channel, ih, iw)
col = img2col(x, 2, 2, 1, 0)
w = wb.W.reshape(output_channel, -1).T
output = np.dot(col, w)
print("x=\n", x)
print("col_x=\n", col)
print("weights=\n", wb.W)
print("col_w=\n", w)
print("output=\n", output)
out2 = output.reshape(batch_size, output_height, output_width, -1)
print("out2=\n", out2)
out3 = np.transpose(out2, axes=(0, 3, 1, 2))
print("conv result=\n", out3)
def understand_4d_col2img_complex():
batch_size = 2
stride = 1
padding = 0
fh = 2
fw = 2
input_channel = 3
output_channel = 2
iw = 3
ih = 3
(output_height,
output_width) = calculate_output_size(ih, iw, fh, fw, padding, stride)
wb = ConvWeightsBias(output_channel, input_channel, fh, fw,
InitialMethod.MSRA, OptimizerName.SGD, 0.1)
wb.Initialize("test", "test", True)
wb.W = np.array(range(output_channel * input_channel * fh * fw)).reshape(
output_channel, input_channel, fh, fw)
wb.B = np.array([0])
x = np.array(range(input_channel * iw * ih * batch_size)).reshape(
batch_size, input_channel, ih, iw)
print("x=\n", x)
col_x = img2col(x, fh, fw, stride, padding)
print("col_x=\n", col_x)
print("w=\n", wb.W)
col_w = wb.W.reshape(output_channel, -1).T
print("col_w=\n", col_w)
# backward
delta_in = np.array(
range(batch_size * output_channel * output_height *
output_width)).reshape(batch_size, output_channel, output_height,
output_width)
print("delta_in=\n", delta_in)
delta_in_2d = np.transpose(delta_in,
axes=(0, 2, 3, 1)).reshape(-1, output_channel)
print("delta_in_2d=\n", delta_in_2d)
dB = np.sum(delta_in_2d, axis=0, keepdims=True).T / batch_size
print("dB=\n", dB)
dW = np.dot(col_x.T, delta_in_2d) / batch_size
print("dW=\n", dW)
dW = np.transpose(dW, axes=(1, 0)).reshape(output_channel, input_channel,
fh, fw)
print("dW=\n", dW)
dcol = np.dot(delta_in_2d, col_w.T)
print("dcol=\n", dcol)
delta_out = col2img(dcol, x.shape, fh, fw, stride, padding, output_height,
output_width)
print("delta_out=\n", delta_out)
