def test_conv(n64_hw32_c32_3x3):
cf = ConvParams(**n64_hw32_c32_3x3)
inputs = ng.placeholder(axes=cf.ax_i)
filters = ng.placeholder(axes=cf.ax_f)
# randomly initialize
input_value = rng.uniform(-0.5, 0.5, cf.ax_i)
filter_value = rng.uniform(-0.5, 0.5, cf.ax_f)
error_value = rng.uniform(-0.5, 0.5, cf.ax_o)
inputs = ng.placeholder(cf.ax_i)
filters = ng.placeholder(cf.ax_f)
errors = ng.placeholder(cf.ax_o)
output = ng.convolution(cf.conv_params, inputs, filters, axes=cf.ax_o)
bprop_out = bprop_conv(errors, inputs, filters, output)
updat_out = update_conv(errors, inputs, filters, output)
with executor([output, bprop_out, updat_out], inputs, filters, errors) as conv_executor:
result_ng, gradI_ng, gradF_ng = conv_executor(input_value, filter_value, error_value)
# Compute reference with NumPy
result_np, gradI_np, gradF_np = reference_conv(cf.dimI, cf.dimF, cf.dimO,
cf.conv_params,
input_value, filter_value, error_value)
# Compare fprop
assert np.allclose(result_ng, result_np, rtol=0, atol=0.5)
# Compare bprop
assert np.allclose(gradI_ng, gradI_np, rtol=0, atol=0.5)
# Compare update
assert np.allclose(gradF_ng, gradF_np, rtol=0, atol=2)
def execute_convolution(image_height,
image_width,
filter_height,
filter_width,
channel=16,
batch_size=32,
filter_count=8,
image_3rd_dim=1,
filter_3rd_dim=1,
padding=(0, 0, 0),
stride=(1, 1, 1),
dilation=1,
np_comparison=False):
pad_h, pad_w, pad_d = padding
str_h, str_w, str_d = stride
cf = ConvParams(C=channel,
N=batch_size,
K=filter_count,
D=image_3rd_dim,
H=image_height,
W=image_width,
T=filter_3rd_dim,
R=filter_height,
S=filter_width,
pad_d=pad_d,
pad_h=pad_h,
pad_w=pad_w,
str_d=str_d,
str_h=str_h,
str_w=str_w,
dil_d=dilation,
dil_h=dilation,
dil_w=dilation)
inputs = ng.placeholder(cf.ax_i)
filters = ng.placeholder(cf.ax_f)
rng = RandomTensorGenerator(0, np.float32)
input_value = rng.uniform(-4, 4, cf.ax_i, dtype=int)
filter_value = rng.uniform(-4, 4, cf.ax_f, dtype=int)
error_value = rng.uniform(-0.5, 0.5, cf.ax_o)
with executor(
ng.convolution(cf.conv_params, inputs, filters, axes=cf.ax_o),
inputs, filters) as const_executor:
out = const_executor(input_value, filter_value)
if np_comparison:
np_out, gradInp, gradF_np = \
reference_conv(cf.dimI, cf.dimF, cf.dimO, cf.conv_params, input_value, filter_value,
error_value)
return out, np_out
return out