def test_conv1d_api(self, batch_size, in_channels_per_group, length,
out_channels_per_group, groups, kernel, stride, pad,
dilation, X_scale, X_zero_point, W_scale, W_zero_point,
Y_scale, Y_zero_point, use_bias, use_fused,
use_channelwise):
# Tests the correctness of the conv2d module.
in_channels = in_channels_per_group * groups
out_channels = out_channels_per_group * groups
input_feature_map_size = (length, )
kernel_size = (kernel, )
stride = (stride, )
pad = (pad, )
dilation = (dilation, )
if torch.backends.quantized.engine == 'qnnpack':
use_channelwise = False
if use_fused:
module_name = "QuantizedConvReLU1d"
qconv_module = nnq_fused.ConvReLU1d(in_channels,
out_channels,
kernel,
stride,
pad,
dilation,
groups,
use_bias,
padding_mode="zeros")
else:
module_name = "QuantizedConv1d"
qconv_module = nnq.Conv1d(in_channels,
out_channels,
kernel,
stride,
pad,
dilation,
groups,
use_bias,
padding_mode="zeros")
conv_module = nn.Conv1d(in_channels,
out_channels,
kernel,
stride,
pad,
dilation,
groups,
use_bias,
padding_mode="zeros")
if use_fused:
relu_module = nn.ReLU()
conv_module = nni.ConvReLU1d(conv_module, relu_module)
conv_module = conv_module.float()
self._test_conv_api_impl(module_name, qconv_module, conv_module,
batch_size, in_channels_per_group,
input_feature_map_size,
out_channels_per_group, groups, kernel_size,
stride, pad, dilation, X_scale, X_zero_point,
W_scale, W_zero_point, Y_scale, Y_zero_point,
use_bias, use_fused, use_channelwise)
def test_conv1d_api(self):
options = itertools.product(
["zeros", "reflect"], # pad_mode
[True, False], # use_bias
[True, False], # use_fused
[True, False], # use_channelwise
)
for pad_mode, use_bias, use_fused, use_channelwise in options:
if torch.backends.quantized.engine == "qnnpack":
use_channelwise = False
batch_size = 2
in_channels_per_group = 2
length = 8
out_channels_per_group = 2
groups = 3
kernel = 3
stride = 2
pad = 1
dilation = 1
# Tests the correctness of the conv2d module.
in_channels = in_channels_per_group * groups
out_channels = out_channels_per_group * groups
input_feature_map_size = (length,)
kernel_size = (kernel, )
stride = (stride, )
pad = (pad, )
dilation = (dilation, )
X_scale = 1.3
X_zero_point = 2
W_scale = [0.5]
W_zero_point = [3]
Y_scale = 5.0
Y_zero_point = 4
if torch.backends.quantized.engine == 'qnnpack':
use_channelwise = False
# use_fused -> quantized class
class_map = {
True: (nniq.ConvReLU1d, "QuantizedConvReLU1d"),
False: (nnq.Conv1d, "QuantizedConv1d")
}
qconv_cls, module_name = class_map[use_fused]
qconv_module = qconv_cls(
in_channels, out_channels, kernel, stride, pad,
dilation, groups, use_bias, padding_mode=pad_mode
)
conv_module = nn.Conv1d(
in_channels, out_channels, kernel, stride, pad,
dilation, groups, use_bias, padding_mode=pad_mode)
if use_fused:
relu_module = nn.ReLU()
conv_module = nni.ConvReLU1d(conv_module, relu_module)
conv_module = conv_module.float()
self._test_conv_api_impl(
module_name, qconv_module, conv_module, batch_size,
in_channels_per_group, input_feature_map_size,
out_channels_per_group, groups, kernel_size, stride, pad, pad_mode,
dilation, X_scale, X_zero_point, W_scale, W_zero_point, Y_scale,
Y_zero_point, use_bias, use_fused, use_channelwise)