def init(self, N, IC, OC, L, G, kernel, stride, pad):
super(QConv1dBenchmark, self).init(N, IC, OC, (L, ), G, (kernel, ), stride, pad)
self.qconv1d = nnq.Conv1d(IC, OC, kernel, stride=stride, padding=pad, groups=G)
self.qconv1d.set_weight_bias(self.qW, None)
self.qconv1d.scale = torch.tensor([self.scale], dtype=torch.double)
self.qconv1d.zero_point = torch.tensor([self.zero_point], dtype=torch.int)
self.set_module_name("QConv1d")
def init(self, IC, OC, kernel, stride, N, L, device):
G = 1
pad = 0
self.scale = 1.0 / 255
self.zero_point = 0
X = torch.randn(N, IC, L, dtype=torch.float32)
qX = torch.quantize_per_tensor(X,
scale=self.scale,
zero_point=self.zero_point,
dtype=torch.quint8)
# Convert the tensor to NHWC format
W = torch.randn(OC, IC // G, kernel, dtype=torch.float32)
self.qW = torch.quantize_per_tensor(W,
scale=self.scale,
zero_point=0,
dtype=torch.qint8)
self.input = qX
self.qconv1d = nnq.Conv1d(IC,
OC,
kernel,
stride=stride,
padding=pad,
groups=G)
self.qconv1d.set_weight_bias(self.qW, None)
self.qconv1d.scale = torch.tensor([self.scale], dtype=torch.double)
self.qconv1d.zero_point = torch.tensor([self.zero_point],
dtype=torch.int)
self.set_module_name("QConv1d")
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)
