def test_conv2d_api(self, batch_size, in_channels_per_group, H, W,
out_channels_per_group, groups, kernel_h, kernel_w,
stride_h, stride_w, pad_h, pad_w, 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 = (H, W)
kernel_size = (kernel_h, kernel_w)
stride = (stride_h, stride_w)
padding = (pad_h, pad_w)
dilation = (dilation, dilation)
if torch.backends.quantized.engine == 'qnnpack':
use_channelwise = False
if use_fused:
module_name = "QuantizedConvReLU2d"
qconv_module = nnq_fused.ConvReLU2d(in_channels,
out_channels,
kernel_size,
stride,
padding,
dilation,
groups,
use_bias,
padding_mode="zeros")
else:
module_name = "QuantizedConv2d"
qconv_module = nnq.Conv2d(in_channels,
out_channels,
kernel_size,
stride,
padding,
dilation,
groups,
use_bias,
padding_mode="zeros")
conv_module = nn.Conv2d(in_channels,
out_channels,
kernel_size,
stride,
padding,
dilation,
groups,
use_bias,
padding_mode="zeros")
if use_fused:
relu_module = nn.ReLU()
conv_module = nni.ConvReLU2d(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, padding,
dilation, X_scale, X_zero_point, W_scale, W_zero_point, Y_scale,
Y_zero_point, use_bias, use_fused, use_channelwise)
def test_conv2d_relu(self):
module = nniq.ConvReLU2d(3,
3,
kernel_size=3,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
padding_mode="zeros")
self._test_op(module, input_size=[1, 3, 6, 6], generate=False)
def test_conv2d_relu(self):
for i, qengine in enumerate(supported_qengines):
with override_quantized_engine(qengine):
module = nniq.ConvReLU2d(3, 3, kernel_size=3, stride=1, padding=0, dilation=1,
groups=1, bias=True, padding_mode="zeros")
self._test_op(module, input_size=[1, 3, 6, 6], generate=False, iter=i)
