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_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 H = 8 W = 8 out_channels_per_group = 2 groups = 3 kernel_h = 3 kernel_w = 3 stride_h = 2 stride_w = 2 pad_h = 1 pad_w = 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 = (H, W) kernel_size = (kernel_h, kernel_w) stride = (stride_h, stride_w) padding = (pad_h, pad_w) dilation = (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 # use_fused -> quantized class class_map = { True: (nniq.ConvReLU2d, "QuantizedConvReLU2d"), False: (nnq.Conv2d, "QuantizedConv2d") } qconv_cls, module_name = class_map[use_fused] qconv_module = qconv_cls( in_channels, out_channels, kernel_size, stride, padding, dilation, groups, use_bias, padding_mode=pad_mode ) conv_module = nn.Conv2d( in_channels, out_channels, kernel_size, stride, padding, dilation, groups, use_bias, padding_mode=pad_mode) 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, pad_mode, dilation, X_scale, X_zero_point, W_scale, W_zero_point, Y_scale, Y_zero_point, use_bias, use_fused, use_channelwise)