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Source code for torch.nn.intrinsic.quantized.modules.bn_relu


import torch
import torch.ao.nn.intrinsic
import torch.nn.intrinsic.qat
import torch.ao.nn.quantized as nnq


[docs]class BNReLU2d(nnq.BatchNorm2d): r""" A BNReLU2d module is a fused module of BatchNorm2d and ReLU We adopt the same interface as :class:`torch.ao.nn.quantized.BatchNorm2d`. Attributes: Same as torch.ao.nn.quantized.BatchNorm2d """ _FLOAT_MODULE = torch.nn.intrinsic.BNReLU2d def __init__(self, num_features, eps=1e-5, momentum=0.1, device=None, dtype=None): super(BNReLU2d, self).__init__(num_features, eps=eps, momentum=momentum, device=device, dtype=dtype) def forward(self, input): # Temporarily using len(shape) instead of ndim due to JIT issue # https://github.com/pytorch/pytorch/issues/23890 if len(input.shape) != 4: raise ValueError("Input shape must be `(N, C, H, W)`!") return torch.ops.quantized.batch_norm2d_relu( input, self.weight, self.bias, self.running_mean, self.running_var, self.eps, self.scale, self.zero_point) def _get_name(self): return 'QuantizedBNReLU2d' @classmethod def from_float(cls, mod): # TODO: Add qat support for BNReLU2d return super(BNReLU2d, cls).from_float(mod) @classmethod def from_reference(cls, bn_relu, output_scale, output_zero_point): return super().from_reference(bn_relu[0], output_scale, output_zero_point)
[docs]class BNReLU3d(nnq.BatchNorm3d): r""" A BNReLU3d module is a fused module of BatchNorm3d and ReLU We adopt the same interface as :class:`torch.ao.nn.quantized.BatchNorm3d`. Attributes: Same as torch.ao.nn.quantized.BatchNorm3d """ _FLOAT_MODULE = torch.nn.intrinsic.BNReLU3d def __init__(self, num_features, eps=1e-5, momentum=0.1, device=None, dtype=None): super(BNReLU3d, self).__init__(num_features, eps=eps, momentum=momentum, device=device, dtype=dtype) def forward(self, input): # Temporarily using len(shape) instead of ndim due to JIT issue # https://github.com/pytorch/pytorch/issues/23890 if len(input.shape) != 5: raise ValueError("Input shape must be `(N, C, D, H, W)`!") return torch.ops.quantized.batch_norm3d_relu( input, self.weight, self.bias, self.running_mean, self.running_var, self.eps, self.scale, self.zero_point) def _get_name(self): return 'QuantizedBNReLU3d' @classmethod def from_float(cls, mod): # TODO: Add qat support for BNReLU3d return super(BNReLU3d, cls).from_float(mod) @classmethod def from_reference(cls, bn_relu, output_scale, output_zero_point): return super().from_reference(bn_relu[0], output_scale, output_zero_point)

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