SparseRCNN/detectron2/layers/batch_norm.py

# Copyright (c) Facebook, Inc. and its affiliates.
import logging
import torch
import torch.distributed as dist
from torch import nn
from torch.autograd.function import Function
from torch.nn import functional as F

from detectron2.utils import comm, env

from .wrappers import BatchNorm2d


class FrozenBatchNorm2d(nn.Module):
    """
    BatchNorm2d where the batch statistics and the affine parameters are fixed.

    It contains non-trainable buffers called
    "weight" and "bias", "running_mean", "running_var",
    initialized to perform identity transformation.

    The pre-trained backbone models from Caffe2 only contain "weight" and "bias",
    which are computed from the original four parameters of BN.
    The affine transform `x * weight + bias` will perform the equivalent
    computation of `(x - running_mean) / sqrt(running_var) * weight + bias`.
    When loading a backbone model from Caffe2, "running_mean" and "running_var"
    will be left unchanged as identity transformation.

    Other pre-trained backbone models may contain all 4 parameters.

    The forward is implemented by `F.batch_norm(..., training=False)`.
    """

    _version = 3

    def __init__(self, num_features, eps=1e-5):
        super().__init__()
        self.num_features = num_features
        self.eps = eps
        self.register_buffer("weight", torch.ones(num_features))
        self.register_buffer("bias", torch.zeros(num_features))
        self.register_buffer("running_mean", torch.zeros(num_features))
        self.register_buffer("running_var", torch.ones(num_features) - eps)

    def forward(self, x):
        if x.requires_grad:
            # When gradients are needed, F.batch_norm will use extra memory
            # because its backward op computes gradients for weight/bias as well.
            scale = self.weight * (self.running_var + self.eps).rsqrt()
            bias = self.bias - self.running_mean * scale
            scale = scale.reshape(1, -1, 1, 1)
            bias = bias.reshape(1, -1, 1, 1)
            out_dtype = x.dtype  # may be half
            return x * scale.to(out_dtype) + bias.to(out_dtype)
        else:
            # When gradients are not needed, F.batch_norm is a single fused op
            # and provide more optimization opportunities.
            return F.batch_norm(
                x,
                self.running_mean,
                self.running_var,
                self.weight,
                self.bias,
                training=False,
                eps=self.eps,
            )

    def _load_from_state_dict(
        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
    ):
        version = local_metadata.get("version", None)

        if version is None or version < 2:
            # No running_mean/var in early versions
            # This will silent the warnings
            if prefix + "running_mean" not in state_dict:
                state_dict[prefix + "running_mean"] = torch.zeros_like(self.running_mean)
            if prefix + "running_var" not in state_dict:
                state_dict[prefix + "running_var"] = torch.ones_like(self.running_var)

        if version is not None and version < 3:
            logger = logging.getLogger(__name__)
            logger.info("FrozenBatchNorm {} is upgraded to version 3.".format(prefix.rstrip(".")))
            # In version < 3, running_var are used without +eps.
            state_dict[prefix + "running_var"] -= self.eps

        super()._load_from_state_dict(
            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
        )

    def __repr__(self):
        return "FrozenBatchNorm2d(num_features={}, eps={})".format(self.num_features, self.eps)

    @classmethod
    def convert_frozen_batchnorm(cls, module):
        """
        Convert BatchNorm/SyncBatchNorm in module into FrozenBatchNorm.

        Args:
            module (torch.nn.Module):

        Returns:
            If module is BatchNorm/SyncBatchNorm, returns a new module.
            Otherwise, in-place convert module and return it.

        Similar to convert_sync_batchnorm in
        https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/batchnorm.py
        """
        bn_module = nn.modules.batchnorm
        bn_module = (bn_module.BatchNorm2d, bn_module.SyncBatchNorm)
        res = module
        if isinstance(module, bn_module):
            res = cls(module.num_features)
            if module.affine:
                res.weight.data = module.weight.data.clone().detach()
                res.bias.data = module.bias.data.clone().detach()
            res.running_mean.data = module.running_mean.data
            res.running_var.data = module.running_var.data
            res.eps = module.eps
        else:
            for name, child in module.named_children():
                new_child = cls.convert_frozen_batchnorm(child)
                if new_child is not child:
                    res.add_module(name, new_child)
        return res


def get_norm(norm, out_channels):
    """
    Args:
        norm (str or callable): either one of BN, SyncBN, FrozenBN, GN;
            or a callable that takes a channel number and returns
            the normalization layer as a nn.Module.

    Returns:
        nn.Module or None: the normalization layer
    """
    if norm is None:
        return None
    if isinstance(norm, str):
        if len(norm) == 0:
            return None
        norm = {
            "BN": BatchNorm2d,
            # Fixed in https://github.com/pytorch/pytorch/pull/36382
            "SyncBN": NaiveSyncBatchNorm if env.TORCH_VERSION <= (1, 5) else nn.SyncBatchNorm,
            "FrozenBN": FrozenBatchNorm2d,
            "GN": lambda channels: nn.GroupNorm(32, channels),
            # for debugging:
            "nnSyncBN": nn.SyncBatchNorm,
            "naiveSyncBN": NaiveSyncBatchNorm,
        }[norm]
    return norm(out_channels)


class AllReduce(Function):
    @staticmethod
    def forward(ctx, input):
        input_list = [torch.zeros_like(input) for k in range(dist.get_world_size())]
        # Use allgather instead of allreduce since I don't trust in-place operations ..
        dist.all_gather(input_list, input, async_op=False)
        inputs = torch.stack(input_list, dim=0)
        return torch.sum(inputs, dim=0)

    @staticmethod
    def backward(ctx, grad_output):
        dist.all_reduce(grad_output, async_op=False)
        return grad_output


class NaiveSyncBatchNorm(BatchNorm2d):
    """
    In PyTorch<=1.5, ``nn.SyncBatchNorm`` has incorrect gradient
    when the batch size on each worker is different.
    (e.g., when scale augmentation is used, or when it is applied to mask head).

    This is a slower but correct alternative to `nn.SyncBatchNorm`.

    Note:
        There isn't a single definition of Sync BatchNorm.

        When ``stats_mode==""``, this module computes overall statistics by using
        statistics of each worker with equal weight.  The result is true statistics
        of all samples (as if they are all on one worker) only when all workers
        have the same (N, H, W). This mode does not support inputs with zero batch size.

        When ``stats_mode=="N"``, this module computes overall statistics by weighting
        the statistics of each worker by their ``N``. The result is true statistics
        of all samples (as if they are all on one worker) only when all workers
        have the same (H, W). It is slower than ``stats_mode==""``.

        Even though the result of this module may not be the true statistics of all samples,
        it may still be reasonable because it might be preferrable to assign equal weights
        to all workers, regardless of their (H, W) dimension, instead of putting larger weight
        on larger images. From preliminary experiments, little difference is found between such
        a simplified implementation and an accurate computation of overall mean & variance.
    """

    def __init__(self, *args, stats_mode="", **kwargs):
        super().__init__(*args, **kwargs)
        assert stats_mode in ["", "N"]
        self._stats_mode = stats_mode

    def forward(self, input):
        if comm.get_world_size() == 1 or not self.training:
            return super().forward(input)

        B, C = input.shape[0], input.shape[1]

        mean = torch.mean(input, dim=[0, 2, 3])
        meansqr = torch.mean(input * input, dim=[0, 2, 3])

        if self._stats_mode == "":
            assert B > 0, 'SyncBatchNorm(stats_mode="") does not support zero batch size.'
            vec = torch.cat([mean, meansqr], dim=0)
            vec = AllReduce.apply(vec) * (1.0 / dist.get_world_size())
            mean, meansqr = torch.split(vec, C)
            momentum = self.momentum
        else:
            if B == 0:
                vec = torch.zeros([2 * C + 1], device=mean.device, dtype=mean.dtype)
                vec = vec + input.sum()  # make sure there is gradient w.r.t input
            else:
                vec = torch.cat(
                    [mean, meansqr, torch.ones([1], device=mean.device, dtype=mean.dtype)], dim=0
                )
            vec = AllReduce.apply(vec * B)

            total_batch = vec[-1].detach()
            momentum = total_batch.clamp(max=1) * self.momentum  # no update if total_batch is 0
            total_batch = torch.max(total_batch, torch.ones_like(total_batch))  # avoid div-by-zero
            mean, meansqr, _ = torch.split(vec / total_batch, C)

        var = meansqr - mean * mean
        invstd = torch.rsqrt(var + self.eps)
        scale = self.weight * invstd
        bias = self.bias - mean * scale
        scale = scale.reshape(1, -1, 1, 1)
        bias = bias.reshape(1, -1, 1, 1)

        self.running_mean += momentum * (mean.detach() - self.running_mean)
        self.running_var += momentum * (var.detach() - self.running_var)
        return input * scale + bias