windows10安装slowfast报错记录

UnicodeDecodeError: ‘gbk’ codec can’t decode byte 0xaf in position 157: illegal multibyte sequence
编码问题，yaml文件的注释中去掉汉字

yaml.scanner.ScannerError: while scanning for the next token found character ‘\t’ that cannot start any token
in “demo\AVA\SLOWFAST_32x2_R101_50_50.yaml”, line 77, column 32
注意在yaml文件中路径格式不能使用\，要使用/

raise IOError(“Video {} cannot be opened”.format(self.source)) OSError: Video ./input/1.mp4 cannot be opened
无法读取视频错误的解决方法：使用绝对路径

from pytorchvideo.layers.distributed import ( # noqa ImportError: cannot import name ‘cat_all_gather’ from ‘pytorchvideo.layers.distributed’ (/home/cxgk/anaconda3/envs/sf/lib/python3.9/site-packages/pytorchvideo/layers/distributed.py)

参考其他博主的解决方案

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
 
"""Distributed helpers."""
 
import torch
import torch.distributed as dist
from torch._C._distributed_c10d import ProcessGroup
from torch.autograd.function import Function
 
_LOCAL_PROCESS_GROUP = None
 
 
def get_world_size() -> int:
    """
    Simple wrapper for correctly getting worldsize in both distributed
    / non-distributed settings
    """
    return (
        torch.distributed.get_world_size()
        if torch.distributed.is_available() and torch.distributed.is_initialized()
        else 1
    )
 
 
def cat_all_gather(tensors, local=False):
    """Performs the concatenated all_reduce operation on the provided tensors."""
    if local:
        gather_sz = get_local_size()
    else:
        gather_sz = torch.distributed.get_world_size()
    tensors_gather = [torch.ones_like(tensors) for _ in range(gather_sz)]
    torch.distributed.all_gather(
        tensors_gather,
        tensors,
        async_op=False,
        group=_LOCAL_PROCESS_GROUP if local else None,
    )
    output = torch.cat(tensors_gather, dim=0)
    return output
 
 
def init_distributed_training(cfg):
    """
    Initialize variables needed for distributed training.
    """
    if cfg.NUM_GPUS <= 1:
        return
    num_gpus_per_machine = cfg.NUM_GPUS
    num_machines = dist.get_world_size() // num_gpus_per_machine
    for i in range(num_machines):
        ranks_on_i = list(
            range(i * num_gpus_per_machine, (i + 1) * num_gpus_per_machine)
        )
        pg = dist.new_group(ranks_on_i)
        if i == cfg.SHARD_ID:
            global _LOCAL_PROCESS_GROUP
            _LOCAL_PROCESS_GROUP = pg
 
 
def get_local_size() -> int:
    """
    Returns:
        The size of the per-machine process group,
        i.e. the number of processes per machine.
    """
    if not dist.is_available():
        return 1
    if not dist.is_initialized():
        return 1
    return dist.get_world_size(group=_LOCAL_PROCESS_GROUP)
 
 
def get_local_rank() -> int:
    """
    Returns:
        The rank of the current process within the local (per-machine) process group.
    """
    if not dist.is_available():
        return 0
    if not dist.is_initialized():
        return 0
    assert _LOCAL_PROCESS_GROUP is not None
    return dist.get_rank(group=_LOCAL_PROCESS_GROUP)
 
 
def get_local_process_group() -> ProcessGroup:
    assert _LOCAL_PROCESS_GROUP is not None
    return _LOCAL_PROCESS_GROUP
 
 
class GroupGather(Function):
    """
    GroupGather performs all gather on each of the local process/ GPU groups.
    """
 
    @staticmethod
    def forward(ctx, input, num_sync_devices, num_groups):
        """
        Perform forwarding, gathering the stats across different process/ GPU
        group.
        """
        ctx.num_sync_devices = num_sync_devices
        ctx.num_groups = num_groups
 
        input_list = [torch.zeros_like(input) for k in range(get_local_size())]
        dist.all_gather(
            input_list, input, async_op=False, group=get_local_process_group()
        )
 
        inputs = torch.stack(input_list, dim=0)
        if num_groups > 1:
            rank = get_local_rank()
            group_idx = rank // num_sync_devices
            inputs = inputs[
                group_idx * num_sync_devices : (group_idx + 1) * num_sync_devices
            ]
        inputs = torch.sum(inputs, dim=0)
        return inputs
 
    @staticmethod
    def backward(ctx, grad_output):
        """
        Perform backwarding, gathering the gradients across different process/ GPU
        group.
        """
        grad_output_list = [
            torch.zeros_like(grad_output) for k in range(get_local_size())
        ]
        dist.all_gather(
            grad_output_list,
            grad_output,
            async_op=False,
            group=get_local_process_group(),
        )
 
        grads = torch.stack(grad_output_list, dim=0)
        if ctx.num_groups > 1:
            rank = get_local_rank()
            group_idx = rank // ctx.num_sync_devices
            grads = grads[
                group_idx
                * ctx.num_sync_devices : (group_idx + 1)
                * ctx.num_sync_devices
            ]
        grads = torch.sum(grads, dim=0)
        return grads, None, None

将这些代码添加到layers/distributed.py

File “/media/cxgk/Linux/work/SlowFast/slowfast/models/losses.py”, line 11, in
from pytorchvideo.losses.soft_target_cross_entropy import ( ModuleNotFoundError: No module named ‘pytorchvideo.losses’

解决方案：打开"/home/cxgk/anaconda3/envs/sf/lib/python3.9/site-packages/pytorchvideo/losses"，在文件夹下新建 soft_target_cross_entropy.py， 并打开添加如下代码：

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
 
import torch
import torch.nn as nn
import torch.nn.functional as F
from pytorchvideo.layers.utils import set_attributes
from pytorchvideo.transforms.functional import convert_to_one_hot
 
 
class SoftTargetCrossEntropyLoss(nn.Module):
    """
    Adapted from Classy Vision: ./classy_vision/losses/soft_target_cross_entropy_loss.py.
    This allows the targets for the cross entropy loss to be multi-label.
    """
 
    def __init__(
        self,
        ignore_index: int = -100,
        reduction: str = "mean",
        normalize_targets: bool = True,
    ) -> None:
        """
        Args:
            ignore_index (int): sample should be ignored for loss if the class is this value.
            reduction (str): specifies reduction to apply to the output.
            normalize_targets (bool): whether the targets should be normalized to a sum of 1
                based on the total count of positive targets for a given sample.
        """
        super().__init__()
        set_attributes(self, locals())
        assert isinstance(self.normalize_targets, bool)
        if self.reduction not in ["mean", "none"]:
            raise NotImplementedError(
                'reduction type "{}" not implemented'.format(self.reduction)
            )
        self.eps = torch.finfo(torch.float32).eps
 
    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
        """
        Args:
            input (torch.Tensor): the shape of the tensor is N x C, where N is the number of
                samples and C is the number of classes. The tensor is raw input without
                softmax/sigmoid.
            target (torch.Tensor): the shape of the tensor is N x C or N. If the shape is N, we
                will convert the target to one hot vectors.
        """
        # Check if targets are inputted as class integers
        if target.ndim == 1:
            assert (
                input.shape[0] == target.shape[0]
            ), "SoftTargetCrossEntropyLoss requires input and target to have same batch size!"
            target = convert_to_one_hot(target.view(-1, 1), input.shape[1])
 
        assert input.shape == target.shape, (
            "SoftTargetCrossEntropyLoss requires input and target to be same "
            f"shape: {input.shape} != {target.shape}"
        )
 
        # Samples where the targets are ignore_index do not contribute to the loss
        N, C = target.shape
        valid_mask = torch.ones((N, 1), dtype=torch.float).to(input.device)
        if 0 <= self.ignore_index <= C - 1:
            drop_idx = target[:, self.ignore_idx] > 0
            valid_mask[drop_idx] = 0
 
        valid_targets = target.float() * valid_mask
        if self.normalize_targets:
            valid_targets /= self.eps + valid_targets.sum(dim=1, keepdim=True)
        per_sample_per_target_loss = -valid_targets * F.log_softmax(input, -1)
 
        per_sample_loss = torch.sum(per_sample_per_target_loss, -1)
        # Perform reduction
        if self.reduction == "mean":
            # Normalize based on the number of samples with > 0 non-ignored targets
            loss = per_sample_loss.sum() / torch.sum(
                (torch.sum(valid_mask, -1) > 0)
            ).clamp(min=1)
        elif self.reduction == "none":
            loss = per_sample_loss
 
        return 

在安装slowfast的配置文件时，会使用到git，这时候要先安装git:conda install git

# 要双引号！！！！！！！
pip install 'git+https://github.com/facebookresearch/fvcore'  
pip install 'git+https://github.com/facebookresearch/fairscale' 
pip install simplejson
pip install -U iopath
pip install psutil tensorboard opencv-python moviepy pytorchvideo
pip install pillow pyyaml pandas matplotlib sklearn

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在安装上述库的时候，gcc、Detectron2的安装遇到了一些困难，参考了以下链接:
配置深度学习环境: https://blog.youkuaiyun.com/wahahhaha/article/details/131605668
gcc https://blog.youkuaiyun.com/qq_44319167/article/details/130439875
Detectron2 https://blog.youkuaiyun.com/Wenyuanbo/article/details/130577744 需要修改一些内容，一一对应

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在ava格式的数据集准备方面，我发现在windows系统中无法直接运行.sh后缀的文件，解决方案如下：
自定义ava数据集的构建：https://blog.youkuaiyun.com/qq_45672807/article/details/123558959
git bash的安装：https://blog.youkuaiyun.com/mukes/article/details/115693833