ubuntu18.04 下slowfast网络环境安装及模型测试( python3.9)

用pip 安装建议用国内源，如 pip install xxx -i https://pypi.tuna.tsinghua.edu.cn/simple

目录

1.conda env 环境创建

2. install pytorch 

3. install fvcore

4. install simplejson

5. gcc版本查看

6. PyAV

7.ffmpeg with PyAV

8. PyYaml , tqdm

9.iopath

10. psutil

11. opencv

12. tensorboard

13. moviepy

14. PyTorchVideo

15. Detectron2

16. FairScale

17. SlowFast

运行Demo测试模型

安装过程中遇到的一些errors

error0 

         error1

error2

error3

error4

error5

error6

error7

---

1.conda env 环境创建

conda create -n py39 python=3.9

2. install pytorch 

先查看cuda版本 , 再对应pytorch版本

查看系统nvidia驱动版本支持最高cuda版本

查看当前cuda版本

根据对应cuda版本安装pytorch torchvision

source activate py39
conda install pytorch torchvision cudatoolkit=11.3 -c pytorch

3. install fvcore

pip install git+https://github.com/facebookresearch/fvcore

4. install simplejson

pip install simplejson 

5. gcc版本查看

gcc -v

版本是 7.5.0

6. PyAV

conda install av -c conda-forge

7.ffmpeg with PyAV

pip install av

8. PyYaml , tqdm

pip list fvcore

9.iopath

pip install -U iopath

10. psutil

pip install psutil

11. opencv

pip install opencv-python

12. tensorboard

查看是否安装tensorboard:

conda list tensorboard

没有安装tensorboard

pip install tensorboard

13. moviepy

pip install moviepy

14. PyTorchVideo

pip install pytorchvideo

15. Detectron2

git clone https://github.com/facebookresearch/detectron2 detectron2_repo

pip install -e detectron2_repo

16. FairScale

pip install git+https://github.com/facebookresearch/fairscale

17. SlowFast

git clone https://github.com/facebookresearch/SlowFast.git

cd SlowFast
python setup.py build develop

运行Demo测试模型

python3 tools/run_net.py --cfg demo/AVA/SLOWFAST_32x2_R101_50_50.yaml

安装过程中遇到的一些errors

error0 

not find PIL 

解决办法：将setup.py 中的 PIL 更改为 Pillow 

error1

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)

解决方式：

方式一：将pytorchvideo/pytorchvideo at main · facebookresearch/pytorchvideo · GitHub文件下内容复制到虚拟环境所对应的文件下，这里是：/home/cxgk/anaconda3/envs/sf/lib/python3.9/site-packages/pytorchvideo/

方式二：
layers/distributed.py添加如下内容

# 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 = Nonedef 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 outputdef init_distributed_training(cfg):"""Initialize variables needed for distributed training."""if cfg.NUM_GPUS <= 1:returnnum_gpus_per_machine = cfg.NUM_GPUSnum_machines = dist.get_world_size() // num_gpus_per_machinefor 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 = pgdef 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 1if not dist.is_initialized():return 1return 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 0if not dist.is_initialized():return 0assert _LOCAL_PROCESS_GROUP is not Nonereturn dist.get_rank(group=_LOCAL_PROCESS_GROUP)def get_local_process_group() -> ProcessGroup:assert _LOCAL_PROCESS_GROUP is not Nonereturn _LOCAL_PROCESS_GROUPclass GroupGather(Function):"""GroupGather performs all gather on each of the local process/ GPU groups."""@staticmethoddef forward(ctx, input, num_sync_devices, num_groups):"""Perform forwarding, gathering the stats across different process/ GPUgroup."""ctx.num_sync_devices = num_sync_devicesctx.num_groups = num_groupsinput_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_devicesinputs = inputs[group_idx * num_sync_devices : (group_idx + 1) * num_sync_devices]inputs = torch.sum(inputs, dim=0)return inputs@staticmethoddef backward(ctx, grad_output):"""Perform backwarding, gathering the gradients across different process/ GPUgroup."""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_devicesgrads = grads[group_idx* ctx.num_sync_devices : (group_idx + 1)* ctx.num_sync_devices]grads = torch.sum(grads, dim=0)return grads, None, None

error2

from scipy.ndimage import gaussian_filter

ModuleNotFoundError: No module named 'scipy'

解决方法：

pip install scipy

error3

from av._core import time_base, library_versions

ImportError: /home/cxgk/anaconda3/envs/sf/lib/python3.9/site-packages/av/../../.././libgnutls.so.30: symbol mpn_copyi version HOGWEED_6 not defined in file libhogweed.so.6 with link time reference
 

解决方法：

先移处av包

使用 pip安装

pip install av

error4

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'

解决办法：

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_hotclass 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 1based 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).epsdef 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 ofsamples and C is the number of classes. The tensor is raw input withoutsoftmax/sigmoid.target (torch.Tensor): the shape of the tensor is N x C or N. If the shape is N, wewill convert the target to one hot vectors."""# Check if targets are inputted as class integersif 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 lossN, C = target.shapevalid_mask = torch.ones((N, 1), dtype=torch.float).to(input.device)if 0 <= self.ignore_index <= C - 1:drop_idx = target[:, self.ignore_idx] > 0valid_mask[drop_idx] = 0valid_targets = target.float() * valid_maskif 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 reductionif self.reduction == "mean":# Normalize based on the number of samples with > 0 non-ignored targetsloss = per_sample_loss.sum() / torch.sum((torch.sum(valid_mask, -1) > 0)).clamp(min=1)elif self.reduction == "none":loss = per_sample_lossreturn 

error5

from sklearn.metrics import confusion_matrix

ModuleNotFoundError: No module named 'sklearn'

解决办法：

pip install scikit-learn

error6

raise KeyError("Non-existent config key: {}".format(full_key))

KeyError: 'Non-existent config key: TENSORBOARD.MODEL_VIS.TOPK'

解决方法：

注释掉如下三行：

TENSORBOARD

MODEL_VIS

TOPK

error7

RuntimeError: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 3.94 GiB total capacity; 2.83 GiB already allocated; 25.44 MiB free; 2.84 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

解决方法：

将yaml里的帧数改小：

DATA:
NUM_FRAMES: 16

Reference:

https://github.com/facebookresearch/pytorchvideo/blob/main/pytorchvideo