# YOLOv5 PyTorch utils

import datetime    # 时间模块  基于time进行了封装 更高级
import logging     # 日志功能生成模块
import math        # 数学函数模块
import os          # 与操作系统进行交互的模块
import platform    # 提供获取操作系统相关信息的模块
import subprocess  # 子进程定义及操作的模块
import time        # 时间模块 更底层
from contextlib import contextmanager  # 用于进行上下文管理的模块
from copy import deepcopy  # 实现深度复制的模块
from pathlib import Path   # Path将str转换为Path对象 使字符串路径易于操作的模块

import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torchvision

try:
    import thop  # 用于Pytorch模型的FLOPS计算工具模块
except ImportError:
    thop = None
logger = logging.getLogger(__name__)


@contextmanager
def torch_distributed_zero_first(local_rank: int):
    """
    Decorator to make all processes in distributed training wait for each local_master to do something.
    """
    if local_rank not in [-1, 0]:
        torch.distributed.barrier()
    yield
    if local_rank == 0:
        torch.distributed.barrier()


def init_torch_seeds(seed=0):
    # Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html
    torch.manual_seed(seed)
    if seed == 0:  # slower, more reproducible
        cudnn.benchmark, cudnn.deterministic = False, True
    else:  # faster, less reproducible
        cudnn.benchmark, cudnn.deterministic = True, False


def date_modified(path=__file__):
    # return human-readable file modification date, i.e. '2021-3-26'
    t = datetime.datetime.fromtimestamp(Path(path).stat().st_mtime)
    return f'{t.year}-{t.month}-{t.day}'


def git_describe(path=Path(__file__).parent):  # path must be a directory
    # return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe
    s = f'git -C {path} describe --tags --long --always'
    try:
        return subprocess.check_output(s, shell=True, stderr=subprocess.STDOUT).decode()[:-1]
    except subprocess.CalledProcessError as e:
        return ''  # not a git repository

# 这个函数才是主角，用于自动选择本机模型训练的设备，并输出日志信息。
def select_device(device='', batch_size=None):
    # device = 'cpu' or '0' or '0,1,2,3'
    """广泛用于train.py、test.py、detect.py等文件中
    用于选择模型训练的设备 并输出日志信息
    :params device: 输入的设备  device = 'cpu' or '0' or '0,1,2,3'
    :params batch_size: 一个批次的图片个数
    """
    # git_describe(): 返回当前文件父文件的描述信息(yolov5)   date_modified(): 返回当前文件的修改日期
    # s: 之后要加入logger日志的显示信息
    s = f'YOLOv5 🚀 {git_describe() or date_modified()} torch {torch.__version__} '  # string
    # 如果device输入为cpu  cpu=True  device.lower(): 将device字符串全部转为小写字母
    cpu = device.lower() == 'cpu'
    if cpu:
        # 如果cpu=True 就强制(force)使用cpu 令torch.cuda.is_available() = False
        os.environ['CUDA_VISIBLE_DEVICES'] = '-1'  # force torch.cuda.is_available() = False
    elif device:  # non-cpu device requested
        # 如果输入device不为空  device=GPU  直接设置 CUDA environment variable = device 加入CUDA可用设备
        os.environ['CUDA_VISIBLE_DEVICES'] = device  # set environment variable
        # 检查cuda的可用性 如果不可用则终止程序
        assert torch.cuda.is_available(), f'CUDA unavailable, invalid device {device} requested'  # check availability
    # 输入device为空 自行根据计算机情况选择相应设备  先看GPU 没有就CPU
    # 如果cuda可用 且 输入device != cpu 则 cuda=True 反正cuda=False
    cuda = not cpu and torch.cuda.is_available()
    if cuda:
        n = torch.cuda.device_count()
        if n > 1 and batch_size:  # check that batch_size is compatible with device_count
            assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}'
        space = ' ' * len(s)
        for i, d in enumerate(device.split(',') if device else range(n)):
            # p: 每个可用显卡的相关属性
            p = torch.cuda.get_device_properties(i)
            # 显示信息s加上每张显卡的属性信息
            s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / 1024 ** 2}MB)\n"  # bytes to MB
    else:
        s += 'CPU\n'

    logger.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s)  # emoji-safe
    # 如果cuda可用就返回第一张显卡的的名称 如: GeForce RTX 2060 反之返回CPU对应的名称
    return torch.device('cuda:0' if cuda else 'cpu')


def time_synchronized():
    # pytorch-accurate time
    if torch.cuda.is_available():
        torch.cuda.synchronize()
    return time.time()


def profile(x, ops, n=100, device=None):
    # profile a pytorch module or list of modules. Example usage:
    #     x = torch.randn(16, 3, 640, 640)  # input
    #     m1 = lambda x: x * torch.sigmoid(x)
    #     m2 = nn.SiLU()
    #     profile(x, [m1, m2], n=100)  # profile speed over 100 iterations

    device = device or torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
    x = x.to(device)
    x.requires_grad = True
    print(torch.__version__, device.type, torch.cuda.get_device_properties(0) if device.type == 'cuda' else '')
    print(f"\n{'Params':>12s}{'GFLOPS':>12s}{'forward (ms)':>16s}{'backward (ms)':>16s}{'input':>24s}{'output':>24s}")
    for m in ops if isinstance(ops, list) else [ops]:
        m = m.to(device) if hasattr(m, 'to') else m  # device
        m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m  # type
        dtf, dtb, t = 0., 0., [0., 0., 0.]  # dt forward, backward
        try:
            flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2  # GFLOPS
        except:
            flops = 0

        for _ in range(n):
            t[0] = time_synchronized()
            y = m(x)
            t[1] = time_synchronized()
            try:
                _ = y.sum().backward()
                t[2] = time_synchronized()
            except:  # no backward method
                t[2] = float('nan')
            dtf += (t[1] - t[0]) * 1000 / n  # ms per op forward
            dtb += (t[2] - t[1]) * 1000 / n  # ms per op backward

        s_in = tuple(x.shape) if isinstance(x, torch.Tensor) else 'list'
        s_out = tuple(y.shape) if isinstance(y, torch.Tensor) else 'list'
        p = sum(list(x.numel() for x in m.parameters())) if isinstance(m, nn.Module) else 0  # parameters
        print(f'{p:12}{flops:12.4g}{dtf:16.4g}{dtb:16.4g}{str(s_in):>24s}{str(s_out):>24s}')


def is_parallel(model):
    return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)


def intersect_dicts(da, db, exclude=()):
    # Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values
    return {k: v for k, v in da.items() if k in db and not any(x in k for x in exclude) and v.shape == db[k].shape}


def initialize_weights(model):
    for m in model.modules():
        t = type(m)
        if t is nn.Conv2d:
            pass  # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
        elif t is nn.BatchNorm2d:
            m.eps = 1e-3
            m.momentum = 0.03
        elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6]:
            m.inplace = True


def find_modules(model, mclass=nn.Conv2d):
    # Finds layer indices matching module class 'mclass'
    return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)]


def sparsity(model):
    # Return global model sparsity
    a, b = 0., 0.
    for p in model.parameters():
        a += p.numel()
        b += (p == 0).sum()
    return b / a


def prune(model, amount=0.3):
    # Prune model to requested global sparsity
    import torch.nn.utils.prune as prune
    print('Pruning model... ', end='')
    for name, m in model.named_modules():
        if isinstance(m, nn.Conv2d):
            prune.l1_unstructured(m, name='weight', amount=amount)  # prune
            prune.remove(m, 'weight')  # make permanent
    print(' %.3g global sparsity' % sparsity(model))


def fuse_conv_and_bn(conv, bn):
    # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/
    fusedconv = nn.Conv2d(conv.in_channels,
                          conv.out_channels,
                          kernel_size=conv.kernel_size,
                          stride=conv.stride,
                          padding=conv.padding,
                          groups=conv.groups,
                          bias=True).requires_grad_(False).to(conv.weight.device)

    # prepare filters
    w_conv = conv.weight.clone().view(conv.out_channels, -1)
    w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
    fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))

    # prepare spatial bias
    b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
    b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
    fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)

    return fusedconv


def model_info(model, verbose=False, img_size=640):
    # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320]
    n_p = sum(x.numel() for x in model.parameters())  # number parameters
    n_g = sum(x.numel() for x in model.parameters() if x.requires_grad)  # number gradients
    if verbose:
        print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
        for i, (name, p) in enumerate(model.named_parameters()):
            name = name.replace('module_list.', '')
            print('%5g %40s %9s %12g %20s %10.3g %10.3g' %
                  (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))

    try:  # FLOPS
        from thop import profile
        stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32
        img = torch.zeros((1, model.yaml.get('ch', 3), stride, stride), device=next(model.parameters()).device)  # input
        flops = profile(deepcopy(model), inputs=(img,), verbose=False)[0] / 1E9 * 2  # stride GFLOPS
        img_size = img_size if isinstance(img_size, list) else [img_size, img_size]  # expand if int/float
        fs = ', %.1f GFLOPS' % (flops * img_size[0] / stride * img_size[1] / stride)  # 640x640 GFLOPS
    except (ImportError, Exception):
        fs = ''

    logger.info(f"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}")


def load_classifier(name='resnet101', n=2):
    # Loads a pretrained model reshaped to n-class output
    model = torchvision.models.__dict__[name](pretrained=True)

    # ResNet model properties
    # input_size = [3, 224, 224]
    # input_space = 'RGB'
    # input_range = [0, 1]
    # mean = [0.485, 0.456, 0.406]
    # std = [0.229, 0.224, 0.225]

    # Reshape output to n classes
    filters = model.fc.weight.shape[1]
    model.fc.bias = nn.Parameter(torch.zeros(n), requires_grad=True)
    model.fc.weight = nn.Parameter(torch.zeros(n, filters), requires_grad=True)
    model.fc.out_features = n
    return model


def scale_img(img, ratio=1.0, same_shape=False, gs=32):  # img(16,3,256,416)
    # scales img(bs,3,y,x) by ratio constrained to gs-multiple
    if ratio == 1.0:
        return img
    else:
        h, w = img.shape[2:]
        s = (int(h * ratio), int(w * ratio))  # new size
        img = F.interpolate(img, size=s, mode='bilinear', align_corners=False)  # resize
        if not same_shape:  # pad/crop img
            h, w = [math.ceil(x * ratio / gs) * gs for x in (h, w)]
        return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447)  # value = imagenet mean


def copy_attr(a, b, include=(), exclude=()):
    # Copy attributes from b to a, options to only include [...] and to exclude [...]
    for k, v in b.__dict__.items():
        if (len(include) and k not in include) or k.startswith('_') or k in exclude:
            continue
        else:
            setattr(a, k, v)


class ModelEMA:
    """ Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models
    Keep a moving average of everything in the model state_dict (parameters and buffers).
    This is intended to allow functionality like
    https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
    A smoothed version of the weights is necessary for some training schemes to perform well.
    This class is sensitive where it is initialized in the sequence of model init,
    GPU assignment and distributed training wrappers.
    """

    def __init__(self, model, decay=0.9999, updates=0):
        # Create EMA
        self.ema = deepcopy(model.module if is_parallel(model) else model).eval()  # FP32 EMA
        # if next(model.parameters()).device.type != 'cpu':
        #     self.ema.half()  # FP16 EMA
        self.updates = updates  # number of EMA updates
        self.decay = lambda x: decay * (1 - math.exp(-x / 2000))  # decay exponential ramp (to help early epochs)
        for p in self.ema.parameters():
            p.requires_grad_(False)

    def update(self, model):
        # Update EMA parameters
        with torch.no_grad():
            self.updates += 1
            d = self.decay(self.updates)

            msd = model.module.state_dict() if is_parallel(model) else model.state_dict()  # model state_dict
            for k, v in self.ema.state_dict().items():
                if v.dtype.is_floating_point:
                    v *= d
                    v += (1. - d) * msd[k].detach()

    def update_attr(self, model, include=(), exclude=('process_group', 'reducer')):
        # Update EMA attributes
        copy_attr(self.ema, model, include, exclude)