import math import os import time from copy import deepcopy import torch import torch.backends.cudnn as cudnn import torch.nn as nn import torch.nn.functional as F import torchvision.models as models def init_seeds(seed=0): torch.manual_seed(seed) # Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html if seed == 0: # slower, more reproducible cudnn.deterministic = True cudnn.benchmark = False else: # faster, less reproducible cudnn.deterministic = False cudnn.benchmark = True def select_device(device='', apex=False, batch_size=None): # device = 'cpu' or '0' or '0,1,2,3' cpu_request = device.lower() == 'cpu' if device and not cpu_request: # if device requested other than 'cpu' os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable assert torch.cuda.is_available(), 'CUDA unavailable, invalid device %s requested' % device # check availablity cuda = False if cpu_request else torch.cuda.is_available() if cuda: c = 1024 ** 2 # bytes to MB ng = torch.cuda.device_count() if ng > 1 and batch_size: # check that batch_size is compatible with device_count assert batch_size % ng == 0, 'batch-size %g not multiple of GPU count %g' % (batch_size, ng) x = [torch.cuda.get_device_properties(i) for i in range(ng)] s = 'Using CUDA ' + ('Apex ' if apex else '') # apex for mixed precision https://github.com/NVIDIA/apex for i in range(0, ng): if i == 1: s = ' ' * len(s) print("%sdevice%g _CudaDeviceProperties(name='%s', total_memory=%dMB)" % (s, i, x[i].name, x[i].total_memory / c)) else: print('Using CPU') print('') # skip a line return torch.device('cuda:0' if cuda else 'cpu') def time_synchronized(): torch.cuda.synchronize() if torch.cuda.is_available() else None return time.time() 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-4 m.momentum = 0.03 elif t in [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 fuse_conv_and_bn(conv, bn): # https://tehnokv.com/posts/fusing-batchnorm-and-conv/ with torch.no_grad(): # init fusedconv = torch.nn.Conv2d(conv.in_channels, conv.out_channels, kernel_size=conv.kernel_size, stride=conv.stride, padding=conv.padding, bias=True) # 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.size())) # prepare spatial bias if conv.bias is not None: b_conv = conv.bias else: b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) 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): # Plots a line-by-line description of a PyTorch model 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 macs, _ = profile(model, inputs=(torch.zeros(1, 3, 480, 640),), verbose=False) fs = ', %.1f GFLOPS' % (macs / 1E9 * 2) except: fs = '' print('Model Summary: %g layers, %g parameters, %g gradients%s' % (len(list(model.parameters())), n_p, n_g, fs)) def load_classifier(name='resnet101', n=2): # Loads a pretrained model reshaped to n-class output model = models.__dict__[name](pretrained=True) # Display 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] for x in [input_size, input_space, input_range, mean, std]: print(x + ' =', eval(x)) # Reshape output to n classes filters = model.fc.weight.shape[1] model.fc.bias = torch.nn.Parameter(torch.zeros(n), requires_grad=True) model.fc.weight = torch.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): # img(16,3,256,416), r=ratio # scales img(bs,3,y,x) by ratio 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 gs = 32 # (pixels) grid size 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 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. E.g. Google's hyper-params for training MNASNet, MobileNet-V3, EfficientNet, etc that use RMSprop with a short 2.4-3 epoch decay period and slow LR decay rate of .96-.99 requires EMA smoothing of weights to match results. Pay attention to the decay constant you are using relative to your update count per epoch. To keep EMA from using GPU resources, set device='cpu'. This will save a bit of memory but disable validation of the EMA weights. Validation will have to be done manually in a separate process, or after the training stops converging. This class is sensitive where it is initialized in the sequence of model init, GPU assignment and distributed training wrappers. I've tested with the sequence in my own train.py for torch.DataParallel, apex.DDP, and single-GPU. """ def __init__(self, model, decay=0.9999, device=''): # make a copy of the model for accumulating moving average of weights self.ema = deepcopy(model) self.ema.eval() self.updates = 0 # number of EMA updates self.decay = lambda x: decay * (1 - math.exp(-x / 2000)) # decay exponential ramp (to help early epochs) self.device = device # perform ema on different device from model if set if device: self.ema.to(device=device) for p in self.ema.parameters(): p.requires_grad_(False) def update(self, model): self.updates += 1 d = self.decay(self.updates) with torch.no_grad(): if type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel): msd, esd = model.module.state_dict(), self.ema.module.state_dict() else: msd, esd = model.state_dict(), self.ema.state_dict() for k, v in esd.items(): if v.dtype.is_floating_point: v *= d v += (1. - d) * msd[k].detach() def update_attr(self, model): # Assign attributes (which may change during training) for k in model.__dict__.keys(): if not k.startswith('_'): setattr(self.ema, k, getattr(model, k))