import os import torch def init_seeds(seed=0): torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) # Remove randomness (may be slower on Tesla GPUs) # https://pytorch.org/docs/stable/notes/randomness.html if seed == 0: torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False def select_device(device='', apex=False): # 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() x = [torch.cuda.get_device_properties(i) for i in range(ng)] cuda_str = 'Using CUDA ' + ('Apex ' if apex else '') # apex for mixed precision https://github.com/NVIDIA/apex for i in range(0, ng): if i == 1: cuda_str = ' ' * len(cuda_str) print("%sdevice%g _CudaDeviceProperties(name='%s', total_memory=%dMB)" % (cuda_str, 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 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)) b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps)) fusedconv.bias.copy_(b_conv + b_bn) return fusedconv def model_info(model, report='summary'): # 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 report is 'full': 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())) print('Model Summary: %g layers, %g parameters, %g gradients' % (len(list(model.parameters())), n_p, n_g))