62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
import torch
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def init_seeds(seed=0):
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torch.cuda.empty_cache()
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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# torch.backends.cudnn.deterministic = True # https://pytorch.org/docs/stable/notes/randomness.html
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def select_device(force_cpu=False):
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cuda = False if force_cpu else torch.cuda.is_available()
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device = torch.device('cuda:0' if cuda else 'cpu')
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if not cuda:
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print('Using CPU')
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if cuda:
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torch.backends.cudnn.benchmark = True # set False for reproducible results
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c = 1024 ** 2 # bytes to MB
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ng = torch.cuda.device_count()
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x = [torch.cuda.get_device_properties(i) for i in range(ng)]
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cuda_str = 'Using CUDA '
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for i in range(0, ng):
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if i == 1:
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# torch.cuda.set_device(0) # OPTIONAL: Set GPU ID
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cuda_str = ' ' * len(cuda_str)
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print("%sdevice%g _CudaDeviceProperties(name='%s', total_memory=%dMB)" %
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(cuda_str, i, x[i].name, x[i].total_memory / c))
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print('') # skip a line
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return device
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def fuse_conv_and_bn(conv, bn):
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# https://tehnokv.com/posts/fusing-batchnorm-and-conv/
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with torch.no_grad():
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# init
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fusedconv = torch.nn.Conv2d(
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conv.in_channels,
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conv.out_channels,
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kernel_size=conv.kernel_size,
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stride=conv.stride,
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padding=conv.padding,
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bias=True
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)
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# prepare filters
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w_conv = conv.weight.clone().view(conv.out_channels, -1)
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w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
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fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.size()))
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# prepare spatial bias
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if conv.bias is not None:
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b_conv = conv.bias
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else:
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b_conv = torch.zeros(conv.weight.size(0))
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b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
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fusedconv.bias.copy_(b_conv + b_bn)
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return fusedconv
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