104 lines
4.3 KiB
Python
104 lines
4.3 KiB
Python
import os
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import time
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import torch
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import torch.backends.cudnn as cudnn
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def init_seeds(seed=0):
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torch.manual_seed(seed)
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# Remove randomness (may be slower on Tesla GPUs) # https://pytorch.org/docs/stable/notes/randomness.html
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if seed == 0:
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cudnn.deterministic = True
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cudnn.benchmark = False
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def select_device(device='', apex=False, batch_size=None):
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# device = 'cpu' or '0' or '0,1,2,3'
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cpu_request = device.lower() == 'cpu'
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if device and not cpu_request: # if device requested other than 'cpu'
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os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable
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assert torch.cuda.is_available(), 'CUDA unavailable, invalid device %s requested' % device # check availablity
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cuda = False if cpu_request else torch.cuda.is_available()
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if cuda:
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c = 1024 ** 2 # bytes to MB
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ng = torch.cuda.device_count()
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if ng > 1 and batch_size: # check that batch_size is compatible with device_count
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assert batch_size % ng == 0, 'batch-size %g not multiple of GPU count %g' % (batch_size, ng)
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x = [torch.cuda.get_device_properties(i) for i in range(ng)]
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s = 'Using CUDA ' + ('Apex ' if apex else '') # apex for mixed precision https://github.com/NVIDIA/apex
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for i in range(0, ng):
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if i == 1:
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s = ' ' * len(s)
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print("%sdevice%g _CudaDeviceProperties(name='%s', total_memory=%dMB)" %
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(s, i, x[i].name, x[i].total_memory / c))
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else:
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print('Using CPU')
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print('') # skip a line
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return torch.device('cuda:0' if cuda else 'cpu')
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def time_synchronized():
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torch.cuda.synchronize() if torch.cuda.is_available() else None
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return time.time()
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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(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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# 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_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
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return fusedconv
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def model_info(model, report='summary'):
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# Plots a line-by-line description of a PyTorch model
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n_p = sum(x.numel() for x in model.parameters()) # number parameters
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n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients
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if report == 'full':
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print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
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for i, (name, p) in enumerate(model.named_parameters()):
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name = name.replace('module_list.', '')
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print('%5g %40s %9s %12g %20s %10.3g %10.3g' %
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(i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
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print('Model Summary: %g layers, %g parameters, %g gradients' % (len(list(model.parameters())), n_p, n_g))
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def load_classifier(name='resnet101', n=2):
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# Loads a pretrained model reshaped to n-class output
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import pretrainedmodels # https://github.com/Cadene/pretrained-models.pytorch#torchvision
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model = pretrainedmodels.__dict__[name](num_classes=1000, pretrained='imagenet')
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# Display model properties
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for x in ['model.input_size', 'model.input_space', 'model.input_range', 'model.mean', 'model.std']:
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print(x + ' =', eval(x))
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# Reshape output to n classes
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filters = model.last_linear.weight.shape[1]
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model.last_linear.bias = torch.nn.Parameter(torch.zeros(n))
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model.last_linear.weight = torch.nn.Parameter(torch.zeros(n, filters))
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model.last_linear.out_features = n
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return model
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