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model_creator.py
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model_creator.py
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import models
import torch.nn
import torch.backends
import torchvision
import pruning.methods as pruning
import sys
import os
def setup_model(params) :
if params.dataset == 'cifar10' :
import models.cifar as models
num_classes = 10
elif params.dataset == 'cifar100' :
import models.cifar as models
num_classes = 100
else :
import models.imagenet as models
num_classes = 1000
print("Creating Model %s" % params.arch)
if params.arch.endswith('resnet'):
model = models.__dict__[params.arch](
num_classes=num_classes,
depth=params.depth
)
else:
model = models.__dict__[params.arch](num_classes=num_classes)
model = torch.nn.DataParallel(model, params.gpu_list)
model = model.cuda()
if params.resume == True or params.branch == True:
checkpoint = torch.load(params.pretrained)
model.load_state_dict(checkpoint, strict=False)
if params.evaluate == True:
checkpoint = torch.load(params.pretrained)
model.load_state_dict(checkpoint['state_dict'])
if params.finetune == True:
checkpoint = torch.load(params.pretrained)
if 'state_dict' in checkpoint.keys() :
model.load_state_dict(checkpoint['state_dict'], strict=False)
else :
model.load_state_dict(checkpoint, strict=False)
torch.backends.cudnn.benchmark = True
print('Total params: %.2fM' % (sum(p.numel() for p in model.parameters())/1000000.0))
criterion = torch.nn.CrossEntropyLoss()
optimiser = torch.optim.SGD(model.parameters(), lr=params.lr, momentum=params.momentum, weight_decay=params.weight_decay)
return (model, criterion, optimiser)