def load_model_from_name(x):
opt = cifar10Manager.load_metadata(x, 0)[0]
#small old bug in the saving of metadata, this is a cheap trick to remedy it
for key, val in opt.items():
if isinstance(val, str):
opt[key] = eval(val)
model = convForwModel.ConvolForwardNet(**opt)
if USE_CUDA: model = model.cuda()
model.load_state_dict(cifar10Manager.load_model_state_dict(x))
return model
TRAIN_TEACHER_MODEL = True
TRAIN_SMALLER_MODEL = False
TRAIN_SMALLER_QUANTIZED_MODEL = False
TRAIN_DISTILLED_MODEL = False
TRAIN_DIFFERENTIABLE_QUANTIZATION = False
CHECK_PM_QUANTIZATION = True
batch_size = 25
cifar10 = datasets.CIFAR10()
train_loader, test_loader = cifar10.getTrainLoader(batch_size), cifar10.getTestLoader(batch_size)
# Teacher model
model_name = 'cifar10_teacher'
teacherModelPath = os.path.join(cifar10modelsFolder, model_name)
teacherModel = convForwModel.ConvolForwardNet(**convForwModel.teacherModelSpec,
useBatchNorm=USE_BATCH_NORM,
useAffineTransformInBatchNorm=AFFINE_BATCH_NORM)
if USE_CUDA: teacherModel = teacherModel.cuda()
if not model_name in cifar10Manager.saved_models:
cifar10Manager.add_new_model(model_name, teacherModelPath,
arguments_creator_function={**convForwModel.teacherModelSpec,
'useBatchNorm':USE_BATCH_NORM,
'useAffineTransformInBatchNorm':AFFINE_BATCH_NORM})
if TRAIN_TEACHER_MODEL:
cifar10Manager.train_model(teacherModel, model_name=model_name,
train_function=convForwModel.train_model,
arguments_train_function={'epochs_to_train': epochsToTrainCIFAR},
train_loader=train_loader, test_loader=test_loader)
teacherModel.load_state_dict(cifar10Manager.load_model_state_dict(model_name))
cnn_hf.evaluateModel(teacherModel, test_loader, k=5)
except:
pass
epochsToTrainCIFAR = args.epochs
USE_BATCH_NORM = True
AFFINE_BATCH_NORM = True
if args.data == 'cifar10':
data = datasets.CIFAR10()
elif args.data == 'cifar100':
data = datasets.CIFAR100()
if args.test_memory:
if not args.train_teacher:
model = convForwModel.ConvolForwardNet(
**smallerModelSpecs[args.stModel],
activation=args.stud_act,
numBins=args.num_bins,
useBatchNorm=USE_BATCH_NORM,
useAffineTransformInBatchNorm=AFFINE_BATCH_NORM)
else:
model = convForwModel.ConvolForwardNet(
**convForwModel.teacherModelSpec,
useBatchNorm=USE_BATCH_NORM,
useAffineTransformInBatchNorm=AFFINE_BATCH_NORM)
if USE_CUDA: model = model.cuda()
test_loader = data.getTestLoader(1)
import time
start = time.time()
cnn_hf.evaluateModel(model, test_loader)
mem = torch.cuda.max_memory_allocated()
end = time.time()
datasets.BASE_DATA_FOLDER = 'data'
batch_size = 50
epochsToTrainCIFAR = 100
TRAIN_TEACHER_MODEL = False
TRAIN_DISTILLED_MODEL = True
TRAIN_SMALLER_MODEL = True
TRAIN_DISTILLED_QUANTIZED_MODEL = True
cifar10 = datasets.CIFAR10() #-> will be saved in /home/saved_datasets/cifar10
train_loader, test_loader = cifar10.getTrainLoader(batch_size), cifar10.getTestLoader(batch_size)
import cnn_models.conv_forward_model as convForwModel
import cnn_models.help_fun as cnn_hf
teacherModel = convForwModel.ConvolForwardNet(**convForwModel.teacherModelSpec, useBatchNorm=True, useAffineTransformInBatchNorm=True)
#convForwModel.train_model(teacherModel, train_loader, test_loader, epochs_to_train=20)
import cnn_models.conv_forward_model as convForwModel
import cnn_models.help_fun as cnn_hf
import model_manager
model_manager_path = 'model_manager_cifar10.tst'
model_save_path ='models'
__mkdir(model_save_path)
if os.path.exists(model_manager_path):
cifar10Manager = model_manager.ModelManager('model_manager_cifar10.tst',
'model_manager', create_new_model_manager=False)#the first t
else:
cifar10Manager = model_manager.ModelManager('model_manager_cifar10.tst',
'model_manager', create_new_model_manager=True)#the first t
distilled_model_names = [
'cifar10_distilled_spec{}'.format(idx_spec)
for idx_spec in range(len(smallerModelSpecs))
]
for distilled_model_name in distilled_model_names:
modelOptions = cifar10Manager.load_metadata(distilled_model_name, 0)[0]
# small old bug in the saving of metadata, this is a cheap trick to remedy it
for key, val in modelOptions.items():
if isinstance(val, str):
modelOptions[key] = eval(val)
for numBit in numBits:
if numBit == 8: continue
distilled_quantized_model_name = distilled_model_name + '_quant_points_{}bits'.format(
numBit)
distilled_quantized_model = convForwModel.ConvolForwardNet(
**modelOptions)
if USE_CUDA:
distilled_quantized_model = distilled_quantized_model.cuda()
save_path = cifar10Manager.get_model_base_path(distilled_model_name) + \
'quant_points_{}bits'.format(numBit)
with open(save_path, 'rb') as p:
quantization_points, infoDict = pickle.load(p)
distilled_quantized_model.load_state_dict(
torch.load(save_path + '_model_state_dict'))
quantization_functions = [
functools.partial(quantization.nonUniformQuantization,
listQuantizationPoints=qp,
bucket_size=256) for qp in quantization_points
]
