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main.py
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main.py
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import model_manager
import torch
import os
import datasets
import cnn_models.conv_forward_model as convForwModel
import cnn_models.help_fun as cnn_hf
import quantization
import pickle
import copy
import quantization.help_functions as qhf
import functools
import helpers.functions as mhf
import argparse
import numpy as np
parser = argparse.ArgumentParser(description = 'LMA for Model Compression')
parser.add_argument('-batch_size', type = int, default=64)
parser.add_argument('-init_lr', type = float, default=1e-2)
parser.add_argument('-epochs', type = int, default=200)
parser.add_argument('-stud_act', type = str, default='relu')
parser.add_argument('-num_bins', type = int, default=8)
parser.add_argument('-plot_title', type=str, default='test')
parser.add_argument('-train_teacher', action='store_true')
parser.add_argument('-train_student', action='store_true')
parser.add_argument('-manager', type = str, default='model_manager_cifar10')
parser.add_argument('-stModel', type = int, default=0)
parser.add_argument('-data', type = str, default='cifar10')
parser.add_argument('-seed', type = int, default=1)
parser.add_argument('-test_memory', action='store_true')
args = parser.parse_args()
args.plot_title = 'summary/'+args.plot_title+'_seed_'+str(args.seed)+'_nb_'+str(args.num_bins)
datasets.BASE_DATA_FOLDER = 'datas'
SAVED_MODELS_FOLDER = 'models'
MANAGER_FOLDER = 'manager'
USE_CUDA = torch.cuda.is_available()
torch.manual_seed(args.seed)
np.random.seed(args.seed)
#Define the architechtures we want to try
smallerModelSpec0 = {'spec_conv_layers': [(75, 5, 5), (50, 5, 5), (50, 5, 5), (25, 5, 5)],
'spec_max_pooling': [(1, 2, 2), (3, 2, 2)],
'spec_dropout_rates': [(1, 0.2), (3, 0.3), (4, 0.4)],
'spec_linear': [500], 'width': 32, 'height': 32}
smallerModelSpec1 = {'spec_conv_layers': [(50, 5, 5), (25, 5, 5), (25, 5, 5), (10, 5, 5)],
'spec_max_pooling': [(1, 2, 2), (3, 2, 2)],
'spec_dropout_rates': [(1, 0.2), (3, 0.3), (4, 0.4)],
'spec_linear': [400], 'width': 32, 'height': 32}
smallerModelSpec2 = {'spec_conv_layers': [(25, 5, 5), (10, 5, 5), (10, 5, 5), (5, 5, 5)],
'spec_max_pooling': [(1, 2, 2), (3, 2, 2)],
'spec_dropout_rates': [(1, 0.2), (3, 0.3), (4, 0.4)],
'spec_linear': [300], 'width': 32, 'height': 32}
smallerModelSpecs = [smallerModelSpec0, smallerModelSpec1, smallerModelSpec2]
print('CUDA_VISIBLE_DEVICES: {}'.format(os.environ['CUDA_VISIBLE_DEVICES']))
try:
os.mkdir(datasets.BASE_DATA_FOLDER)
except:pass
try:
os.mkdir(SAVED_MODELS_FOLDER)
except:pass
try:
os.mkdir(MANAGER_FOLDER)
except:pass
manager_path = os.path.join(MANAGER_FOLDER, args.manager+'.tst')
create_new = True
if os.path.exists(manager_path):
create_new = False
Manager = model_manager.ModelManager(manager_path,
args.manager,
create_new_model_manager=create_new)
modelsFolder = os.path.join(SAVED_MODELS_FOLDER, args.data)
try:
os.mkdir(modelsFolder)
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()
avg_time = (end-start)*1000 / len(test_loader)
if args.train_teacher:
str2save = 'teacher_cifar10: time: {} ms, memory: {} M'.format(avg_time, mem/(1024**2))
print(str2save)
else:
str2save = 's_{}_{}_nb_{}cifar10: time: {} ms, memory: {} M'.format(args.stModel, args.stud_act, args.num_bins, avg_time, mem/(1024**2))
print(str2save)
with open('memory.txt', 'a') as fr:
fr.write(str2save+'\n')
torch.cuda.empty_cache()
import sys
sys.exit()
train_loader, test_loader = data.getTrainLoader(args.batch_size), data.getTestLoader(args.batch_size)
# Teacher model
model_name = args.manager+'_%s_teacher'%args.data
teacherModelPath = os.path.join(modelsFolder, model_name)
teacherModel = convForwModel.ConvolForwardNet(**convForwModel.teacherModelSpec,
useBatchNorm=USE_BATCH_NORM,
useAffineTransformInBatchNorm=AFFINE_BATCH_NORM)
if USE_CUDA: teacherModel = teacherModel.cuda()
print('Parameters number of teacher model: '+str(sum(p.numel() for p in teacherModel.parameters())))
if not model_name in Manager.saved_models:
Manager.add_new_model(model_name, teacherModelPath,
arguments_creator_function={**convForwModel.teacherModelSpec,
'useBatchNorm':USE_BATCH_NORM,
'useAffineTransformInBatchNorm':AFFINE_BATCH_NORM})
if args.train_teacher:
Manager.train_model(teacherModel, model_name=model_name,
train_function=convForwModel.train_model,
arguments_train_function={'epochs_to_train': epochsToTrainCIFAR,
'initial_learning_rate': args.init_lr,
'plot_path': args.plot_title+model_name},
train_loader=train_loader, test_loader=test_loader)
teacherModel.load_state_dict(Manager.load_model_state_dict(model_name))
#train normal distilled
model = convForwModel.ConvolForwardNet(**smallerModelSpecs[args.stModel],
activation=args.stud_act,
numBins=args.num_bins,
useBatchNorm=USE_BATCH_NORM,
useAffineTransformInBatchNorm=AFFINE_BATCH_NORM)
model_name = args.manager + '_%s_smaller_distilled_'%args.data + args.stud_act + '_s_' + str(args.stModel) + 'tmp'
model_path = os.path.join(modelsFolder, model_name)
if USE_CUDA: model = model.cuda()
print('Parameters number of student model: '+str(sum(p.numel() for p in model.parameters())))
if args.train_student:
Manager.remove_model(model_name)
Manager.add_new_model(model_name, model_path,
arguments_creator_function={**smallerModelSpecs[args.stModel],
'activation':args.stud_act,
'numBins':args.num_bins,
'useBatchNorm':USE_BATCH_NORM,
'useAffineTransformInBatchNorm':AFFINE_BATCH_NORM})
Manager.train_model(model, model_name=model_name,
train_function=convForwModel.train_model,
arguments_train_function={'epochs_to_train': epochsToTrainCIFAR,
'initial_learning_rate': args.init_lr,
'use_distillation_loss': True,
'plot_path': args.plot_title+model_name,
'teacher_model': teacherModel},
train_loader=train_loader, test_loader=test_loader)