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SOL4Py Sample: TorchCIFARModel
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#******************************************************************************
#
# Copyright (c) 2018-2019 Antillia.com TOSHIYUKI ARAI. ALL RIGHTS RESERVED.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
#******************************************************************************
# 2019/06/28
# See: https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#loading-and-normalizing-cifar10
# TorchCIFARModel.py
# encodig: utf-8
import sys
import os
import cv2
import time
import traceback
import pandas as pd
import seaborn as sns
import socket
import matplotlib.pyplot as plt
import numpy as np
sys.path.append('../../')
from SOL4Py.ZMain import *
from SOL4Py.ZMLModel import *
from SOL4Py.torch.ZTorchEpochChangeNotifier import *
from SOL4Py.torch.ZTorchSimpleModel import *
CIFAR10 = 0
CIFAR100 = 1
############################################################
# Classifier Model class
class TorchCIFARModel(ZMLModel):
##
# Constructor
def __init__(self, dataset_id, epochs=0, mainv=None, ipaddress="127.0.0.1", port=7777):
super(TorchCIFARModel, self).__init__(0, mainv)
#self.view = mainv
self._start(self.__init__.__name__)
self.write("dataset_id:{}, ephochs:{}, mainv:{}".format(dataset_id, epochs, mainv) )
self.ipaddress = ipaddress
self.port = port
self.model = None #
self.dataset_id = dataset_id # CIFAR10 or CIFAR100
self.dataset = None
self.epochs = epochs
self.set_dataset_id(dataset_id)
notifier = self.__class__.__name__+str("-") + str(self.dataset_id)
self.callbacks = [ZTorchEpochChangeNotifier(ipaddress, port, notifier, int(self.epochs)+10)]
self._end(self.__init__.__name__)
def set_dataset_id(self, dataset_id):
self._start(self.set_dataset_id.__name__)
self.dataset_id = dataset_id
self.model_filename = self.__class__.__name__ + "_" + str(self.dataset_id) + ".pt"
self.nclasses = 0
self.write("model_filename " + self.model_filename)
self._end(self.set_dataset_id.__name__)
def build(self):
self.write("====================================")
self._start(self.build.__name__)
if self.is_trained() != True:
try:
self.load_dataset()
self.create()
self.train()
#self.evaluate()
self.save()
except:
traceback.print_exc()
self._end(self.build.__name__)
#
def load_dataset(self, data_root = "./data",
batch_size_train= 128,
batch_size_test = 64):
self._start(self.load_dataset.__name__)
self.train_transformer = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
self.test_transformer = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
# Load CIFAR10
if self.dataset_id == CIFAR10:
self.trainset = torchvision.datasets.CIFAR10(
root=data_root, train=True, download=True, transform=self.train_transformer)
self.train_loader = torch.utils.data.DataLoader(
self.trainset, batch_size=batch_size_train, shuffle=True, num_workers=2)
self.testset = torchvision.datasets.CIFAR10(
root=data_root, train=False, download=True, transform=self.test_transformer)
self.test_loader = torch.utils.data.DataLoader(
self.testset, batch_size=batch_size_test, shuffle=False, num_workers=2)
self.nclasses = 10
# Load CIFAR100
if self.dataset_id == CIFAR100:
self.trainset = torchvision.datasets.CIFAR100(
root=data_root, train=True, download=True, transform=self.train_transformer)
self.train_loader = torch.utils.data.DataLoader(
self.trainset, batch_size=batch_size_train, shuffle=True, num_workers=2)
self.testset = torchvision.datasets.CIFAR100(
root=data_root, train=False, download=True, transform=self.test_transformer)
self.test_loader = torch.utils.data.DataLoader(
self.testset, batch_size=batch_size_test, shuffle=False, num_workers=2)
self.nclasses = 100
self._end(self.load_dataset.__name__)
# Create a sequential model
def create(self):
self._start(self.create.__name__)
self.image_size = (3, 32, 32)
print("classes {}".format(self.nclasses))
self.model = ZTorchSimpleModel(self.image_size, self.nclasses, self.model_filename)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.model = self.model.to(device)
self._end(self.create.__name__)
def train(self):
self._start(self.train.__name__)
start = time.time()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(self.model.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)
self.model.fit(self.train_loader,
self.test_loader,
self.callbacks,
self.epochs,
criterion,
optimizer)
elapsed_time = time.time() - start
elapsed = str("Train elapsed_time:{0}".format(elapsed_time) + "[sec]")
self.write(elapsed)
self.model.summary()
self._end(self.train.__name__)
def predict(self, input):
#image_tensor = self.test_transformer(image).float()
#image_tensor = image_tensor.unsqueeze_(0)
#input = Variable(image_tensor)
prediction = self.model.predict(input)
return prediction
def save(self):
self._start(self.save.__name__)
self.model.save()
self._end(self.save.__name__)
def load(self):
self._start(self.load.__name__)
try:
self.model.load_model()
#self.write("Loaded a weight file:{}".format(self.model_file))
except:
self.write( formatted_traceback() )
self._end(self.load.__name__)
def get_model(self):
return self.model
def is_trained(self):
rc = False
if os.path.isfile(self.model_filename) == True:
self.write("Found model_filename:'{}'".format(self.model_filename))
rc = True
return rc
def evaluate(self):
self._start(self.evaluate.__name__)
try:
score = 0 # self.model.evaluate(self.X_test, self.y_test, verbose=0)
#self.write("Test loss :{}".format(score[0]))
#self.write("Test accuracy:{}".format(score[1]))
pass
except:
self.write(formatted_traceback())
self._end(self.evaluate.__name__)
############################################################
#
if main(__name__):
try:
app_name = os.path.basename(sys.argv[0])
dataset_id = CIFAR10
epochs = 20 #2019/04/25
if len(sys.argv) >= 2:
dataset_id = int(sys.argv[1])
if len(sys.argv) >= 3:
epochs = int(sys.argv[2])
print("dataset_id:{} epochs:{}".format(dataset_id, epochs))
if dataset_id == CIFAR10 or dataset_id == CIFAR100 :
model = TorchCIFARModel(dataset_id, epochs, None)
model.build()
else:
print("Invalid dataset_id: {}".format(dataset_id))
except:
traceback.print_exc()
Last modified:20 Sep. 2019