prepare_static.py

# -*- coding: utf-8 -*-

from __future__ import print_function, division

import argparse
import torch
from torchvision import datasets, transforms
import time
import os

version =  torch.__version__

######################################################################
# Options
# --------
parser = argparse.ArgumentParser(description='Training')
parser.add_argument('--data_dir',default='/home/zzd/Market/pytorch',type=str, help='training dir path')
parser.add_argument('--train_all', action='store_true', help='use all training data' )
parser.add_argument('--color_jitter', action='store_true', help='use color jitter in training' )
parser.add_argument('--batchsize', default=128, type=int, help='batchsize')
opt = parser.parse_args()

data_dir = opt.data_dir

######################################################################
# Load Data
# ---------
#

transform_train_list = [
        #transforms.RandomResizedCrop(size=128, scale=(0.75,1.0), ratio=(0.75,1.3333), interpolation=3), #Image.BICUBIC)
        transforms.Resize((288,144), interpolation=3),
        #transforms.RandomCrop((256,128)),
        #transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ]

transform_val_list = [
        transforms.Resize(size=(256,128),interpolation=3), #Image.BICUBIC
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ]


print(transform_train_list)
data_transforms = {
    'train': transforms.Compose( transform_train_list ),
    'val': transforms.Compose(transform_val_list),
}


train_all = ''
if opt.train_all:
     train_all = '_all'

image_datasets = {}
image_datasets['train'] = datasets.ImageFolder(os.path.join(data_dir, 'train' + train_all),
                                          data_transforms['train'])
image_datasets['val'] = datasets.ImageFolder(os.path.join(data_dir, 'val'),
                                          data_transforms['val'])

dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=opt.batchsize,
                                             shuffle=True, num_workers=16)
              for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes

use_gpu = torch.cuda.is_available()

######################################################################
# prepare_dataset
# ------------------
#
# Now, let's write a general function to train a model. Here, we will
# illustrate:
#
# -  Scheduling the learning rate
# -  Saving the best model
#
# In the following, parameter ``scheduler`` is an LR scheduler object from
# ``torch.optim.lr_scheduler``.

def prepare_model():
    since = time.time()

    num_epochs = 1
    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)

        # Each epoch has a training and validation phase
        for phase in ['train']:

            mean = torch.zeros(3)
            std = torch.zeros(3)
            # Iterate over data.
            for data in dataloaders[phase]:
                # get the inputs
                inputs, labels = data
                now_batch_size,c,h,w = inputs.shape
                mean += torch.sum(torch.mean(torch.mean(inputs,dim=3),dim=2),dim=0)
                std += torch.sum(torch.std(inputs.view(now_batch_size,c,h*w),dim=2),dim=0)
                
            print(mean/dataset_sizes['train'])
            print(std/dataset_sizes['train'])

    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(
        time_elapsed // 60, time_elapsed % 60))
    return 



prepare_model()