mindsporelite-image-classification/np.py

import os
import random
import shutil
import time
import warnings

import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing as mp
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets



def convert(image_folder, gpu_id=None, batch_size=1):

    if gpu_id != None:
        torch.cuda.set_device(gpu_id)

    # prepare valid dataloader
    val_transform = transforms.Compose([
        transforms.Resize(342),
        transforms.CenterCrop(299),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.5, 0.5, 0.5],
                             std=[0.5, 0.5, 0.5])
    ])
    val_dataset = datasets.ImageFolder(image_folder, val_transform)
    val_loader = torch.utils.data.DataLoader(
        val_dataset, batch_size=batch_size, shuffle=False,
        num_workers=1, pin_memory=False)
    # valid model in the valid dataloader
    validate(val_loader, gpu_id)

def validate(val_loader, gpu_id):
    with torch.no_grad():
        if gpu_id != None:
            torch.cuda.synchronize()
        for i, (images, target) in enumerate(val_loader):
            images = images.permute(0, 2, 3, 1)
            #print(images.shape)
            #print(target.item())
            inpy = images.numpy()
            f = open('calib_data_c/%05d.bin'%i, 'wb')
            f.write(inpy.tobytes('C'))
            f.close()
            


convert('calib_data', 0)
convert('val_Data', 0)