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@@ -0,0 +1,60 @@
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+import os
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+import random
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+import shutil
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+import time
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+import warnings
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+
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+import numpy as np
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+import torch
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+import torch.nn as nn
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+import torch.nn.parallel
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+import torch.backends.cudnn as cudnn
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+import torch.distributed as dist
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+import torch.optim
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+import torch.multiprocessing as mp
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+import torch.utils.data
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+import torch.utils.data.distributed
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+import torchvision.transforms as transforms
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+import torchvision.datasets as datasets
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+
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+
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+
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+def convert(image_folder, gpu_id=None, batch_size=1):
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+
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+ if gpu_id != None:
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+ torch.cuda.set_device(gpu_id)
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+
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+ # prepare valid dataloader
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+ val_transform = transforms.Compose([
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+ transforms.Resize(342),
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+ transforms.CenterCrop(299),
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+ transforms.ToTensor(),
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+ transforms.Normalize(mean=[0.5, 0.5, 0.5],
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+ std=[0.5, 0.5, 0.5])
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+ ])
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+ val_dataset = datasets.ImageFolder(image_folder, val_transform)
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+ val_loader = torch.utils.data.DataLoader(
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+ val_dataset, batch_size=batch_size, shuffle=False,
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+ num_workers=1, pin_memory=False)
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+ # valid model in the valid dataloader
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+ validate(val_loader, gpu_id)
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+
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+def validate(val_loader, gpu_id):
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+ with torch.no_grad():
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+ if gpu_id != None:
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+ torch.cuda.synchronize()
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+ for i, (images, target) in enumerate(val_loader):
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+ images = images.permute(0, 2, 3, 1)
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+ #print(images.shape)
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+ #print(target.item())
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+ inpy = images.numpy()
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+ f = open('calib_data_c/%05d.bin'%i, 'wb')
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+ f.write(inpy.tobytes('C'))
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+ f.close()
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+
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+
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+
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+convert('calib_data', 0)
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+convert('val_Data', 0)
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+
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+
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