add cviruntime
commit 3f4938648950a7f3bf9a19c320ca9fae7c52de20 Author: sophgo-forum-service <forum_service@sophgo.com> Date: Mon May 13 13:44:23 2024 +0800 [feat] cviruntime opensource for cv18xx soc. - a4b6a3, add cumsum and gatherelements_pt.
This commit is contained in:
carbon
215
cviruntime/python/eval_imagenet.py
Executable file
215
cviruntime/python/eval_imagenet.py
Executable file
@ -0,0 +1,215 @@
|
||||
#!/usr/bin/env python3
|
||||
# use pytorch for dataloader
|
||||
# https://github.com/pytorch/examples/blob/master/imagenet/main.py
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import random
|
||||
import shutil
|
||||
import time
|
||||
import warnings
|
||||
import numpy as np
|
||||
import pyruntime
|
||||
from PIL import Image
|
||||
from PIL import ImageFile
|
||||
ImageFile.LOAD_TRUNCATED_IMAGES = True
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.utils.data
|
||||
import torchvision.transforms as transforms
|
||||
import torchvision.datasets as datasets
|
||||
|
||||
class AverageMeter(object):
|
||||
"""Computes and stores the average and current value"""
|
||||
def __init__(self, name, fmt=':f'):
|
||||
self.name = name
|
||||
self.fmt = fmt
|
||||
self.reset()
|
||||
|
||||
def reset(self):
|
||||
self.val = 0
|
||||
self.avg = 0
|
||||
self.sum = 0
|
||||
self.count = 0
|
||||
|
||||
def update(self, val, n=1):
|
||||
self.val = val
|
||||
self.sum += val * n
|
||||
self.count += n
|
||||
self.avg = self.sum / self.count
|
||||
|
||||
def __str__(self):
|
||||
fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
|
||||
return fmtstr.format(**self.__dict__)
|
||||
|
||||
class ProgressMeter(object):
|
||||
def __init__(self, num_batches, meters, prefix=""):
|
||||
self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
|
||||
self.meters = meters
|
||||
self.prefix = prefix
|
||||
|
||||
def display(self, batch):
|
||||
entries = [self.prefix + self.batch_fmtstr.format(batch)]
|
||||
entries += [str(meter) for meter in self.meters]
|
||||
print('\t'.join(entries))
|
||||
|
||||
def _get_batch_fmtstr(self, num_batches):
|
||||
num_digits = len(str(num_batches // 1))
|
||||
fmt = '{:' + str(num_digits) + 'd}'
|
||||
return '[' + fmt + '/' + fmt.format(num_batches) + ']'
|
||||
|
||||
def accuracy(output, target, topk=(1,)):
|
||||
"""Computes the accuracy over the k top predictions for the specified values of k"""
|
||||
maxk = max(topk)
|
||||
batch_size = target.size(0)
|
||||
|
||||
_, pred = output.topk(maxk, 1, True, True)
|
||||
pred = pred.t()
|
||||
correct = pred.eq(target.view(1, -1).expand_as(pred))
|
||||
|
||||
res = []
|
||||
for k in topk:
|
||||
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
|
||||
res.append(correct_k.mul_(100.0 / batch_size))
|
||||
return res
|
||||
|
||||
def datasetLoader(args):
|
||||
image_resize_dims = [int(s) for s in args.image_resize_dims.split(',')]
|
||||
net_input_dims = [int(s) for s in args.net_input_dims.split(',')]
|
||||
image_resize_dims = [ max(x,y) for (x,y) in zip(image_resize_dims, net_input_dims)]
|
||||
|
||||
valdir = os.path.join(args.dataset, 'val')
|
||||
if (args.loader_transforms):
|
||||
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
|
||||
std=[0.229, 0.224, 0.225])
|
||||
val_loader = torch.utils.data.DataLoader(
|
||||
datasets.ImageFolder(valdir, transforms.Compose([
|
||||
transforms.Resize(image_resize_dims),
|
||||
transforms.CenterCrop(net_input_dims),
|
||||
transforms.ToTensor(),
|
||||
normalize,
|
||||
])),
|
||||
batch_size=args.batch_size, shuffle=True)
|
||||
else:
|
||||
val_loader = torch.utils.data.DataLoader(
|
||||
datasets.ImageFolder(valdir, transforms.Compose([
|
||||
transforms.Resize(image_resize_dims),
|
||||
transforms.CenterCrop(net_input_dims),
|
||||
transforms.ToTensor()
|
||||
])),
|
||||
batch_size=args.batch_size, shuffle=True)
|
||||
return val_loader
|
||||
|
||||
def imagePreprocssing(args, images, mean, qscale):
|
||||
inputs = np.array([])
|
||||
for image in images:
|
||||
if args.loader_transforms:
|
||||
# loader do normalize already
|
||||
x = image.numpy()
|
||||
else:
|
||||
# pytorch ToTensor() will do HWC to CHW, and change range to [0.0, 1.0]
|
||||
# for pytorch, seeing errors if not include ToTensor in transforms
|
||||
# change to range [0, 255]
|
||||
x = image.numpy() * 255
|
||||
x = x.astype('uint8')
|
||||
# transposed already in ToTensor()
|
||||
# x = np.transpose(x, (2, 0, 1))
|
||||
# still need the swap for caffe models
|
||||
x = x[[2,1,0], :, :]
|
||||
x = x.astype(np.float32)
|
||||
if args.raw_scale != 255.0:
|
||||
x = x * args.raw_scale / 255.0
|
||||
# apply mean
|
||||
if mean.size != 0:
|
||||
x -= mean
|
||||
if qscale != 0:
|
||||
x = x * qscale
|
||||
# expand to 4-D again
|
||||
x = np.expand_dims(x, axis=0)
|
||||
if inputs.size:
|
||||
inputs = np.append(inputs, x, axis=0)
|
||||
else:
|
||||
inputs = x
|
||||
|
||||
if args.input_scale != 1.0:
|
||||
inputs *= args.input_scale
|
||||
return inputs
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description="Classification Evaluation on ImageNet Dataset.")
|
||||
parser.add_argument("--cvimodel", type=str)
|
||||
parser.add_argument("--batch_size", type=int, default=1)
|
||||
parser.add_argument("--dataset", type=str, help="The root directory of the ImageNet dataset.")
|
||||
parser.add_argument("--image_resize_dims", type=str, default='256,256')
|
||||
parser.add_argument("--net_input_dims", type=str, default='224,224')
|
||||
parser.add_argument("--raw_scale", type=float, help="Multiply raw input image data by this scale.", default=255.0)
|
||||
parser.add_argument("--mean", help="Per Channel image mean values")
|
||||
parser.add_argument("--input_scale", type=float, help="Multiply input features by this scale.", default=1.0)
|
||||
parser.add_argument("--count", type=int, default=50000)
|
||||
parser.add_argument("--loader_transforms", type=int, help="image transform by torch loader", default=0)
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.mean:
|
||||
mean = np.array([float(s) for s in args.mean.split(',')], dtype=np.float32)
|
||||
mean = mean[:, np.newaxis, np.newaxis]
|
||||
else:
|
||||
mean = np.array([])
|
||||
|
||||
# load model
|
||||
model = pyruntime.Model(args.cvimodel, args.batch_size)
|
||||
print('load model {}'.format(args.cvimodel))
|
||||
|
||||
val_loader = datasetLoader(args)
|
||||
|
||||
batch_time = AverageMeter('Time', ':6.3f')
|
||||
losses = AverageMeter('Loss', ':.4e')
|
||||
top1 = AverageMeter('Acc@1', ':6.2f')
|
||||
top5 = AverageMeter('Acc@5', ':6.2f')
|
||||
progress = ProgressMeter(len(val_loader) * args.batch_size,
|
||||
[batch_time, losses, top1, top5],
|
||||
prefix='Test: ')
|
||||
|
||||
# define loss function (criterion) and optimizer
|
||||
criterion = nn.CrossEntropyLoss()
|
||||
threshold = ((50 + args.batch_size - 1) // args.batch_size) * args.batch_size
|
||||
total = len(val_loader) * args.batch_size
|
||||
count = 0
|
||||
end = time.time()
|
||||
for i, (images, target) in enumerate(val_loader):
|
||||
# preprocessing
|
||||
x = imagePreprocssing(args, images, mean, model.inputs[0].qscale)
|
||||
# inference
|
||||
model.inputs[0].data[:] = x
|
||||
model.forward()
|
||||
|
||||
# validate output prob
|
||||
assert(len(model.outputs) == 1)
|
||||
res = model.outputs[0].data
|
||||
prob = np.reshape(res, (res.shape[0], res.shape[1]))
|
||||
output = torch.from_numpy(prob)
|
||||
|
||||
# loss
|
||||
loss = criterion(output, target)
|
||||
|
||||
# measure accuracy and record loss
|
||||
acc1, acc5 = accuracy(output, target, topk=(1, 5))
|
||||
losses.update(loss.item(), images.size(0))
|
||||
top1.update(acc1[0], images.size(0))
|
||||
top5.update(acc5[0], images.size(0))
|
||||
|
||||
# measure elapsed time
|
||||
batch_time.update(time.time() - end)
|
||||
end = time.time()
|
||||
|
||||
count += args.batch_size
|
||||
if count % threshold == 0:
|
||||
progress.display(count)
|
||||
if count >= args.count:
|
||||
progress.display(count)
|
||||
break
|
||||
if count + args.batch_size > total:
|
||||
progress.display(count)
|
||||
break
|
||||
print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
|
||||
.format(top1=top1, top5=top5))
|
||||
Reference in New Issue
Block a user