我要转换的模型：https ://github.com/ultralytics/yolov3
我正在尝试将此 pytorch yolov3 模型转换为 coreML，为此我使用了 ONNX，它用于将模型从一个平台转换为另一个平台。它正在转换模型，当我在 xcode 中运行它时，我可以看到它的输入和输出不同，它没有检测到任何对象，也没有在屏幕上显示任何矩形。
我已经尝试过本教程并遵循了类似的步骤，但这次是针对 YOLOV3。谷歌文档：https ://drive.google.com/drive/folders/1uxgUBemJVw9wZsdpboYbzUN4bcRhsuAI
我还检查了 Yolov3 自己也提供了“onnx”文件，所以我什至尝试使用该文件并将其转换为 coreML，但它仍然没有检测到对象并提供错误的输入/输出值。
将 Pytroch 转换为 ONNX
名为“yoloToOnnx.py”的文件内容：
import argparse
import os
import sys
import time
import re
import numpy as np
import torch
from torch.optim import Adam
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision import transforms
import torch.onnx
import utils
from transformer_net import TransformerNet
from vgg import Vgg16
def check_paths(args):
try:
if not os.path.exists(args.save_model_dir):
os.makedirs(args.save_model_dir)
if args.checkpoint_model_dir is not None and not (os.path.exists(args.checkpoint_model_dir)):
os.makedirs(args.checkpoint_model_dir)
except OSError as e:
print(e)
sys.exit(1)
def train(args):
device = torch.device("cuda" if args.cuda else "cpu")
np.random.seed(args.seed)
torch.manual_seed(args.seed)
transform = transforms.Compose([
transforms.Resize(args.image_size),
transforms.CenterCrop(args.image_size),
transforms.ToTensor(),
transforms.Lambda(lambda x: x.mul(255))
])
train_dataset = datasets.ImageFolder(args.dataset, transform)
train_loader = DataLoader(train_dataset, batch_size=args.batch_size)
transformer = TransformerNet().to(device)
optimizer = Adam(transformer.parameters(), args.lr)
mse_loss = torch.nn.MSELoss()
vgg = Vgg16(requires_grad=False).to(device)
style_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Lambda(lambda x: x.mul(255))
])
style = utils.load_image(args.style_image, size=args.style_size)
style = style_transform(style)
style = style.repeat(args.batch_size, 1, 1, 1).to(device)
features_style = vgg(utils.normalize_batch(style))
gram_style = [utils.gram_matrix(y) for y in features_style]
for e in range(args.epochs):
transformer.train()
agg_content_loss = 0.
agg_style_loss = 0.
count = 0
for batch_id, (x, _) in enumerate(train_loader):
n_batch = len(x)
count += n_batch
optimizer.zero_grad()
x = x.to(device)
y = transformer(x)
y = utils.normalize_batch(y)
x = utils.normalize_batch(x)
features_y = vgg(y)
features_x = vgg(x)
content_loss = args.content_weight * mse_loss(features_y.relu2_2, features_x.relu2_2)
style_loss = 0.
for ft_y, gm_s in zip(features_y, gram_style):
gm_y = utils.gram_matrix(ft_y)
style_loss += mse_loss(gm_y, gm_s[:n_batch, :, :])
style_loss *= args.style_weight
total_loss = content_loss + style_loss
total_loss.backward()
optimizer.step()
agg_content_loss += content_loss.item()
agg_style_loss += style_loss.item()
if (batch_id + 1) % args.log_interval == 0:
mesg = "{}\tEpoch {}:\t[{}/{}]\tcontent: {:.6f}\tstyle: {:.6f}\ttotal: {:.6f}".format(
time.ctime(), e + 1, count, len(train_dataset),
agg_content_loss / (batch_id + 1),
agg_style_loss / (batch_id + 1),
(agg_content_loss + agg_style_loss) / (batch_id + 1)
)
print(mesg)
if args.checkpoint_model_dir is not None and (batch_id + 1) % args.checkpoint_interval == 0:
transformer.eval().cpu()
ckpt_model_filename = "ckpt_epoch_" + str(e) + "_batch_id_" + str(batch_id + 1) + ".pth"
ckpt_model_path = os.path.join(args.checkpoint_model_dir, ckpt_model_filename)
torch.save(transformer.state_dict(), ckpt_model_path)
transformer.to(device).train()
# save model
transformer.eval().cpu()
save_model_filename = "epoch_" + str(args.epochs) + "_" + str(time.ctime()).replace(' ', '_') + "_" + str(
args.content_weight) + "_" + str(args.style_weight) + ".model"
save_model_path = os.path.join(args.save_model_dir, save_model_filename)
torch.save(transformer.state_dict(), save_model_path)
print("\nDone, trained model saved at", save_model_path)
def stylize(args):
device = torch.device("cuda" if args.cuda else "cpu")
content_image = utils.load_image(args.content_image, scale=args.content_scale)
content_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Lambda(lambda x: x.mul(255))
])
content_image = content_transform(content_image)
content_image = content_image.unsqueeze(0).to(device)
if args.model.endswith(".onnx"):
output = stylize_onnx_caffe2(content_image, args)
else:
with torch.no_grad():
style_model = TransformerNet()
state_dict = torch.load(args.model)
# remove saved deprecated running_* keys in InstanceNorm from the checkpoint
for k in list(state_dict.keys()):
if re.search(r'in\d+\.running_(mean|var)$', k):
del state_dict[k]
style_model.load_state_dict(state_dict)
style_model.to(device)
if args.export_onnx:
assert args.export_onnx.endswith(".onnx"), "Export model file should end with .onnx"
output = torch.onnx._export(style_model, content_image, args.export_onnx).cpu()
else:
output = style_model(content_image).cpu()
utils.save_image(args.output_image, output[0])
def stylize_onnx_caffe2(content_image, args):
"""
Read ONNX model and run it using Caffe2
"""
assert not args.export_onnx
import onnx
import onnx_caffe2.backend
model = onnx.load(args.model)
prepared_backend = onnx_caffe2.backend.prepare(model, device='CUDA' if args.cuda else 'CPU')
inp = {model.graph.input[0].name: content_image.numpy()}
c2_out = prepared_backend.run(inp)[0]
return torch.from_numpy(c2_out)
def main():
main_arg_parser = argparse.ArgumentParser(description="parser for fast-neural-style")
subparsers = main_arg_parser.add_subparsers(title="subcommands", dest="subcommand")
train_arg_parser = subparsers.add_parser("train", help="parser for training arguments")
train_arg_parser.add_argument("--epochs", type=int, default=2,
help="number of training epochs, default is 2")
train_arg_parser.add_argument("--batch-size", type=int, default=4,
help="batch size for training, default is 4")
train_arg_parser.add_argument("--dataset", type=str, required=True,
help="path to training dataset, the path should point to a folder "
"containing another folder with all the training images")
train_arg_parser.add_argument("--style-image", type=str, default="images/style-images/mosaic.jpg",
help="path to style-image")
train_arg_parser.add_argument("--save-model-dir", type=str, required=True,
help="path to folder where trained model will be saved.")
train_arg_parser.add_argument("--checkpoint-model-dir", type=str, default=None,
help="path to folder where checkpoints of trained models will be saved")
train_arg_parser.add_argument("--image-size", type=int, default=256,
help="size of training images, default is 256 X 256")
train_arg_parser.add_argument("--style-size", type=int, default=None,
help="size of style-image, default is the original size of style image")
train_arg_parser.add_argument("--cuda", type=int, required=True,
help="set it to 1 for running on GPU, 0 for CPU")
train_arg_parser.add_argument("--seed", type=int, default=42,
help="random seed for training")
train_arg_parser.add_argument("--content-weight", type=float, default=1e5,
help="weight for content-loss, default is 1e5")
train_arg_parser.add_argument("--style-weight", type=float, default=1e10,
help="weight for style-loss, default is 1e10")
train_arg_parser.add_argument("--lr", type=float, default=1e-3,
help="learning rate, default is 1e-3")
train_arg_parser.add_argument("--log-interval", type=int, default=500,
help="number of images after which the training loss is logged, default is 500")
train_arg_parser.add_argument("--checkpoint-interval", type=int, default=2000,
help="number of batches after which a checkpoint of the trained model will be created")
eval_arg_parser = subparsers.add_parser("eval", help="parser for evaluation/stylizing arguments")
eval_arg_parser.add_argument("--content-image", type=str, required=True,
help="path to content image you want to stylize")
eval_arg_parser.add_argument("--content-scale", type=float, default=None,
help="factor for scaling down the content image")
eval_arg_parser.add_argument("--output-image", type=str, required=True,
help="path for saving the output image")
eval_arg_parser.add_argument("--model", type=str, required=True,
help="saved model to be used for stylizing the image. If file ends in .pth - PyTorch path is used, if in .onnx - Caffe2 path")
eval_arg_parser.add_argument("--cuda", type=int, required=True,
help="set it to 1 for running on GPU, 0 for CPU")
eval_arg_parser.add_argument("--export_onnx", type=str,
help="export ONNX model to a given file")
args = main_arg_parser.parse_args()
if args.subcommand is None:
print("ERROR: specify either train or eval")
sys.exit(1)
if args.cuda and not torch.cuda.is_available():
print("ERROR: cuda is not available, try running on CPU")
sys.exit(1)
if args.subcommand == "train":
check_paths(args)
train(args)
else:
stylize(args)
if __name__ == "__main__":
main()
命令：
python ./yoloToOnnx.py eval --content-image dummy.jpg --output-image dummy-out.jpg --model ./yolov3.pt --cuda 0 --export_onnx ./yolov3.onnx
将 ONNX 模型转换为 CoreML 模型：
文件 onnx_to_coreml.py 内容：
import sys
from onnx import onnx_pb
from onnx_coreml import convert
model_in = sys.argv[1]
model_out = sys.argv[2]
model_file = open(model_in, 'rb')
model_proto = onnx_pb.ModelProto()
model_proto.ParseFromString(model_file.read())
coreml_model = convert(model_proto, image_input_names=['0'], image_output_names=['186'])
coreml_model.save(model_out)
命令：
python onnx_to_coreml.py ./yolov3.onnx ./yolov3.mlmodel
我期待转换后我应该能够在我的 IOS 应用程序中使用这个模型，它将检测带有矩形及其名称的不同对象。
奇怪的是，在转换它时没有抛出任何错误，它给出了编译成功的消息，但在 IOS App 中使用时没有给出预期的输出。