A PyTorch implementation of " EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks."

Related tags

Deep Learning deep-learning pytorch efficientnet
Overview

EfficientNet

A PyTorch implementation of EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.

[arxiv] [Official TF Repo]

Implement based on Official TF Repo. Only opened EfficientNet is included.
This repo not contains baseline network search(Mnas-Net) and compound coefficient search methods.

Some details(HyperParams, transform, EMA ...) are different with Original repo.

Pretrained network

This is not end-to-end imagenet trainning weight. Using the Official TF Pretrained weight.

Please check the conversion/readme.md and pretrained_example.ipynb

How to use:

python3 main.py -h
usage: main.py [-h] --save_dir SAVE_DIR [--root ROOT] [--gpus GPUS]
               [--num_workers NUM_WORKERS] [--model {b0}] [--epoch EPOCH]
               [--batch_size BATCH_SIZE] [--test]
               [--dropout_rate DROPOUT_RATE]
               [--dropconnect_rate DROPCONNECT_RATE] [--optim {adam,rmsprop}]
               [--lr LR] [--beta [BETA [BETA ...]]] [--momentum MOMENTUM]
               [--eps EPS] [--decay DECAY]

Pytorch EfficientNet

optional arguments:
  -h, --help            show this help message and exit
  --save_dir SAVE_DIR   Directory name to save the model
  --root ROOT           The Directory of data path.
  --gpus GPUS           Select GPU Numbers | 0,1,2,3 |
  --num_workers NUM_WORKERS
                        Select CPU Number workers
  --model {b0}          The type of Efficient net.
  --epoch EPOCH         The number of epochs
  --batch_size BATCH_SIZE
                        The size of batch
  --test                Only Test
  --dropout_rate DROPOUT_RATE
  --dropconnect_rate DROPCONNECT_RATE
  --optim {adam,rmsprop}
  --lr LR               Base learning rate when train batch size is 256.
  --beta [BETA [BETA ...]]
  --momentum MOMENTUM
  --eps EPS
  --decay DECAY

TODO

  • Hyper Parameter / Imagenet Transformation Check
  • Implementation of Resolution Change
  • Validation on Imagenet Dataset
  • Clean up logging
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Comments
  • convert Tensorflow weights to Pytorch weights

    convert Tensorflow weights to Pytorch weights

    I have converted Tensorflow weights to Pytorch weights perfectly using your code in EfficientNet-b0, but I got some wrong from EfficientNet-b1 to EfficientNet-b3. I would like to ask if you have completed the code, and can you help fix these bugs? Traceback (most recent call last): File "convert.py", line 117, in convert_MBConv(mbconv, key_to_param(tf_mbconv)) File "convert.py", line 68, in convert_MBConv convert_se(m.se, tf_params[10:14]) File "convert.py", line 55, in convert_se convert_conv(m.se[1], tf_params[0], tf_params[1]) File "convert.py", line 19, in convert_conv m.weight.data = torch.from_numpy(np.transpose(weight, (3, 2, 0, 1))) File "/home/xux/anaconda3/envs/tf1.9_py3.6/lib/python3.6/site-packages/numpy/core/fromnumeric.py", line 639, in transpose return _wrapfunc(a, 'transpose', axes) File "/home/xux/anaconda3/envs/tf1.9_py3.6/lib/python3.6/site-packages/numpy/core/fromnumeric.py", line 56, in _wrapfunc return getattr(obj, method)(*args, **kwds) ValueError: axes don't match array

    I download eval_ckpt_main.py and some other files from Official TF Repo, I don't know if they are necessary. Can you give me some help?

    opened by xbpgithub 2
  • Stochastic depth drop connect rate schedule

    Stochastic depth drop connect rate schedule

    HI @zsef123 Great work! You said in another issue that you are gonna implement the stochastic depth. Just a quick note that original Efficient Net uses linear schedule which is the same as the Stochastic depth paper, where Layer 0 has drop rate 0 and the last layer has drop rate 0.2.

    Thank you!

    opened by wuhy08 0
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