EfficientNet-PyTorch | A PyTorch implementation of EfficientNet and EfficientNetV2 (coming soon!) | Machine Learning library
kandi X-RAY | EfficientNet-PyTorch Summary
kandi X-RAY | EfficientNet-PyTorch Summary
This repository contains an op-for-op PyTorch reimplementation of EfficientNet, along with pre-trained models and examples. The goal of this implementation is to be simple, highly extensible, and easy to integrate into your own projects. This implementation is a work in progress -- new features are currently being implemented.
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Top functions reviewed by kandi - BETA
- Main worker function
- Adjust the learning rate
- Save a checkpoint
- Train the model
- Connects the convolution layer
- Round a set of filters
- Calculate the number of repeat repeats
- Call the model
- Wrapper for squeeze
- Loads the efficiency netnet
- Load parameter from a checkpoint file
- Call the block
- Evaluate inference
- Create model function
- Create a model from pretrained pretrained weights
- Encode a list of blocks
- Build model base
- Runs inference on images
- Evaluate example images
- Perform the forward computation
- Calculate the image size
- Get the size of a given model
- Call stem layers
- Forward convolutional layer
- Evaluate an image
- Load a temporary tensorflow model
EfficientNet-PyTorch Key Features
EfficientNet-PyTorch Examples and Code Snippets
Lines of Code : 48License : Permissive (Apache-2.0)
Copyfrom efficientnet_pytorch import EfficientNet
model = EfficientNet.from_name('efficientnet-b0')
from efficientnet_pytorch import EfficientNet
model = EfficientNet.from_pretrained('efficientnet-b0')
import json
from PIL import Image
import torch
fro annotation{
"id": int,
"image_id": int,
"category_id": int,
"segmentation": RLE or [polygon],
"area": float,
"bbox": [x,y,width,height],
"iscrowd": 0 or 1
}
categories[{
"id": int,
"name": str,
"supercategory": str
}]
model = EfficientNet.from_pretrained("efficientnet-b0", advprop=True)
if advprop: # for models using advprop pretrained weights
normalize = transforms.Lambda(lambda img: img * 2.0 - 1.0)
else:
normalize = transforms.Normalize(mean=[0.485, 0 """
Evaluate on ImageNet. Note that at the moment, training is not implemented (I am working on it).
that being said, evaluation is working.
"""
import argparse
import os
import random
import shutil
import time
import warnings
import PIL
import tor def my_func():
print(datetime.now())
from datetime import datetime
exec(open("./mod.py").read())
my_func()
from datetime import datetime
import mod
mod.my_func()
03_install_cv:
command: "sudo apt-get update"
command: "sudo apt-get install -y libsm6 libxext6 libxrender-dev"
03_install_cv:
command: "yum update -y && yum install -y libXext libSM libXrenmissing_keys=['_conv_stem.weight', '_bn0.weight', '_bn0.bias', ...]
unexpected_keys=['module._conv_stem.weight', 'module._bn0.weight', 'module._bn0.bias', ...]
# original saved file with DataParallel
checkpoint = ttry:
import something
except ImportError:
print("import failed")
Community Discussions
Trending Discussions on EfficientNet-PyTorch
QUESTION
I'm modifying a pretrained efficient-net model in pytorch. I'm doing the following in order:
Create the default model, load the imagenet weights.
Then, change the number of channels in the first layer, and delete few layers while adding few.
ANSWER
Answered 2021-Jul-27 at 05:44If you are re-defining some of the layers, which you seem to be doing with model._conv_stem and model._fc. Then, yes those will be randomly initialized, thus meaning the loaded weights will no longer be used on those layers. The rest of the model will of course stay untouched and will use the loaded weights.
QUESTION
I'm using the EfficientNet pre-trained model for my image classification project in Pytorch, and my purpose is to change the number of classes which is initially 1000 to 4. However, for that when I try adding a model._fc layer, I keep on seeing this error "EfficientNet' object has no attribute 'classifier". Here is my code (Config.NUM_CLASSES = 4):
...ANSWER
Answered 2021-Jun-19 at 16:59EfficentNet class doesn't have attribute classifier, you need to change in_features=model.classifier.in_features to in_features=model._fc.in_features.
QUESTION
I have made a switch from TensorFlow to PyTorch recently. I use a famous Github repo for training on EfficientNets. I wrote the model initiation class as follows:
ANSWER
Answered 2020-Dec-31 at 10:47Yes you can use different input sizes when it comes to transfer learning, after all the model that you load is just the set of weights of the fixed sequence of layers and fixed convolution kernel sizes. But I believe that there is some sort of minimum size that the model needs to work efficiently. You would still need to re-train the model but it will still converge quite quickly.
You would have to check the official implementation on the minimum size of the model like the one in VGG16 where they specify that the width and height need to be at least 32.
QUESTION
I have a classification problem to predict 8 classes for example, I am using EfficientNetB3 in pytorch from here. However, I got confused on whether my custom class is correctly written. I think I want to strip the last layer of the pre-trained model to suit the 8 outputs right? Did I do it correctly? Because when I print y_preds = model(images) in my DataLoader, it seems to give me 1536 predictions. Is this an expected behavior?
ANSWER
Answered 2020-Nov-21 at 06:00You're not even using self.fc in forward pass.
Either just introduce it as:
QUESTION
I'm deploying a Django app on AWS elastic beanstalk, and I run into the following error:
...ANSWER
Answered 2020-Sep-11 at 00:33apt-get is for Ubuntu. Elastic Beanstalk is based on Amazon Linux 2, and you should be using yum. Thus the following will not work:
QUESTION
ANSWER
Answered 2020-Jul-14 at 15:41Here how I will handle it.
Add an upstream
QUESTION
I trained a model of Efficentnet-b6(Architechture is as follows):
https://github.com/lukemelas/EfficientNet-PyTorch
Now, I tried to load a model I trained with it:
...ANSWER
Answered 2020-May-20 at 10:03If you compare the missing_keys and unexpected_keys, you may realize what is happening.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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