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125 lines
4.3 KiB
125 lines
4.3 KiB
# Summary
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**Progressive Neural Architecture Search**, or **PNAS**, is a method for learning the structure of convolutional neural networks (CNNs). It uses a sequential model-based optimization (SMBO) strategy, where we search the space of cell structures, starting with simple (shallow) models and progressing to complex ones, pruning out unpromising structures as we go.
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## How do I use this model on an image?
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To load a pretrained model:
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```python
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import timm
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model = timm.create_model('pnasnet5large', pretrained=True)
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model.eval()
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```
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To load and preprocess the image:
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```python
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import urllib
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from PIL import Image
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from timm.data import resolve_data_config
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from timm.data.transforms_factory import create_transform
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config = resolve_data_config({}, model=model)
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transform = create_transform(**config)
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url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
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urllib.request.urlretrieve(url, filename)
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img = Image.open(filename).convert('RGB')
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tensor = transform(img).unsqueeze(0) # transform and add batch dimension
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```
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To get the model predictions:
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```python
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import torch
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with torch.no_grad():
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out = model(tensor)
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probabilities = torch.nn.functional.softmax(out[0], dim=0)
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print(probabilities.shape)
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# prints: torch.Size([1000])
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```
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To get the top-5 predictions class names:
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```python
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# Get imagenet class mappings
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url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
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urllib.request.urlretrieve(url, filename)
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with open("imagenet_classes.txt", "r") as f:
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categories = [s.strip() for s in f.readlines()]
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# Print top categories per image
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top5_prob, top5_catid = torch.topk(probabilities, 5)
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for i in range(top5_prob.size(0)):
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print(categories[top5_catid[i]], top5_prob[i].item())
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# prints class names and probabilities like:
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# [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
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```
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Replace the model name with the variant you want to use, e.g. `pnasnet5large`. You can find the IDs in the model summaries at the top of this page.
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To extract image features with this model, follow the [timm feature extraction examples](https://rwightman.github.io/pytorch-image-models/feature_extraction/), just change the name of the model you want to use.
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## How do I finetune this model?
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You can finetune any of the pre-trained models just by changing the classifier (the last layer).
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```python
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model = timm.create_model('pnasnet5large', pretrained=True).reset_classifier(NUM_FINETUNE_CLASSES)
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```
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To finetune on your own dataset, you have to write a training loop or adapt [timm's training
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script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
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## How do I train this model?
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You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
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## Citation
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```BibTeX
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@misc{liu2018progressive,
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title={Progressive Neural Architecture Search},
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author={Chenxi Liu and Barret Zoph and Maxim Neumann and Jonathon Shlens and Wei Hua and Li-Jia Li and Li Fei-Fei and Alan Yuille and Jonathan Huang and Kevin Murphy},
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year={2018},
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eprint={1712.00559},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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<!--
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Models:
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- Name: pnasnet5large
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Metadata:
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FLOPs: 31458865950
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Batch Size: 1600
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Training Data:
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- ImageNet
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Training Techniques:
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- Label Smoothing
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- RMSProp
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- Weight Decay
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Training Resources: 100x NVIDIA P100 GPUs
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Architecture:
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- Average Pooling
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- Batch Normalization
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- Convolution
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- Depthwise Separable Convolution
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- Dropout
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- ReLU
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File Size: 345153926
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Tasks:
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- Image Classification
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ID: pnasnet5large
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LR: 0.015
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Dropout: 0.5
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Crop Pct: '0.911'
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Momentum: 0.9
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Image Size: '331'
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Interpolation: bicubic
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Label Smoothing: 0.1
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/pnasnet.py#L343
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In Collection: PNASNet
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Collections:
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- Name: PNASNet
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Paper:
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title: Progressive Neural Architecture Search
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url: https://paperswithcode.com//paper/progressive-neural-architecture-search
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type: model-index
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Type: model-index
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--> |