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119 lines
4.3 KiB
Markdown
119 lines
4.3 KiB
Markdown
# Inference with existing models
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This tutorial will show how to use the following APIs：
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1. [**`list_models`**](mmpretrain.apis.list_models) & [**`get_model`**](mmpretrain.apis.get_model) ：list models in MMPreTrain and get a specific model.
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2. [**`ImageClassificationInferencer`**](mmpretrain.apis.ImageClassificationInferencer): inference on given images.
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3. [**`FeatureExtractor`**](mmpretrain.apis.FeatureExtractor): extract features from the image files directly.
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## List models and Get model
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list all the models in MMPreTrain.
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```
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>>> from mmpretrain import list_models
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>>> list_models()
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['barlowtwins_resnet50_8xb256-coslr-300e_in1k',
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'beit-base-p16_beit-in21k-pre_3rdparty_in1k',
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.................]
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```
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`list_models` supports fuzzy matching, you can use **\*** to match any character.
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```
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>>> from mmpretrain import list_models
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>>> list_models("*convnext-b*21k")
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['convnext-base_3rdparty_in21k',
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'convnext-base_in21k-pre-3rdparty_in1k-384px',
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'convnext-base_in21k-pre_3rdparty_in1k']
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```
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you can use `get_model` get the model.
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```
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>>> from mmpretrain import get_model
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# model without pre-trained weight
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>>> model = get_model("convnext-base_in21k-pre_3rdparty_in1k")
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# model with default weight in MMPreTrain
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>>> model = get_model("convnext-base_in21k-pre_3rdparty_in1k", pretrained=True)
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# model with weight in local
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>>> model = get_model("convnext-base_in21k-pre_3rdparty_in1k", pretrained="your_local_checkpoint_path")
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# you can also do some modification, like modify the num_classes in head.
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>>> model = get_model("convnext-base_in21k-pre_3rdparty_in1k", head=dict(num_classes=10))
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# you can get model without neck, head, and output from stage 1, 2, 3 in backbone
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>>> model_headless = get_model("resnet18_8xb32_in1k", head=None, neck=None, backbone=dict(out_indices=(1, 2, 3)))
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```
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Then you can do the forward:
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```
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>>> import torch
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>>> from mmpretrain import get_model
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>>> model = get_model('convnext-base_in21k-pre_3rdparty_in1k', pretrained=True)
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>>> x = torch.rand((1, 3, 224, 224))
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>>> y = model(x)
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>>> print(type(y), y.shape)
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<class 'torch.Tensor'> torch.Size([1, 1000])
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```
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## Inference on a given image
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Here is an example of building the inferencer on a [given image](https://github.com/open-mmlab/mmpretrain/raw/main/demo/demo.JPEG) by using ImageNet-1k pre-trained checkpoint.
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```python
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>>> from mmpretrain import ImageClassificationInferencer
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>>> inferencer = ImageClassificationInferencer('resnet50_8xb32_in1k')
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>>> results = inferencer('https://github.com/open-mmlab/mmpretrain/raw/main/demo/demo.JPEG')
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>>> print(results[0]['pred_class'])
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sea snake
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```
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`result` is a dictionary containing `pred_label`, `pred_score`, `pred_scores` and `pred_class`, the result is as follows:
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```text
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{"pred_label":65,"pred_score":0.6649366617202759,"pred_class":"sea snake", "pred_scores": [..., 0.6649366617202759, ...]}
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```
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If you want to use your own config and checkpoint:
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```
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>>> from mmpretrain import ImageClassificationInferencer
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>>> inferencer = ImageClassificationInferencer(
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model='configs/resnet/resnet50_8xb32_in1k.py',
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pretrained='https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth',
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device='cuda')
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>>> inferencer('https://github.com/open-mmlab/mmpretrain/raw/main/demo/demo.JPEG')
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```
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You can also inference multiple images by batch on CUDA:
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```python
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>>> from mmpretrain import ImageClassificationInferencer
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>>> inferencer = ImageClassificationInferencer('resnet50_8xb32_in1k', device='cuda')
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>>> imgs = ['https://github.com/open-mmlab/mmpretrain/raw/main/demo/demo.JPEG'] * 5
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>>> results = inferencer(imgs, batch_size=2)
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>>> print(results[1]['pred_class'])
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sea snake
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```
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## Extract Features From Image
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Compared with `model.extract_feat`, `FeatureExtractor` is used to extract features from the image files directly, instead of a batch of tensors.
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In a word, the input of `model.extract_feat` is `torch.Tensor`, the input of `FeatureExtractor` is images.
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```
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>>> from mmpretrain import FeatureExtractor, get_model
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>>> model = get_model('resnet50_8xb32_in1k', backbone=dict(out_indices=(0, 1, 2, 3)))
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>>> extractor = FeatureExtractor(model)
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>>> features = extractor('https://github.com/open-mmlab/mmpretrain/raw/main/demo/demo.JPEG')[0]
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>>> features[0].shape, features[1].shape, features[2].shape, features[3].shape
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(torch.Size([256]), torch.Size([512]), torch.Size([1024]), torch.Size([2048]))
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```
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