77 lines
2.5 KiB
Markdown
77 lines
2.5 KiB
Markdown
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## Introduction
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This repo serve as source code storage for the Standalone SATRN Text Recognizer packages.
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Installing this package requires 3 additional packages: PyTorch, MMCV, and colorama.
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## Installation
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```shell
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conda create -n sdsvtr-env python=3.8
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conda activate sdsvtr-env
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conda install pytorch torchvision pytorch-cuda=11.6 -c pytorch -c nvidia
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pip install -U openmim
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mim install mmcv-full
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pip install colorama
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git clone https://github.com/moewiee/sdsvtr.git
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cd sdsvtr
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pip install -v -e .
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```
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## Basic Usage
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```python
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from sdsvtr import StandaloneSATRNRunner
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runner = StandaloneSATRNRunner(version='satrn-lite-general-pretrain-20230106', return_confident=False, device='cpu')
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```
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The `version` parameter accepts version names declared in `sdsvtr.factory.online_model_factory` or a local path such as `$DIR\model.pth`. To check for available versions in the hub, run:
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```python
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import sdsvtr
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print(sdsvtr.__hub_available_versions__)
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```
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Naturally, a `StandaloneSATRNRunner` instance assumes the input to be one of the following: an instance of `np.ndarray`, an instance of `str`, a list of `np.ndarray`, or a list of `str`, for examples:
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```python
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import numpy as np
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from sdsvtr import StandaloneSATRNRunner
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runner = StandaloneSATRNRunner(version='satrn-lite-general-pretrain-20230106', return_confident=False, device='cpu')
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dummy_list = [np.ndarray((32,128,3)) for _ in range(100)]
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result = runner(dummy_list)
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```
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To run with a specific batchsize, try:
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```python
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import numpy as np
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from sdsvtr import StandaloneSATRNRunner
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runner = StandaloneSATRNRunner(version='satrn-lite-general-pretrain-20230106', return_confident=False, device='cuda:0')
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dummy_list = [np.ndarray(1,3,32,128) for _ in range(100)]
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bs = min(32, len(imageFiles)) # batchsize = 32
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all_results = []
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while len(dummy_list) > 0:
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dummy_batch = dummy_list[:bs]
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dummy_list = dummy_list[bs:]
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all_results += runner(dummy_batch)
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```
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## Version Changelog
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* **[0.0.1]**
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Initial version with specified features.
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* **[0.0.2]**
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Update online model hub.
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* **[0.0.3]**
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Update API now able to inference with 4 types of inputs: list/instance of `np.ndarray`/`str`
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Update API interface with `return_confident` parameter.
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Update `wget` check and `sha256` check for model hub retrieval.
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* **[0.0.4]**
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Update decoder module with EarlyStopping mechanism to possibly improve inference speed on short sequences.
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Update API interface with optional argument `max_seq_len_overwrite` to overwrite checkpoint's `max_seq_len` config.
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* **[0.0.5]**
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Allow inference on a specific device
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