Approximate Convolutional Sparse Coding (ACSC)

A pytorch implementation of a ACSC model based on Lerned Convolutional Sparse Coding model proposed here and or here.

ACSC block description

Evaluation of different ASCS variants

Image | Lena | House | Pepper | Couple | Fgpr | Boat | Hill | Man | Barbara |---|---|---|---|---|---|---|---|---|---| ACSC1 |31.28 |31.69 |29.88 |29.11| 26.43| 29.31| 29.35| 29.36| 27.85 ACSC2 |30.7| 30.83| 29.23| 28.61| 26.2| 28.95| 29.04| 28.98| 27.48 ACSC3| 32.15| 32.87| 30.67| 29.87| 27.18| 29.96| 29.833| 29.81| 29.41| ACSC4| 32.268| 33.25| 30.7| 30.03 |27.24| 30.04| 29.93| 29.87| 29.71 BM3D| 31.97| 32.68| 29.63| 29.51| 27.73| 29.7| 29.72| 29.42 |30.64

Setup

For a linux with a cuda simply run bash pip install pipenv && pipenv install --dev This will install pipenv and pipenv will install the rest of the rest of dependencies specified in the Pipfile. If Pipfile does not work then use Python version >= 3.6 and install using pip all dependencies (mostly just pytorch)

Usage

Their are 3 main entry points:

1. train_denoise.py
2. test_denoise.py
3. analyze_model.py

all entry points are run in the same fashion: if PipFile is used bash pipenv run python xx.py --args_file params.json or if pip is used bash python3 xx.py --args_file params.json Example for how to set params.json file can be found in ./saved_models/acsc[1-4].

Train

train will build model and run train session using parameters that are givin in params.json. params.json is updated and coped to log-path specified in params.json. train will automatically run test and analyze. bash pipenv run python train_denoise.py --args_file params.json

Trainset

Download npz PASCAL VOC images provided google drive make sure to specify dataset_path in params.json.

Test

test will build the model specified in params.json. Two types of tests are run:

1. PASCAL VOC2012 testset (must be downloaded)
2. famous images

all result are saved in log_dir provided in params.json. bash pipenv run python test_denoise.py --args_file params.json

Analyze

Evaluate model beyond test accuracy. Sparsity factors etc. bash pipenv run python analyze_denoise.py --args_file params.json

Params file

Param file is first set with model + train + test parameter. During train and test the Params file is constantly updated. When A model is done training it saved its weights and the final state of the params.json. Thus running train on the saved params.json will load the last state of the saved model proceed to continue training session. [Exmaple of initial params file] json { "train_args": { "noise": 25, "epoch": 30, "batch_size": 15, "learning_rate": 1e-4, "dataset_path": "./pascal_120.npz", "log_dir": "saved_models/", "name": "acsc" }, "model_args": { "num_input_channels": 1, "num_output_channels": 1, "kc": 64, "ks": 7, "ista_iters": 20, "iter_weight_share": true, "share_decoder": false }, "test_args": { "noise": 25, "load_path": "", "name": "acsc", "testset_famous_path": "test_images/", "testset_pascal_path": "VOCdevkit/VOC2010/JPEGImages/" } }

Issues

Bump ipython from 7.2.0 to 7.16.3

opened on 2022-01-21 19:53:11 by dependabot[bot]

Bumps ipython from 7.2.0 to 7.16.3.

Commits

• d43c7c7 release 7.16.3
• 5fa1e40 Merge pull request from GHSA-pq7m-3gw7-gq5x
• 8df8971 back to dev
• 9f477b7 release 7.16.2
• 138f266 bring back release helper from master branch
• 5aa3634 Merge pull request #13341 from meeseeksmachine/auto-backport-of-pr-13335-on-7...
• bcae8e0 Backport PR #13335: What's new 7.16.2
• 8fcdcd3 Pin Jedi to <0.17.2.
• 2486838 release 7.16.1
• 20bdc6f fix conda build
• Additional commits viewable in compare view

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