├── .github └── ISSUE_TEMPLATE │ ├── bug_report.md │ ├── custom.md │ └── feature_request.md ├── CODE_OF_CONDUCT.md ├── LICENSE ├── MANIFEST.in ├── README.md ├── bin ├── predict.py └── train.py ├── fetch ├── __init__.py ├── data_sequence.py ├── models │ ├── a_FT_DenseNet121_2_DMT_Xception_13_256 │ │ ├── ft_DenseNet121_2_dt_Xception_13_256.json │ │ └── ft_DenseNet121_2_dt_Xception_13_256.yaml │ ├── b_FT_DenseNet121_2_DMT_VGG16_1_32 │ │ ├── ft_DenseNet121_2_dt_VGG16_1_32.json │ │ └── ft_DenseNet121_2_dt_VGG16_1_32.yaml │ ├── c_FT_DenseNet169_6_DMT_Xception_13_112 │ │ ├── ft_DenseNet169_6_dt_Xception_13_112.json │ │ └── ft_DenseNet169_6_dt_Xception_13_112.yaml │ ├── d_FT_DenseNet201_4_DMT_Xception_13_32 │ │ ├── ft_DenseNet201_4_dt_Xception_13_32.json │ │ └── ft_DenseNet201_4_dt_Xception_13_32.yaml │ ├── e_FT_VGG19_3_DMT_Xception_13_128 │ │ ├── ft_VGG19_3_dt_Xception_13_128.json │ │ └── ft_VGG19_3_dt_Xception_13_128.yaml │ ├── f_FT_DenseNet169_6_DMT_VGG16_1_512 │ │ ├── ft_DenseNet169_6_dt_VGG16_1_512.json │ │ └── ft_DenseNet169_6_dt_VGG16_1_512.yaml │ ├── g_FT_VGG19_3_DMT_VGG16_1_128 │ │ ├── ft_VGG19_3_dt_VGG16_1_128.json │ │ └── ft_VGG19_3_dt_VGG16_1_128.yaml │ ├── h_FT_DenseNet201_4_DMT_InceptionResNetV2_20_160 │ │ └── ft_DenseNet201_4_dt_InceptionResNetV2_20_160.yaml │ ├── i_FT_DenseNet201_4_DMT_VGG16_1_32 │ │ └── ft_DenseNet201_4_dt_VGG16_1_32.yaml │ ├── j_FT_VGG19_3_DMT_InceptionResNetV2_20_512 │ │ └── ft_VGG19_3_dt_InceptionResNetV2_20_512.yaml │ ├── k_FT_DenseNet121_2_DMT_InceptionV3_18_64 │ │ ├── ft_DenseNet121_2_dt_InceptionV3_18_64.json │ │ └── ft_DenseNet121_2_dt_InceptionV3_18_64.yaml │ └── model_list.csv └── utils.py ├── requirements.txt └── setup.py /.github/ISSUE_TEMPLATE/bug_report.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Bug report 3 | about: Create a report to help us improve 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | **Describe the bug** 11 | A clear and concise description of what the bug is. 12 | 13 | **To Reproduce** 14 | Steps to reproduce the behavior: 15 | 1. Go to '...' 16 | 2. Click on '....' 17 | 3. Scroll down to '....' 18 | 4. See error 19 | 20 | **Expected behavior** 21 | A clear and concise description of what you expected to happen. 22 | 23 | **Screenshots** 24 | If applicable, add screenshots to help explain your problem. 25 | 26 | **Additional context** 27 | Add any other context about the problem here. 28 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/custom.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Custom issue template 3 | about: Describe this issue template's purpose here. 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | 11 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/feature_request.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Feature request 3 | about: Suggest an idea for this project 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | **Is your feature request related to a problem? Please describe.** 11 | A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] 12 | 13 | **Describe the solution you'd like** 14 | A clear and concise description of what you want to happen. 15 | 16 | **Describe alternatives you've considered** 17 | A clear and concise description of any alternative solutions or features you've considered. 18 | 19 | **Additional context** 20 | Add any other context or screenshots about the feature request here. 21 | -------------------------------------------------------------------------------- /CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Contributor Covenant Code of Conduct 2 | 3 | ## Our Pledge 4 | 5 | In the interest of fostering an open and welcoming environment, we as 6 | contributors and maintainers pledge to making participation in our project and 7 | our community a harassment-free experience for everyone, regardless of age, body 8 | size, disability, ethnicity, sex characteristics, gender identity and expression, 9 | level of experience, education, socio-economic status, nationality, personal 10 | appearance, race, religion, or sexual identity and orientation. 11 | 12 | ## Our Standards 13 | 14 | Examples of behavior that contributes to creating a positive environment 15 | include: 16 | 17 | * Using welcoming and inclusive language 18 | * Being respectful of differing viewpoints and experiences 19 | * Gracefully accepting constructive criticism 20 | * Focusing on what is best for the community 21 | * Showing empathy towards other community members 22 | 23 | Examples of unacceptable behavior by participants include: 24 | 25 | * The use of sexualized language or imagery and unwelcome sexual attention or 26 | advances 27 | * Trolling, insulting/derogatory comments, and personal or political attacks 28 | * Public or private harassment 29 | * Publishing others' private information, such as a physical or electronic 30 | address, without explicit permission 31 | * Other conduct which could reasonably be considered inappropriate in a 32 | professional setting 33 | 34 | ## Our Responsibilities 35 | 36 | Project maintainers are responsible for clarifying the standards of acceptable 37 | behavior and are expected to take appropriate and fair corrective action in 38 | response to any instances of unacceptable behavior. 39 | 40 | Project maintainers have the right and responsibility to remove, edit, or 41 | reject comments, commits, code, wiki edits, issues, and other contributions 42 | that are not aligned to this Code of Conduct, or to ban temporarily or 43 | permanently any contributor for other behaviors that they deem inappropriate, 44 | threatening, offensive, or harmful. 45 | 46 | ## Scope 47 | 48 | This Code of Conduct applies both within project spaces and in public spaces 49 | when an individual is representing the project or its community. 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /MANIFEST.in: -------------------------------------------------------------------------------- 1 | include fetch/models/*.csv 2 | include fetch/models/*/*yaml 3 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # FETCH 2 | 3 | 4 | [![DOI](https://zenodo.org/badge/165734093.svg?style=flat-square)](https://zenodo.org/badge/latestdoi/165734093) 5 | [![issues](https://img.shields.io/github/issues/devanshkv/fetch)](https://github.com/devanshkv/fetch/issues) 6 | [![forks](https://img.shields.io/github/forks/devanshkv/fetch)](https://github.com/devanshkv/fetch/network/members) 7 | [![stars](https://img.shields.io/github/stars/devanshkv/fetch)](https://github.com/devanshkv/fetch/stargazers) 8 | [![GitHub license](https://img.shields.io/github/license/devanshkv/fetch)](https://github.com/devanshkv/fetch/blob/master/LICENSE) 9 | [![HitCount](http://hits.dwyl.com/devanshkv/fetch.svg)](http://hits.dwyl.com/devanshkv/fetch) 10 | [![arXiv](https://img.shields.io/badge/arXiv-1902.06343-brightgreen.svg)](https://arxiv.org/abs/1902.06343) 11 | [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) 12 | 13 | 14 | fetch is Fast Extragalactic Transient Candidate Hunter. It has been detailed in the paper [Towards deeper neural networks for Fast Radio Burst detection](https://arxiv.org/abs/1902.06343). 15 | 16 | This is the `tensorflow>=2` version of the fetch, if you are looking for the older tensorflow version click [here](https://github.com/devanshkv/fetch/archive/0.1.8.tar.gz). 17 | 18 | Install 19 | --- 20 | git clone https://github.com/devanshkv/fetch.git 21 | cd fetch 22 | pip install -r requirements.txt 23 | python setup.py install 24 | 25 | The installation will put `predict.py` and `train.py` in your `PYTHONPATH`. 26 | 27 | Usage 28 | --- 29 | To use fetch, you would first have to create candidates. Use [`your`](https://thepetabyteproject.github.io/your/) for this purpose, [this notebook](https://thepetabyteproject.github.io/your/ipynb/Candidate/) explains the whole process. Your also comes with a command line script [`your_candmaker.py`](https://thepetabyteproject.github.io/your/bin/your_candmaker/) which allows you to use CPU or single/multiple GPUs. 30 | 31 | To predict a candidate h5 files living in the directory `/data/candidates/` use `predict.py` for model `a` as follows: 32 | 33 | predict.py --data_dir /data/candidates/ --model a 34 | 35 | To fine-tune the model `a`, with a bunch of candidates, put them in a pandas readable csv, `candidate.csv` with headers 'h5' and 'label'. Use 36 | 37 | train.py --data_csv candidates.csv --model a --output_path ./ 38 | 39 | This would train the model `a` and save the training log, and model weights in the output path. 40 | 41 | Example 42 | --- 43 | 44 | Test filterbank data can be downloaded from [here](http://astro.phys.wvu.edu/files/askap_frb_180417.tgz). The folder contains three filterbanks: 28.fil 29.fil 34.fil. 45 | Heimdall results for each of the files are as follows: 46 | 47 | for 28.fil 48 | 49 | 16.8128 1602 2.02888 1 127 475.284 22 1601 1604 50 | for 29.fil 51 | 52 | 18.6647 1602 2.02888 1 127 475.284 16 1601 1604 53 | for 34.fil 54 | 55 | 13.9271 1602 2.02888 1 127 475.284 12 1602 1604 56 | 57 | The `cand.csv` would look like the following: 58 | 59 | file,snr,stime,width,dm,label,chan_mask_path,num_files 60 | 28.fil,16.8128,2.02888,1,475.284,1,,1 61 | 29.fil,18.6647,2.02888,1,475.284,1,,1 62 | 34.fil,13.9271,2.02888,1,475.284,1,,1 63 | 64 | Running `your_candmaker.py` will create three files: 65 | 66 | cand_tstart_58682.620316710374_tcand_2.0288800_dm_475.28400_snr_13.92710.h5 67 | cand_tstart_58682.620316710374_tcand_2.0288800_dm_475.28400_snr_16.81280.h5 68 | cand_tstart_58682.620316710374_tcand_2.0288800_dm_475.28400_snr_18.66470.h5 69 | 70 | Running `predict.py` with model `a` will give `results_a.csv`: 71 | 72 | ,candidate,probability,label 73 | 0,cand_tstart_58682.620316710374_tcand_2.0288800_dm_475.28400_snr_18.66470.h5,1.0,1.0 74 | 1,cand_tstart_58682.620316710374_tcand_2.0288800_dm_475.28400_snr_16.81280.h5,1.0,1.0 75 | 2,cand_tstart_58682.620316710374_tcand_2.0288800_dm_475.28400_snr_13.92710.h5,1.0,1.0 76 | 77 | Training Data 78 | --- 79 | 80 | The training data is available at [astro.phys.wvu.edu/fetch](http://astro.phys.wvu.edu/fetch/). 81 | 82 | ## Citating this work 83 | ___ 84 | 85 | If you use this work please cite: 86 | 87 | @article{Agarwal2020, 88 | doi = {10.1093/mnras/staa1856}, 89 | url = {https://doi.org/10.1093/mnras/staa1856}, 90 | year = {2020}, 91 | month = jun, 92 | publisher = {Oxford University Press ({OUP})}, 93 | author = {Devansh Agarwal and Kshitij Aggarwal and Sarah Burke-Spolaor and Duncan R Lorimer and Nathaniel Garver-Daniels}, 94 | title = {{FETCH}: A deep-learning based classifier for fast transient classification}, 95 | journal = {Monthly Notices of the Royal Astronomical Society} 96 | } 97 | @software{agarwal_aggarwal_2020, 98 | author = {Devansh Agarwal and 99 | Kshitij Aggarwal}, 100 | title = {{devanshkv/fetch: Software release with the 101 | manuscript}}, 102 | month = jun, 103 | year = 2020, 104 | publisher = {Zenodo}, 105 | version = {0.1.8}, 106 | doi = {10.5281/zenodo.3905437}, 107 | url = {https://doi.org/10.5281/zenodo.3905437} 108 | } 109 | -------------------------------------------------------------------------------- /bin/predict.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | 3 | import argparse 4 | import glob 5 | import logging 6 | import os 7 | import string 8 | 9 | import numpy as np 10 | import pandas as pd 11 | 12 | from fetch.data_sequence import DataGenerator 13 | from fetch.utils import get_model 14 | 15 | logger = logging.getLogger(__name__) 16 | 17 | if __name__ == "__main__": 18 | parser = argparse.ArgumentParser( 19 | description="Fast Extragalactic Transient Candiate Hunter (FETCH)", 20 | formatter_class=argparse.ArgumentDefaultsHelpFormatter, 21 | ) 22 | parser.add_argument("-v", "--verbose", help="Be verbose", action="store_true") 23 | parser.add_argument( 24 | "-g", 25 | "--gpu_id", 26 | help="GPU ID (use -1 for CPU)", 27 | type=int, 28 | required=False, 29 | default=0, 30 | ) 31 | parser.add_argument( 32 | "-n", "--nproc", help="Number of processors for training", default=4, type=int 33 | ) 34 | parser.add_argument( 35 | "-c", 36 | "--data_dir", 37 | help="Directory with candidate h5s.", 38 | required=True, 39 | type=str, 40 | action='append' 41 | ) 42 | parser.add_argument( 43 | "-b", "--batch_size", help="Batch size for training data", default=8, type=int 44 | ) 45 | parser.add_argument( 46 | "-m", "--model", help="Index of the model to train", required=True 47 | ) 48 | parser.add_argument( 49 | "-p", "--probability", help="Detection threshold", default=0.5, type=float 50 | ) 51 | args = parser.parse_args() 52 | 53 | logging_format = ( 54 | "%(asctime)s - %(funcName)s -%(name)s - %(levelname)s - %(message)s" 55 | ) 56 | 57 | if args.verbose: 58 | logging.basicConfig(level=logging.DEBUG, format=logging_format) 59 | else: 60 | logging.basicConfig(level=logging.INFO, format=logging_format) 61 | 62 | if args.model not in list(string.ascii_lowercase)[:11]: 63 | raise ValueError(f"Model only range from a -- j.") 64 | 65 | if args.gpu_id >= 0: 66 | os.environ["CUDA_VISIBLE_DEVICES"] = f"{args.gpu_id}" 67 | else: 68 | os.environ["CUDA_VISIBLE_DEVICES"] = "" 69 | 70 | if args.nproc > 1: 71 | use_multiprocessing = True 72 | logging.info(f"Using multiprocessing with {args.nproc} workers") 73 | else: 74 | use_multiprocessing = False 75 | 76 | if args.model not in list(string.ascii_lowercase)[:11]: 77 | raise ValueError(f"Model only range from a -- j.") 78 | 79 | model = get_model(args.model) 80 | 81 | for data_dir in args.data_dir: 82 | 83 | cands_to_eval = glob.glob(f"{data_dir}/*h5") 84 | 85 | if len(cands_to_eval) == 0: 86 | logger.warning(f"No candidates to evaluate in directory: {data_dir}") 87 | continue 88 | 89 | logging.debug(f"Read {len(cands_to_eval)} candidates") 90 | 91 | # Get the data generator, make sure noise and shuffle are off. 92 | cand_datagen = DataGenerator( 93 | list_IDs=cands_to_eval, 94 | labels=[0] * len(cands_to_eval), 95 | shuffle=False, 96 | noise=False, 97 | batch_size=args.batch_size, 98 | ) 99 | 100 | # get's get predicting 101 | probs = model.predict_generator( 102 | generator=cand_datagen, 103 | verbose=1, 104 | use_multiprocessing=use_multiprocessing, 105 | workers=args.nproc, 106 | steps=len(cand_datagen), 107 | ) 108 | 109 | # Save results 110 | results_dict = {} 111 | results_dict["candidate"] = cands_to_eval 112 | results_dict["probability"] = probs[:, 1] 113 | results_dict["label"] = np.round(probs[:, 1] >= args.probability) 114 | results_file = data_dir + f"/results_{args.model}.csv" 115 | pd.DataFrame(results_dict).to_csv(results_file) 116 | 117 | -------------------------------------------------------------------------------- /bin/train.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | 3 | import argparse 4 | import logging 5 | import os 6 | import string 7 | 8 | import pandas as pd 9 | from tensorflow.keras.callbacks import EarlyStopping, CSVLogger, ModelCheckpoint 10 | from tensorflow.keras.models import Model 11 | from sklearn.model_selection import train_test_split 12 | 13 | from fetch.data_sequence import DataGenerator 14 | from fetch.utils import get_model 15 | from fetch.utils import ready_for_train 16 | 17 | logger = logging.getLogger(__name__) 18 | 19 | os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE" 20 | 21 | 22 | def train(model, epochs, patience, output_path, nproc, train_obj, val_obj): 23 | """ 24 | 25 | :param model: model to train (must be compiled) 26 | :type model: Model 27 | :param epochs: max number of epochs to train. 28 | :type epochs: int 29 | :param patience: Stop after these many layers if val. loss doesn't decrease 30 | :type patience: int 31 | :param output_path: paths to save weights and logs 32 | :type output_path: str 33 | :param nproc: number of processors for training 34 | :type nproc: int 35 | :param train_obj: DataGenerator training object for training 36 | :type train_obj: DataGenerator 37 | :param val_obj: DataGenerator training object for validation 38 | :type val_obj: DataGenerator 39 | :return: model, history object 40 | """ 41 | if nproc == 1: 42 | use_multiprocessing = False 43 | else: 44 | use_multiprocessing = True 45 | 46 | # Callbacks for training and validation 47 | ES = EarlyStopping( 48 | monitor="val_loss", 49 | min_delta=1e-3, 50 | patience=patience, 51 | verbose=1, 52 | mode="min", 53 | restore_best_weights=True, 54 | ) 55 | CK = ModelCheckpoint( 56 | output_path + "weights.h5", 57 | monitor="val_loss", 58 | verbose=1, 59 | save_best_only=True, 60 | save_weights_only=False, 61 | mode="min", 62 | ) 63 | csv_name = output_path + "training_log.csv" 64 | LO = CSVLogger(csv_name, append=False) 65 | 66 | callbacks = [ES, CK, LO] 67 | 68 | train_history = model.fit_generator( 69 | generator=train_obj, 70 | validation_data=val_obj, 71 | epochs=epochs, 72 | use_multiprocessing=use_multiprocessing, 73 | max_queue_size=10, 74 | workers=nproc, 75 | shuffle=True, 76 | callbacks=callbacks, 77 | verbose=1, 78 | ) 79 | return model, train_history 80 | 81 | 82 | if __name__ == "__main__": 83 | parser = argparse.ArgumentParser( 84 | description="Fast Extragalactic Transient Candiate Hunter (FETCH)" 85 | ) 86 | parser.add_argument("-v", "--verbose", help="Be verbose", action="store_true") 87 | parser.add_argument( 88 | "-g", "--gpu_id", help="GPU ID", type=int, required=False, default=0 89 | ) 90 | parser.add_argument( 91 | "-n", "--nproc", help="Number of processors for training", default=4, type=int 92 | ) 93 | parser.add_argument( 94 | "-c", 95 | "--data_csv", 96 | help="CSV with candidate h5 paths and labels", 97 | required=True, 98 | type=str, 99 | ) 100 | parser.add_argument( 101 | "-b", "--batch_size", help="Batch size for training data", default=8, type=int 102 | ) 103 | parser.add_argument( 104 | "-e", "--epochs", help="Number of epochs for training", default=15, type=int 105 | ) 106 | parser.add_argument( 107 | "-p", 108 | "--patience", 109 | help="Layer patience, stop training if validation loss does not decreate", 110 | default=3, 111 | type=int, 112 | ) 113 | parser.add_argument( 114 | "-nft", 115 | "--n_ft_layers", 116 | help="Number of layers in FT model to train", 117 | default=0, 118 | type=int, 119 | ) 120 | parser.add_argument( 121 | "-ndt", 122 | "--n_dt_layers", 123 | help="Number of layers in DT model to train", 124 | default=0, 125 | type=int, 126 | ) 127 | parser.add_argument( 128 | "-nf", 129 | "--n_fusion_layers", 130 | help="Number of layers to train post FT and DT models", 131 | default=1, 132 | type=int, 133 | ) 134 | parser.add_argument( 135 | "-o", 136 | "--output_path", 137 | help="Place to save the weights and training logs", 138 | type=str, 139 | required=True, 140 | ) 141 | parser.add_argument( 142 | "-vs", 143 | "--val_split", 144 | help="Percent of data to use for validation", 145 | type=float, 146 | default=0.2, 147 | ) 148 | parser.add_argument( 149 | "-m", "--model", help="Index of the model to train", required=True, type=str 150 | ) 151 | args = parser.parse_args() 152 | 153 | logging_format = ( 154 | "%(asctime)s - %(funcName)s -%(name)s - %(levelname)s - %(message)s" 155 | ) 156 | 157 | if args.verbose: 158 | logging.basicConfig(level=logging.DEBUG, format=logging_format) 159 | else: 160 | logging.basicConfig(level=logging.INFO, format=logging_format) 161 | 162 | if args.model not in list(string.ascii_lowercase)[:11]: 163 | raise ValueError(f"Model only range from a -- j.") 164 | 165 | if args.gpu_id: 166 | os.environ["CUDA_VISIBLE_DEVICES"] = f"{args.gpu_id}" 167 | 168 | if args.n_fusion_layers >= 9: 169 | raise ValueError( 170 | f"Cannot open {args.n_fusion_layers} for training. Models only have 6 layers after FT and DT models." 171 | ) 172 | 173 | data_df = pd.read_csv(args.data_csv) 174 | 175 | train_df, val_df = train_test_split( 176 | data_df, test_size=(1 - args.val_split), random_state=1993 177 | ) 178 | train_data_generator = DataGenerator( 179 | list_IDs=list(train_df["h5"]), 180 | labels=list(train_df["label"]), 181 | noise=True, 182 | shuffle=True, 183 | ) 184 | validate_data_generator = DataGenerator( 185 | list_IDs=list(val_df["h5"]), 186 | labels=list(val_df["label"]), 187 | noise=False, 188 | shuffle=False, 189 | ) 190 | 191 | model_to_train = get_model(args.model) 192 | 193 | model_to_train = ready_for_train( 194 | model_to_train, 195 | ndt=args.n_dt_layers, 196 | nft=args.n_ft_layers, 197 | nf=args.n_fusion_layers, 198 | ) 199 | 200 | trained_model, history = train( 201 | model_to_train, 202 | epochs=args.epochs, 203 | patience=args.patience, 204 | output_path=args.output_path, 205 | nproc=args.nproc, 206 | train_obj=train_data_generator, 207 | val_obj=validate_data_generator, 208 | ) 209 | -------------------------------------------------------------------------------- /fetch/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/devanshkv/fetch/5b85488f93c199f6a2efc8b2060defc84080f54a/fetch/__init__.py -------------------------------------------------------------------------------- /fetch/data_sequence.py: -------------------------------------------------------------------------------- 1 | """ 2 | https://stanford.edu/~shervine/blog/keras-how-to-generate-data-on-the-fly 3 | """ 4 | import logging 5 | import os 6 | 7 | import h5py 8 | from tensorflow import keras 9 | import numpy as np 10 | import scipy.signal as s 11 | 12 | os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE" 13 | logger = logging.getLogger(__name__) 14 | 15 | 16 | class DataGenerator(keras.utils.Sequence): 17 | def __init__( 18 | self, 19 | list_IDs, 20 | labels, 21 | batch_size=32, 22 | ft_dim=(256, 256), 23 | dt_dim=(256, 256), 24 | n_channels=1, 25 | n_classes=2, 26 | shuffle=True, 27 | noise=False, 28 | noise_mean=0.0, 29 | noise_std=1.0, 30 | ): 31 | """ 32 | 33 | :param list_IDs: list of h5 files 34 | :type list_IDs: list 35 | :param labels: list of labels (use fake labels when using predict) 36 | :type labels: list 37 | :param batch_size: Batch size (def = 32) 38 | :type batch_size: int 39 | :param ft_dim: 2D shape (def (256, 256) 40 | :type dt_dim tuple 41 | :param dt_dim: 2D shape (def (256, 256) 42 | :type ft_dim tuple 43 | :param n_channels: number of channels in data (def = 1) 44 | :type n_channels: int 45 | :param n_classes: number of classes to classify data into (def = 2) 46 | :type n_classes: ints 47 | :param shuffle: to shuffle or not to shuffle? 48 | :type shuffle: bool 49 | :param noise: to add noise or not to? 50 | :type noise: bool 51 | :param noise_mean: mean of gaussian noise 52 | :type noise_mean: float 53 | :param noise_std: standard deviation of gaussian noise 54 | :type noise_std: float 55 | """ 56 | self.ft_dim = ft_dim 57 | self.dt_dim = dt_dim 58 | self.batch_size = batch_size 59 | self.list_IDs = list_IDs 60 | self.n_channels = n_channels 61 | self.n_classes = n_classes 62 | self.shuffle = shuffle 63 | self.on_epoch_end() 64 | self.noise = noise 65 | self.labels = labels 66 | self.noise_mean = noise_mean 67 | self.noise_std = noise_std 68 | 69 | def __len__(self): 70 | """ 71 | 72 | :return: Number of batches per epoch 73 | """ 74 | return int(np.ceil(len(self.list_IDs) / self.batch_size)) 75 | 76 | def __getitem__(self, index): 77 | """ 78 | 79 | :param index: index 80 | :return: Data dictionary and categorical labels 81 | """ 82 | if index < self.__len__(): 83 | indexes = self.indexes[ 84 | index * self.batch_size : (index + 1) * self.batch_size 85 | ] 86 | else: 87 | indexes = self.indexes[index * self.batch_size :] 88 | # Find list of IDs 89 | list_IDs_temp = [self.list_IDs[k] for k in indexes] 90 | 91 | # Generate data 92 | X, y = self.__data_generation(list_IDs_temp, indexes) 93 | 94 | return X, y 95 | 96 | def on_epoch_end(self): 97 | """ 98 | 99 | :return: Updates the indices at the end of the epoch 100 | """ 101 | self.indexes = np.arange(len(self.list_IDs)) 102 | if self.shuffle == True: 103 | np.random.shuffle(self.indexes) 104 | 105 | def __data_generation(self, list_IDs_temp, indexes): 106 | """ 107 | 108 | :param list_IDs_temp: list of h5 files to read 109 | :param indexes: indexes 110 | :return: Batch of data and labels 111 | """ 112 | X = np.empty((len(list_IDs_temp), *self.ft_dim, self.n_channels)) 113 | Y = np.empty((len(list_IDs_temp), *self.dt_dim, self.n_channels)) 114 | y = np.empty((len(list_IDs_temp)), dtype=int) 115 | 116 | # Generate data 117 | for i, ID in enumerate(list_IDs_temp): 118 | try: 119 | with h5py.File(ID, "r") as f: 120 | data_ft = s.detrend( 121 | np.nan_to_num(np.array(f["data_freq_time"], dtype=np.float32).T) 122 | ) 123 | data_ft /= np.std(data_ft) 124 | data_ft -= np.median(data_ft) 125 | data_dt = np.nan_to_num( 126 | np.array(f["data_dm_time"], dtype=np.float32) 127 | ) 128 | data_dt /= np.std(data_dt) 129 | data_dt -= np.median(data_dt) 130 | X[ 131 | i, 132 | ] = np.reshape(data_ft, (*self.ft_dim, self.n_channels)) 133 | Y[ 134 | i, 135 | ] = np.reshape(data_dt, (*self.dt_dim, self.n_channels)) 136 | except KeyError: 137 | print(ID) 138 | y[i] = self.labels[indexes[i]] 139 | X[X != X] = 0.0 140 | Y[Y != Y] = 0.0 141 | 142 | if self.noise: 143 | X += np.random.normal( 144 | loc=self.noise_mean, scale=self.noise_std, size=X.shape 145 | ) 146 | return {"data_freq_time": X, "data_dm_time": Y}, keras.utils.to_categorical( 147 | y, num_classes=self.n_classes 148 | ) 149 | -------------------------------------------------------------------------------- /fetch/models/e_FT_VGG19_3_DMT_Xception_13_128/ft_VGG19_3_dt_Xception_13_128.json: -------------------------------------------------------------------------------- 1 | {"class_name": 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/fetch/models/g_FT_VGG19_3_DMT_VGG16_1_128/ft_VGG19_3_dt_VGG16_1_128.yaml: -------------------------------------------------------------------------------- 1 | backend: tensorflow 2 | class_name: Model 3 | config: 4 | input_layers: 5 | - [data_freq_time, 0, 0] 6 | - [data_dm_time, 0, 0] 7 | layers: 8 | - class_name: InputLayer 9 | config: 10 | batch_input_shape: !!python/tuple [null, 256, 256, 1] 11 | dtype: float32 12 | name: data_freq_time 13 | sparse: false 14 | inbound_nodes: [] 15 | name: data_freq_time 16 | - class_name: InputLayer 17 | config: 18 | batch_input_shape: !!python/tuple [null, 256, 256, 1] 19 | dtype: float32 20 | name: data_dm_time 21 | sparse: false 22 | inbound_nodes: [] 23 | name: data_dm_time 24 | - class_name: Conv2D 25 | config: 26 | activation: relu 27 | activity_regularizer: null 28 | bias_constraint: null 29 | bias_initializer: 30 | class_name: Zeros 31 | config: {} 32 | bias_regularizer: null 33 | data_format: channels_last 34 | dilation_rate: &id001 !!python/tuple [1, 1] 35 | filters: 3 36 | kernel_constraint: null 37 | kernel_initializer: 38 | class_name: VarianceScaling 39 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 40 | kernel_regularizer: null 41 | kernel_size: &id002 !!python/tuple [2, 2] 42 | name: conv2d_1__0 43 | padding: valid 44 | strides: &id003 !!python/tuple [1, 1] 45 | trainable: true 46 | use_bias: true 47 | inbound_nodes: 48 | - - - data_freq_time 49 | - 0 50 | - 0 51 | - {} 52 | name: conv2d_1__0 53 | - class_name: Conv2D 54 | config: 55 | activation: relu 56 | activity_regularizer: null 57 | bias_constraint: null 58 | bias_initializer: 59 | class_name: Zeros 60 | config: {} 61 | bias_regularizer: null 62 | data_format: channels_last 63 | dilation_rate: *id001 64 | filters: 3 65 | kernel_constraint: null 66 | kernel_initializer: 67 | class_name: VarianceScaling 68 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 69 | kernel_regularizer: null 70 | kernel_size: *id002 71 | name: conv2d_2__1 72 | padding: valid 73 | strides: *id003 74 | trainable: true 75 | use_bias: true 76 | inbound_nodes: 77 | - - - data_dm_time 78 | - 0 79 | - 0 80 | - {} 81 | name: conv2d_2__1 82 | - class_name: Model 83 | config: 84 | input_layers: 85 | - [input_2, 0, 0] 86 | layers: 87 | - class_name: InputLayer 88 | config: 89 | batch_input_shape: !!python/tuple [null, null, null, 3] 90 | dtype: float32 91 | name: input_2 92 | sparse: false 93 | inbound_nodes: [] 94 | name: input_2 95 | - class_name: Conv2D 96 | config: 97 | activation: relu 98 | activity_regularizer: null 99 | bias_constraint: null 100 | bias_initializer: 101 | class_name: Zeros 102 | config: {} 103 | bias_regularizer: null 104 | data_format: channels_last 105 | dilation_rate: *id001 106 | filters: 64 107 | kernel_constraint: null 108 | kernel_initializer: 109 | class_name: VarianceScaling 110 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 111 | kernel_regularizer: null 112 | kernel_size: !!python/tuple [3, 3] 113 | name: block1_conv1 114 | padding: same 115 | strides: &id004 !!python/tuple [1, 1] 116 | trainable: &id005 !!python/object/apply:numpy.core.multiarray.scalar 117 | - &id006 !!python/object/apply:numpy.dtype 118 | args: [b1, 0, 1] 119 | state: !!python/tuple [3, '|', null, null, null, -1, -1, 0] 120 | - !!binary | 121 | AA== 122 | use_bias: true 123 | inbound_nodes: 124 | - - - input_2 125 | - 0 126 | - 0 127 | - {} 128 | name: block1_conv1 129 | - class_name: Conv2D 130 | config: 131 | activation: relu 132 | activity_regularizer: null 133 | bias_constraint: null 134 | bias_initializer: 135 | class_name: Zeros 136 | config: {} 137 | bias_regularizer: null 138 | data_format: channels_last 139 | dilation_rate: *id001 140 | filters: 64 141 | kernel_constraint: null 142 | kernel_initializer: 143 | class_name: VarianceScaling 144 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 145 | kernel_regularizer: null 146 | kernel_size: !!python/tuple [3, 3] 147 | name: block1_conv2 148 | padding: same 149 | strides: *id004 150 | trainable: *id005 151 | use_bias: true 152 | inbound_nodes: 153 | - - - block1_conv1 154 | - 0 155 | - 0 156 | - {} 157 | name: block1_conv2 158 | - class_name: MaxPooling2D 159 | config: 160 | data_format: channels_last 161 | name: block1_pool 162 | padding: valid 163 | pool_size: !!python/tuple [2, 2] 164 | strides: !!python/tuple [2, 2] 165 | trainable: *id005 166 | inbound_nodes: 167 | - - - block1_conv2 168 | - 0 169 | - 0 170 | - {} 171 | name: block1_pool 172 | - class_name: Conv2D 173 | config: 174 | activation: relu 175 | activity_regularizer: null 176 | bias_constraint: null 177 | bias_initializer: 178 | class_name: Zeros 179 | config: {} 180 | bias_regularizer: null 181 | data_format: channels_last 182 | dilation_rate: *id001 183 | filters: 128 184 | kernel_constraint: null 185 | kernel_initializer: 186 | class_name: VarianceScaling 187 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 188 | kernel_regularizer: null 189 | kernel_size: !!python/tuple [3, 3] 190 | name: block2_conv1 191 | padding: same 192 | strides: *id004 193 | trainable: *id005 194 | use_bias: true 195 | inbound_nodes: 196 | - - - block1_pool 197 | - 0 198 | - 0 199 | - {} 200 | name: block2_conv1 201 | - class_name: Conv2D 202 | config: 203 | activation: relu 204 | activity_regularizer: null 205 | bias_constraint: null 206 | bias_initializer: 207 | class_name: Zeros 208 | config: {} 209 | bias_regularizer: null 210 | data_format: channels_last 211 | dilation_rate: *id001 212 | filters: 128 213 | kernel_constraint: null 214 | kernel_initializer: 215 | class_name: VarianceScaling 216 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 217 | kernel_regularizer: null 218 | kernel_size: !!python/tuple [3, 3] 219 | name: block2_conv2 220 | padding: same 221 | strides: *id004 222 | trainable: *id005 223 | use_bias: true 224 | inbound_nodes: 225 | - - - block2_conv1 226 | - 0 227 | - 0 228 | - {} 229 | name: block2_conv2 230 | - class_name: MaxPooling2D 231 | config: 232 | data_format: channels_last 233 | name: block2_pool 234 | padding: valid 235 | pool_size: !!python/tuple [2, 2] 236 | strides: !!python/tuple [2, 2] 237 | trainable: *id005 238 | inbound_nodes: 239 | - - - block2_conv2 240 | - 0 241 | - 0 242 | - {} 243 | name: block2_pool 244 | - class_name: Conv2D 245 | config: 246 | activation: relu 247 | activity_regularizer: null 248 | bias_constraint: null 249 | bias_initializer: 250 | class_name: Zeros 251 | config: {} 252 | bias_regularizer: null 253 | data_format: channels_last 254 | dilation_rate: *id001 255 | filters: 256 256 | kernel_constraint: null 257 | kernel_initializer: 258 | class_name: VarianceScaling 259 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 260 | kernel_regularizer: null 261 | kernel_size: !!python/tuple [3, 3] 262 | name: block3_conv1 263 | padding: same 264 | strides: *id004 265 | trainable: *id005 266 | use_bias: true 267 | inbound_nodes: 268 | - - - block2_pool 269 | - 0 270 | - 0 271 | - {} 272 | name: block3_conv1 273 | - class_name: Conv2D 274 | config: 275 | activation: relu 276 | activity_regularizer: null 277 | bias_constraint: null 278 | bias_initializer: 279 | class_name: Zeros 280 | config: {} 281 | bias_regularizer: null 282 | data_format: channels_last 283 | dilation_rate: *id001 284 | filters: 256 285 | kernel_constraint: null 286 | kernel_initializer: 287 | class_name: VarianceScaling 288 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 289 | kernel_regularizer: null 290 | kernel_size: !!python/tuple [3, 3] 291 | name: block3_conv2 292 | padding: same 293 | strides: *id004 294 | trainable: *id005 295 | use_bias: true 296 | inbound_nodes: 297 | - - - block3_conv1 298 | - 0 299 | - 0 300 | - {} 301 | name: block3_conv2 302 | - class_name: Conv2D 303 | config: 304 | activation: relu 305 | activity_regularizer: null 306 | bias_constraint: null 307 | bias_initializer: 308 | class_name: Zeros 309 | config: {} 310 | bias_regularizer: null 311 | data_format: channels_last 312 | dilation_rate: *id001 313 | filters: 256 314 | kernel_constraint: null 315 | kernel_initializer: 316 | class_name: VarianceScaling 317 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 318 | kernel_regularizer: null 319 | kernel_size: !!python/tuple [3, 3] 320 | name: block3_conv3 321 | padding: same 322 | strides: *id004 323 | trainable: *id005 324 | use_bias: true 325 | inbound_nodes: 326 | - - - block3_conv2 327 | - 0 328 | - 0 329 | - {} 330 | name: block3_conv3 331 | - class_name: Conv2D 332 | config: 333 | activation: relu 334 | activity_regularizer: null 335 | bias_constraint: null 336 | bias_initializer: 337 | class_name: Zeros 338 | config: {} 339 | bias_regularizer: null 340 | data_format: channels_last 341 | dilation_rate: *id001 342 | filters: 256 343 | kernel_constraint: null 344 | kernel_initializer: 345 | class_name: VarianceScaling 346 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 347 | kernel_regularizer: null 348 | kernel_size: !!python/tuple [3, 3] 349 | name: block3_conv4 350 | padding: same 351 | strides: *id004 352 | trainable: *id005 353 | use_bias: true 354 | inbound_nodes: 355 | - - - block3_conv3 356 | - 0 357 | - 0 358 | - {} 359 | name: block3_conv4 360 | - class_name: MaxPooling2D 361 | config: 362 | data_format: channels_last 363 | name: block3_pool 364 | padding: valid 365 | pool_size: !!python/tuple [2, 2] 366 | strides: !!python/tuple [2, 2] 367 | trainable: *id005 368 | inbound_nodes: 369 | - - - block3_conv4 370 | - 0 371 | - 0 372 | - {} 373 | name: block3_pool 374 | - class_name: Conv2D 375 | config: 376 | activation: relu 377 | activity_regularizer: null 378 | bias_constraint: null 379 | bias_initializer: 380 | class_name: Zeros 381 | config: {} 382 | bias_regularizer: null 383 | data_format: channels_last 384 | dilation_rate: *id001 385 | filters: 512 386 | kernel_constraint: null 387 | kernel_initializer: 388 | class_name: VarianceScaling 389 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 390 | kernel_regularizer: null 391 | kernel_size: !!python/tuple [3, 3] 392 | name: block4_conv1 393 | padding: same 394 | strides: *id004 395 | trainable: *id005 396 | use_bias: true 397 | inbound_nodes: 398 | - - - block3_pool 399 | - 0 400 | - 0 401 | - {} 402 | name: block4_conv1 403 | - class_name: Conv2D 404 | config: 405 | activation: relu 406 | activity_regularizer: null 407 | bias_constraint: null 408 | bias_initializer: 409 | class_name: Zeros 410 | config: {} 411 | bias_regularizer: null 412 | data_format: channels_last 413 | dilation_rate: *id001 414 | filters: 512 415 | kernel_constraint: null 416 | kernel_initializer: 417 | class_name: VarianceScaling 418 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 419 | kernel_regularizer: null 420 | kernel_size: !!python/tuple [3, 3] 421 | name: block4_conv2 422 | padding: same 423 | strides: *id004 424 | trainable: *id005 425 | use_bias: true 426 | inbound_nodes: 427 | - - - block4_conv1 428 | - 0 429 | - 0 430 | - {} 431 | name: block4_conv2 432 | - class_name: Conv2D 433 | config: 434 | activation: relu 435 | activity_regularizer: null 436 | bias_constraint: null 437 | bias_initializer: 438 | class_name: Zeros 439 | config: {} 440 | bias_regularizer: null 441 | data_format: channels_last 442 | dilation_rate: *id001 443 | filters: 512 444 | kernel_constraint: null 445 | kernel_initializer: 446 | class_name: VarianceScaling 447 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 448 | kernel_regularizer: null 449 | kernel_size: !!python/tuple [3, 3] 450 | name: block4_conv3 451 | padding: same 452 | strides: *id004 453 | trainable: *id005 454 | use_bias: true 455 | inbound_nodes: 456 | - - - block4_conv2 457 | - 0 458 | - 0 459 | - {} 460 | name: block4_conv3 461 | - class_name: Conv2D 462 | config: 463 | activation: relu 464 | activity_regularizer: null 465 | bias_constraint: null 466 | bias_initializer: 467 | class_name: Zeros 468 | config: {} 469 | bias_regularizer: null 470 | data_format: channels_last 471 | dilation_rate: *id001 472 | filters: 512 473 | kernel_constraint: null 474 | kernel_initializer: 475 | class_name: VarianceScaling 476 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 477 | kernel_regularizer: null 478 | kernel_size: !!python/tuple [3, 3] 479 | name: block4_conv4 480 | padding: same 481 | strides: *id004 482 | trainable: *id005 483 | use_bias: true 484 | inbound_nodes: 485 | - - - block4_conv3 486 | - 0 487 | - 0 488 | - {} 489 | name: block4_conv4 490 | - class_name: MaxPooling2D 491 | config: 492 | data_format: channels_last 493 | name: block4_pool 494 | padding: valid 495 | pool_size: !!python/tuple [2, 2] 496 | strides: !!python/tuple [2, 2] 497 | trainable: *id005 498 | inbound_nodes: 499 | - - - block4_conv4 500 | - 0 501 | - 0 502 | - {} 503 | name: block4_pool 504 | - class_name: Conv2D 505 | config: 506 | activation: relu 507 | activity_regularizer: null 508 | bias_constraint: null 509 | bias_initializer: 510 | class_name: Zeros 511 | config: {} 512 | bias_regularizer: null 513 | data_format: channels_last 514 | dilation_rate: *id001 515 | filters: 512 516 | kernel_constraint: null 517 | kernel_initializer: 518 | class_name: VarianceScaling 519 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 520 | kernel_regularizer: null 521 | kernel_size: !!python/tuple [3, 3] 522 | name: block5_conv1 523 | padding: same 524 | strides: *id004 525 | trainable: *id005 526 | use_bias: true 527 | inbound_nodes: 528 | - - - block4_pool 529 | - 0 530 | - 0 531 | - {} 532 | name: block5_conv1 533 | - class_name: Conv2D 534 | config: 535 | activation: relu 536 | activity_regularizer: null 537 | bias_constraint: null 538 | bias_initializer: 539 | class_name: Zeros 540 | config: {} 541 | bias_regularizer: null 542 | data_format: channels_last 543 | dilation_rate: *id001 544 | filters: 512 545 | kernel_constraint: null 546 | kernel_initializer: 547 | class_name: VarianceScaling 548 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 549 | kernel_regularizer: null 550 | kernel_size: !!python/tuple [3, 3] 551 | name: block5_conv2 552 | padding: same 553 | strides: *id004 554 | trainable: &id007 !!python/object/apply:numpy.core.multiarray.scalar 555 | - *id006 556 | - !!binary | 557 | AQ== 558 | use_bias: true 559 | inbound_nodes: 560 | - - - block5_conv1 561 | - 0 562 | - 0 563 | - {} 564 | name: block5_conv2 565 | - class_name: Conv2D 566 | config: 567 | activation: relu 568 | activity_regularizer: null 569 | bias_constraint: null 570 | bias_initializer: 571 | class_name: Zeros 572 | config: {} 573 | bias_regularizer: null 574 | data_format: channels_last 575 | dilation_rate: *id001 576 | filters: 512 577 | kernel_constraint: null 578 | kernel_initializer: 579 | class_name: VarianceScaling 580 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 581 | kernel_regularizer: null 582 | kernel_size: !!python/tuple [3, 3] 583 | name: block5_conv3 584 | padding: same 585 | strides: *id004 586 | trainable: *id007 587 | use_bias: true 588 | inbound_nodes: 589 | - - - block5_conv2 590 | - 0 591 | - 0 592 | - {} 593 | name: block5_conv3 594 | - class_name: Conv2D 595 | config: 596 | activation: relu 597 | activity_regularizer: null 598 | bias_constraint: null 599 | bias_initializer: 600 | class_name: Zeros 601 | config: {} 602 | bias_regularizer: null 603 | data_format: channels_last 604 | dilation_rate: *id001 605 | filters: 512 606 | kernel_constraint: null 607 | kernel_initializer: 608 | class_name: VarianceScaling 609 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 610 | kernel_regularizer: null 611 | kernel_size: !!python/tuple [3, 3] 612 | name: block5_conv4 613 | padding: same 614 | strides: *id004 615 | trainable: *id007 616 | use_bias: true 617 | inbound_nodes: 618 | - - - block5_conv3 619 | - 0 620 | - 0 621 | - {} 622 | name: block5_conv4 623 | - class_name: MaxPooling2D 624 | config: 625 | data_format: channels_last 626 | name: block5_pool 627 | padding: valid 628 | pool_size: !!python/tuple [2, 2] 629 | strides: !!python/tuple [2, 2] 630 | trainable: *id005 631 | inbound_nodes: 632 | - - - block5_conv4 633 | - 0 634 | - 0 635 | - {} 636 | name: block5_pool 637 | - class_name: GlobalMaxPooling2D 638 | config: 639 | data_format: channels_last 640 | name: global_max_pooling2d_1 641 | trainable: *id005 642 | inbound_nodes: 643 | - - - block5_pool 644 | - 0 645 | - 0 646 | - {} 647 | name: global_max_pooling2d_1 648 | name: vgg19__0 649 | output_layers: 650 | - [global_max_pooling2d_1, 0, 0] 651 | inbound_nodes: 652 | - - - conv2d_1__0 653 | - 0 654 | - 0 655 | - {} 656 | name: vgg19__0 657 | - class_name: Model 658 | config: 659 | input_layers: 660 | - [input_4, 0, 0] 661 | layers: 662 | - class_name: InputLayer 663 | config: 664 | batch_input_shape: !!python/tuple [null, null, null, 3] 665 | dtype: float32 666 | name: input_4 667 | sparse: false 668 | inbound_nodes: [] 669 | name: input_4 670 | - class_name: Conv2D 671 | config: 672 | activation: relu 673 | activity_regularizer: null 674 | bias_constraint: null 675 | bias_initializer: 676 | class_name: Zeros 677 | config: {} 678 | bias_regularizer: null 679 | data_format: channels_last 680 | dilation_rate: *id001 681 | filters: 64 682 | kernel_constraint: null 683 | kernel_initializer: 684 | class_name: VarianceScaling 685 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 686 | kernel_regularizer: null 687 | kernel_size: !!python/tuple [3, 3] 688 | name: block1_conv1 689 | padding: same 690 | strides: *id004 691 | trainable: *id005 692 | use_bias: true 693 | inbound_nodes: 694 | - - - input_4 695 | - 0 696 | - 0 697 | - {} 698 | name: block1_conv1 699 | - class_name: Conv2D 700 | config: 701 | activation: relu 702 | activity_regularizer: null 703 | bias_constraint: null 704 | bias_initializer: 705 | class_name: Zeros 706 | config: {} 707 | bias_regularizer: null 708 | data_format: channels_last 709 | dilation_rate: *id001 710 | filters: 64 711 | kernel_constraint: null 712 | kernel_initializer: 713 | class_name: VarianceScaling 714 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 715 | kernel_regularizer: null 716 | kernel_size: !!python/tuple [3, 3] 717 | name: block1_conv2 718 | padding: same 719 | strides: *id004 720 | trainable: *id005 721 | use_bias: true 722 | inbound_nodes: 723 | - - - block1_conv1 724 | - 0 725 | - 0 726 | - {} 727 | name: block1_conv2 728 | - class_name: MaxPooling2D 729 | config: 730 | data_format: channels_last 731 | name: block1_pool 732 | padding: valid 733 | pool_size: !!python/tuple [2, 2] 734 | strides: !!python/tuple [2, 2] 735 | trainable: *id005 736 | inbound_nodes: 737 | - - - block1_conv2 738 | - 0 739 | - 0 740 | - {} 741 | name: block1_pool 742 | - class_name: Conv2D 743 | config: 744 | activation: relu 745 | activity_regularizer: null 746 | bias_constraint: null 747 | bias_initializer: 748 | class_name: Zeros 749 | config: {} 750 | bias_regularizer: null 751 | data_format: channels_last 752 | dilation_rate: *id001 753 | filters: 128 754 | kernel_constraint: null 755 | kernel_initializer: 756 | class_name: VarianceScaling 757 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 758 | kernel_regularizer: null 759 | kernel_size: !!python/tuple [3, 3] 760 | name: block2_conv1 761 | padding: same 762 | strides: *id004 763 | trainable: *id005 764 | use_bias: true 765 | inbound_nodes: 766 | - - - block1_pool 767 | - 0 768 | - 0 769 | - {} 770 | name: block2_conv1 771 | - class_name: Conv2D 772 | config: 773 | activation: relu 774 | activity_regularizer: null 775 | bias_constraint: null 776 | bias_initializer: 777 | class_name: Zeros 778 | config: {} 779 | bias_regularizer: null 780 | data_format: channels_last 781 | dilation_rate: *id001 782 | filters: 128 783 | kernel_constraint: null 784 | kernel_initializer: 785 | class_name: VarianceScaling 786 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 787 | kernel_regularizer: null 788 | kernel_size: !!python/tuple [3, 3] 789 | name: block2_conv2 790 | padding: same 791 | strides: *id004 792 | trainable: *id005 793 | use_bias: true 794 | inbound_nodes: 795 | - - - block2_conv1 796 | - 0 797 | - 0 798 | - {} 799 | name: block2_conv2 800 | - class_name: MaxPooling2D 801 | config: 802 | data_format: channels_last 803 | name: block2_pool 804 | padding: valid 805 | pool_size: !!python/tuple [2, 2] 806 | strides: !!python/tuple [2, 2] 807 | trainable: *id005 808 | inbound_nodes: 809 | - - - block2_conv2 810 | - 0 811 | - 0 812 | - {} 813 | name: block2_pool 814 | - class_name: Conv2D 815 | config: 816 | activation: relu 817 | activity_regularizer: null 818 | bias_constraint: null 819 | bias_initializer: 820 | class_name: Zeros 821 | config: {} 822 | bias_regularizer: null 823 | data_format: channels_last 824 | dilation_rate: *id001 825 | filters: 256 826 | kernel_constraint: null 827 | kernel_initializer: 828 | class_name: VarianceScaling 829 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 830 | kernel_regularizer: null 831 | kernel_size: !!python/tuple [3, 3] 832 | name: block3_conv1 833 | padding: same 834 | strides: *id004 835 | trainable: *id005 836 | use_bias: true 837 | inbound_nodes: 838 | - - - block2_pool 839 | - 0 840 | - 0 841 | - {} 842 | name: block3_conv1 843 | - class_name: Conv2D 844 | config: 845 | activation: relu 846 | activity_regularizer: null 847 | bias_constraint: null 848 | bias_initializer: 849 | class_name: Zeros 850 | config: {} 851 | bias_regularizer: null 852 | data_format: channels_last 853 | dilation_rate: *id001 854 | filters: 256 855 | kernel_constraint: null 856 | kernel_initializer: 857 | class_name: VarianceScaling 858 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 859 | kernel_regularizer: null 860 | kernel_size: !!python/tuple [3, 3] 861 | name: block3_conv2 862 | padding: same 863 | strides: *id004 864 | trainable: *id005 865 | use_bias: true 866 | inbound_nodes: 867 | - - - block3_conv1 868 | - 0 869 | - 0 870 | - {} 871 | name: block3_conv2 872 | - class_name: Conv2D 873 | config: 874 | activation: relu 875 | activity_regularizer: null 876 | bias_constraint: null 877 | bias_initializer: 878 | class_name: Zeros 879 | config: {} 880 | bias_regularizer: null 881 | data_format: channels_last 882 | dilation_rate: *id001 883 | filters: 256 884 | kernel_constraint: null 885 | kernel_initializer: 886 | class_name: VarianceScaling 887 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 888 | kernel_regularizer: null 889 | kernel_size: !!python/tuple [3, 3] 890 | name: block3_conv3 891 | padding: same 892 | strides: *id004 893 | trainable: *id005 894 | use_bias: true 895 | inbound_nodes: 896 | - - - block3_conv2 897 | - 0 898 | - 0 899 | - {} 900 | name: block3_conv3 901 | - class_name: MaxPooling2D 902 | config: 903 | data_format: channels_last 904 | name: block3_pool 905 | padding: valid 906 | pool_size: !!python/tuple [2, 2] 907 | strides: !!python/tuple [2, 2] 908 | trainable: *id005 909 | inbound_nodes: 910 | - - - block3_conv3 911 | - 0 912 | - 0 913 | - {} 914 | name: block3_pool 915 | - class_name: Conv2D 916 | config: 917 | activation: relu 918 | activity_regularizer: null 919 | bias_constraint: null 920 | bias_initializer: 921 | class_name: Zeros 922 | config: {} 923 | bias_regularizer: null 924 | data_format: channels_last 925 | dilation_rate: *id001 926 | filters: 512 927 | kernel_constraint: null 928 | kernel_initializer: 929 | class_name: VarianceScaling 930 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 931 | kernel_regularizer: null 932 | kernel_size: !!python/tuple [3, 3] 933 | name: block4_conv1 934 | padding: same 935 | strides: *id004 936 | trainable: *id005 937 | use_bias: true 938 | inbound_nodes: 939 | - - - block3_pool 940 | - 0 941 | - 0 942 | - {} 943 | name: block4_conv1 944 | - class_name: Conv2D 945 | config: 946 | activation: relu 947 | activity_regularizer: null 948 | bias_constraint: null 949 | bias_initializer: 950 | class_name: Zeros 951 | config: {} 952 | bias_regularizer: null 953 | data_format: channels_last 954 | dilation_rate: *id001 955 | filters: 512 956 | kernel_constraint: null 957 | kernel_initializer: 958 | class_name: VarianceScaling 959 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 960 | kernel_regularizer: null 961 | kernel_size: !!python/tuple [3, 3] 962 | name: block4_conv2 963 | padding: same 964 | strides: *id004 965 | trainable: *id005 966 | use_bias: true 967 | inbound_nodes: 968 | - - - block4_conv1 969 | - 0 970 | - 0 971 | - {} 972 | name: block4_conv2 973 | - class_name: Conv2D 974 | config: 975 | activation: relu 976 | activity_regularizer: null 977 | bias_constraint: null 978 | bias_initializer: 979 | class_name: Zeros 980 | config: {} 981 | bias_regularizer: null 982 | data_format: channels_last 983 | dilation_rate: *id001 984 | filters: 512 985 | kernel_constraint: null 986 | kernel_initializer: 987 | class_name: VarianceScaling 988 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 989 | kernel_regularizer: null 990 | kernel_size: !!python/tuple [3, 3] 991 | name: block4_conv3 992 | padding: same 993 | strides: *id004 994 | trainable: *id005 995 | use_bias: true 996 | inbound_nodes: 997 | - - - block4_conv2 998 | - 0 999 | - 0 1000 | - {} 1001 | name: block4_conv3 1002 | - class_name: MaxPooling2D 1003 | config: 1004 | data_format: channels_last 1005 | name: block4_pool 1006 | padding: valid 1007 | pool_size: !!python/tuple [2, 2] 1008 | strides: !!python/tuple [2, 2] 1009 | trainable: *id005 1010 | inbound_nodes: 1011 | - - - block4_conv3 1012 | - 0 1013 | - 0 1014 | - {} 1015 | name: block4_pool 1016 | - class_name: Conv2D 1017 | config: 1018 | activation: relu 1019 | activity_regularizer: null 1020 | bias_constraint: null 1021 | bias_initializer: 1022 | class_name: Zeros 1023 | config: {} 1024 | bias_regularizer: null 1025 | data_format: channels_last 1026 | dilation_rate: *id001 1027 | filters: 512 1028 | kernel_constraint: null 1029 | kernel_initializer: 1030 | class_name: VarianceScaling 1031 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 1032 | kernel_regularizer: null 1033 | kernel_size: !!python/tuple [3, 3] 1034 | name: block5_conv1 1035 | padding: same 1036 | strides: *id004 1037 | trainable: *id005 1038 | use_bias: true 1039 | inbound_nodes: 1040 | - - - block4_pool 1041 | - 0 1042 | - 0 1043 | - {} 1044 | name: block5_conv1 1045 | - class_name: Conv2D 1046 | config: 1047 | activation: relu 1048 | activity_regularizer: null 1049 | bias_constraint: null 1050 | bias_initializer: 1051 | class_name: Zeros 1052 | config: {} 1053 | bias_regularizer: null 1054 | data_format: channels_last 1055 | dilation_rate: *id001 1056 | filters: 512 1057 | kernel_constraint: null 1058 | kernel_initializer: 1059 | class_name: VarianceScaling 1060 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 1061 | kernel_regularizer: null 1062 | kernel_size: !!python/tuple [3, 3] 1063 | name: block5_conv2 1064 | padding: same 1065 | strides: *id004 1066 | trainable: *id005 1067 | use_bias: true 1068 | inbound_nodes: 1069 | - - - block5_conv1 1070 | - 0 1071 | - 0 1072 | - {} 1073 | name: block5_conv2 1074 | - class_name: Conv2D 1075 | config: 1076 | activation: relu 1077 | activity_regularizer: null 1078 | bias_constraint: null 1079 | bias_initializer: 1080 | class_name: Zeros 1081 | config: {} 1082 | bias_regularizer: null 1083 | data_format: channels_last 1084 | dilation_rate: *id001 1085 | filters: 512 1086 | kernel_constraint: null 1087 | kernel_initializer: 1088 | class_name: VarianceScaling 1089 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 1090 | kernel_regularizer: null 1091 | kernel_size: !!python/tuple [3, 3] 1092 | name: block5_conv3 1093 | padding: same 1094 | strides: *id004 1095 | trainable: *id007 1096 | use_bias: true 1097 | inbound_nodes: 1098 | - - - block5_conv2 1099 | - 0 1100 | - 0 1101 | - {} 1102 | name: block5_conv3 1103 | - class_name: MaxPooling2D 1104 | config: 1105 | data_format: channels_last 1106 | name: block5_pool 1107 | padding: valid 1108 | pool_size: !!python/tuple [2, 2] 1109 | strides: !!python/tuple [2, 2] 1110 | trainable: *id005 1111 | inbound_nodes: 1112 | - - - block5_conv3 1113 | - 0 1114 | - 0 1115 | - {} 1116 | name: block5_pool 1117 | - class_name: GlobalMaxPooling2D 1118 | config: 1119 | data_format: channels_last 1120 | name: global_max_pooling2d_2 1121 | trainable: *id005 1122 | inbound_nodes: 1123 | - - - block5_pool 1124 | - 0 1125 | - 0 1126 | - {} 1127 | name: global_max_pooling2d_2 1128 | name: vgg16__1 1129 | output_layers: 1130 | - [global_max_pooling2d_2, 0, 0] 1131 | inbound_nodes: 1132 | - - - conv2d_2__1 1133 | - 0 1134 | - 0 1135 | - {} 1136 | name: vgg16__1 1137 | - class_name: BatchNormalization 1138 | config: 1139 | axis: -1 1140 | beta_constraint: null 1141 | beta_initializer: 1142 | class_name: Zeros 1143 | config: {} 1144 | beta_regularizer: null 1145 | center: true 1146 | epsilon: 0.001 1147 | gamma_constraint: null 1148 | gamma_initializer: 1149 | class_name: Ones 1150 | config: {} 1151 | gamma_regularizer: null 1152 | momentum: 0.99 1153 | moving_mean_initializer: 1154 | class_name: Zeros 1155 | config: {} 1156 | moving_variance_initializer: 1157 | class_name: Ones 1158 | config: {} 1159 | name: batch_normalization_1 1160 | scale: true 1161 | trainable: true 1162 | inbound_nodes: 1163 | - - - vgg19__0 1164 | - 1 1165 | - 0 1166 | - {} 1167 | name: batch_normalization_1 1168 | - class_name: BatchNormalization 1169 | config: 1170 | axis: -1 1171 | beta_constraint: null 1172 | beta_initializer: 1173 | class_name: Zeros 1174 | config: {} 1175 | beta_regularizer: null 1176 | center: true 1177 | epsilon: 0.001 1178 | gamma_constraint: null 1179 | gamma_initializer: 1180 | class_name: Ones 1181 | config: {} 1182 | gamma_regularizer: null 1183 | momentum: 0.99 1184 | moving_mean_initializer: 1185 | class_name: Zeros 1186 | config: {} 1187 | moving_variance_initializer: 1188 | class_name: Ones 1189 | config: {} 1190 | name: batch_normalization_2 1191 | scale: true 1192 | trainable: true 1193 | inbound_nodes: 1194 | - - - vgg16__1 1195 | - 1 1196 | - 0 1197 | - {} 1198 | name: batch_normalization_2 1199 | - class_name: Dropout 1200 | config: {name: dropout_1, noise_shape: null, rate: 0.3, seed: null, trainable: true} 1201 | inbound_nodes: 1202 | - - - batch_normalization_1 1203 | - 0 1204 | - 0 1205 | - {} 1206 | name: dropout_1 1207 | - class_name: Dropout 1208 | config: {name: dropout_2, noise_shape: null, rate: 0.3, seed: null, trainable: true} 1209 | inbound_nodes: 1210 | - - - batch_normalization_2 1211 | - 0 1212 | - 0 1213 | - {} 1214 | name: dropout_2 1215 | - class_name: Dense 1216 | config: 1217 | activation: linear 1218 | activity_regularizer: null 1219 | bias_constraint: null 1220 | bias_initializer: 1221 | class_name: Zeros 1222 | config: {} 1223 | bias_regularizer: null 1224 | kernel_constraint: null 1225 | kernel_initializer: 1226 | class_name: VarianceScaling 1227 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 1228 | kernel_regularizer: null 1229 | name: dense_1 1230 | trainable: true 1231 | units: 128 1232 | use_bias: true 1233 | inbound_nodes: 1234 | - - - dropout_1 1235 | - 0 1236 | - 0 1237 | - {} 1238 | name: dense_1 1239 | - class_name: Dense 1240 | config: 1241 | activation: linear 1242 | activity_regularizer: null 1243 | bias_constraint: null 1244 | bias_initializer: 1245 | class_name: Zeros 1246 | config: {} 1247 | bias_regularizer: null 1248 | kernel_constraint: null 1249 | kernel_initializer: 1250 | class_name: VarianceScaling 1251 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 1252 | kernel_regularizer: null 1253 | name: dense_2 1254 | trainable: true 1255 | units: 128 1256 | use_bias: true 1257 | inbound_nodes: 1258 | - - - dropout_2 1259 | - 0 1260 | - 0 1261 | - {} 1262 | name: dense_2 1263 | - class_name: Multiply 1264 | config: {name: multiply_1, trainable: true} 1265 | inbound_nodes: 1266 | - - - dense_1 1267 | - 0 1268 | - 0 1269 | - &id008 {} 1270 | - - dense_2 1271 | - 0 1272 | - 0 1273 | - *id008 1274 | name: multiply_1 1275 | - class_name: BatchNormalization 1276 | config: 1277 | axis: -1 1278 | beta_constraint: null 1279 | beta_initializer: 1280 | class_name: Zeros 1281 | config: {} 1282 | beta_regularizer: null 1283 | center: true 1284 | epsilon: 0.001 1285 | gamma_constraint: null 1286 | gamma_initializer: 1287 | class_name: Ones 1288 | config: {} 1289 | gamma_regularizer: null 1290 | momentum: 0.99 1291 | moving_mean_initializer: 1292 | class_name: Zeros 1293 | config: {} 1294 | moving_variance_initializer: 1295 | class_name: Ones 1296 | config: {} 1297 | name: batch_normalization_3 1298 | scale: true 1299 | trainable: true 1300 | inbound_nodes: 1301 | - - - multiply_1 1302 | - 0 1303 | - 0 1304 | - {} 1305 | name: batch_normalization_3 1306 | - class_name: Activation 1307 | config: {activation: relu, name: activation_1, trainable: true} 1308 | inbound_nodes: 1309 | - - - batch_normalization_3 1310 | - 0 1311 | - 0 1312 | - {} 1313 | name: activation_1 1314 | - class_name: Dense 1315 | config: 1316 | activation: softmax 1317 | activity_regularizer: null 1318 | bias_constraint: null 1319 | bias_initializer: 1320 | class_name: Zeros 1321 | config: {} 1322 | bias_regularizer: null 1323 | kernel_constraint: null 1324 | kernel_initializer: 1325 | class_name: VarianceScaling 1326 | config: {distribution: uniform, mode: fan_avg, scale: 1.0, seed: null} 1327 | kernel_regularizer: null 1328 | name: dense_3 1329 | trainable: true 1330 | units: 2 1331 | use_bias: true 1332 | inbound_nodes: 1333 | - - - activation_1 1334 | - 0 1335 | - 0 1336 | - {} 1337 | name: dense_3 1338 | name: model_3 1339 | output_layers: 1340 | - [dense_3, 0, 0] 1341 | keras_version: 2.2.2 1342 | -------------------------------------------------------------------------------- /fetch/models/model_list.csv: -------------------------------------------------------------------------------- 1 | ,model,hash 2 | 0,a_ft_DenseNet121_2_dt_Xception_13_256.h5,cfbdec1c229b0578899635b0235653bd 3 | 1,b_ft_DenseNet121_2_dt_VGG16_1_32.h5,d384b231c6ccb2fff96d5a7c305f3076 4 | 2,c_ft_DenseNet169_6_dt_Xception_13_112.h5,666775321a871d88beb83361f50cf9e1 5 | 3,d_ft_DenseNet201_4_dt_Xception_13_32.h5,a3712763ab78ae96a4ce422e4eeab1de 6 | 4,e_ft_VGG19_3_dt_Xception_13_128.h5,b951b674aa20192ca801d424b0103efc 7 | 5,f_ft_DenseNet169_6_dt_VGG16_1_512.h5,63da8f0ea980ee95d8b41284f444e6b9 8 | 6,g_ft_VGG19_3_dt_VGG16_1_128.h5,f407cbbe38a1a266b552fcc73b06a82e 9 | 7,h_ft_DenseNet201_4_dt_InceptionResNetV2_20_160.h5,605b39a0191ca07079c53026786821fb 10 | 8,i_ft_DenseNet201_4_dt_VGG16_1_32.h5,2621f23f19ddad1fb692bbd6c59a364f 11 | 9,j_ft_VGG19_3_dt_InceptionResNetV2_20_512.h5,25addb221183e34de569d2d11ef39576 12 | 10,k_ft_DenseNet121_2_dt_InceptionV3_18_64.h5,aa30fc3d9a6566d9ea07068acc45c2ec 13 | -------------------------------------------------------------------------------- /fetch/utils.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | 3 | import glob 4 | import logging 5 | import os 6 | import string 7 | 8 | import numpy as np 9 | import pandas as pd 10 | from tensorflow.keras.models import Model 11 | from tensorflow.keras.models import model_from_json 12 | from tensorflow.keras.optimizers import Adam 13 | from tensorflow.keras.utils import get_file 14 | 15 | os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE" 16 | PATH_TO_WEIGHTS = "https://zenodo.org/records/5029590/files/" 17 | 18 | logger = logging.getLogger(__name__) 19 | 20 | 21 | def open_n_layers_for_training(model, nlayers): 22 | """ 23 | Makes nlayers of the model trainable. 24 | nlayers start from the top of the model. 25 | Top (or head) refers to the classification layer. The opening of layers for training starts from top. 26 | 27 | :param model: Model to open layers of 28 | :type model: Model 29 | :param nlayers: Number of (trainable) layers to open. 30 | :type nlayers: int 31 | :return: model 32 | """ 33 | mask = np.zeros(len(model.layers), dtype=np.bool) 34 | mask[-nlayers:] = True 35 | for layer, mask_val in zip(model.layers, mask): 36 | layer.trainable = mask_val 37 | return model 38 | 39 | 40 | def ready_for_train(model, nf, ndt, nft): 41 | """ 42 | This makes the model ready for training, it opens the layers for training and complies it. 43 | 44 | :param model: model to train 45 | :type: Model 46 | :param nf: Number of layers to train post FT and DT models 47 | :type nf: int 48 | :param ndt: Number of layers in DT model to train 49 | :type ndt: int 50 | :param nft: Number of layers in FT model to train 51 | :type nft : int 52 | :return: compiled model ready for training 53 | """ 54 | 55 | # Make all layers non trainable first 56 | model.trainable = False 57 | model = open_n_layers_for_training(model, nf) 58 | 59 | # Get the FT and DT models to open them up for training 60 | model.layers[4] = open_n_layers_for_training(model.layers[4], nft) 61 | model.layers[5] = open_n_layers_for_training(model.layers[5], ndt) 62 | 63 | model_trainable = Model(model.inputs, model.outputs) 64 | 65 | # Adam optimizer with imagenet defaults 66 | optimizer = Adam( 67 | lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False 68 | ) 69 | 70 | # Compile 71 | model_trainable.compile( 72 | optimizer=optimizer, loss="binary_crossentropy", metrics=["accuracy"] 73 | ) 74 | 75 | return model_trainable 76 | 77 | 78 | def get_model(model_idx): 79 | """ 80 | 81 | :param model_idx: model string between a--j 82 | :type model_idx: str 83 | :return: Model 84 | """ 85 | # Get the model from the folder 86 | logger.info(f"Getting model {model_idx}") 87 | path = os.path.split(__file__)[0] 88 | model_json = glob.glob(f"{path}/models/{model_idx}_FT*/*json")[0] 89 | 90 | # Read the model from the json 91 | with open(model_json, "r") as j: 92 | model = model_from_json(j.read()) 93 | 94 | # get the model weights, if not present download them. 95 | model_list = pd.read_csv(f"{path}/models/model_list.csv") 96 | model_index = string.ascii_lowercase.index(model_idx) 97 | 98 | weights = get_file( 99 | model_list["model"][model_index], 100 | PATH_TO_WEIGHTS + model_list["model"][model_index], 101 | file_hash=model_list["hash"][model_index], 102 | cache_subdir="models", 103 | hash_algorithm="md5", 104 | ) 105 | 106 | # dump weights 107 | model.load_weights(weights) 108 | 109 | return model 110 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | pandas>=1.2.2 2 | tensorflow[and-cuda]>=2.12 3 | numpy>=1.19.5 4 | h5py>=3.1.0 5 | scipy>=1.6.0 6 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup 2 | 3 | setup( 4 | name="fetch", 5 | version="0.2.0", 6 | packages=["fetch"], 7 | scripts=["bin/predict.py", "bin/train.py"], 8 | package_dir={"fetch": "fetch"}, 9 | package_data={"fetch": ["models/model_list.csv", "models/*/*"]}, 10 | url="https://github.com/devanshkv/fetch", 11 | tests_require=["pytest", "pytest-cov"], 12 | license="GNU General Public License v3.0", 13 | author=["Devansh Agarwal", "Kshitij Aggarwal"], 14 | author_email=["devansh.kv@gmail.com", "ka0064@mix.wvu.edu"], 15 | description="FETCH (Fast Extragalactic Transient Candidate Hunter)", 16 | classifiers=[ 17 | "Natural Language :: English", 18 | "Intended Audience :: Science/Research", 19 | "Programming Language :: Python", 20 | "Programming Language :: Python :: 3.6", 21 | "Programming Language :: Python :: 3.7", 22 | "Programming Language :: Python :: 3.8", 23 | "Programming Language :: Python :: 3.9", 24 | "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", 25 | "Topic :: Scientific/Engineering :: Astronomy", 26 | ], 27 | ) 28 | --------------------------------------------------------------------------------