From 97208a5d170ae553d79d17b8d24e3abddadc514c Mon Sep 17 00:00:00 2001 From: Parsa Torabian Date: Thu, 5 May 2022 12:52:46 -0400 Subject: [PATCH 1/9] Refactored into a module --- README.md | 2 - README.rst | 193 ++++++++++++++++++ environment.yml | 31 +++ experiments/config.yml | 46 +++++ logging.yml | 43 ++++ {mouse_cnn => mousenet}/__init__.py | 0 {cmouse => mousenet/cmouse}/__init__.py | 0 {cmouse => mousenet/cmouse}/anatomy.py | 0 .../cmouse}/exps/cifar/config.py | 0 .../cifar/myresults/network_(3,64,64).pkl | Bin {cmouse => mousenet/cmouse}/exps/cifar/run.sh | 0 .../cmouse}/exps/cifar/train_config.py | 0 .../exps/imagenet/1111_model_best.pth.tar | Bin .../cmouse}/exps/imagenet/config.py | 0 .../cmouse}/exps/imagenet/imagenet_demo.ipynb | 0 ...twork_complete_updated_number(3,64,64).pkl | Bin .../cmouse}/exps/imagenet/run.sh | 0 .../cmouse}/exps/imagenet/train_config.py | 0 {cmouse => mousenet/cmouse}/helper.py | 0 {cmouse => mousenet/cmouse}/main.py | 0 .../cmouse}/mousenet_complete_pool.py | 0 {cmouse => mousenet/cmouse}/network.py | 0 {cmouse => mousenet/cmouse}/run_tests.sh | 0 {cmouse => mousenet/cmouse}/train_cifar.py | 0 .../example}/MouseNet_Demo.html | 0 .../example}/MouseNet_Demo.ipynb | 0 {example => mousenet/example}/config.py | 0 .../construct mousenet architecture.ipynb | 0 .../example}/construct-larger-font.jpg | Bin .../example}/framework2-cropped.jpg | Bin {example => mousenet/example}/network.jpg | Bin ...twork_complete_updated_number(3,64,64).pkl | Bin .../example}/using mousenet.ipynb | 0 {test => mousenet/mouse_cnn}/__init__.py | 0 .../mouse_cnn}/architecture.py | 0 {mouse_cnn => mousenet/mouse_cnn}/data.py | 0 {mouse_cnn => mousenet/mouse_cnn}/flatmap.py | 0 {mouse_cnn => mousenet/mouse_cnn}/voxel.py | 4 +- setup.cfg | 7 + setup.py | 40 ++++ tests/__init__.py | 0 {test => tests}/test_kernel_multimodality.py | 6 +- tests/test_model_import.py | 7 + tests/test_pytorch.py | 7 + 44 files changed, 379 insertions(+), 7 deletions(-) delete mode 100644 README.md create mode 100644 README.rst create mode 100644 environment.yml create mode 100644 experiments/config.yml create mode 100644 logging.yml rename {mouse_cnn => mousenet}/__init__.py (100%) rename {cmouse => mousenet/cmouse}/__init__.py (100%) rename {cmouse => mousenet/cmouse}/anatomy.py (100%) rename {cmouse => mousenet/cmouse}/exps/cifar/config.py (100%) rename {cmouse => mousenet/cmouse}/exps/cifar/myresults/network_(3,64,64).pkl (100%) rename {cmouse => mousenet/cmouse}/exps/cifar/run.sh (100%) rename {cmouse => mousenet/cmouse}/exps/cifar/train_config.py (100%) rename {cmouse => mousenet/cmouse}/exps/imagenet/1111_model_best.pth.tar (100%) rename {cmouse => mousenet/cmouse}/exps/imagenet/config.py (100%) rename {cmouse => mousenet/cmouse}/exps/imagenet/imagenet_demo.ipynb (100%) rename {cmouse => mousenet/cmouse}/exps/imagenet/myresults/network_complete_updated_number(3,64,64).pkl (100%) rename {cmouse => mousenet/cmouse}/exps/imagenet/run.sh (100%) rename {cmouse => mousenet/cmouse}/exps/imagenet/train_config.py (100%) rename {cmouse => mousenet/cmouse}/helper.py (100%) rename {cmouse => mousenet/cmouse}/main.py (100%) rename {cmouse => mousenet/cmouse}/mousenet_complete_pool.py (100%) rename {cmouse => mousenet/cmouse}/network.py (100%) rename {cmouse => mousenet/cmouse}/run_tests.sh (100%) mode change 100755 => 100644 rename {cmouse => mousenet/cmouse}/train_cifar.py (100%) rename {example => mousenet/example}/MouseNet_Demo.html (100%) rename {example => mousenet/example}/MouseNet_Demo.ipynb (100%) rename {example => mousenet/example}/config.py (100%) rename {example => mousenet/example}/construct mousenet architecture.ipynb (100%) rename {example => mousenet/example}/construct-larger-font.jpg (100%) rename {example => mousenet/example}/framework2-cropped.jpg (100%) rename {example => mousenet/example}/network.jpg (100%) rename {example => mousenet/example}/network_complete_updated_number(3,64,64).pkl (100%) rename {example => mousenet/example}/using mousenet.ipynb (100%) rename {test => mousenet/mouse_cnn}/__init__.py (100%) rename {mouse_cnn => mousenet/mouse_cnn}/architecture.py (100%) rename {mouse_cnn => mousenet/mouse_cnn}/data.py (100%) rename {mouse_cnn => mousenet/mouse_cnn}/flatmap.py (100%) rename {mouse_cnn => mousenet/mouse_cnn}/voxel.py (99%) create mode 100644 setup.cfg create mode 100644 setup.py create mode 100644 tests/__init__.py rename {test => tests}/test_kernel_multimodality.py (94%) create mode 100644 tests/test_model_import.py create mode 100644 tests/test_pytorch.py diff --git a/README.md b/README.md deleted file mode 100644 index 66124a3..0000000 --- a/README.md +++ /dev/null @@ -1,2 +0,0 @@ -# Mouse_CNN -This is the repo for the ongoing project of CNN MouseNet -- a convolutional neural network constrained by the architecture of the mouse visual cortex. \ No newline at end of file diff --git a/README.rst b/README.rst new file mode 100644 index 0000000..1f91668 --- /dev/null +++ b/README.rst @@ -0,0 +1,193 @@ +========= +Mouse_CNN +========= +This is the repo for the ongoing project of CNN MouseNet -- a convolutional neural network constrained by the architecture of the mouse visual cortex. + +.. contents:: Table of Contents + :depth: 2 + +Folder Structure +================ + +:: + + Mouse_CNN/ + │ + ├── mousenet/ + │ │ + │ ├── cli.py - command line interface + │ ├── main.py - main script to start train/test + │ │ + │ ├── base/ - abstract base classes + │ │ ├── base_data_loader.py - abstract base class for data loaders + │ │ ├── base_model.py - abstract base class for models + │ │ └── base_trainer.py - abstract base class for trainers + │ │ + │ ├── data_loader/ - anything about data loading goes here + │ │ ├── augmentation.py + │ │ └── data_loaders.py + │ │ + │ ├── model/ - models, losses, and metrics + │ │ ├── loss.py + │ │ ├── metric.py + │ │ └── model.py + │ │ + │ ├── trainer/ - trainers + │ │ └── trainer.py + │ │ + │ └── utils/ + │ ├── logger.py - class for train logging + │ ├── visualization.py - class for Tensorboard visualization support + │ └── saving.py - manages pathing for saving models + logs + │ + ├── logging.yml - logging configuration + │ + ├── data/ - directory for storing input data + │ + ├── experiments/ - directory for storing configuration files + │ + ├── saved/ - directory for checkpoints and logs + │ + └── tests/ - tests folder + + +Usage +===== + +.. code-block:: + + $ conda env create --file environment.yml + $ conda activate mousenet + +The code in this repo is an MNIST example of the template. You can run the tests, +and the example project using: + +.. code-block:: + + $ pytest tests + $ mousenet train -c experiments/config.yml + +Config file format +------------------ +Config files are in `.yml` format: + +.. code-block:: HTML + + name: Mnist_LeNet + n_gpu: 1 + save_dir: saved/ + seed: 1234 + + arch: + type: MnistModel + args: {} + + data_loader: + type: MnistDataLoader + args: + batch_size: 128 + data_dir: data/ + num_workers: 2 + shuffle: true + validation_split: 0.1 + + loss: nll_loss + + lr_scheduler: + type: StepLR + args: + gamma: 0.1 + step_size: 50 + + metrics: + - my_metric + - my_metric2 + + optimizer: + type: Adam + args: + lr: 0.001 + weight_decay: 0 + + training: + early_stop: 10 + epochs: 100 + monitor: min val_loss + save_period: 1 + tensorboard: true + + testing: + data_dir: data/ + batch_size: 128 + num_workers: 8 + + +Add addional configurations if you need. + +Using config files +------------------ +Modify the configurations in `.yml` config files, then run: + +.. code-block:: + + $ mousenet train -c experiments/config.yml + +Resuming from checkpoints +------------------------- +You can resume from a previously saved checkpoint by: + +.. code-block:: + + mousenet train -c experiments/config.yml -r path/to/checkpoint + +Checkpoints +----------- +You can specify the name of the training session in config files: + +.. code-block:: HTML + + "name": "MNIST_LeNet" + +The checkpoints will be saved in `save_dir/name/timestamp/checkpoint_epoch_n`, with timestamp in +mmdd_HHMMSS format. + +A copy of config file will be saved in the same folder. + +**Note**: checkpoints contain: + +.. code-block:: python + + checkpoint = { + 'arch': arch, + 'epoch': epoch, + 'state_dict': self.model.state_dict(), + 'optimizer': self.optimizer.state_dict(), + 'monitor_best': self.mnt_best, + 'config': self.config + } + +Tensorboard Visualization +-------------------------- +This template supports ``_ visualization. + +1. Run training + + Set `tensorboard` option in config file true. + +2. Open tensorboard server + + Type `tensorboard --logdir saved/runs/` at the project root, then server will open at + `http://localhost:6006` + +By default, values of loss and metrics specified in config file, input images, and histogram of +model parameters will be logged. If you need more visualizations, use `add_scalar('tag', data)`, +`add_image('tag', image)`, etc in the `trainer._train_epoch` method. `add_something()` methods in +this template are basically wrappers for those of `tensorboard.SummaryWriter` module. + +**Note**: You don't have to specify current steps, since `TensorboardWriter` class defined at +`logger/visualization.py` will track current steps. + +Acknowledgments +=============== +This project was created using +`Cookiecutter PyTorch `_ diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000..75d15b1 --- /dev/null +++ b/environment.yml @@ -0,0 +1,31 @@ +name: mousenet + +channels: + - pytorch + - conda-forge + +dependencies: + - python=3.8 + - numpy + - scipy + - matplotlib + - pandas + - pytest + - flake8 + - rope + - autopep8 + - black + - click + - jupyter + - pyyaml + - pytorch>=1.1.0 + - torchvision + - cudatoolkit=10.0 + - tensorboard>=1.14 + - absl-py + - future + - tqdm + - pip + - pip: + - -e . # install package in development mode + - git+https://github.com/AllenInstitute/mouse_connectivity_models.git diff --git a/experiments/config.yml b/experiments/config.yml new file mode 100644 index 0000000..f97d7b7 --- /dev/null +++ b/experiments/config.yml @@ -0,0 +1,46 @@ +name: Mnist_LeNet +save_dir: saved/ +seed: 1234 +target_devices: [0] + +arch: + type: MnistModel + args: {} + +augmentation: + type: MNISTTransforms + args: {} + +data_loader: + type: MnistDataLoader + args: + batch_size: 128 + data_dir: data/ + nworkers: 2 + shuffle: true + validation_split: 0.1 + +loss: nll_loss + +lr_scheduler: + type: StepLR + args: + gamma: 0.1 + step_size: 50 + +metrics: +- top_1_acc +- top_3_acc + +optimizer: + type: Adam + args: + lr: 0.001 + weight_decay: 0 + +training: + early_stop: 10 + epochs: 100 + monitor: min val_loss + save_period: 1 + tensorboard: true diff --git a/logging.yml b/logging.yml new file mode 100644 index 0000000..c0b4887 --- /dev/null +++ b/logging.yml @@ -0,0 +1,43 @@ +version: 1 +disable_existing_loggers: False +formatters: + simple: + format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s" + +handlers: + console: + class: logging.StreamHandler + level: DEBUG + formatter: simple + stream: ext://sys.stdout + + debug_file_handler: + class: logging.handlers.RotatingFileHandler + level: DEBUG + formatter: simple + filename: debug.log + maxBytes: 10485760 # 10MB + backupCount: 10 + encoding: utf8 + + info_file_handler: + class: logging.handlers.RotatingFileHandler + level: INFO + formatter: simple + filename: info.log + maxBytes: 10485760 # 10MB + backupCount: 10 + encoding: utf8 + + error_file_handler: + class: logging.handlers.RotatingFileHandler + level: ERROR + formatter: simple + filename: error.log + maxBytes: 10485760 # 10MB + backupCount: 10 + encoding: utf8 + +root: + level: INFO + handlers: [console, debug_file_handler, info_file_handler, error_file_handler] diff --git a/mouse_cnn/__init__.py b/mousenet/__init__.py similarity index 100% rename from mouse_cnn/__init__.py rename to mousenet/__init__.py diff --git a/cmouse/__init__.py b/mousenet/cmouse/__init__.py similarity index 100% rename from cmouse/__init__.py rename to mousenet/cmouse/__init__.py diff --git a/cmouse/anatomy.py b/mousenet/cmouse/anatomy.py similarity index 100% rename from cmouse/anatomy.py rename to mousenet/cmouse/anatomy.py diff --git a/cmouse/exps/cifar/config.py b/mousenet/cmouse/exps/cifar/config.py similarity index 100% rename from cmouse/exps/cifar/config.py rename to mousenet/cmouse/exps/cifar/config.py diff --git a/cmouse/exps/cifar/myresults/network_(3,64,64).pkl b/mousenet/cmouse/exps/cifar/myresults/network_(3,64,64).pkl similarity index 100% rename from cmouse/exps/cifar/myresults/network_(3,64,64).pkl rename to mousenet/cmouse/exps/cifar/myresults/network_(3,64,64).pkl diff --git a/cmouse/exps/cifar/run.sh b/mousenet/cmouse/exps/cifar/run.sh similarity index 100% rename from cmouse/exps/cifar/run.sh rename to 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mousenet/mouse_cnn/architecture.py diff --git a/mouse_cnn/data.py b/mousenet/mouse_cnn/data.py similarity index 100% rename from mouse_cnn/data.py rename to mousenet/mouse_cnn/data.py diff --git a/mouse_cnn/flatmap.py b/mousenet/mouse_cnn/flatmap.py similarity index 100% rename from mouse_cnn/flatmap.py rename to mousenet/mouse_cnn/flatmap.py diff --git a/mouse_cnn/voxel.py b/mousenet/mouse_cnn/voxel.py similarity index 99% rename from mouse_cnn/voxel.py rename to mousenet/mouse_cnn/voxel.py index d2a0558..54c70fc 100644 --- a/mouse_cnn/voxel.py +++ b/mousenet/mouse_cnn/voxel.py @@ -2,8 +2,8 @@ import numpy as np import pickle from mcmodels.core import VoxelModelCache -from mouse_cnn.flatmap import FlatMap -from mouse_cnn.data import Data +from mousenet.mouse_cnn.flatmap import FlatMap +from mousenet.mouse_cnn.data import Data import matplotlib.pyplot as plt from scipy.spatial import ConvexHull from scipy.ndimage.filters import gaussian_filter diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 0000000..8209289 --- /dev/null +++ b/setup.cfg @@ -0,0 +1,7 @@ +[flake8] +max-line-length = 88 +ignore = E122,E123,E126,E127,E128,E203,E221,E241,E731,E722,W503 +exclude = tests,.git,__init__.py + +[bdist_wheel] +universal=1 diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..40ed5e6 --- /dev/null +++ b/setup.py @@ -0,0 +1,40 @@ +import io +import os +import re + +from setuptools import find_packages +from setuptools import setup + + +def read(filename): + filename = os.path.join(os.path.dirname(__file__), filename) + text_type = type(u"") + with io.open(filename, mode="r", encoding="utf-8") as fd: + return re.sub(text_type(r":[a-z]+:`~?(.*?)`"), text_type(r"``\1``"), fd.read()) + + +requirements = [ + # use environment.yml +] + + +setup( + name="mousenet", + version="0.0.1", + url="https://github.com/mabuice/Mouse_CNN", + author="Iris Jianghong Shi ", + author_email="jhshi@uw.edu", + description="A cnn built to structurally look like mouse visual cortex", + long_description=read("README.rst"), + packages=find_packages(exclude=("tests",)), + entry_points={ + "console_scripts": [ + "mousenet=mousenet.cli:cli" + ] + }, + install_requires=requirements, + classifiers=[ + "Programming Language :: Python", + "Programming Language :: Python :: 3.8", + ], +) diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/test/test_kernel_multimodality.py b/tests/test_kernel_multimodality.py similarity index 94% rename from test/test_kernel_multimodality.py rename to tests/test_kernel_multimodality.py index f5b1a9a..199982b 100644 --- a/test/test_kernel_multimodality.py +++ b/tests/test_kernel_multimodality.py @@ -2,9 +2,9 @@ import numpy as np import matplotlib.pyplot as plt from sklearn import svm -from mouse_cnn.voxel import VoxelModel, is_multimodal_or_eccentric, fit_image, get_gaussian_fit -from mouse_cnn.voxel import get_fraction_peak_at_center_of_mass, get_multimodal_depth_fraction -from mouse_cnn.flatmap import FlatMap +from mousenet.mouse_cnn.voxel import VoxelModel, is_multimodal_or_eccentric, fit_image, get_gaussian_fit +from mousenet.mouse_cnn.voxel import get_fraction_peak_at_center_of_mass, get_multimodal_depth_fraction +from mousenet.mouse_cnn.flatmap import FlatMap def save_example_kernels(): diff --git a/tests/test_model_import.py b/tests/test_model_import.py new file mode 100644 index 0000000..1f8968f --- /dev/null +++ b/tests/test_model_import.py @@ -0,0 +1,7 @@ +import mousenet + +def test_generic_import(): + assert True #todo + +def test_retinotopic_import(): + assert True #todo \ No newline at end of file diff --git a/tests/test_pytorch.py b/tests/test_pytorch.py new file mode 100644 index 0000000..2dd7380 --- /dev/null +++ b/tests/test_pytorch.py @@ -0,0 +1,7 @@ +import torch + +def test_pytorch(): + x = torch.zeros(3, 4) + x += 1 + print(x) + pass From 0e91027f3cda5c194f46765422a639b45685df6d Mon Sep 17 00:00:00 2001 From: Parsa Torabian Date: Thu, 12 May 2022 12:15:55 -0400 Subject: [PATCH 2/9] Rollback after too large a commit. Refactor almost done --- environment.yml | 2 + mousenet/__init__.py | 1 + mousenet/cmouse/__init__.py | 1 - mousenet/cmouse/anatomy.py | 5 +- mousenet/cmouse/main.py | 2 +- mousenet/cmouse/mousenet_complete_pool.py | 44 +- mousenet/cmouse/network.py | 6 +- mousenet/cmouse/train_cifar.py | 2 +- mousenet/data_files/readme.txt | 1 + .../construct mousenet architecture.ipynb | 6 +- mousenet/loader.py | 54 +++ mousenet/mouse_cnn/architecture.py | 16 +- mousenet/mouse_cnn/data.py | 2 +- mousenet/mouse_cnn/voxel.py | 4 +- mousenet/retinotopics/Polygon Debug.ipynb | 394 ++++++++++++++++++ mousenet/retinotopics/area_derivations.py | 133 ++++++ .../retinotopics/digitized_data/VisAM.csv | 122 ++++++ .../digitized_data/VisAM.csv:Zone.Identifier | 3 + .../retinotopics/digitized_data/VisAl.csv | 63 +++ .../digitized_data/VisAl.csv:Zone.Identifier | 3 + mousenet/retinotopics/digitized_data/VisL.csv | 87 ++++ .../digitized_data/VisL.csv:Zone.Identifier | 3 + .../retinotopics/digitized_data/VisLi.csv | 54 +++ .../digitized_data/VisLi.csv:Zone.Identifier | 3 + mousenet/retinotopics/digitized_data/VisP.csv | 199 +++++++++ .../digitized_data/VisP.csv:Zone.Identifier | 3 + .../retinotopics/digitized_data/VisPM.csv | 82 ++++ .../digitized_data/VisPM.csv:Zone.Identifier | 3 + .../retinotopics/digitized_data/VisPOR.csv | 31 ++ .../digitized_data/VisPOR.csv:Zone.Identifier | 3 + .../retinotopics/digitized_data/VisPl.csv | 110 +++++ .../digitized_data/VisPl.csv:Zone.Identifier | 3 + .../retinotopics/digitized_data/VisRL.csv | 93 +++++ .../digitized_data/VisRL.csv:Zone.Identifier | 3 + mousenet/retinotopics/images/VisAL.jpg | Bin 0 -> 33431 bytes .../images/VisAL.jpg:Zone.Identifier | 3 + mousenet/retinotopics/images/VisAM.jpg | Bin 0 -> 33434 bytes .../images/VisAM.jpg:Zone.Identifier | 3 + mousenet/retinotopics/images/VisL.jpg | Bin 0 -> 33159 bytes .../images/VisL.jpg:Zone.Identifier | 3 + mousenet/retinotopics/images/VisLI.jpg | Bin 0 -> 34049 bytes .../images/VisLI.jpg:Zone.Identifier | 3 + mousenet/retinotopics/images/VisP.jpg | Bin 0 -> 31100 bytes .../images/VisP.jpg:Zone.Identifier | 3 + mousenet/retinotopics/images/VisPL.jpg | Bin 0 -> 33151 bytes .../images/VisPL.jpg:Zone.Identifier | 3 + mousenet/retinotopics/images/VisPM.jpg | Bin 0 -> 31667 bytes .../images/VisPM.jpg:Zone.Identifier | 3 + mousenet/retinotopics/images/VisPOR.jpg | Bin 0 -> 32849 bytes .../images/VisPOR.jpg:Zone.Identifier | 3 + mousenet/retinotopics/images/VisRL.jpg | Bin 0 -> 33424 bytes .../images/VisRL.jpg:Zone.Identifier | 3 + mousenet/retinotopics/images/main.jpg | Bin 0 -> 262798 bytes .../images/main.jpg:Zone.Identifier | 4 + mousenet/retinotopics/retinomap.pkl | Bin 0 -> 1641 bytes mousenet/retinotopics/test.py | 23 + tests/test_model_import.py | 10 +- 57 files changed, 1574 insertions(+), 31 deletions(-) create mode 100644 mousenet/data_files/readme.txt create mode 100644 mousenet/loader.py create mode 100644 mousenet/retinotopics/Polygon Debug.ipynb create mode 100644 mousenet/retinotopics/area_derivations.py create mode 100644 mousenet/retinotopics/digitized_data/VisAM.csv create mode 100644 mousenet/retinotopics/digitized_data/VisAM.csv:Zone.Identifier create mode 100644 mousenet/retinotopics/digitized_data/VisAl.csv create mode 100644 mousenet/retinotopics/digitized_data/VisAl.csv:Zone.Identifier create mode 100644 mousenet/retinotopics/digitized_data/VisL.csv create mode 100644 mousenet/retinotopics/digitized_data/VisL.csv:Zone.Identifier create mode 100644 mousenet/retinotopics/digitized_data/VisLi.csv create mode 100644 mousenet/retinotopics/digitized_data/VisLi.csv:Zone.Identifier create mode 100644 mousenet/retinotopics/digitized_data/VisP.csv create mode 100644 mousenet/retinotopics/digitized_data/VisP.csv:Zone.Identifier create mode 100644 mousenet/retinotopics/digitized_data/VisPM.csv create mode 100644 mousenet/retinotopics/digitized_data/VisPM.csv:Zone.Identifier create mode 100644 mousenet/retinotopics/digitized_data/VisPOR.csv create mode 100644 mousenet/retinotopics/digitized_data/VisPOR.csv:Zone.Identifier create mode 100644 mousenet/retinotopics/digitized_data/VisPl.csv create mode 100644 mousenet/retinotopics/digitized_data/VisPl.csv:Zone.Identifier create mode 100644 mousenet/retinotopics/digitized_data/VisRL.csv create mode 100644 mousenet/retinotopics/digitized_data/VisRL.csv:Zone.Identifier create mode 100644 mousenet/retinotopics/images/VisAL.jpg create mode 100644 mousenet/retinotopics/images/VisAL.jpg:Zone.Identifier create mode 100644 mousenet/retinotopics/images/VisAM.jpg create mode 100644 mousenet/retinotopics/images/VisAM.jpg:Zone.Identifier create mode 100644 mousenet/retinotopics/images/VisL.jpg create mode 100644 mousenet/retinotopics/images/VisL.jpg:Zone.Identifier create mode 100644 mousenet/retinotopics/images/VisLI.jpg create mode 100644 mousenet/retinotopics/images/VisLI.jpg:Zone.Identifier create mode 100644 mousenet/retinotopics/images/VisP.jpg create mode 100644 mousenet/retinotopics/images/VisP.jpg:Zone.Identifier create mode 100644 mousenet/retinotopics/images/VisPL.jpg create mode 100644 mousenet/retinotopics/images/VisPL.jpg:Zone.Identifier create mode 100644 mousenet/retinotopics/images/VisPM.jpg create mode 100644 mousenet/retinotopics/images/VisPM.jpg:Zone.Identifier create mode 100644 mousenet/retinotopics/images/VisPOR.jpg create mode 100644 mousenet/retinotopics/images/VisPOR.jpg:Zone.Identifier create mode 100644 mousenet/retinotopics/images/VisRL.jpg create mode 100644 mousenet/retinotopics/images/VisRL.jpg:Zone.Identifier create mode 100644 mousenet/retinotopics/images/main.jpg create mode 100644 mousenet/retinotopics/images/main.jpg:Zone.Identifier create mode 100644 mousenet/retinotopics/retinomap.pkl create mode 100644 mousenet/retinotopics/test.py diff --git a/environment.yml b/environment.yml index 75d15b1..1d92a5f 100644 --- a/environment.yml +++ b/environment.yml @@ -12,6 +12,8 @@ dependencies: - pandas - pytest - flake8 + - networkx + - network - rope - autopep8 - black diff --git a/mousenet/__init__.py b/mousenet/__init__.py index e69de29..5d173db 100644 --- a/mousenet/__init__.py +++ b/mousenet/__init__.py @@ -0,0 +1 @@ +from .loader import load \ No newline at end of file diff --git a/mousenet/cmouse/__init__.py b/mousenet/cmouse/__init__.py index 112d4f6..e69de29 100644 --- a/mousenet/cmouse/__init__.py +++ b/mousenet/cmouse/__init__.py @@ -1 +0,0 @@ -from . import mousenet diff --git a/mousenet/cmouse/anatomy.py b/mousenet/cmouse/anatomy.py index 11d2a38..df429e3 100644 --- a/mousenet/cmouse/anatomy.py +++ b/mousenet/cmouse/anatomy.py @@ -2,10 +2,7 @@ import networkx as nx import matplotlib.pyplot as plt import sys -sys.path.append('../') -sys.path.append('../../') -sys.path.append('../../../') -from mouse_cnn.architecture import * +from ..mouse_cnn.architecture import * # from config import get_output_shrinkage class AnatomicalLayer: diff --git a/mousenet/cmouse/main.py b/mousenet/cmouse/main.py index 75752de..c32562f 100644 --- a/mousenet/cmouse/main.py +++ b/mousenet/cmouse/main.py @@ -158,7 +158,7 @@ def main_worker(gpu, ngpus_per_node, args): if NET == 1: net_name = 'network_complete_updated_number(%s,%s,%s)'%(INPUT_SIZE[0],INPUT_SIZE[1],INPUT_SIZE[2]) - architecture = Architecture(data_folder=DATA_DIR) + architecture = Architecture() net = gen_network(net_name, architecture) if FIXMASK != 0: np.random.seed(FIXMASK) diff --git a/mousenet/cmouse/mousenet_complete_pool.py b/mousenet/cmouse/mousenet_complete_pool.py index d5a13b6..6bee361 100644 --- a/mousenet/cmouse/mousenet_complete_pool.py +++ b/mousenet/cmouse/mousenet_complete_pool.py @@ -2,7 +2,20 @@ from torch import nn import networkx as nx import numpy as np -from config import INPUT_SIZE, EDGE_Z, OUTPUT_AREAS, HIDDEN_LINEAR, NUM_CLASSES +from .exps.imagenet.config import INPUT_SIZE, EDGE_Z, OUTPUT_AREAS, HIDDEN_LINEAR, NUM_CLASSES + +def get_retinotopic_mask(layer, retinomap): + mask = torch.zeros(32, 32) + for area in retinomap: + if area.lower() in layer: + normalized_polygon = area[1] + x, y = normalized_polygon.exterior.coords.xy + x, y = list(x), list(y) + mask[min(x):max(x), min(y), max(y)] = 1 + return mask + + raise ValueError(f"Could not find area for layer {layer} in retinomap") + class Conv2dMask(nn.Conv2d): """ @@ -66,15 +79,25 @@ class MouseNetCompletePool(nn.Module): """ torch model constructed by parameters provided in network. """ - def __init__(self, network, mask=3): + def __init__(self, network, mask=3, retinomap=None): super(MouseNetCompletePool, self).__init__() self.Convs = nn.ModuleDict() self.BNs = nn.ModuleDict() self.network = network + self.layer_masks = nn.ModuleDict() + self.retinomap = retinomap G, _ = network.make_graph() self.top_sort = list(nx.topological_sort(G)) + + if self.retinomap is not None: + for layer in self.top_sort: + self.layer_masks[layer] = get_retinotopic_mask(layer) + else: + for layer in self.top_sort: + self.layer_masks[layer] = torch.ones(32, 32) + for layer in network.layers: params = layer.params self.Convs[layer.source_name + layer.target_name] = Conv2dMask(params.in_channels, params.out_channels, params.kernel_size, @@ -131,17 +154,28 @@ def get_img_feature(self, x, area_list, flatten=True): if area == 'LGNd' or area == 'LGNv': layer = self.network.find_conv_source_target('input', area) layer_name = layer.source_name + layer.target_name - calc_graph[area] = nn.ReLU(inplace=True)(self.BNs[area](self.Convs[layer_name](x))) + calc_graph[area] = nn.ReLU(inplace=True)( + self.BNs[area]( + self.Convs[layer_name](x) + ) + ) continue for layer in self.network.layers: if layer.target_name == area: + mask = self.layer_masks[layer.source_name] layer_name = layer.source_name + layer.target_name if area not in calc_graph: - calc_graph[area] = self.Convs[layer_name](calc_graph[layer.source_name]) + calc_graph[area] = self.Convs[layer_name]( + mask(calc_graph[layer.source_name]) + ) else: calc_graph[area] = calc_graph[area] + self.Convs[layer_name](calc_graph[layer.source_name]) - calc_graph[area] = nn.ReLU(inplace=True)(self.BNs[area](calc_graph[area])) + calc_graph[area] = nn.ReLU(inplace=True)( + self.BNs[area]( + mask(calc_graph[area]) + ) + ) if len(area_list) == 1: if flatten: diff --git a/mousenet/cmouse/network.py b/mousenet/cmouse/network.py index 17f4962..3d85ab8 100644 --- a/mousenet/cmouse/network.py +++ b/mousenet/cmouse/network.py @@ -1,9 +1,9 @@ import numpy as np import networkx as nx -from anatomy import gen_anatomy +from .anatomy import gen_anatomy import torch from torch import nn -from config import INPUT_SIZE, EDGE_Z, INPUT_GSH, INPUT_GSW, get_out_sigma +from .exps.imagenet.config import INPUT_SIZE, EDGE_Z, INPUT_GSH, INPUT_GSW, get_out_sigma import os import pickle import matplotlib.pyplot as plt @@ -166,7 +166,7 @@ def load_network_from_pickle(file_path): net = pickle.load(f) return net -def gen_network(net_name, architecture, data_folder=''): +def gen_network(net_name, architecture): file_path = './myresults/%s.pkl'%net_name if os.path.exists(file_path): net = load_network_from_pickle(file_path) diff --git a/mousenet/cmouse/train_cifar.py b/mousenet/cmouse/train_cifar.py index 869a186..d9484f9 100644 --- a/mousenet/cmouse/train_cifar.py +++ b/mousenet/cmouse/train_cifar.py @@ -193,7 +193,7 @@ def __call__(self, tensor): # get the mouse network net_name = 'network_(%s,%s,%s)'%(INPUT_SIZE[0],INPUT_SIZE[1],INPUT_SIZE[2]) -architecture = Architecture(data_folder=DATA_DIR) +architecture = Architecture() net = gen_network(net_name, architecture) mousenet = MouseNetCompletePool(net, mask=MASK) diff --git a/mousenet/data_files/readme.txt b/mousenet/data_files/readme.txt new file mode 100644 index 0000000..8f44179 --- /dev/null +++ b/mousenet/data_files/readme.txt @@ -0,0 +1 @@ +deprecated folder. todo remove references to "data_files" everywhere \ No newline at end of file diff --git a/mousenet/example/construct mousenet architecture.ipynb b/mousenet/example/construct mousenet architecture.ipynb index bbada6f..2b23789 100644 --- a/mousenet/example/construct mousenet architecture.ipynb +++ b/mousenet/example/construct mousenet architecture.ipynb @@ -36,7 +36,7 @@ "metadata": {}, "outputs": [], "source": [ - "architecture = Architecture(data_folder=DATA_DIR)\n", + "architecture = Architecture()\n", "anet = gen_anatomy(architecture)" ] }, @@ -47,7 +47,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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\n", 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", 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" ] diff --git a/mousenet/loader.py b/mousenet/loader.py new file mode 100644 index 0000000..ec45437 --- /dev/null +++ b/mousenet/loader.py @@ -0,0 +1,54 @@ +import pickle +import os +from .cmouse.mousenet_complete_pool import MouseNetCompletePool +import torch.nn as nn +import pathlib +from .cmouse.anatomy import gen_anatomy +from .mouse_cnn.architecture import Architecture +from .cmouse import network + + +def generate_net(): + cached = "net_cache.pkl" + if os.path.isfile(cached): + return network.load_network_from_pickle(cached) + architecture = Architecture() + anet = gen_anatomy(architecture) + net = network.Network() + net.construct_from_anatomy(anet, architecture) + network.save_network_to_pickle(net, cached) + return net + +def load(architecture, pretraining=None): + if architecture not in ("default", "retinotopic"): + raise ValueError("Architecture must be one of default or retinotopic") + + if pretraining not in (None, "imagenet", "kaiming"): + raise ValueError("Pretraining must be one of imagenet, kaiming, or None") + + #path to this file + path = pathlib.Path(__file__).parent.resolve() + # with open(os.path.join(path, "example", "network_complete_updated_number(3,64,64).pkl"), "rb") as file: + # net = pickle.load(file) + # pdb.set_trace() + + net = generate_net() + mousenet = MouseNetCompletePool(net) + + retinomap = None + if architecture ==" retinotopic": + with open(os.path.join(path, "retinotopics", "retinomap.pkl", "rb")) as file: + retinomap = pickle.load(file) + + model = MouseNetCompletePool(net, retinomask = retinomap) + + if pretraining == "kaiming" or None: + def _kaiming_init_ (m): + if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear): + nn.init.kaiming_uniform_(m.weight.data) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight.data, 1) + nn.init.constant_(m.bias.data, 0) + model.apply(_kaiming_init_) + + return model \ No newline at end of file diff --git a/mousenet/mouse_cnn/architecture.py b/mousenet/mouse_cnn/architecture.py index 9cb842e..105e830 100644 --- a/mousenet/mouse_cnn/architecture.py +++ b/mousenet/mouse_cnn/architecture.py @@ -1,7 +1,7 @@ import numpy as np -from config import EDGE_Z -from mouse_cnn.data import Data -from mouse_cnn.voxel import Target, get_surface_area_mm2 +from ..cmouse.exps.imagenet.config import EDGE_Z +from .data import Data +from .voxel import Target, get_surface_area_mm2 # TODO: review hard-coded values for LGN @@ -14,9 +14,9 @@ class Architecture(Data): width estimates in micrometers to kernel width estimates in pixels. """ - def __init__(self, data_folder='data_files'): - super(Architecture, self).__init__(data_folder=data_folder) - self.targets = _get_targets(self, data_folder=data_folder) + def __init__(self): + super(Architecture, self).__init__() + self.targets = _get_targets(self) self.channels = {} def set_num_channels(self, area, layer, channels): @@ -91,14 +91,14 @@ def _get_name(area, layer): return area + layer -def _get_targets(data, data_folder='data_files/'): +def _get_targets(data): # build dictionary of voxel.Target instances per target layer targets = {} for area in data.get_areas(): if data.get_hierarchical_level(area) > 0: for layer in data.get_layers(): in_degree = data.get_extrinsic_in_degree(area, layer) - targets[_get_name(area, layer)] = Target(area, layer, external_in_degree=in_degree, data_folder=data_folder) + targets[_get_name(area, layer)] = Target(area, layer, external_in_degree=in_degree) return targets diff --git a/mousenet/mouse_cnn/data.py b/mousenet/mouse_cnn/data.py index 5c83e88..0fa0456 100644 --- a/mousenet/mouse_cnn/data.py +++ b/mousenet/mouse_cnn/data.py @@ -10,7 +10,7 @@ class Data: - def __init__(self, data_folder = 'data_files'): + def __init__(self): # self.e18 = Ero2018(data_folder) # this data is no longer used self.p11 = Perin11() self.b19 = Billeh19() diff --git a/mousenet/mouse_cnn/voxel.py b/mousenet/mouse_cnn/voxel.py index 54c70fc..290c15f 100644 --- a/mousenet/mouse_cnn/voxel.py +++ b/mousenet/mouse_cnn/voxel.py @@ -2,8 +2,8 @@ import numpy as np import pickle from mcmodels.core import VoxelModelCache -from mousenet.mouse_cnn.flatmap import FlatMap -from mousenet.mouse_cnn.data import Data +from .flatmap import FlatMap +from .data import Data import matplotlib.pyplot as plt from scipy.spatial import ConvexHull from scipy.ndimage.filters import gaussian_filter diff --git a/mousenet/retinotopics/Polygon Debug.ipynb b/mousenet/retinotopics/Polygon Debug.ipynb new file mode 100644 index 0000000..36150cb --- /dev/null +++ b/mousenet/retinotopics/Polygon Debug.ipynb @@ -0,0 +1,394 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from shapely.geometry import box, Polygon, LinearRing\n", + "import numpy as np\n", + "from scipy.spatial import ConvexHull\n", + "from shapely.affinity import scale, translate\n", + "import itertools\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import geopandas as gpd\n", + "\n", + "from area_derivations import iou, minimum_bounding_rectangle, get_best_fit_rect" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "v1_ref = (60, 92)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "areas = [\"VisP\", \"VisRL\", \"VisAL\", \"VisL\", \"VisLI\", \"VisPL\", \"VisPOR\", \"VisAM\", \"VisPM\"]\n", + "\n", + "plt.figure(figsize=(12, 8))\n", + "\n", + "for area in areas:\n", + " polygon = Polygon(pd.read_csv(f\"digitized_data/{area}.csv\").values)\n", + " x, y = polygon.exterior.xy\n", + " plt.plot(list(x), list(y), label=area)\n", + "\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# area_pairs = list(itertools.product([areas[0]], areas[1:]))\n", + "# area_pairs" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "bounding_rectangles = []\n", + "visp_polygon = Polygon(pd.read_csv(f\"digitized_data/VisP.csv\").values)\n", + "visp_bound = Polygon(minimum_bounding_rectangle(visp_polygon))\n", + "\n", + "for area in areas:\n", + " polygon = Polygon(pd.read_csv(f\"digitized_data/{area}.csv\").values)\n", + " min_bounding_rect = Polygon(minimum_bounding_rectangle(polygon))\n", + " bounding_rectangles.append(\n", + " visp_bound.intersection(min_bounding_rect)\n", + " )\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(199, 2)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.array(visp_polygon.exterior.xy).T.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "best_rects = []\n", + "#AM and PM don't work for some reason? Which is kind of convienent...\n", + "areas = [\"VisP\", \"VisRL\", \"VisAL\", \"VisL\", \"VisLI\", \"VisPL\", \"VisPOR\"]\n", + "for area in areas:\n", + " best_rect, best_iou = get_best_fit_rect(area)\n", + " best_rects.append((best_rect, best_iou))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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hd5OBFgHjbhSRlSLyo4gMrq7AjTHGlE95SvST0E65Av3L6U0ueqKdOv0dQES6orft6uZ95nHvZgrGGGN8Umaid87NQXtQCxwW2F90XYr7aT4D7Yozz+ltxlYCx1VRrMYYYyqgwu3oReRu9JZiO9C7mIP2iRzYWVcGFb87jjHRZdcWyPwONiwGEUhuBMkNoY73XPQ+IcnvSE2UqXCid87dDNwsIjeifTrfRug794TsTEdExgPjAVq3Dnm/XWMi1+6tsP47fWQuhPXfw4415ftsUoqX9BscuBMo6X2dxrrzMCaEqrgy9mXgbTTRZ6A3WijSCr3DzwGccxOBiQDp6enWs5qJXEVJPXOhl9yDknrDdtDqGDjuMmjeE5p3h7gEyN2mn83dBrlbvdfb9XXguB0Z3jTbwBWGjqHTcBj9P4gPi4vdTZip0FYhIh2dcyu8t6ejtzsDmAm8LCIPoXcD6ojeqciY6LBfUvcepSb1HloyD6VWKjQ4iKPZwkLI2xmwI/CS/4ZFMPc/8MEtMPTeCn81E73KTPQiMgW9hVkTEclAS+7DRKQTeku61ei9KXHOLRGRaehtyPKBK51zBdUUuzHVKzipZy6E7SGS+rGXQoujSk/qVSEuTucfvIzuo6CwAL56DNIOh/RLqi8GE5HCoj/69PR0Z52aGd9tXgHL3ixO7sFJvUVPLaW36Okl9Yb+xBlKYQFMGQM/fwwXTIf2/f2OyNQAEVngnEsvazqr0DMmYwF8/hAsfxtwmtRbHgPpl4ZnUg8lLh7OfhaeHQTTLoLLP4bGHfyOyoQJS/QmNjkHv3wCnz8Mq+ZA7frQ73o47nJIaep3dBVTux6cNxWeOQVeHg2XfRT+OyhTIyzRm2rx8tdrmLFwnd9hHEBcAcfv+YIzcqbRPn8lW+Ma83bq5XxUZyh7VtSBFT+jt/eNXJ2Tb+TWLRNY+vCZ3NvoTgrE/uaBvl6l13/+eu9wnyOpObYFmGoxY+E6lmbupGvzen6HAkCC20u/3FmcnvMqzQvWsz6+JU/Wv5bPkk8mX6LrAqXlSUcwsf7V/HHHQ1y08ymer3+l3yEZn1miN9Wma/N6vPL7Xv4GsWcnLJgEXz4GORv0ZGrfe2jR5TSuiIvX5mJRqRd8kM+Quf9hyPE9oN91dkGV59ynvvQ7hBpnid5Ep5ws+PpJ+OZp2LMD2vWHM5+E9gNiJ+ENvB1yNsEnd2n7+0H/iJ3vbvZjid5El9zt8PFd8N0LkJ8HXU6DvtdqK5pYExcPI5+AWil6QVVeNgx/UIebmGKJ3kSPvGx48SzI/B56jIHe1+gFRLEsLg6GPaBX4X7+MOzN0eQfn+h3ZKYGWaI30WHvbnh5jF7Beu4L0Dl2WlSUSUSrcWrVg1l3QF4OjJoEibX9jszUELuVoIl8+Xkw7UJY/QWcNdGSfElO/ItW3fz0Hrw8ShO+iQmW6E1kK8iH1y+FlR/Baf+GI8/xO6LwduxlcOZT8OsX8L8ztD8fE/Us0ZvIVVgIM67U/mkG3wPHjPM7osjQ41zt0njDIpg0ArI3+h2RqWaW6E1kcg7e+Sssmgon3wK9/uh3RJGlywg4fxpsWwXPD4Xta/2OyFQjS/Qm8jgHH94K85+DPtfCidf5HVFk6nASXDQDdm+G54bAlsju+sGUzBK9iTyf3g9zH4VjL9fWJHYRUMUdehz87m3YtxteHacntk3UsURvIsvc/8Lsf0LPsTD0fkvyVaHZkTDycdjwg15sZqKOJXoTOeY/Dx/cDF1HwumP6sVApmp0GgrHXKxHSqvm+B2NqWL2TzHhr7BAr+p868/QcTCc9bRdxl8dBt+tNyt54wq9F62JGpboTXjbvAKeGwwf3a791oyeDAnR1a1w2EiqC2c/AzkbdacaBrcZNVXDEr0JT4UF2rXwk3012Z/9rLb9Tkz2O7Lo1uIoOOkmWPIGfD/V72hMFbG+bkz42fKzXgi15ks4fCic9gikNvM7qtjR51pYOQveuR7a9IKGbf2OyFRSmSV6EXlORDaJyOKAYf8SkeUiskhE3hCRBgHjbhSRlSLyo4gMrqa4TTQqLISvn4In+sDGpTDySThviiX5mhYXr333SxxMH6/dTJiIVp6qm0nAkKBhHwJHOOe6Az8BNwKISFdgDNDN+8zjImJnzUzZtq6CyafBu3+Dtn3hyq+g53nWfNIvDVprB2hrv4bPH/I7GlNJZVbdOOfmiEjboGEfBLz9CijqSeoMYKpzLg9YJSIrgeOA2Lt3lymfwkJY8Bx88HctSZ7+XzjqAkvw4aD7KFjxPsy+F1wh9LnGzpFEqKqoo78EeMV73RJN/EUyvGEHEJHxwHiA1q1bV0EYJuJsWw0zr9J22+1P0rbxDQ71OyoTaPhDemJ89j2w8GUYep+2uTcRpVKtbkTkZiAfeKloUIjJQrbRcs5NdM6lO+fS09LSKhOGiUSLp8MTvWHdtzDiEbjwDUvy4ah2PRj1PFw0ExJqw5Qx8NJo2PqL35GZg1DhRC8i44ARwFjnfmtwmwEE/ltbAesrHp6JWt+9qLez+8NcSL/YqmrCXfv+8Icv4NS79AYvj50AH9+td/YyYa9CiV5EhgA3AKc75wJ/6ZnAGBGpJSLtgI7AvMqHaaJOy2Ngzw6o08jvSEx5xSdC7z/BVfP14rU598Njx8Oyt+ziqjBXnuaVU9CTqZ1EJENELgX+C6QCH4rIQhF5EsA5twSYBiwF3gOudM4VVFv0JnK1Pl5P8GXM9zsSc7DqNYdznoVxb+nVtK+MhZfOsW6Ow1h5Wt2cF2Lws6VMfzdwd2WCMjGg1bGAwNp52i+6iTztToQrPoN5E+GTe+DxE7TEf+JfdQdgwoZ1gWD8Ubs+NO0Ka78qe1oTvuITodeV8Kf50O1M+OxB+O9xsHSGVeeEEUv0xj+tj4e132jzPRPZUpvBWRPh4nd1Jz7tIph2IeRl+x2ZwRK98dOhJ8DebNi01O9ITFVp0xt+PwcG3gHL34ZnBllTzDBgid74p/Xx+rzGqm+iSnwC9L0WLpgO2Znw9Mnwy2y/o4ppluiNfxq0gZRm2p+KiT4dToLLP4aUQ+CFs+CrJ63e3ieW6I1/RLRUv+YrSwDRqnEHuPRDOHwwvHeDdnlhNyCvcZbojb86nAw71sKGRX5HYqpL7Xpw7kvQ73q9InrSCMje6HdUMcUSvfFXl9MhLgEWv+53JKY6xcXBybfAqEmwcTFMHKD9HJkaYYne+KtOIy3VL55u1TexoNuZcMn72iX180Nh0TS/I4oJluiN/444R6tv1lq3SDGheXcYP1v7O5p+OXxwq11LUc0s0Rv/dRqqXeBa9U3sqNsELvw/SL8E5v4HXj4Xcrf7HVXUskRv/Fe7HnQ8FZa8YSW7WJKQBCMe1pub/PIJPHMKbF7hd1RRyRK9CQ9HnA27NsGvn/kdialpx16qNzbJ3QYTT4IvH4P8vX5HFVUs0ZvwcPhgSEqx6ptY1baP1tu3Ph7ev0nvPrbiI7+jihqW6E14SEyGzsNh6UwrzcWqBq1h7Gtw/jS9V8FLZ+ttCzev9DuyiGeJ3oSPI86GPdth+Zt+R2L8IqJHd3/8CgbdCavnaj/3H9wCe3b6HV3EskRvwkeHU7SP+o9uh317/I7G+CkhCfpcDVd/Cz3Ohbn/hUePhm9fgMJCv6OLOJboTfiIT4Ah98D2NfDVY35HY8JBSlM44zHtHK1Re+0r5+mTYI11hHcwLNGb8NJ+AHQeAXMehOwNfkdjwkXLo/WK2rOegZxN8Nxg+PFdv6OKGJboTfg59U4o3Aez/uF3JCaciED3UXDVN3p17f/9AXZk+B1VRCgz0YvIcyKySUQWBwwbJSJLRKRQRNKDpr9RRFaKyI8iMrg6gjZRrlF7OOEPsPAlWLfA72hMuKmVAuc8DwX74PXLoSDf74jCXnlK9JOAIUHDFgNnAXMCB4pIV2AM0M37zOMiEl/5ME3MOfE6qNsU3rvROjszB2rcQa+qXTMXPr3P72jCXpmJ3jk3B9gaNGyZc+7HEJOfAUx1zuU551YBK4HjqiRSE1tq14NTbtW7T9lFVCaU7qOh51iY8y9YNafs6WNYVdfRtwTWBrzP8IYdQETGi8h8EZmflZVVxWGYqNBzLDTvAR/+Hfbu9jsaE46G/QsaH6ZVOLs2+x1N2KrqRC8hhoU87nbOTXTOpTvn0tPS0qo4DBMV4uJhyL2wc532cGhMsKS6MOp57SfnjSusjX0JqjrRZwCHBrxvBayv4mWYWNKmt96s4vNHrIWFCa3ZkTD4blj5oV1/UYKqTvQzgTEiUktE2gEdAbubhKmcQf8AHHx4m9+RmHB17GXQ5TS9qjrDWmoFK0/zyinAl0AnEckQkUtF5EwRyQB6AW+LyPsAzrklwDRgKfAecKVzzjoYN5XToDX0uQYWvwbznvY7GhOOROD0RyG1BUy7CLb87HdEYSWhrAmcc+eVMOqNEqa/G7i7MkEZc4B+f4PMRfDO9ZDcEI48x++ITLhJbghjXoQXzoRnT4Wxr+oVtcaujDURIj5BT7q17qUn3VbO8jsiE46a94BLPoCkOjBpBPz8sd8RhQVL9CZyJCbDeVMgrTO8cgGs/cbviEw4anIYXPohNGqn/dn/8JrfEfnOEr2JLMkN4ILXIeUQeHkUbFrud0QmHKU2g4vfgUOPh9cvha+e8DsiX1miN5En9RC48A2IT9L62O1r/I7IhKPa9bVQ0OU0eG+CtsiJ0e40LNGbyNSoHVwwHfbu0mRvV0WaUBJrw6jJkH4JfP4wzLiSuBhsCGiJ3kSuZkfA+a/ohVTv3uB3NCZcxcXD8IdgwI2w8CWu2/YPklxs3cHMEr2JbG16QWpziMFSmjkIIjBgAgx/iKPyvuGWLTfC7q1lfy5KWKI3kS0/D7avhsYd/Y7ERIJjL+XhBjfRbt9KeH4Y5GX7HVGNsERvItvWX8AVQhNL9KZ85iX35f5Gd8DmH+HdCX6HUyMs0ZvItnmFPjc+zN84TET5odbR0PcvsPBFWDrD73CqnSV6E9m2eIneSvTmYA2YAC2OhplXw87o7mTXEr2JbJtX6snYWql+R2IiTXwinPU0FOyN+r7sLdGbyLb5J6u2MRXX5DAYcg+s+hS+etzvaKqNJXoTuZzTqpsmh/sdiYlkR4+DTsNh1h2w4Qe/o6kWluhN5Nq1GfbssPp5UzlFfdknN4TXL4N9uX5HVOUs0ZvIVXQi1trQm8qq2xhGPg5Zy6PyTmaW6E3kKmpa2cTq6E0VOGwgHH8FzHsKVnzodzRVyhK9iVybf4KE2lD/0LKnNaY8Bt4OaV1gxlVR1UWCJXoTubb+oveTjYv3OxITLRKT4cwnYVeWdm0cJSzRm8iV1gm2rIStq/yOxESTFj2h33Ww6BVY/rbf0VSJMhO9iDwnIptEZHHAsEYi8qGIrPCeGwaMu1FEVorIjyIyuLoCN4bjfg8SD18+5nckJtqceB0cciS8eW1UVOGUp0Q/CRgSNGwCMMs51xGY5b1HRLoCY4Bu3mceFxE7rjbVo15z6HEufPei3XjEVK2EJDjzCcjdCu9c73c0lVZmonfOzQGCd2lnAJO915OBkQHDpzrn8pxzq4CVwHFVE6oxIfS+GvJzYd7Tfkdiok2zI6H/DbD4NVg60+9oKqWidfSHOOcyAbznpt7wlsDagOkyvGEHEJHxIjJfROZnZWVVMAwT89I6QadhMG8i7N3tdzQm2vT9MzTvAW/9OaKPGqv6ZKyEGBbybrzOuYnOuXTnXHpaWloVh2FiSu+r9RB74Ut+R2KiTXwijHxCr8B+5zq/o6mwiib6jSLSHMB73uQNzwACGzW3AqK7/0/jv9YnQKvjYO6jUJDvdzQm2hzSTbs0XvKGPiJQRRP9TGCc93ocMCNg+BgRqSUi7YCOwLzKhWhMGUSgzzV6S8Fl0X8TCeODPtdCi6Pg7b9CTuRVNZeneeUU4Eugk4hkiMilwL3AIBFZAQzy3uOcWwJMA5YC7wFXOmd3bTY1oNMw7a74i39rr5bGVKX4BBj5pN5j9u2/RNw2Vp5WN+c555o75xKdc62cc88657Y4505xznX0nrcGTH+3c66Dc66Tc+7d6g3fGE9cHPT+E2R+D6vm+B2NiUZNO8NJN8GymbBkut/RHBS7MtZEj+5joG5TLdUbUx16/UmrcN67SUv3EcISvYkeibXhhCvg51lRewMJ47P4BBj2AORsgM8e9DuacrNEb6JL+iWQlAJf/MfvSEy0apUOPc7Xrje2/Ox3NOViid5El+SGemu4xa/D9jV+R2Oi1cDbIL4WvH+z35GUiyV6E316/REkDl6/HPbs9DsaE41Sm0H/6+Gnd2HFR35HUyZL9Cb61G8FZz8N6+bDCyMhd5vfEZlodPwftEnvexMgf6/f0ZTKEr2JTt3OhNEv6EnZyadFdD8lJkwlJMHge/TexfOe8juaUlmiN9Gr8zA4b4reW3bScMje6HdEJtocfip0HAyz7wvr7csSvYluhw2Esa/C9rUwaRjsWOd3RCbaDLkH8vfArH/4HUmJLNGb6NeuH1w4XUtczw+Fbav9jshEk8YdtAHAwhchY4Hf0YRkid7EhtYnwLgZ2t3s88Mipv2ziRD9roeUQ+Ddv0Fhod/RHMASvYkdLY+B372ld6R6fhhsWu53RCZa1EqFgXdoS69FU/2O5gCW6E1saXYk/O4dwOkJWusqwVSV7udCy3T46Pawu37DEr2JPU07w8XvQkItmDQC1n3rd0QmGsTFwbD7IWcjzPmX39HsxxK9iU2NO8DF70Dt+vC/M2DN135HZKJBy2Og5wXw1ROweaXf0fzGEr2JXQ3basm+bhq8cGbYtpgwEWbgbZCYDG9eDYXhcd8lS/QmttVvqcke4PuX/Y3FRIeUpjD0Plj9RdjcG8ESvTGph0BSHXDh1yzORKge50HXkfDJ3bD+O7+jsURvjDFVTgRGPKx3PHv9cti7y9dwLNEbY0x1qNMIznwStqz0vd/6SiV6EblGRBaLyBIRudYb1khEPhSRFd5zwyqJ1BhjIk37/tD7KljwPCx/x7cwKpzoReQI4HLgOKAHMEJEOgITgFnOuY7ALO+9McbEppNv1Qv1Zl7lWw+XlSnRdwG+cs7tds7lA58CZwJnAJO9aSYDIysVoTHGRLKEWnDWM1pPP+OP4FyNh1CZRL8Y6CcijUWkDjAMOBQ4xDmXCeA9N618mMYYE8GadoZBd8LKj2De0zW++AoneufcMuA+4EPgPeB7IL+8nxeR8SIyX0TmZ2VlVTQMY6pGXALk5/kdhYlmx10OHU+FD26p8atmK3Uy1jn3rHPuaOdcP2ArsALYKCLNAbznTSV8dqJzLt05l56WllaZMIypvEbt9U5UxlQXETj9UX3+4uEaXXRlW9009Z5bA2cBU4CZwDhvknHAjMosw5gakdYZspb7Un9qYkhqMzjqAvj+FdiZWWOLrWw7+tdFZCnwJnClc24bcC8wSERWAIO898aEt6ZdIG8n7FzvdyQm2vW6ElwBfP1kjS0yoTIfds6dGGLYFuCUyszXmBqX1lmfs5Zp/zfGVJdG7aHrGTD/OTjxr1C7XrUv0q6MNQa0RA921ylTM3pfrUeQCybVyOIs0RsDULcJ1GmiJXpjqlvLo6Htidpvff7eal+cJXpjijTtYiV6U3P6XAvZ6+GHV6t9UZbojSmS1hmyfrSWN6ZmtO8P8Uk10o2xJXpjijTtDHuzYUeG35GYWLB6LhTshfYDqn1RluiNAdj6C/zwmr7eFfIaP2Oq1vK3ISEZOpxc7YuqVPNKYyJeYSHMewo+ugPiE/XKxRZH+x2ViXbOwY/vQIeT9O5m1cwSvYldm1fCjCth7VfaB8mIR6wNvakZGxbBjrUwoGZ6cbdEb2JPYQF8+ZjezzOhFox8EnqM0T5IjKkJy98GiYPDh9TI4izRm9iyabmW4tfNh07DYcRD2v+IMTVp+Ttw6Al6/UYNsERvYkNBPsz9N8y+F5JS4Oxn4YizrRRvat62X2HjD3DqXTW2SEv0JvptXAL/90fIXKh9jAx7AFLsfjjGJz++q8+dhtXYIi3Rm+hVsA8+ewjm/Atq14dRk6HbSL+jMrFu+duQ1gUad6ixRVqiN9Epc5GW4jf+AEecA0Pvh7qN/Y7KxLrdW/VCqb7X1uhiLdGb6LIvV0vxnz8EyY3g3Jegywi/ozJGLXxZ+6LvOrJGF2uJ3kSHvGzt33vuf/XK1u5jYMg9UKeR35EZowoL4JuntbVN8+41umhL9Cay5W6HeRPhq8chd5v2G9LveWjb1+/IjNnfig+1xc0pf6/xRVuiN5Fp12a96Gne09oR2eFDod910Crd78iMCW3eU5DaHLqcXuOLtkRvIsvOTJj7KCx4Xuvju43U27E1O9LvyIwp2eYV8PPHcNLN2qdSDbNEbyLDttXwxSPw3Yta19l9NPT9C6Qd7ndkxpRt3tPa9/wxv/Nl8ZboTXjbvEJb0Sx6BeLioedY6HMNNGrnd2TGlE9etra26XaWbxfqVSrRi8ifgcsAB/wAXAzUAV4B2gK/AqOdc9sqFaWJPRsWw2cPwpI3IKE2HDceev/Jepc0kWfhFD2PdPx430KocKIXkZbA1UBX51yuiEwDxgBdgVnOuXtFZAIwAbihSqI10S9jAXz2gPbVnZSqF5accCWkpPkdmTEHr7BQW4W1TIeWx/gWRmWrbhKAZBHZh5bk1wM3AgO88ZOB2ViiN2X59QvtquCXT6B2Axhwk5aAkhv6HZkxFffLJ7BlBZz1tK9hVDjRO+fWicgDwBogF/jAOfeBiBzinMv0pskUkZCVUiIyHhgP0Lp164qGYSKZc/DLbPj0flgzF+qmwcA74NhLoVaq39EZU3nzJkLdpjV+JWywylTdNATOANoB24FXReSC8n7eOTcRmAiQnp7uKhqHiVCr58LHd8PqzyG1hfZFc/RFkJjsd2TGVI2NS+Cn96H/3yAhyddQKlN1MxBY5ZzLAhCR6UBvYKOINPdK880Bu9OyKZaxAD65S9sUpxziJfhxkFjb78iMqTo5m2DKGG1lc+zlfkdTqUS/BjhBROqgVTenAPOBXcA44F7veUZlgzRRIHMRfPJP+OldqNMYBt0Jx15WIzdGNqZG7cuFqedDThZc/HZYNCSoTB391yLyGvAtkA98h1bFpADTRORSdGcwqioCNRFq03KY/U9YOkP7hD/5Fjj+CquDN9GpsFC7x874Bka/4GtLm0CVanXjnLsNuC1ocB5aujcxrFn+Os7JfhEenw1JdaHf36DXlZDcwO/QjKk+s/8JS6Zro4KuNd+nTUnsylhTtbavgU/v56Gsl8gnEfpcDb2vsZt+mOi3cIo2ET7qQr16O4xYojdVY+d6vZJ1wWQQ4f06p/N/KaOZOGi435EZU/1+/QJm/gna9YPhD4XdTect0ZvKycmCzx+Gb57RO+ccfRGceB2Tp67xOzJjasaWn+GVsdCwLYz+n+9NKUOxRG8qZvdWmPsf+PopyN8DPc6H/tfrxg7oeXhjotzurfDSKEBg7LSwvZLbEr05OPl79XZos++DvJ1w5DnQfwI0OczvyIypWfl74ZULYcdauGgmNGrvd0QlskRvym/Fh/Dejdp3x2EDtS38IV39jsqYmrdrC8z4o17ZfdbT0KaX3xGVyhK9KdvmlfD+TbDifWh8GJz/Khx+qt9RGeOPlR9pW/ncbTDsAb0JTpizRG9KtmeHdjj29VPaJ/ypd8Fxvw/Lk03GVLt9e+Cj2+DrJyGtM1zwesTcwtISvTlQYSEsfBFm/UNvwn3UBXrnep/ujmOM7zYshtcvg6xlWtgZdEdEdcBnid4UKyzU7oLfvxkyF8KhJ8DYV6HFUX5HZow/Cgvhq8dh1h16n4Sxr0PHgX5HddAs0ceygn2Q+b12GbzmS33kbtNug89+Fo44O+wu/DCmxuxcD//3B71nQqfhcPp/oG4Tv6OqEEv0sWTvbu1sac2XmtwzvoF9u3Vcow7QeTi06QNdz9D+aYyJVUtnwJvXQH4enPZv7Uo7ggs9luij2e6tsPbr4hL7+u+gMB8QaHaE9snRphe07g2ph/gdrTH+y8uGdyfoOaoWR8FZz0TFNSKW6KNJThas+tQrsX8Jm5bo8PgkaHE09P6TJvVDj7NeJI0JtnYeTL9cO+Y78ToYMAHiE/2OqkpYoo8Wy9+G6eNhbw4kpWgy73amlthbHhNRLQSMqVH7crW/pjkPQL2W8Lu3oU1vv6OqUpboI51z8PlDMOtOPdQc/gA06wHx9tMaU6r8PPj2f5rgczZA93Nh2L/0BjlRxrJBJNuXq12j/vAqHDkKTn/USu7GlCHOFdAv9yN4dDzsWKPVmec8B237+B1atbFEH6l2Zup9Kdd/B6fcBn3/HNGtAoypdoUFsHg6D2bdRouCdXre6rRHoMPJUf/fsUQfidYtgKljYc9OGPOSNos0xoTmHCx/Cz6+G7KWsS+hHfc3vI2/XR47hSNL9JHmh9dgxpXaHcFlH8Ih3fyOyJjw5Jx2QPbxXXqld+OOcM5z3PB5M5zExUySh0okehHpBLwSMKg98Hfgf97wtsCvwGjn3LaKh2gAvdjpswfhswe0TvHcFyL2Kj1jqt2qOZrg134NDVrDyCfgyNEQn4D74ku/o6txFU70zrkfgZ4AIhIPrAPeACYAs5xz94rIBO/9DZUPNcbs2QFrvobVX+gFT+u/g8J9equ+YQ9aD5LGBCss0O4Kvvi3Xk+S2lzv33rUhTH/f6mqqptTgJ+dc6tF5AxggDd8MjAbS/Rly8nSDsVWe48NPwAO4hKh5dHQ+ypoPwDa9Y+pQ05jyrR5BSx8Gb6fCtnroU4TGHwPpF9srdA8VZXoxwBTvNeHOOcyAZxzmSISsm9bERkPjAdo3bp1FYURQbav9a5g9Ursm3/S4QnJerHTgBu9i53SIamOv7EaE25yt8OSNzTBZ8wDiYPDBsGQe6DTUEio5XeEYaXSiV5EkoDTgRsP5nPOuYnARID09HRX2TjCmnN6p/iipL56rrbfBahVH1qfAD3HaodizXvE/GGmMSEVFsAvn2hyX/YWFORBWhe9pWX30ZDazO8Iw1ZVlOiHAt865zZ67zeKSHOvNN8c2FQFy4g8OZu0xFGU3Hdl6fC6aXp5de+r9LlpV4iL9zdWY8JZ1k/wfVHVTCYkN4RjxkGP8/RqcKvKLFNVJPrzKK62AZgJjAPu9Z5nVMEyIkf+Xr3V2Kf3w95sqH+oXpDRpreW2BsfZhumMWXJ3QaLp8P3U7Q7bYmHjoNg6H1w+BCrmjlIlUr0IlIHGAT8PmDwvcA0EbkUWAOMqswyIoZz8NP7ehPtrT/rxjjwDmja2e/IjIkMhQXw8yew8CXtpK8gT494T71bu/iwrrQrrFKJ3jm3G2gcNGwL2gondmT9BO9NgJ9nQZPDI/Z2Y8b4IutHrXdf9EpA1czvoOf5es7KjoArza6MrYzc7fDpfTBvIiTW1SZdx10eNX1YG1PlCgsga7lWx2R8Axnz9b3EQ8dTYej9cPhgq5qpYpboK6KwQLs3/fhOvYvTMePgpFsgJc3vyIwJL9kbYd18TegZ3+iFf3tzdFxyI2iVrhcBHjlKu/Uw1cIS/cH69Qt47wa9oKl1bxh6rx5eGhPr9u2BDYuKk/q6+Xq3JoC4BGh2pFbHtEzXBN+ovVXL1BBL9OW1fS18eKs2mazXSvuv7naWbagmNjkH237dP6lnLtJuOkBbm7U8Bo77PbQ6Fpp3t6tUfWSJvizbVsO3k+HLx/R9/wnQ5xq7WtXEjoJ82LYKNi3Tx/pvNbnv3qLjE+to3+69rtSSest0qNfc35jNfizRh7JnByydoRdorP5Ch3U7U6/Aa3Cov7EZU10KC7SUnrVcE3rR8+afoGCvN5Foy7LDh2hSb3WsXp1qt64Ma/brFCnI18urv5+ibXjz9+jFTSffoveSbBCD/fGY6FRYCDvWeol8KWxaDlnLtJlwfm7xdPUPhaZd9IK/pl0grTOkdYKkuv7FbirEEv2GxZrcf3gVcjZqG96jLtDLq1seY3XwJjI5B/t2w67N2rtj1rLihL5pOezbVTxtagu9sC/9Ek3oTbtoQq+V6l/8pkrFZqLP3qiJ/fupsPEH7Qr48MHQY4y25bU2vFViaeZOzn0q9m7yUNUS3F5SC7NJKcwmpXAnKS6b1MKd3vuA1y5gmsJskti333y2xzVkbUIb1iYOJKNOGzIS2rA2oQ2741JgN7Dae7APWFzzX7SGLM3cSdfm9fwOo0bFTqLfl6tVMt9P1StYXaGeNBr2ABxxNtRp5HeEUeWMni39DiGsiCsk2e0mpTCbui5HnwtzqOty9LkwhxSnw/ZL4G4ntV1eifPdRyLZcansikslO64eG+JbkJPYiRzvfU5cKpnxrVib2IacuNhKbiXp2rxezG2f4pz/PQSnp6e7+fPnV/2MCwu1z/fvp+jJ1bydWu/Y/VwtvTfpWPXLNNFtX662Ntm9RTveyt0Oe7brc+624tcHPO8ASvmvxSVqtWFyA++5kT7XaeQNa+S9DhqXWMeqF2OYiCxwzqWXNV10lui3/Kwl90VT9YKNpBToeoYm9zZ9IS7O7whNOCgs0CubixL37i2we7P3vFXrt38bvlXH7dtd8vziEjUp126gz3XTtDBRu0FxEi8aF/xsCdtUo+hJ9Lu36sVM308tvuNM+wFw8q3Qebi1FIgWzkF+Huzdpd1A792lj7yA13tzvMcuyAt4vTcH9uwsTua52ymxlJ2UqiXmOo310vymXfT1bw+vVB2YxC1ZmzAV2Yl+1xb49F5N7nk7i4e3P0n7zii6aGPt16XPR+Khdr3i0lWt+lbqr04F+VqVkbtVd9C52/R17rYD3+/ZuX+i3rsLCvPLtxyJ06O5pBTd0SfV1d+5Wff9k3bdxkFJvLGdkDdRJbIT/cIXtefIYL98oo8KE6hdv/RD7aLn30p13rBa9WJnJ1FYqDvY35J0KQl799aA5L2j5HlKXFA9dGO9hiEpBWoFJOyk1OLXtQKTecDrxGQrYRtDpCf6Yy7WQ/N2/SrXNXBhUQlze9BJtICTbTvWFY8r3FfyvCROk31yA22HHF8L4pP0PrDxRY/EgNdBwxJqBY1P9OYRPKykaZMOnL6sHY9zWloOmZy3l5y8c7dp66WS1K5fnLTrNILGHYJOMha9blg8XSztKI2pIZGd6GvXg5NvrtllFl2IEmpnEPycl62Xjhfs09Yae3borQYL9hYP/+05T+ueS2uZUVESX5z0EwJ2ChKvMeZuK33nlZTiJeUGmqDrtwxK2MGtRBppkrfL4o0JC/ZPPFgixVUG9auhLW5hQfGO4ICdQl7QDsJ7nZ934LCCvd70pUxbuE9L0CUl62SvpJ2QVPXf0xhTYyzRh5u4eIhLti5djTFVxipDjTEmylUq0YtIAxF5TUSWi8gyEeklIo1E5EMRWeE9N6yqYI0xxhy8ypbo/w2855zrDPQAlgETgFnOuY7ALO+9McYYn1Q40YtIPaAf8CyAc26vc247cAYw2ZtsMjCyciEaY4ypjMqU6NsDWcDzIvKdiDwjInWBQ5xzmQDec8hbu4vIeBGZLyLzs7KyKhGGMcaY0lQm0ScARwNPOOeOAnZxENU0zrmJzrl051x6WlpaJcIwxhhTmsok+gwgwzlX1JHMa2ji3ygizQG8502VC9EYY0xlVDjRO+c2AGtFpJM36BRgKTATGOcNGwfMqFSExhhjKqVSNx4RkZ7AM0AS8AtwMbrzmAa0BtYAo5xzW8uYTxbeTcwqoQmwuZLziBa2LvZn66OYrYti0bAu2jjnyqz7Dos7TFUFEZlfnjutxAJbF/uz9VHM1kWxWFoXdmWsMcZEOUv0xhgT5aIp0Ye4A0nMsnWxP1sfxWxdFIuZdRE1dfTGGGNCi6YSvTHGmBAs0RtjTJSL2EQvIvFeHztvee9jtntkEflVRH4QkYUiMt8bFpPrw7rOViLSydseih47ReTaWFwXRUTkzyKyREQWi8gUEakdK+sjYhM9cA3aLXKRWO8e+STnXM+AdsGxuj6s62zAOfejtz30BI4BdgNvEIPrAkBEWgJXA+nOuSOAeGAMMbI+IjLRi0grYDh6VW4R6x55fzG3Pqzr7BKdAvzsnFtNbK+LBCBZRBKAOsB6YmR9RGSiBx4B/gYUBgwrV/fIUcoBH4jIAhEZ7w2LxfVRqa6zo9gYYIr3OibXhXNuHfAA2i1LJrDDOfcBMbI+Ii7Ri8gIYJNzboHfsYSRPs65o4GhwJUi0s/vgHxSqa6zo5GIJAGnA6/6HYufvLr3M4B2QAugrohc4G9UNSfiEj3QBzhdRH4FpgIni8iLxHD3yM659d7zJrQe9jhic31Y19kHGgp865zb6L2P1XUxEFjlnMtyzu0DpgO9iZH1EXGJ3jl3o3OulXOuLXpI+rFz7gJitHtkEakrIqlFr4FTgcXE4PqwrrNDOo/iahuI3XWxBjhBROqIiKDbxjJiZH1E9JWxIjIAuM45N0JEGnOQ3SNHAxFpj5biQasuXnbO3R3D66MnVdB1djQQkTrAWqC9c26HNywmtwsAEbkDOBfIB74DLgNSiIH1EdGJ3hhjTNkirurGGGPMwbFEb4wxUc4SvTHGRDlL9MYYE+Us0RtjTJSzRG+MMVHOEr0xxkS5/weniwgu7ucTRgAAAABJRU5ErkJggg==\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for i in range(len(areas)):\n", + " plt.figure()\n", + " area_df = pd.read_csv(f\"digitized_data/{areas[i]}.csv\", header=None)\n", + " area_polygon = Polygon(area_df.values)\n", + "\n", + " best_rect, best_iou = best_rects[i]\n", + "\n", + " plt.plot(*best_rect.exterior.xy)\n", + " plt.plot(*area_polygon.exterior.xy)\n", + "\n", + " plt.title(f\"Mouse Region: {areas[i]} with an IOU of: {best_iou}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 7))\n", + "for i in range(len(areas)):\n", + "\n", + " area_df = pd.read_csv(f\"digitized_data/{areas[i]}.csv\", header=None)\n", + " area_polygon = Polygon(area_df.values)\n", + "\n", + " best_rect, best_iou = best_rects[i]\n", + "\n", + " plt.plot(*best_rect.exterior.xy, label=areas[i])\n", + "\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "visP_poly = best_rects[0][0]\n", + "x, y = visP_poly.exterior.xy\n", + "centroid = visP_poly.centroid\n", + "\n", + "width = abs(list(x)[0] - list(x)[2])\n", + "height = abs(list(y)[0] - list(y)[1])\n", + "\n", + "scaled_polys = []\n", + "for result in best_rects:\n", + " poly = result[0]\n", + " poly = scale(poly, xfact=92/width, yfact=60/height, origin=centroid)\n", + " scaled_polys.append(\n", + " poly\n", + " )\n", + "\n", + "visP_poly = scaled_polys[0]\n", + "x, y = visP_poly.exterior.xy\n", + "centroid = visP_poly.centroid\n", + "\n", + "minX = min(list(x))\n", + "minY = min(list(y))\n", + "\n", + "final_polys = []\n", + "for poly in scaled_polys:\n", + " poly = translate(poly, xoff= -1*minX, yoff=-1*minY)\n", + " final_polys.append(poly)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(5,5))\n", + "visp_final = \n", + "for pp in final_polys:\n", + " x, y = pp.exterior.xy\n", + " x = list(x)\n", + " y = list(y)\n", + " plt.plot(x, y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "saver = list(zip(final_result, final_polys))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "pickle.dump(saver, open( \"retinomap.pkl\", \"wb\" ) )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/mousenet/retinotopics/area_derivations.py b/mousenet/retinotopics/area_derivations.py new file mode 100644 index 0000000..87c895d --- /dev/null +++ b/mousenet/retinotopics/area_derivations.py @@ -0,0 +1,133 @@ +import pandas as pd +import numpy as np +from shapely.geometry import box, Polygon, LinearRing +import numpy as np +from scipy.spatial import ConvexHull +from shapely.affinity import scale +import itertools + +import matplotlib.pyplot as plt +import geopandas as gpd + + +def iou(A, B): + """ + Ratio of the intersection of two shapely polygons with their union + + :param A: A shapely polygon + :param B: A shapely polygon + + Returns + ------- + float: ratio + """ + if type(A) != Polygon: + A = Polygon(A) + if type(B) != Polygon: + B = Polygon(B) + + return ( + A.intersection(B).area / + A.union(B).area + ) + +def minimum_bounding_rectangle(points): + """ + Find the smallest bounding rectangle for a set of points. + Returns a set of points representing the corners of the bounding box. + + :param points: an nx2 matrix of coordinates + :rval: an nx2 matrix of coordinates + + Returns + 4x2 array: the bounding rectangle + ------- + + """ + if type(points) == Polygon: + points = np.array(points.exterior.xy).T + + from scipy.ndimage.interpolation import rotate + pi2 = np.pi/2. + + # get the convex hull for the points + hull_points = points[ConvexHull(points).vertices] + + # calculate edge angles + edges = np.zeros((len(hull_points)-1, 2)) + edges = hull_points[1:] - hull_points[:-1] + + angles = np.zeros((len(edges))) + angles = np.arctan2(edges[:, 1], edges[:, 0]) + + angles = np.abs(np.mod(angles, pi2)) + angles = np.unique(angles) + + # find rotation matrices + # XXX both work + rotations = np.vstack([ + np.cos(angles), + np.cos(angles-pi2), + np.cos(angles+pi2), + np.cos(angles)]).T +# rotations = np.vstack([ +# np.cos(angles), +# -np.sin(angles), +# np.sin(angles), +# np.cos(angles)]).T + rotations = rotations.reshape((-1, 2, 2)) + + # apply rotations to the hull + rot_points = np.dot(rotations, hull_points.T) + + # find the bounding points + min_x = np.nanmin(rot_points[:, 0], axis=1) + max_x = np.nanmax(rot_points[:, 0], axis=1) + min_y = np.nanmin(rot_points[:, 1], axis=1) + max_y = np.nanmax(rot_points[:, 1], axis=1) + + # find the box with the best area + areas = (max_x - min_x) * (max_y - min_y) + best_idx = np.argmin(areas) + + # return the best box + x1 = max_x[best_idx] + x2 = min_x[best_idx] + y1 = max_y[best_idx] + y2 = min_y[best_idx] + r = rotations[best_idx] + + rval = np.zeros((4, 2)) + rval[0] = np.dot([x1, y2], r) + rval[1] = np.dot([x2, y2], r) + rval[2] = np.dot([x2, y1], r) + rval[3] = np.dot([x1, y1], r) + + return rval + + +def get_best_fit_rect(area, step=5): + area_df = pd.read_csv(f"digitized_data/{area}.csv", header=None) + area_polygon = Polygon(area_df.values) + + x1 = np.arange(area_df[0].min(), area_df[0].max(), step) + x2 = np.arange(area_df[0].max(), area_df[0].min(), -1*step) + y1 = np.arange(area_df[1].min(), area_df[1].max(), step) + y2 = np.arange(area_df[1].max(), area_df[1].min(), -1*step) + + possible_rectangles = list(itertools.product(x1, x2, y1, y2)) + + best_rect = possible_rectangles[0] + best_iou = 0 + for r in possible_rectangles: + candidate = Polygon(np.array([ + [r[0], r[2]], [r[0], r[3]], + [r[1], r[3]], [r[1], r[2]]]) + ) + + current_iou = iou(candidate, area_polygon) + if current_iou > best_iou: + best_iou = current_iou + best_rect = candidate + + return best_rect, best_iou \ No newline at end of file diff --git a/mousenet/retinotopics/digitized_data/VisAM.csv b/mousenet/retinotopics/digitized_data/VisAM.csv new file mode 100644 index 0000000..fd3f32f --- /dev/null +++ b/mousenet/retinotopics/digitized_data/VisAM.csv @@ -0,0 +1,122 @@ +71.75, 111.125 +74.5, 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b/mousenet/retinotopics/digitized_data/VisAM.csv:Zone.Identifier @@ -0,0 +1,3 @@ +[ZoneTransfer] +ZoneId=3 +HostUrl=about:internet diff --git a/mousenet/retinotopics/digitized_data/VisAl.csv b/mousenet/retinotopics/digitized_data/VisAl.csv new file mode 100644 index 0000000..6e19fbd --- /dev/null +++ b/mousenet/retinotopics/digitized_data/VisAl.csv @@ -0,0 +1,63 @@ +26.5, 107.66666666666667 +27.716535433070867, 108.03149606299213 +28.976377952755907, 108.34645669291339 +30.236220472440948, 108.03149606299213 +31.5, 107.5 +33.07086614173229, 107.55905511811024 +34.5, 107.16666666666667 +37.5, 106.5 +40.5, 106.83333333333333 +43.5, 107 +46.5, 107.16666666666667 +49.5, 108.5 +52.5, 109.83333333333333 +55.5, 111.66666666666667 +58.5, 114 +61.5, 116.25 +63.7, 118.6 +65.83333333333333, 120.83333333333333 +67.5, 123.33333333333333 +70, 126.5 +72.5, 128.33333333333334 +75.5, 128.5 +81.5, 127 +84.5, 125.5 +87.5, 123.5 +90.5, 121 +93.5, 118.83333333333333 +96.5, 116.33333333333333 +99.5, 114.5 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b/mousenet/retinotopics/digitized_data/VisAl.csv:Zone.Identifier @@ -0,0 +1,3 @@ +[ZoneTransfer] +ZoneId=3 +HostUrl=about:internet diff --git a/mousenet/retinotopics/digitized_data/VisL.csv b/mousenet/retinotopics/digitized_data/VisL.csv new file mode 100644 index 0000000..ca80019 --- /dev/null +++ b/mousenet/retinotopics/digitized_data/VisL.csv @@ -0,0 +1,87 @@ +2, 126.5 +2.5, 122.5 +2.5, 117.5 +3.5, 113.125 +4.5, 109.33333333333333 +5.75, 106 +6.5, 102.83333333333333 +7.5, 98.25 +8.5, 94.33333333333333 +9.75, 91 +10, 87.5 +12, 84.25 +13.5, 80.875 +15, 77.5 +17.5, 73.5 +20.75, 71.625 +23.7, 70.5 +26.5, 70.25 +29.25, 69 +30.75, 70.75 +33.25, 70.625 +36.5, 70.83333333333333 +39.5, 70.5 +42.5, 70.5 +44.5, 70.5 +53.5, 71.5 +56.5, 72.33333333333333 +59.5, 73 +62.5, 73.33333333333333 +65.5, 73 +68.5, 73.16666666666667 +71.5, 74 +74.5, 74.5 +77.5, 75.66666666666667 +80.5, 76 +83.5, 77 +86.5, 78.16666666666667 +89.5, 80 +92.25, 82.625 +95.5, 86.5 +97, 89.75 +97.7, 93.7 +98.16666666666667, 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a/mousenet/retinotopics/digitized_data/VisLi.csv b/mousenet/retinotopics/digitized_data/VisLi.csv new file mode 100644 index 0000000..86a6e9d --- /dev/null +++ b/mousenet/retinotopics/digitized_data/VisLi.csv @@ -0,0 +1,54 @@ +39.75, 63 +42.25, 63 +45.5, 62.5 +48.5, 62.333333333333336 +51.5, 62.166666666666664 +54.5, 62.666666666666664 +57.5, 63.166666666666664 +60.5, 63.5 +63.25, 64 +66.5, 64.5 +69.5, 65 +72.5, 66.16666666666667 +75.5, 67.66666666666667 +78.5, 70.25 +80.5, 72.625 +82.5, 75.875 +83.75, 79.25 +84.5, 83.3 +85, 87 +84.5, 89.5 +83.5, 92.75 +82.16666666666667, 98 +81, 102 +80, 105.25 +78.5, 108.375 +77.5, 111.5 +76, 113.5 +74, 117.25 +72, 120 +71, 123.25 +69.5, 126.5 +68.5, 129.25 +65.5, 129.75 +61.7, 128 +59.5, 125.625 +58, 122 +56.166666666666664, 119.16666666666667 +56.5, 115.83333333333333 +57, 112.5 +58, 108.5 +59, 106 +58.7, 101.5 +59.7, 96.5 +61.5, 92.5 +61.5, 89.66666666666667 +61.75, 86.25 +59.5, 82.83333333333333 +56.5, 79.5 +53.75, 76.25 +52.25, 74.25 +49.75, 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64 and i>= 0 for i in y]) diff --git a/tests/test_model_import.py b/tests/test_model_import.py index 1f8968f..dbe10ef 100644 --- a/tests/test_model_import.py +++ b/tests/test_model_import.py @@ -1,7 +1,13 @@ import mousenet def test_generic_import(): - assert True #todo + model = mousenet.load(architecture="default", pretraining=None) def test_retinotopic_import(): - assert True #todo \ No newline at end of file + model = mousenet.load(architecture="retinotopic", pretraining=None) + +def test_imagenet_import(): + model = mousenet.load(architecture="default", pretraining="imagenet") + +def test_kaiming_import(): + model = mousenet.load(architecture="default", pretraining="kaiming") From a42cc588272f77d44aaead9826b03972489dd884 Mon Sep 17 00:00:00 2001 From: Parsa Torabian Date: Thu, 19 May 2022 15:18:22 -0400 Subject: [PATCH 3/9] Added in retinotopics --- environment.yml | 1 + mousenet/cmouse/mousenet_complete_pool.py | 34 ++++++++++++++++---- mousenet/cmouse/network.py | 15 +++++++-- mousenet/loader.py | 28 +++++++++------- mousenet/mouse_cnn/voxel.py | 39 +++++++++-------------- 5 files changed, 73 insertions(+), 44 deletions(-) diff --git a/environment.yml b/environment.yml index 1d92a5f..4154c4c 100644 --- a/environment.yml +++ b/environment.yml @@ -6,6 +6,7 @@ channels: dependencies: - python=3.8 + - shapely - numpy - scipy - matplotlib diff --git a/mousenet/cmouse/mousenet_complete_pool.py b/mousenet/cmouse/mousenet_complete_pool.py index 6bee361..9f2fbd9 100644 --- a/mousenet/cmouse/mousenet_complete_pool.py +++ b/mousenet/cmouse/mousenet_complete_pool.py @@ -1,20 +1,39 @@ +from copyreg import pickle import torch from torch import nn import networkx as nx import numpy as np +import pathlib, os +import pickle from .exps.imagenet.config import INPUT_SIZE, EDGE_Z, OUTPUT_AREAS, HIDDEN_LINEAR, NUM_CLASSES def get_retinotopic_mask(layer, retinomap): mask = torch.zeros(32, 32) + if layer == "input": + return + if layer == "VisP": + return mask + for area in retinomap: - if area.lower() in layer: + area_name = area[0].lower() + if area_name == ''.join(x for x in layer.lower() if x.isalpha()): normalized_polygon = area[1] x, y = normalized_polygon.exterior.coords.xy x, y = list(x), list(y) - mask[min(x):max(x), min(y), max(y)] = 1 + xshift= yshift = int(0) + if area_name != "visp": + xshift = int((max(x) - min(x))/4) + yshift = int((max(y) - min(y))/4) + x1, x2 = int(max(min(x)+xshift, 0)), int(min(max(x) - xshift, 32)) + y1, y2 = int(max(min(y) + yshift, 0)), int(min(max(y) - yshift, 32)) + mask[x1:x2, y1:y2] = 1 + mask_sum = mask.sum() + project_root = pathlib.Path(__file__).parent.parent.resolve() + file = os.path.join(project_root, "retinotopics", "mask_areas", f"{area_name}.pkl") + pickle.dump(mask_sum, open(file,"wb")) return mask - raise ValueError(f"Could not find area for layer {layer} in retinomap") + # raise ValueError(f"Could not find area for layer {layer} in retinomap") class Conv2dMask(nn.Conv2d): @@ -84,7 +103,7 @@ def __init__(self, network, mask=3, retinomap=None): self.Convs = nn.ModuleDict() self.BNs = nn.ModuleDict() self.network = network - self.layer_masks = nn.ModuleDict() + self.layer_masks = dict() self.retinomap = retinomap G, _ = network.make_graph() @@ -93,7 +112,7 @@ def __init__(self, network, mask=3, retinomap=None): if self.retinomap is not None: for layer in self.top_sort: - self.layer_masks[layer] = get_retinotopic_mask(layer) + self.layer_masks[layer] = get_retinotopic_mask(layer, self.retinomap) else: for layer in self.top_sort: self.layer_masks[layer] = torch.ones(32, 32) @@ -167,13 +186,13 @@ def get_img_feature(self, x, area_list, flatten=True): layer_name = layer.source_name + layer.target_name if area not in calc_graph: calc_graph[area] = self.Convs[layer_name]( - mask(calc_graph[layer.source_name]) + mask*calc_graph[layer.source_name] ) else: calc_graph[area] = calc_graph[area] + self.Convs[layer_name](calc_graph[layer.source_name]) calc_graph[area] = nn.ReLU(inplace=True)( self.BNs[area]( - mask(calc_graph[area]) + mask*calc_graph[area] ) ) @@ -186,6 +205,7 @@ def get_img_feature(self, x, area_list, flatten=True): else: re = None for area in area_list: + print(area, (calc_graph[area]).shape) if re is None: re = torch.flatten(torch.nn.AdaptiveAvgPool2d(4) (calc_graph[area]), 1) # re = torch.flatten( diff --git a/mousenet/cmouse/network.py b/mousenet/cmouse/network.py index 3d85ab8..78d96de 100644 --- a/mousenet/cmouse/network.py +++ b/mousenet/cmouse/network.py @@ -7,7 +7,8 @@ import os import pickle import matplotlib.pyplot as plt - +import pathlib +import pdb class ConvParam: def __init__(self, in_channels, out_channels, gsh, gsw, out_sigma): """ @@ -50,10 +51,11 @@ class Network: """ network class that contains all conv paramters needed to construct torch model. """ - def __init__(self): + def __init__(self, retinotopic=False): self.layers = [] self.area_channels = {} self.area_size = {} + self.retinotopic = retinotopic def find_conv_source_target(self, source_name, target_name): for layer in self.layers: @@ -113,6 +115,15 @@ def construct_from_anatomy(self, anet, architecture): out_size = in_size * out_sigma self.area_size[e[1].area+e[1].depth] = out_size out_channels = np.floor(out_anat_layer.num/out_size**2) + if self.retinotopic: + project_root = pathlib.Path(__file__).parent.parent.resolve() + mask_pickle = ''.join(x for x in in_layer_name.lower() if x.isalpha()) + mask_path = os.path.join(project_root, "retinotopics", "mask_areas", f"{mask_pickle}.pkl") + if os.path.exists(mask_path): + mask_size = pickle.load(open(mask_path, "rb")) + out_channels = out_channels*int((32*32)/mask_size) + + architecture.set_num_channels(e[1].area, e[1].depth, out_channels) self.area_channels[e[1].area+e[1].depth] = out_channels diff --git a/mousenet/loader.py b/mousenet/loader.py index ec45437..8bbfbd3 100644 --- a/mousenet/loader.py +++ b/mousenet/loader.py @@ -1,3 +1,4 @@ +from doctest import REPORTING_FLAGS import pickle import os from .cmouse.mousenet_complete_pool import MouseNetCompletePool @@ -6,15 +7,17 @@ from .cmouse.anatomy import gen_anatomy from .mouse_cnn.architecture import Architecture from .cmouse import network +import pathlib +import os, pdb - -def generate_net(): - cached = "net_cache.pkl" - if os.path.isfile(cached): - return network.load_network_from_pickle(cached) +def generate_net(retinotopic=False): + root = pathlib.Path(__file__).parent.resolve() + cached = os.path.join(root, "data_files", "net_cache.pkl") + # if os.path.isfile(cached): + # return network.load_network_from_pickle(cached) architecture = Architecture() anet = gen_anatomy(architecture) - net = network.Network() + net = network.Network(retinotopic=retinotopic) net.construct_from_anatomy(anet, architecture) network.save_network_to_pickle(net, cached) return net @@ -32,15 +35,18 @@ def load(architecture, pretraining=None): # net = pickle.load(file) # pdb.set_trace() - net = generate_net() + net = generate_net(retinotopic= architecture=="retinotopic") mousenet = MouseNetCompletePool(net) retinomap = None - if architecture ==" retinotopic": - with open(os.path.join(path, "retinotopics", "retinomap.pkl", "rb")) as file: + if architecture =="retinotopic": + with open(os.path.join(path, "retinotopics", "retinomap.pkl"), "rb") as file: retinomap = pickle.load(file) - - model = MouseNetCompletePool(net, retinomask = retinomap) + + if architecture=="retinotopic": + pdb.set_trace() + model = MouseNetCompletePool(net, retinomap = retinomap) + if pretraining == "kaiming" or None: def _kaiming_init_ (m): diff --git a/mousenet/mouse_cnn/voxel.py b/mousenet/mouse_cnn/voxel.py index 290c15f..3a540a5 100644 --- a/mousenet/mouse_cnn/voxel.py +++ b/mousenet/mouse_cnn/voxel.py @@ -1,6 +1,7 @@ import os import numpy as np import pickle +import pathlib from mcmodels.core import VoxelModelCache from .flatmap import FlatMap from .data import Data @@ -17,32 +18,22 @@ of mouse connectome (Knox et al. 2019). """ - +def get_data_folder(): + project_root = pathlib.Path(__file__).parent.resolve().parent.absolute() + return os.path.join(project_root, 'data_files') class VoxelModel(): # we make a shared instance because the model's state doesn't change # but it takes several seconds to instantiate, so we only want to do it once _instance = None - def __init__(self, data_folder='data_files/'): - cache = VoxelModelCache(manifest_file='connectivity/voxel_model_manifest.json') + def __init__(self): + manifest_file = os.path.join(get_data_folder(), "voxel_model_manifest.json") + cache = VoxelModelCache(manifest_file=manifest_file) self.source_mask = cache.get_source_mask() self.source_keys = self.source_mask.get_key(structure_ids=None) - weight_file = data_folder + '/voxel-weights.pkl' - node_file = data_folder + '/voxel-nodes.pkl' - if os.path.isfile(weight_file) and os.path.isfile(node_file): - with open(weight_file, 'rb') as file: - self.weights = pickle.load(file) - with open(node_file, 'rb') as file: - self.nodes = pickle.load(file) - else: - print('Loading weights from cache (takes several minutes) ...') - self.weights = cache.get_weights() - self.nodes = cache.get_nodes() - with open(weight_file, 'wb') as file: - pickle.dump(self.weights, file) - with open(node_file, 'wb') as file: - pickle.dump(self.nodes, file) + self.weights = cache.get_weights() + self.nodes = cache.get_nodes() self.structure_tree = cache.get_structure_tree() def get_weights(self, source_name, target_name): @@ -76,12 +67,12 @@ def get_positions(self, source_name): return pre_positions @staticmethod - def get_instance(data_folder='data_files/'): + def get_instance(): """ :return: Shared instance of VoxelModel """ if VoxelModel._instance is None: - VoxelModel._instance = VoxelModel(data_folder=data_folder) + VoxelModel._instance = VoxelModel() return VoxelModel._instance @@ -103,19 +94,19 @@ class Target(): be true either, but it allows us to estimate numbers of connections from voxel weights. """ - def __init__(self, area, layer, external_in_degree, data_folder='data_files/'): + def __init__(self, area, layer, external_in_degree): """ :param area: name of area :param layer: name of layer :param external_in_degree: Total neurons providing feedforward input to average neuron, from other cortical areas. """ - self.data_folder=data_folder + self.data_folder=get_data_folder() self.target_area = area self.target_name = area + layer self.e = external_in_degree - self.voxel_model = VoxelModel.get_instance(data_folder=data_folder) + self.voxel_model = VoxelModel.get_instance() self.num_voxels = len(self.voxel_model.get_positions(self.target_name)) self.gamma = None # scale factor for total inbound voxel weight -> extrinsic in-degree @@ -129,7 +120,7 @@ def _set_external_sources(self): including only lower areas in the visual hierarchy """ self.source_names = [] - data = Data(data_folder=self.data_folder) + data = Data() for area in data.get_areas(): if data.get_hierarchical_level(area) < data.get_hierarchical_level(self.target_area): if 'LGN' not in area: #TODO: handle LGN->VISp as special case From 1c2c5b65806afcc2fb8026b43ffbf84af72c0203 Mon Sep 17 00:00:00 2001 From: Parsa Torabian Date: Tue, 24 May 2022 23:47:25 -0400 Subject: [PATCH 4/9] Intermittent Commit --- mousenet/data_files/readme.txt | 1 - mousenet/retinotopics/mask_areas/visal.pkl | Bin 0 -> 392 bytes mousenet/retinotopics/mask_areas/visl.pkl | Bin 0 -> 392 bytes mousenet/retinotopics/mask_areas/visli.pkl | Bin 0 -> 392 bytes mousenet/retinotopics/mask_areas/visp.pkl | Bin 0 -> 392 bytes mousenet/retinotopics/mask_areas/vispl.pkl | Bin 0 -> 392 bytes mousenet/retinotopics/mask_areas/vispor.pkl | Bin 0 -> 392 bytes mousenet/retinotopics/mask_areas/visrl.pkl | Bin 0 -> 392 bytes mousenet/retinotopics/retinomap.pkl | Bin 1641 -> 836 bytes tests/test_retinotopics.py | 6 ++++++ 10 files changed, 6 insertions(+), 1 deletion(-) delete mode 100644 mousenet/data_files/readme.txt create mode 100644 mousenet/retinotopics/mask_areas/visal.pkl create mode 100644 mousenet/retinotopics/mask_areas/visl.pkl create mode 100644 mousenet/retinotopics/mask_areas/visli.pkl create mode 100644 mousenet/retinotopics/mask_areas/visp.pkl create mode 100644 mousenet/retinotopics/mask_areas/vispl.pkl create mode 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.../retinotopics/digitized_data/VisAM.csv:Zone.Identifier | 3 --- .../retinotopics/digitized_data/VisAl.csv:Zone.Identifier | 3 --- mousenet/retinotopics/digitized_data/VisL.csv:Zone.Identifier | 3 --- .../retinotopics/digitized_data/VisLi.csv:Zone.Identifier | 3 --- mousenet/retinotopics/digitized_data/VisP.csv:Zone.Identifier | 3 --- .../retinotopics/digitized_data/VisPM.csv:Zone.Identifier | 3 --- .../retinotopics/digitized_data/VisPOR.csv:Zone.Identifier | 3 --- .../retinotopics/digitized_data/VisPl.csv:Zone.Identifier | 3 --- .../retinotopics/digitized_data/VisRL.csv:Zone.Identifier | 3 --- mousenet/retinotopics/images/VisAL.jpg:Zone.Identifier | 3 --- mousenet/retinotopics/images/VisAM.jpg:Zone.Identifier | 3 --- mousenet/retinotopics/images/VisL.jpg:Zone.Identifier | 3 --- mousenet/retinotopics/images/VisLI.jpg:Zone.Identifier | 3 --- mousenet/retinotopics/images/VisP.jpg:Zone.Identifier | 3 --- mousenet/retinotopics/images/VisPL.jpg:Zone.Identifier | 3 --- mousenet/retinotopics/images/VisPM.jpg:Zone.Identifier | 3 --- mousenet/retinotopics/images/VisPOR.jpg:Zone.Identifier | 3 --- mousenet/retinotopics/images/VisRL.jpg:Zone.Identifier | 3 --- mousenet/retinotopics/images/main.jpg:Zone.Identifier | 4 ---- 19 files changed, 58 deletions(-) delete mode 100644 mousenet/retinotopics/digitized_data/VisAM.csv:Zone.Identifier delete mode 100644 mousenet/retinotopics/digitized_data/VisAl.csv:Zone.Identifier delete mode 100644 mousenet/retinotopics/digitized_data/VisL.csv:Zone.Identifier delete mode 100644 mousenet/retinotopics/digitized_data/VisLi.csv:Zone.Identifier delete mode 100644 mousenet/retinotopics/digitized_data/VisP.csv:Zone.Identifier delete mode 100644 mousenet/retinotopics/digitized_data/VisPM.csv:Zone.Identifier delete mode 100644 mousenet/retinotopics/digitized_data/VisPOR.csv:Zone.Identifier delete mode 100644 mousenet/retinotopics/digitized_data/VisPl.csv:Zone.Identifier delete mode 100644 mousenet/retinotopics/digitized_data/VisRL.csv:Zone.Identifier delete mode 100644 mousenet/retinotopics/images/VisAL.jpg:Zone.Identifier delete mode 100644 mousenet/retinotopics/images/VisAM.jpg:Zone.Identifier delete mode 100644 mousenet/retinotopics/images/VisL.jpg:Zone.Identifier delete mode 100644 mousenet/retinotopics/images/VisLI.jpg:Zone.Identifier delete mode 100644 mousenet/retinotopics/images/VisP.jpg:Zone.Identifier delete mode 100644 mousenet/retinotopics/images/VisPL.jpg:Zone.Identifier delete mode 100644 mousenet/retinotopics/images/VisPM.jpg:Zone.Identifier delete mode 100644 mousenet/retinotopics/images/VisPOR.jpg:Zone.Identifier delete mode 100644 mousenet/retinotopics/images/VisRL.jpg:Zone.Identifier delete mode 100644 mousenet/retinotopics/images/main.jpg:Zone.Identifier diff --git a/mousenet/retinotopics/digitized_data/VisAM.csv:Zone.Identifier b/mousenet/retinotopics/digitized_data/VisAM.csv:Zone.Identifier deleted file mode 100644 index 053d112..0000000 --- a/mousenet/retinotopics/digitized_data/VisAM.csv:Zone.Identifier +++ /dev/null @@ -1,3 +0,0 @@ -[ZoneTransfer] -ZoneId=3 -HostUrl=about:internet diff --git a/mousenet/retinotopics/digitized_data/VisAl.csv:Zone.Identifier b/mousenet/retinotopics/digitized_data/VisAl.csv:Zone.Identifier deleted file mode 100644 index 053d112..0000000 --- a/mousenet/retinotopics/digitized_data/VisAl.csv:Zone.Identifier +++ /dev/null @@ -1,3 +0,0 @@ -[ZoneTransfer] -ZoneId=3 -HostUrl=about:internet diff --git a/mousenet/retinotopics/digitized_data/VisL.csv:Zone.Identifier b/mousenet/retinotopics/digitized_data/VisL.csv:Zone.Identifier deleted file mode 100644 index 053d112..0000000 --- a/mousenet/retinotopics/digitized_data/VisL.csv:Zone.Identifier +++ /dev/null @@ -1,3 +0,0 @@ -[ZoneTransfer] -ZoneId=3 -HostUrl=about:internet diff --git a/mousenet/retinotopics/digitized_data/VisLi.csv:Zone.Identifier b/mousenet/retinotopics/digitized_data/VisLi.csv:Zone.Identifier deleted file mode 100644 index 053d112..0000000 --- a/mousenet/retinotopics/digitized_data/VisLi.csv:Zone.Identifier +++ /dev/null @@ -1,3 +0,0 @@ -[ZoneTransfer] -ZoneId=3 -HostUrl=about:internet diff --git a/mousenet/retinotopics/digitized_data/VisP.csv:Zone.Identifier b/mousenet/retinotopics/digitized_data/VisP.csv:Zone.Identifier deleted file mode 100644 index 053d112..0000000 --- a/mousenet/retinotopics/digitized_data/VisP.csv:Zone.Identifier +++ /dev/null @@ -1,3 +0,0 @@ -[ZoneTransfer] -ZoneId=3 -HostUrl=about:internet diff --git a/mousenet/retinotopics/digitized_data/VisPM.csv:Zone.Identifier b/mousenet/retinotopics/digitized_data/VisPM.csv:Zone.Identifier deleted file mode 100644 index 053d112..0000000 --- a/mousenet/retinotopics/digitized_data/VisPM.csv:Zone.Identifier +++ /dev/null @@ -1,3 +0,0 @@ -[ZoneTransfer] -ZoneId=3 -HostUrl=about:internet diff --git a/mousenet/retinotopics/digitized_data/VisPOR.csv:Zone.Identifier b/mousenet/retinotopics/digitized_data/VisPOR.csv:Zone.Identifier deleted file mode 100644 index 053d112..0000000 --- a/mousenet/retinotopics/digitized_data/VisPOR.csv:Zone.Identifier +++ /dev/null @@ -1,3 +0,0 @@ -[ZoneTransfer] -ZoneId=3 -HostUrl=about:internet diff --git a/mousenet/retinotopics/digitized_data/VisPl.csv:Zone.Identifier b/mousenet/retinotopics/digitized_data/VisPl.csv:Zone.Identifier deleted file mode 100644 index 053d112..0000000 --- a/mousenet/retinotopics/digitized_data/VisPl.csv:Zone.Identifier +++ /dev/null @@ -1,3 +0,0 @@ -[ZoneTransfer] -ZoneId=3 -HostUrl=about:internet diff --git a/mousenet/retinotopics/digitized_data/VisRL.csv:Zone.Identifier b/mousenet/retinotopics/digitized_data/VisRL.csv:Zone.Identifier deleted file mode 100644 index 053d112..0000000 --- a/mousenet/retinotopics/digitized_data/VisRL.csv:Zone.Identifier +++ /dev/null @@ -1,3 +0,0 @@ -[ZoneTransfer] -ZoneId=3 -HostUrl=about:internet diff --git a/mousenet/retinotopics/images/VisAL.jpg:Zone.Identifier b/mousenet/retinotopics/images/VisAL.jpg:Zone.Identifier deleted file mode 100644 index 60f0f97..0000000 --- a/mousenet/retinotopics/images/VisAL.jpg:Zone.Identifier +++ /dev/null @@ -1,3 +0,0 @@ -[ZoneTransfer] -LastWriterPackageFamilyName=Microsoft.Windows.Photos_8wekyb3d8bbwe -ZoneId=3 diff --git a/mousenet/retinotopics/images/VisAM.jpg:Zone.Identifier b/mousenet/retinotopics/images/VisAM.jpg:Zone.Identifier deleted file mode 100644 index 60f0f97..0000000 --- a/mousenet/retinotopics/images/VisAM.jpg:Zone.Identifier +++ /dev/null @@ -1,3 +0,0 @@ -[ZoneTransfer] -LastWriterPackageFamilyName=Microsoft.Windows.Photos_8wekyb3d8bbwe -ZoneId=3 diff --git a/mousenet/retinotopics/images/VisL.jpg:Zone.Identifier b/mousenet/retinotopics/images/VisL.jpg:Zone.Identifier deleted file mode 100644 index 60f0f97..0000000 --- a/mousenet/retinotopics/images/VisL.jpg:Zone.Identifier +++ /dev/null @@ -1,3 +0,0 @@ -[ZoneTransfer] -LastWriterPackageFamilyName=Microsoft.Windows.Photos_8wekyb3d8bbwe -ZoneId=3 diff --git a/mousenet/retinotopics/images/VisLI.jpg:Zone.Identifier b/mousenet/retinotopics/images/VisLI.jpg:Zone.Identifier deleted file mode 100644 index 60f0f97..0000000 --- a/mousenet/retinotopics/images/VisLI.jpg:Zone.Identifier +++ /dev/null @@ -1,3 +0,0 @@ -[ZoneTransfer] -LastWriterPackageFamilyName=Microsoft.Windows.Photos_8wekyb3d8bbwe -ZoneId=3 diff --git a/mousenet/retinotopics/images/VisP.jpg:Zone.Identifier b/mousenet/retinotopics/images/VisP.jpg:Zone.Identifier deleted file mode 100644 index 60f0f97..0000000 --- a/mousenet/retinotopics/images/VisP.jpg:Zone.Identifier +++ /dev/null @@ -1,3 +0,0 @@ -[ZoneTransfer] -LastWriterPackageFamilyName=Microsoft.Windows.Photos_8wekyb3d8bbwe -ZoneId=3 diff --git a/mousenet/retinotopics/images/VisPL.jpg:Zone.Identifier b/mousenet/retinotopics/images/VisPL.jpg:Zone.Identifier deleted file mode 100644 index 60f0f97..0000000 --- a/mousenet/retinotopics/images/VisPL.jpg:Zone.Identifier +++ /dev/null @@ -1,3 +0,0 @@ -[ZoneTransfer] -LastWriterPackageFamilyName=Microsoft.Windows.Photos_8wekyb3d8bbwe -ZoneId=3 diff --git a/mousenet/retinotopics/images/VisPM.jpg:Zone.Identifier b/mousenet/retinotopics/images/VisPM.jpg:Zone.Identifier deleted file mode 100644 index 60f0f97..0000000 --- a/mousenet/retinotopics/images/VisPM.jpg:Zone.Identifier +++ /dev/null @@ -1,3 +0,0 @@ -[ZoneTransfer] -LastWriterPackageFamilyName=Microsoft.Windows.Photos_8wekyb3d8bbwe -ZoneId=3 diff --git a/mousenet/retinotopics/images/VisPOR.jpg:Zone.Identifier b/mousenet/retinotopics/images/VisPOR.jpg:Zone.Identifier deleted file mode 100644 index 60f0f97..0000000 --- a/mousenet/retinotopics/images/VisPOR.jpg:Zone.Identifier +++ /dev/null @@ -1,3 +0,0 @@ -[ZoneTransfer] -LastWriterPackageFamilyName=Microsoft.Windows.Photos_8wekyb3d8bbwe -ZoneId=3 diff --git a/mousenet/retinotopics/images/VisRL.jpg:Zone.Identifier b/mousenet/retinotopics/images/VisRL.jpg:Zone.Identifier deleted file mode 100644 index 60f0f97..0000000 --- a/mousenet/retinotopics/images/VisRL.jpg:Zone.Identifier +++ /dev/null @@ -1,3 +0,0 @@ -[ZoneTransfer] -LastWriterPackageFamilyName=Microsoft.Windows.Photos_8wekyb3d8bbwe -ZoneId=3 diff --git a/mousenet/retinotopics/images/main.jpg:Zone.Identifier b/mousenet/retinotopics/images/main.jpg:Zone.Identifier deleted file mode 100644 index 26a3ce7..0000000 --- a/mousenet/retinotopics/images/main.jpg:Zone.Identifier +++ /dev/null @@ -1,4 +0,0 @@ -[ZoneTransfer] -ZoneId=3 -ReferrerUrl=https://iiif.elifesciences.org/lax/18372%2Felife-18372-fig8-v2.tif/full/1500,/0/default.jpg -HostUrl=https://iiif.elifesciences.org/lax/18372%2Felife-18372-fig8-v2.tif/full/1500,/0/default.jpg From 57ad8a1515f6e99e6069779fec7bcd4d6716004f Mon Sep 17 00:00:00 2001 From: Parsa Date: Thu, 26 May 2022 15:02:23 -0400 Subject: [PATCH 6/9] Testing on GPU still failing --- mousenet/cmouse/mousenet_complete_pool.py | 20 ++++++++++++++++---- mousenet/loader.py | 8 +++----- mousenet/retinotopics/mask_areas/visal.pkl | Bin 392 -> 390 bytes mousenet/retinotopics/mask_areas/visl.pkl | Bin 392 -> 390 bytes mousenet/retinotopics/mask_areas/visli.pkl | Bin 392 -> 390 bytes mousenet/retinotopics/mask_areas/visp.pkl | Bin 392 -> 390 bytes mousenet/retinotopics/mask_areas/vispl.pkl | Bin 392 -> 390 bytes mousenet/retinotopics/mask_areas/vispor.pkl | Bin 392 -> 390 bytes mousenet/retinotopics/mask_areas/visrl.pkl | Bin 392 -> 390 bytes tests/test_compare_visual_subfields.py | 6 ++++++ tests/test_retinotopics.py | 19 +++++++++++++++++-- 11 files changed, 42 insertions(+), 11 deletions(-) create mode 100644 tests/test_compare_visual_subfields.py diff --git a/mousenet/cmouse/mousenet_complete_pool.py b/mousenet/cmouse/mousenet_complete_pool.py index 9f2fbd9..00e5009 100644 --- a/mousenet/cmouse/mousenet_complete_pool.py +++ b/mousenet/cmouse/mousenet_complete_pool.py @@ -6,17 +6,19 @@ import pathlib, os import pickle from .exps.imagenet.config import INPUT_SIZE, EDGE_Z, OUTPUT_AREAS, HIDDEN_LINEAR, NUM_CLASSES +import pdb def get_retinotopic_mask(layer, retinomap): + region_name = ''.join(x for x in layer.lower() if x.isalpha()) mask = torch.zeros(32, 32) if layer == "input": return - if layer == "VisP": - return mask + if region_name == "visp": + return 1 for area in retinomap: area_name = area[0].lower() - if area_name == ''.join(x for x in layer.lower() if x.isalpha()): + if area_name == region_name: normalized_polygon = area[1] x, y = normalized_polygon.exterior.coords.xy x, y = list(x), list(y) @@ -31,6 +33,8 @@ def get_retinotopic_mask(layer, retinomap): project_root = pathlib.Path(__file__).parent.parent.resolve() file = os.path.join(project_root, "retinotopics", "mask_areas", f"{area_name}.pkl") pickle.dump(mask_sum, open(file,"wb")) + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + mask.to(device) return mask # raise ValueError(f"Could not find area for layer {layer} in retinomap") @@ -182,8 +186,16 @@ def get_img_feature(self, x, area_list, flatten=True): for layer in self.network.layers: if layer.target_name == area: - mask = self.layer_masks[layer.source_name] + mask = None + if layer.source_name in self.layer_masks: + mask = self.layer_masks[layer.source_name] + if mask is None: + mask = 1 layer_name = layer.source_name + layer.target_name + if isinstance(mask, int): + print(area, mask) + else: + print(area, mask.shape) if area not in calc_graph: calc_graph[area] = self.Convs[layer_name]( mask*calc_graph[layer.source_name] diff --git a/mousenet/loader.py b/mousenet/loader.py index 8bbfbd3..db78d43 100644 --- a/mousenet/loader.py +++ b/mousenet/loader.py @@ -12,9 +12,9 @@ def generate_net(retinotopic=False): root = pathlib.Path(__file__).parent.resolve() - cached = os.path.join(root, "data_files", "net_cache.pkl") - # if os.path.isfile(cached): - # return network.load_network_from_pickle(cached) + cached = os.path.join(root, "data_files", f"net_cache_{'retino' if retinotopic else ''}.pkl") + if os.path.isfile(cached): + return network.load_network_from_pickle(cached) architecture = Architecture() anet = gen_anatomy(architecture) net = network.Network(retinotopic=retinotopic) @@ -43,8 +43,6 @@ def load(architecture, pretraining=None): with open(os.path.join(path, "retinotopics", "retinomap.pkl"), "rb") as file: retinomap = pickle.load(file) - if architecture=="retinotopic": - pdb.set_trace() model = MouseNetCompletePool(net, retinomap = retinomap) diff --git a/mousenet/retinotopics/mask_areas/visal.pkl b/mousenet/retinotopics/mask_areas/visal.pkl index fc9266e21c53815b19c4e465a3a42868d82a6c32..0532c60bd2856551b407244ad5149b87a2e89dee 100644 GIT binary patch delta 61 zcmeBRZewO?V3}GyktKxj@5IQBg1ih23`PdV7DguK=B7r5W(JdG7^Sd?dN4Ww0MZf- Aj{pDw delta 63 zcmZo;?qFtVV3}GwktKxj|HR0RLVOGi43;J)md0l0hK5Flh88B1r5UBL3A-^m008lV B4h{eS diff --git a/mousenet/retinotopics/mask_areas/visl.pkl b/mousenet/retinotopics/mask_areas/visl.pkl index 7b5722be70aa96b496c2b399a2677af7aa18b27e..4e6880b87d9c74b18a8528f7c9ab50ea2be25fde 100644 GIT binary patch delta 61 zcmeBRZewO?V3}GyktKxj@5IQBg1ih23`PdV7DguK=B8$5=0=lc7^Sd?dN4Ww0Moz? ArvLx| delta 63 zcmZo;?qFtVV3}GwktKxj|HR0RLVOGi43;J)mc}L~rbZSPMkWT6r5UBL3A-^m008oi B4jKRe diff --git a/mousenet/retinotopics/mask_areas/visli.pkl b/mousenet/retinotopics/mask_areas/visli.pkl index 1ccb4eb8d707d0cb4f2d9ca6e7643e481d6afd90..21fdf41080b625824eeff1cb203a471835276886 100644 GIT binary patch delta 61 ycmeBRZewO?V3}GyktKxj@5IQBg1ih23`PdV7DguK=BDOAYO)NY6c$krMh5`X#0{qa delta 63 zcmZo;?qFtVV3}GwktKxj|HR0RLVOGi43;J)mc}L~rbZSPW+n!cr5UBL3A-^m008qM B4k7>m diff --git a/mousenet/retinotopics/mask_areas/visp.pkl b/mousenet/retinotopics/mask_areas/visp.pkl index 77a1bbd5d5ea311158760e3abce77824aa6033a1..b6cd952e978f6f11dc34b07ac8708c87d0244d6d 100644 GIT binary patch delta 61 zcmeBRZewO?V3}GyktKxj@5IQBg1ih23`WKVhUP}bMn>j_W)_oW7^Sd?dN4Ww0MQZ+ Ai2wiq delta 63 zcmZo;?qFtVV3}GwktKxj|HR0RLVOGi43;J)md0jgCI%)J#s(&nr5UBL3A-^m008nt B4i*3a diff --git a/mousenet/retinotopics/mask_areas/vispl.pkl b/mousenet/retinotopics/mask_areas/vispl.pkl index 12ec1ae62d852596d34e51522434b93ba7d839aa..5cf1a47bf1a6bb62e30a651c308e0c5ea1e2672f 100644 GIT binary patch delta 61 zcmeBRZewO?V3}GyktKxj@5IQBg1ih23`PdV7DguK=B7p##zvE67^Sd?dN4Ww0Mh6U An*aa+ delta 63 zcmZo;?qFtVV3}GwktKxj|HR0RLVOGi43;J)md0l0hK5E478VARr5UBL3A-^m008mY B4iW$W diff --git a/mousenet/retinotopics/mask_areas/vispor.pkl b/mousenet/retinotopics/mask_areas/vispor.pkl index c9726267025c67e80367c00f51c076eea3840c87..9fb4d041a9ac73a8f787e5f898958ac4a5163561 100644 GIT binary patch delta 61 zcmeBRZewO?V3}GyktKxj@5IQBg1ih23`PdV7DguK=BB15MkbSG7^Sd?dN4Ww0Mh6U An*aa+ delta 63 zcmZo;?qFtVV3}GwktKxj|HR0RLVOGi43;J)md0l0hK5E)29{=%r5UBL3A-^m008mn B4io?Y diff --git a/mousenet/retinotopics/mask_areas/visrl.pkl b/mousenet/retinotopics/mask_areas/visrl.pkl index 4d14610c38b46cef6e5b52a246bba685c63a1327..271a5a841f82b27562f8e463b40eef10cb7928e0 100644 GIT binary patch delta 59 ycmeBRZewO?V3}GyktKxj@5IQB0=x_i3`PdV7DguK=B8%G7L%nJr7;EF865$*3=KU1 delta 61 zcmZo;?qFtVV3}GwktKxj|HR0Rf_w}N43;J)mc}L~rbZSPrY4i67^Sg@x-vQf0M^+J Az5oCK diff --git a/tests/test_compare_visual_subfields.py b/tests/test_compare_visual_subfields.py new file mode 100644 index 0000000..c5ff860 --- /dev/null +++ b/tests/test_compare_visual_subfields.py @@ -0,0 +1,6 @@ +import mousenet +import torch + +def test_retinotopics_fits_in_memory(): + retinotopic_model = mousenet.load(architecture="retinotopic", pretraining=None) + \ No newline at end of file diff --git a/tests/test_retinotopics.py b/tests/test_retinotopics.py index d367de0..1d803ba 100644 --- a/tests/test_retinotopics.py +++ b/tests/test_retinotopics.py @@ -1,6 +1,21 @@ import mousenet import torch +import pdb -def test_fits_in_memory(): +def test_retinotopics_fits_in_memory(): + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model = mousenet.load(architecture="retinotopic", pretraining=None) - input = + model.to(device) + input = torch.rand(1, 3, 64, 64).to(device) + results = model(input) + print(results.shape) + pdb.set_trace() + +def test_stock_fits_in_memory(): + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + model = mousenet.load(architecture="default", pretraining=None) + model.to(device) + input = torch.rand(1, 3, 64, 64).to(device) + results = model(input) + print(results.shape) + pdb.set_trace() \ No newline at end of file From a8f75257d37e2b427bb6c0f8189fc1a84ac0147f Mon Sep 17 00:00:00 2001 From: Parsa Date: Thu, 23 Jun 2022 13:22:55 -0400 Subject: [PATCH 7/9] Intermittent commit --- mousenet/cmouse/exps/__init__.py | 0 mousenet/cmouse/mousenet_complete_pool.py | 55 +++++++++++------------ 2 files changed, 26 insertions(+), 29 deletions(-) create mode 100644 mousenet/cmouse/exps/__init__.py diff --git a/mousenet/cmouse/exps/__init__.py b/mousenet/cmouse/exps/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mousenet/cmouse/mousenet_complete_pool.py b/mousenet/cmouse/mousenet_complete_pool.py index 00e5009..18e1b1d 100644 --- a/mousenet/cmouse/mousenet_complete_pool.py +++ b/mousenet/cmouse/mousenet_complete_pool.py @@ -107,19 +107,19 @@ def __init__(self, network, mask=3, retinomap=None): self.Convs = nn.ModuleDict() self.BNs = nn.ModuleDict() self.network = network - self.layer_masks = dict() + # self.layer_masks = dict() self.retinomap = retinomap G, _ = network.make_graph() self.top_sort = list(nx.topological_sort(G)) - if self.retinomap is not None: - for layer in self.top_sort: - self.layer_masks[layer] = get_retinotopic_mask(layer, self.retinomap) - else: - for layer in self.top_sort: - self.layer_masks[layer] = torch.ones(32, 32) + # if self.retinomap is not None: + # for layer in self.top_sort: + # self.layer_masks[layer] = get_retinotopic_mask(layer, self.retinomap) + # else: + # for layer in self.top_sort: + # self.layer_masks[layer] = torch.ones(32, 32) for layer in network.layers: params = layer.params @@ -149,8 +149,8 @@ def __init__(self, network, mask=3, retinomap=None): # layer = network.find_conv_source_target('%s2/3'%area[:-1],'%s'%area) # total_size += int(layer.out_size*layer.out_size*layer.params.out_channels) - self.classifier = nn.Sequential( - nn.Linear(int(total_size), NUM_CLASSES), + # self.classifier = nn.Sequential( + # nn.Linear(int(total_size), NUM_CLASSES), # nn.Linear(int(total_size), HIDDEN_LINEAR), # nn.ReLU(True), # nn.Dropout(), @@ -158,9 +158,9 @@ def __init__(self, network, mask=3, retinomap=None): # nn.ReLU(True), # nn.Dropout(), # nn.Linear(HIDDEN_LINEAR, NUM_CLASSES), - ) + # ) - def get_img_feature(self, x, area_list, flatten=True): + def get_img_feature(self, x, area_list, flatten=False): """ function for get activations from a list of layers for input x :param x: input image set Tensor with size (num_img, INPUT_SIZE[0], INPUT_SIZE[1], INPUT_SIZE[2]) @@ -186,25 +186,25 @@ def get_img_feature(self, x, area_list, flatten=True): for layer in self.network.layers: if layer.target_name == area: - mask = None - if layer.source_name in self.layer_masks: - mask = self.layer_masks[layer.source_name] - if mask is None: - mask = 1 + # mask = None + # if layer.source_name in self.layer_masks: + # mask = self.layer_masks[layer.source_name] + # if mask is None: + # mask = 1 layer_name = layer.source_name + layer.target_name - if isinstance(mask, int): - print(area, mask) - else: - print(area, mask.shape) + # if isinstance(mask, int): + # print(area, mask) + # else: + # print(area, mask.shape) if area not in calc_graph: calc_graph[area] = self.Convs[layer_name]( - mask*calc_graph[layer.source_name] + calc_graph[layer.source_name] ) else: calc_graph[area] = calc_graph[area] + self.Convs[layer_name](calc_graph[layer.source_name]) calc_graph[area] = nn.ReLU(inplace=True)( self.BNs[area]( - mask*calc_graph[area] + calc_graph[area] ) ) @@ -217,16 +217,13 @@ def get_img_feature(self, x, area_list, flatten=True): else: re = None for area in area_list: - print(area, (calc_graph[area]).shape) if re is None: - re = torch.flatten(torch.nn.AdaptiveAvgPool2d(4) (calc_graph[area]), 1) + re = torch.nn.AdaptiveAvgPool2d(4) (calc_graph[area]) # re = torch.flatten( # nn.ReLU(inplace=True)(self.BNs['%s_downsample'%area](self.Convs['%s_downsample'%area](calc_graph[area]))), # 1) else: - re=torch.cat([torch.flatten( - torch.nn.AdaptiveAvgPool2d(4) (calc_graph[area]), - 1), re], axis=1) + re=torch.cat([torch.nn.AdaptiveAvgPool2d(4) (calc_graph[area]), re], axis=1) # re=torch.cat([ # torch.flatten( # nn.ReLU(inplace=True)(self.BNs['%s_downsample'%area](self.Convs['%s_downsample'%area](calc_graph[area]))), @@ -242,6 +239,6 @@ def get_img_feature(self, x, area_list, flatten=True): return re def forward(self, x): - x = self.get_img_feature(x, OUTPUT_AREAS) - x = self.classifier(x) + x = self.get_img_feature(x, OUTPUT_AREAS, flatten=False) + # x = self.classifier(x) return x From e4d19317ea2a3b4b78ab27c5049c7fd78d750ed4 Mon Sep 17 00:00:00 2001 From: Parsa Torabian Date: Thu, 23 Jun 2022 14:16:54 -0400 Subject: [PATCH 8/9] Update README.rst --- README.rst | 173 ++++++++--------------------------------------------- 1 file changed, 24 insertions(+), 149 deletions(-) diff --git a/README.rst b/README.rst index 1f91668..7496182 100644 --- a/README.rst +++ b/README.rst @@ -15,38 +15,27 @@ Folder Structure │ ├── mousenet/ │ │ - │ ├── cli.py - command line interface - │ ├── main.py - main script to start train/test │ │ - │ ├── base/ - abstract base classes - │ │ ├── base_data_loader.py - abstract base class for data loaders - │ │ ├── base_model.py - abstract base class for models - │ │ └── base_trainer.py - abstract base class for trainers + │ ├── cmouse/ - Code related to constructing the PyTorch model + │ │ └── __init__.py │ │ - │ ├── data_loader/ - anything about data loading goes here - │ │ ├── augmentation.py - │ │ └── data_loaders.py + │ ├── example/ - Example code and resources │ │ - │ ├── model/ - models, losses, and metrics - │ │ ├── loss.py - │ │ ├── metric.py - │ │ └── model.py + │ ├── mouse_cnn/ - Code related to deriving architecture from data + │ │ └── __init__.py │ │ - │ ├── trainer/ - trainers - │ │ └── trainer.py + │ ├── retinotopics/ - Code related to calculating visual subfields + │ │ └── __init__.py │ │ - │ └── utils/ - │ ├── logger.py - class for train logging - │ ├── visualization.py - class for Tensorboard visualization support - │ └── saving.py - manages pathing for saving models + logs + │ └── / + │ ├── loader.py - load function for loading a mousenet model with a particular initialization + │ └── __init__.py - manages pathing for saving models + logs │ - ├── logging.yml - logging configuration + ├── environment.yml - Conda environment and dependancies │ - ├── data/ - directory for storing input data + ├── setup.cfg - Development configurations and linting │ - ├── experiments/ - directory for storing configuration files - │ - ├── saved/ - directory for checkpoints and logs + ├── setup.py - package definitions │ └── tests/ - tests folder @@ -54,140 +43,26 @@ Folder Structure Usage ===== -.. code-block:: - - $ conda env create --file environment.yml - $ conda activate mousenet - -The code in this repo is an MNIST example of the template. You can run the tests, -and the example project using: +Installation: +Change directory to cloned folder .. code-block:: + $ pip install -e . - $ pytest tests - $ mousenet train -c experiments/config.yml - -Config file format ------------------- -Config files are in `.yml` format: - -.. code-block:: HTML - - name: Mnist_LeNet - n_gpu: 1 - save_dir: saved/ - seed: 1234 - - arch: - type: MnistModel - args: {} - - data_loader: - type: MnistDataLoader - args: - batch_size: 128 - data_dir: data/ - num_workers: 2 - shuffle: true - validation_split: 0.1 - loss: nll_loss - lr_scheduler: - type: StepLR - args: - gamma: 0.1 - step_size: 50 - - metrics: - - my_metric - - my_metric2 - - optimizer: - type: Adam - args: - lr: 0.001 - weight_decay: 0 - - training: - early_stop: 10 - epochs: 100 - monitor: min val_loss - save_period: 1 - tensorboard: true - - testing: - data_dir: data/ - batch_size: 128 - num_workers: 8 - - -Add addional configurations if you need. - -Using config files ------------------- -Modify the configurations in `.yml` config files, then run: +To load a mousenet model .. code-block:: - $ mousenet train -c experiments/config.yml + $ import mousenet + $ model = mousenet.load(architecture="stock", pretraining=None) + +Architecture can be one of "stock" or "retinotopic" for visual subfields. Pretraining can be on of: None, "kaiming" for kaiming initialization, or "Imagenet" for imagenet pretraining. -Resuming from checkpoints -------------------------- -You can resume from a previously saved checkpoint by: -.. code-block:: +To test the code - mousenet train -c experiments/config.yml -r path/to/checkpoint - -Checkpoints ------------ -You can specify the name of the training session in config files: - -.. code-block:: HTML - - "name": "MNIST_LeNet" - -The checkpoints will be saved in `save_dir/name/timestamp/checkpoint_epoch_n`, with timestamp in -mmdd_HHMMSS format. - -A copy of config file will be saved in the same folder. - -**Note**: checkpoints contain: - -.. code-block:: python - - checkpoint = { - 'arch': arch, - 'epoch': epoch, - 'state_dict': self.model.state_dict(), - 'optimizer': self.optimizer.state_dict(), - 'monitor_best': self.mnt_best, - 'config': self.config - } - -Tensorboard Visualization --------------------------- -This template supports ``_ visualization. - -1. Run training - - Set `tensorboard` option in config file true. - -2. Open tensorboard server - - Type `tensorboard --logdir saved/runs/` at the project root, then server will open at - `http://localhost:6006` - -By default, values of loss and metrics specified in config file, input images, and histogram of -model parameters will be logged. If you need more visualizations, use `add_scalar('tag', data)`, -`add_image('tag', image)`, etc in the `trainer._train_epoch` method. `add_something()` methods in -this template are basically wrappers for those of `tensorboard.SummaryWriter` module. - -**Note**: You don't have to specify current steps, since `TensorboardWriter` class defined at -`logger/visualization.py` will track current steps. +.. code-block:: -Acknowledgments -=============== -This project was created using -`Cookiecutter PyTorch `_ + $ pytest From a377de583cfef6e1d9b3f5117441310d61ba33aa Mon Sep 17 00:00:00 2001 From: Parsa Date: Mon, 29 Aug 2022 12:42:44 -0400 Subject: [PATCH 9/9] Indentation bug fix --- mousenet/cmouse/mousenet_complete_pool.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/mousenet/cmouse/mousenet_complete_pool.py b/mousenet/cmouse/mousenet_complete_pool.py index 18e1b1d..dbf7641 100644 --- a/mousenet/cmouse/mousenet_complete_pool.py +++ b/mousenet/cmouse/mousenet_complete_pool.py @@ -202,11 +202,15 @@ def get_img_feature(self, x, area_list, flatten=False): ) else: calc_graph[area] = calc_graph[area] + self.Convs[layer_name](calc_graph[layer.source_name]) - calc_graph[area] = nn.ReLU(inplace=True)( - self.BNs[area]( - calc_graph[area] - ) - ) + + calc_graph2 = calc_graph.copy() + calc_graph[area] = nn.ReLU(inplace=True)( + self.BNs[area]( + calc_graph[area] + ) + ) + if calc_graph[area].sum() == 0: + pdb.set_trace() if len(area_list) == 1: if flatten: