python_code stringlengths 0 4.04M | repo_name stringlengths 7 58 | file_path stringlengths 5 147 |
|---|---|---|
"""Script to plot example augmentations generated by the ImageAugmenter."""
from __future__ import print_function
# make sure that ImageAugmenter can be imported from parent directory
if __name__ == '__main__' and __package__ is None:
from os import sys, path
sys.path.append(path.dirname(path.dirname(path.absp... | imgaug-master | old_version/tests/CheckPlotImages.py |
"""Rough measurements of the performance of the ImageAugmenter."""
from __future__ import print_function
# make sure that ImageAugmenter can be imported from parent directory
if __name__ == '__main__' and __package__ is None:
from os import sys, path
sys.path.append(path.dirname(path.dirname(path.abspath(__fil... | imgaug-master | old_version/tests/CheckPerformance.py |
from __future__ import absolute_import
from .imgaug import *
from . import augmenters
from . import parameters
__version__ = '0.1'
| imgaug-master | imgaug/__init__.py |
from __future__ import print_function, division, absolute_import
from abc import ABCMeta, abstractmethod
import random
import numpy as np
import copy
import numbers
import cv2
import math
from scipy import misc
import six
import six.moves as sm
"""
try:
xrange
except NameError: # python3
xrange = range
"""
A... | imgaug-master | imgaug/imgaug.py |
from __future__ import print_function, division, absolute_import
from . import imgaug as ia
from .parameters import StochasticParameter, Deterministic, Binomial, Choice, DiscreteUniform, Normal, Uniform
from abc import ABCMeta, abstractmethod
import random
import numpy as np
import copy as copy_module
import re
import ... | imgaug-master | imgaug/augmenters.py |
from __future__ import print_function, division, absolute_import
from . import imgaug as ia
from abc import ABCMeta, abstractmethod
import numpy as np
import copy as copy_module
import six
@six.add_metaclass(ABCMeta)
class StochasticParameter(object):
def __init__(self):
super(StochasticParameter, self).__... | imgaug-master | imgaug/parameters.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# Code by Samarth Brahmbhatt
import os
from scripts.download_data import ContactPoseDownloader
osp = os.path
def startup(data_dir=None, default_dir=osp.join('data', 'contactpose_data')):
# check that the provided data_dir is OK
if data_dir is not None:
asser... | ContactPose-main | startup.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# Code by Samarth Brahmbhatt | ContactPose-main | __init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# Code by Samarth Brahmbhatt
import numpy as np
import logging
import math
import transforms3d.euler as txe
import transforms3d.quaternions as txq
import argparse
import cv2
import matplotlib.pyplot as plt
try:
from thirdparty.mano.webuser.smpl_handpca_wrapper_HAND_... | ContactPose-main | utilities/misc.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# Code by Samarth Brahmbhatt
from utilities.import_open3d import *
from open3d import pipelines
import utilities.misc as mutils
assert(mutils.load_mano_model is not None)
import numpy as np
import chumpy as ch
import os
import json
import transforms3d.quaternions as t... | ContactPose-main | utilities/mano_fitting.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# Code by Samarth Brahmbhatt
import sys
sys.path.append('.') | ContactPose-main | utilities/init_paths.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# Code by Samarth Brahmbhatt
import os
os.environ["PYOPENGL_PLATFORM"] = "osmesa"
import trimesh
import pyrender
import numpy as np
import transforms3d.euler as txe
import utilities.misc as mutils
import cv2
osp = os.path
class DepthRenderer(object):
"""
Renders... | ContactPose-main | utilities/rendering.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# Code by Samarth Brahmbhatt
from open3d import io as o3dio
from open3d import visualization as o3dv
from open3d import utility as o3du
from open3d import geometry as o3dg | ContactPose-main | utilities/import_open3d.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# Code by Samarth Brahmbhatt | ContactPose-main | utilities/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# Code by Samarth Brahmbhatt
"""
ContactPose dataset loading utilities
"""
import os
import json
import numpy as np
import pickle
from . import misc as mutils
osp = os.path
def get_object_names(p_num, intent, ignore_hp=True):
"""
returns list of objects grasped... | ContactPose-main | utilities/dataset.py |
import datetime
try:
import dropbox
DROPBOX_FOUND = True
except ImportError:
DROPBOX_FOUND = False
import json
import math
import os
import random
import requests
from requests.exceptions import ConnectionError
import time
from tqdm.autonotebook import tqdm
osp = os.path
if DROPBOX_FOUND:
dropbox_app_key = os.... | ContactPose-main | utilities/networking.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# Code by Samarth Brahmbhatt
import sys
sys.path.append('.') | ContactPose-main | scripts/init_paths.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# Code by Samarth Brahmbhatt
import matplotlib.pyplot as plt
import numpy as np
import init_paths
from utilities.import_open3d import *
from utilities.dataset import ContactPose
import utilities.misc as mutils
def apply_colormap_to_mesh(mesh, sigmoid_a=0.05, invert=... | ContactPose-main | scripts/show_contactmap.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# Code by Samarth Brahmbhatt
"""
Preprocesses images for ML training by cropping (RGB and depth), and
randomizing background (RGB only)
NOTE: Requites rendering setup, see docs/rendering.py
"""
import init_paths
from utilities.dataset import ContactPose, get_object_na... | ContactPose-main | scripts/preprocess_images.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# Code by Samarth Brahmbhatt | ContactPose-main | scripts/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# Code by Samarth Brahmbhatt
"""
script to download ContactPose data from Dropbox
URLs in data/urls.json
"""
import init_paths
import cv2
import os
import json
import shutil
from tqdm.autonotebook import tqdm
import utilities.networking as nutils
from zipfile import Zi... | ContactPose-main | scripts/download_data.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# Code by Samarth Brahmbhatt
"""
Discovers 'active areas' i.e. areas on the object surface most frequently
touched by a certain part of the hand. See Figure 7 in the paper
https://arxiv.org/pdf/2007.09545.pdf.
"""
import init_paths
from utilities.import_open3d impo... | ContactPose-main | scripts/data_analysis/active_areas.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# Code by Samarth Brahmbhatt
import sys
sys.path.append('.') | ContactPose-main | scripts/data_analysis/init_paths.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# Code by Samarth Brahmbhatt
"""
Calculates and shows the contact probability for hand points
Figure 5(a) in the paper
"""
import os
import matplotlib.pyplot as plt
import numpy as np
import init_paths
from utilities.import_open3d import *
from utilities.dataset impor... | ContactPose-main | scripts/data_analysis/hand_contact_prob.py |
ContactPose-main | scripts/data_analysis/__init__.py | |
# Copyright (c) Facebook, Inc. and its affiliates.
# Code by Samarth Brahmbhatt
import init_paths
import dropbox
import json
from requests.exceptions import ConnectionError
import os
from utilities.dataset import get_object_names
osp = os.path
dbx = dropbox.Dropbox(os.environ['DROPBOX_APP_KEY'])
def move(p_num, inten... | ContactPose-main | scripts/maintenance/move_videos_dropbox.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# Code by Samarth Brahmbhatt
import sys
sys.path.append('.') | ContactPose-main | scripts/maintenance/init_paths.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# Code by Samarth Brahmbhatt
import requests
import json
from copy import deepcopy
import os
osp = os.path
data_template = {
'path': '/contactpose/videos_full/{:s}/{:s}/color',
'settings': {
'requested_visibility': 'public',
'audience': 'public',
'acc... | ContactPose-main | scripts/maintenance/get_urls.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# Code by Samarth Brahmbhatt
import os
import shutil
import sys
osp = os.path
def remove(p_num):
for ins in ('use', 'handoff'):
p_id = 'full{:s}_{:s}'.format(p_num, ins)
sess_dir = osp.join('..', '..', 'data', 'contactpose_data', p_id)
for object_name ... | ContactPose-main | scripts/maintenance/remove_videos.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# Code by Samarth Brahmbhatt
| ContactPose-main | scripts/maintenance/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# Code by Samarth Brahmbhatt
import init_paths
from scripts.download_data import ContactPoseDownloader
import ffmpeg
import os
import shutil
import json
import itertools
from multiprocessing import Pool
import argparse
from functools import partial
import utilities.ne... | ContactPose-main | scripts/maintenance/produce_videos.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# Code by Samarth Brahmbhatt | ContactPose-main | thirdparty/__init__.py |
#!/usr/bin/env python3
"""
Copyright (c) Meta Platforms, Inc. and affiliates.
Calculate cumulative distribution functions for standard Brownian motions.
Running as a script tests assertions that closed-form, analytical expressions
for the means match numerical evaluations of the means for the cumulative
distribution... | cdeets-main | codes/dists.py |
#!/usr/bin/env python3
"""
Copyright (c) Meta Platforms, Inc. and affiliates.
Plot the subpopulation deviations for the American Community Survey of USCB.
This script creates a directory, "weighted," in the working directory if the
directory does not already exist, then creates subdirectories there for each
of the c... | cdeets-main | codes/acs.py |
#!/usr/bin/env python3
"""
Copyright (c) Meta Platforms, Inc. and affiliates.
Plots of deviation of a subpop. from the full pop., with weighted sampling
*
This implementation considers responses r that can take arbitrary values,
not necesssarily restricted to taking values 0 or 1.
*
Functions
---------
cumulative
... | cdeets-main | codes/subpop_weighted.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#Common imports
import sys
import os
import argparse
import random
import copy
import torch
import torch.utils.data as data_utils
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from to... | CausalRepID-main | test.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#Common imports
import sys
import os
import argparse
import random
import copy
import torch
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
from... | CausalRepID-main | train.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import sys
import math
import torch
import torch.utils.data as data_utils
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
from torch.a... | CausalRepID-main | algorithms/poly_auto_encoder.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import sys
import math
import torch
import torch.utils.data as data_utils
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
from torch.a... | CausalRepID-main | algorithms/base_auto_encoder.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import sys
import math
import torch
import torch.utils.data as data_utils
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
from torch.a... | CausalRepID-main | algorithms/image_auto_encoder.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import sys
import math
import torch
import torch.utils.data as data_utils
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
from torch.a... | CausalRepID-main | algorithms/ioss_auto_encoder.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import sys
import copy
import torch
import torchvision
import numpy as np
from sklearn.metrics import r2_score
from sklearn.linear_model import LinearRegression, Lasso, Ridge, LassoCV, RidgeCV
from sklearn.linear_model import LogisticRegression
fro... | CausalRepID-main | utils/metrics.py |
CausalRepID-main | utils/__init__.py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import sys
import numpy as np
import torch
import torch.utils.data as data_utils
path= os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.append(path)
from data.data_loader import BaseDataLoader
from data.fine_tune... | CausalRepID-main | utils/helper.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
from torch import nn
class LinearAutoEncoder(torch.nn.Module):
def __init__(self, data_dim, latent_dim, batch_norm= False):
super(LinearAutoEncoder, self).__init__()
self.data_dim = data_dim
... | CausalRepID-main | models/linear_auto_encoder.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
import torch.utils.data
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
from torch.autograd import Variable
from torchvision.models.... | CausalRepID-main | models/decoder.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
import torch.utils.data
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
from torch.autograd import Variable
from torchvision.models.... | CausalRepID-main | models/image_resnet_decoder.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
import torch.utils.data
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
from torch.autograd import Variable
from torchvision.models.... | CausalRepID-main | models/poly_decoder.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
from torch import nn
class Encoder(torch.nn.Module):
def __init__(self, data_dim, latent_dim):
super(Encoder, self).__init__()
self.data_dim = data_dim
self.latent_dim = latent_dim
... | CausalRepID-main | models/encoder.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
import torch.utils.data
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
from torch.autograd import Variable
from torchvision.models.... | CausalRepID-main | models/image_decoder.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
from torch import nn
from torchvision import models as vision_models
from torchvision.models import resnet18, resnet50
from torchvision import transforms
class ImageEncoder(torch.nn.Module):
def __init__(self, latent_dim):
... | CausalRepID-main | models/image_encoder.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
import torch.utils.data
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
from torch.autograd import Variable
from torchvision.models.... | CausalRepID-main | models/image_slot_attention_decoder.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import sys
import numpy as np
import pandas as pd
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--case', type=str, default='log',
help= 'test; log; debug')
parser.add_argument('--target_latent... | CausalRepID-main | scripts/main_exps_images.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import numpy as np
import scipy
import copy
from sklearn.linear_model import LinearRegression, Lasso, Ridge, LassoCV, RidgeCV
'''
z: True Latent (dataset_size, latent_dim) (.npy file)
Pred_z: Inferred Latent (dataset_size, latent_dim) (.npy file)
... | CausalRepID-main | scripts/test_metrics.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import sys
import numpy as np
import pandas as pd
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--case', type=str, default='log',
help= 'train; test; log; debug')
parser.add_argument('--interv... | CausalRepID-main | scripts/main_exps.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import random
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
import os
import sys
path= os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.append(path)
from data.dag_generator import DagGenerator
ran... | CausalRepID-main | scripts/dag_gen_debug.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import math
import argparse
import numpy as np
import time
## Imports for plotting
import matplotlib.pyplot as plt
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('svg', 'pdf') # For export
from matplotlib.colors... | CausalRepID-main | scripts/ioss.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import copy
import numpy as np
import torch
import torch.utils.data as data_utils
from torchvision import datasets, transforms
from sklearn.preprocessing import StandardScaler
# Base Class
from data.data_loader import BaseDataLoader
cl... | CausalRepID-main | data/balls_dataset_loader.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#Common imports
import sys
import os
import argparse
import random
import copy
import math
import networkx as nx
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
from sklearn.linear_model... | CausalRepID-main | data/synthetic_polynomial_dgp.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""Defining a set of classes that represent causal functions/ mechanisms.
Author: Diviyan Kalainathan
Modified by Philippe Brouillard, July 24th 2019
Modified by Divyat Mahajan, December 30th 2022
.. MIT License
..
.. Copyright (c) 2018 Diviyan K... | CausalRepID-main | data/causal_mechanisms.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import sys
import copy
import numpy as np
import torch
import torch.utils.data as data_utils
from torchvision import datasets, transforms
class BaseDataLoader(data_utils.Dataset):
def __init__(self, data_dir='', data_case='train', se... | CausalRepID-main | data/data_loader.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import sys
import random
import argparse
import torch
import numpy as np
import pygame
from pygame import gfxdraw, init
from typing import Callable, Optional
from matplotlib import pyplot as plt
if "SDL_VIDEODRIVER" not in os.environ:
... | CausalRepID-main | data/balls_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
""" DAG Generator.
Generates a dataset out of an acyclic FCM.
Author : Olivier Goudet and Diviyan Kalainathan
Modified by Philippe Brouillard, June 25th 2019
Modified by Divyat Mahajan, December 30th 2022
.. MIT License
..
.. Copyright (c) 2018 D... | CausalRepID-main | data/dag_generator.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import copy
import numpy as np
import torch
import torch.utils.data as data_utils
from torchvision import datasets, transforms
from sklearn.preprocessing import StandardScaler
#Base Class
from data.data_loader import BaseDataLoader
clas... | CausalRepID-main | data/fine_tune_loader.py |
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import argparse
import glob
import os
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
plt.rc('fo... | DeepConvexity-master | plot.py |
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from torch.optim.lr_scheduler import ReduceLROnPlateau
import argparse
import torch
import time
import math
import os
def res... | DeepConvexity-master | main.py |
from setuptools import setup
if __name__ == "__main__":
setup(name='epoxy',
version='0.0.0a0',
description='Interactive Model Iteration with Weak Supervision and Pre-Trained Embeddings',
url='https://github.com/HazyResearch/epoxy',
author='Dan Fu',
author_email='da... | epoxy-master | setup.py |
from flyingsquid.label_model import LabelModel
from sklearn.metrics import precision_recall_fscore_support, accuracy_score
import numpy as np
def train_fs_model_spam(L_train):
label_model = LabelModel(L_train.shape[1])
label_model.fit(
L_train,
# These parameters tuned for spam
... | epoxy-master | examples/helpers.py |
from .epoxy import preprocess_lfs, extend_lfs, Epoxy
__all__ = [
'preprocess_lfs',
'extend_lfs',
'Epoxy'
] | epoxy-master | epoxy/__init__.py |
import numpy as np
import faiss
from sklearn.metrics import pairwise
import cytoolz as tz
import torch
class Epoxy:
'''
Class wrapping the functionality to extend LF's.
Uses FAISS for nearest-neighbor search under the hood.
'''
def __init__(
self,
L_train,
train_em... | epoxy-master | epoxy/epoxy.py |
from pathlib import Path
import torch
from einops import rearrange
try:
from cauchy_mult import cauchy_mult_sym_fwd, cauchy_mult_sym_bwd
except ImportError:
from torch.utils.cpp_extension import load
current_dir = Path(__file__).parent.absolute()
cauchy_mult_extension = load(
name='cauchy_mult... | H3-main | csrc/cauchy/cauchy.py |
# Adapted from https://github.com/NVIDIA/apex/blob/master/setup.py
import torch
from torch.utils.cpp_extension import BuildExtension, CppExtension, CUDAExtension, CUDA_HOME
from setuptools import setup, find_packages
import subprocess
import sys
import warnings
import os
# ninja build does not work unless include_dir... | H3-main | csrc/cauchy/setup.py |
import math
from functools import partial
import torch
from einops import rearrange
from cauchy import cauchy_mult_torch, cauchy_mult_keops, cauchy_mult
from benchmarks.utils import benchmark_all, benchmark_combined, benchmark_forward, benchmark_backward, pytorch_profiler
def generate_data(batch_size, N, L, symmet... | H3-main | csrc/cauchy/benchmark_cauchy.py |
import importlib
import json
import argparse
import torch
from benchmark.utils import benchmark_forward
def generate_data(batch_size, N, L, symmetric=True, device='cuda'):
if not symmetric:
v = torch.randn(batch_size, N, dtype=torch.complex64, device=device, requires_grad=True)
w = torch.randn(b... | H3-main | csrc/cauchy/benchmark_cauchy_tune.py |
import os
import shutil
import subprocess
import sys
# import tempfile
# import importlib
import random
import string
import json
from functools import partial
from multiprocessing import Pipe, Pool, Process
from pathlib import Path
from tqdm import tqdm
import numpy as np
def read_file(filename):
""" return ... | H3-main | csrc/cauchy/tuner.py |
import os
from setuptools import setup
from pathlib import Path
import torch.cuda
from torch.utils.cpp_extension import CppExtension, CUDAExtension, BuildExtension
from torch.utils.cpp_extension import CUDA_HOME
extensions_dir = Path(os.getenv('TUNING_SOURCE_DIR')).absolute()
assert extensions_dir.exists()
source_fi... | H3-main | csrc/cauchy/tuning_setup.py |
import math
import json
import argparse
import itertools
from pathlib import Path
from tuner import KernelTuner
def forward_params_list(N):
blocksize_params = ('MAX_BLOCK_SIZE_VALUE', [64, 128, 256, 512, 1024])
thread_value_default = [2, 4, 8, 16, 32, 32, 32, 32, 32, 32]
thread_values_supported = [2, 4, ... | H3-main | csrc/cauchy/tune_cauchy.py |
import math
import torch
import pytest
from einops import rearrange
from cauchy import cauchy_mult_torch, cauchy_mult_keops, cauchy_mult
def generate_data(batch_size, N, L, symmetric=True, device='cuda'):
if not symmetric:
v = torch.randn(batch_size, N, dtype=torch.complex64, device=device, requires_gr... | H3-main | csrc/cauchy/test_cauchy.py |
import torch
import torch.nn.functional as F
from einops import rearrange
from fftconv import fftconv_fwd, fftconv_bwd
def fftconv_ref(u, k, D, dropout_mask):
seqlen = u.shape[-1]
fft_size = 2 * seqlen
k_f = torch.fft.rfft(k, n=fft_size) / fft_size
u_f = torch.fft.rfft(u.to(dtype=k.dtype), n=fft_siz... | H3-main | csrc/fftconv/launch_fftconv.py |
# Adapted from https://github.com/NVIDIA/apex/blob/master/setup.py
import torch
from torch.utils.cpp_extension import BuildExtension, CppExtension, CUDAExtension, CUDA_HOME
from setuptools import setup, find_packages
import subprocess
import sys
import warnings
import os
# ninja build does not work unless include_dir... | H3-main | csrc/fftconv/setup.py |
import math
import re
import numpy as np
# N = 8192
N = 16384
# The case of 0 / N is special, we want to simplify it to 0 / 2 instead of 0 / 1
numerator = np.arange(1, N // 8 + 1)
gcd = np.gcd(numerator, N)
num = numerator // gcd
denom = N // gcd
lut_vals = ['T_2_0'] + [f'T_{d}_{n}' for n, d in zip(num, denom)]
lut_... | H3-main | csrc/fftconv/lut_code_gen.py |
import math
import torch
import torch.nn.functional as F
import pytest
from einops import rearrange
from src.ops.fftconv import fftconv_ref, fftconv_h3_ref, fftconv_func
@pytest.mark.parametrize('output_hbl_layout', [False, True])
# @pytest.mark.parametrize('output_hbl_layout', [False])
@pytest.mark.parametrize('i... | H3-main | tests/ops/test_fftconv.py |
import argparse
import torch
from transformers import GPT2Tokenizer
from src.models.ssm_seq import SSMLMHeadModel
parser = argparse.ArgumentParser(description='H3 text generation')
parser.add_argument('--dmodel', type=int, default=2048)
parser.add_argument('--nlayer', type=int, default=24)
parser.add_argument('--a... | H3-main | examples/generate_text_h3.py |
from typing import Optional
import argparse
import time
import torch
import torch.nn.functional as F
from einops import rearrange
from transformers import GPT2Tokenizer, GPT2Config, GPT2LMHeadModel
from src.models.ssm.h3 import H3
from src.models.ssm_seq import SSMLMHeadModel
from flash_attn.utils.generation impor... | H3-main | benchmarks/benchmark_generation_h3.py |
import logging
from pytorch_lightning.utilities import rank_zero_only
# Copied from https://docs.python.org/3/howto/logging-cookbook.html#using-a-context-manager-for-selective-logging
class LoggingContext:
def __init__(self, logger, level=None, handler=None, close=True):
self.logger = logger
self... | H3-main | src/utils/utils.py |
# Copyright (c) 2023, Tri Dao, Dan Fu.
import math
import re
from functools import partial
from collections import namedtuple, OrderedDict
from collections.abc import Sequence
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.models.gpt2.configuration_gpt2 import GPT2Config
from ... | H3-main | src/models/ssm_seq.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from src.models.ssm.ss_kernel import SSKernel
try:
from src.ops.fftconv import fftconv_func
except ImportError:
fftconv_func = None
@torch.jit.script
def mul_sum(q, y):
return (q * y).sum(dim=1)
class H3(n... | H3-main | src/models/ssm/h3.py |
# TD: [2023-01-05]: Extracted the OptimModule class from
# https://github.com/HazyResearch/state-spaces/blob/06dbbdfd0876501a7f12bf3262121badbc7658af/src/models/sequence/ss/kernel.py
import torch.nn as nn
class OptimModule(nn.Module):
""" Interface for Module that allows registering buffers/parameters with confi... | H3-main | src/models/ssm/ssm_utils.py |
# TD: [2023-01-05]: Extracted the SSKernel class from
# https://github.com/HazyResearch/state-spaces/blob/06dbbdfd0876501a7f12bf3262121badbc7658af/src/models/sequence/ss/kernel.py
# We add option to use the shift kernel, and remove the option of SSKernelNPLR
"""SSM convolution kernels.
SSKernel wraps different kernels... | H3-main | src/models/ssm/ss_kernel.py |
# Copied from https://github.com/HazyResearch/state-spaces/blob/06dbbdfd0876501a7f12bf3262121badbc7658af/src/models/hippo/hippo.py
""" Definitions of A and B matrices for various HiPPO operators. """
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from scipy import special as ss
... | H3-main | src/models/ssm/hippo.py |
# TD: [2023-01-05]: Extracted the SSKernelDiag class from
# https://github.com/HazyResearch/state-spaces/blob/06dbbdfd0876501a7f12bf3262121badbc7658af/src/models/sequence/ss/kernel.py
# We make a small change to use the log_vandermonde CUDA code.
"""SSKernelDiag is the S4D kernel, a simpler algorithm for computing the... | H3-main | src/models/ssm/ss_kernel_shift.py |
# TD: [2023-01-05]: Extracted the SSKernelDiag class from
# https://github.com/HazyResearch/state-spaces/blob/06dbbdfd0876501a7f12bf3262121badbc7658af/src/models/sequence/ss/kernel.py
# We make a small change to use the log_vandermonde CUDA code.
"""SSKernelDiag is the S4D kernel, a simpler algorithm for computing the... | H3-main | src/models/ssm/ss_kernel_diag.py |
# Copied from https://github.com/HazyResearch/state-spaces/blob/06dbbdfd0876501a7f12bf3262121badbc7658af/src/models/sequence/ss/dplr.py
"""Initializations of structured state space models"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
from src.mo... | H3-main | src/models/ssm/dplr.py |
import math
import torch
import torch.nn.functional as F
from einops import rearrange
from fftconv import fftconv_fwd, fftconv_bwd
@torch.jit.script
def _mul_sum(y, q):
return (y * q).sum(dim=1)
# reference convolution with residual connection
def fftconv_ref(u, k, D, dropout_mask, gelu=True, k_rev=None):
... | H3-main | src/ops/fftconv.py |
# TD [2023-01-05]: Copied from https://github.com/HazyResearch/state-spaces/blob/06dbbdfd0876501a7f12bf3262121badbc7658af/src/models/functional/vandermonde.py
# We add the interface to the log vandermonde CUDA code
"""pykeops implementations of the Vandermonde matrix multiplication kernel used in the S4D kernel."""
im... | H3-main | src/ops/vandermonde.py |
# Downloaded from https://github.com/HazyResearch/state-spaces/blob/06dbbdfd0876501a7f12bf3262121badbc7658af/src/models/functional/krylov.py
""" Compute a Krylov function efficiently. (S4 renames the Krylov function to a "state space kernel")
A : (N, N)
b : (N,)
c : (N,)
Return: [c^T A^i b for i in [L]]
"""
import tor... | H3-main | src/ops/krylov.py |
# Downloaded from https://github.com/HazyResearch/state-spaces/blob/06dbbdfd0876501a7f12bf3262121badbc7658af/src/models/functional/toeplitz.py
""" Utilities for computing convolutions.
There are 3 equivalent views:
1. causal convolution
2. multiplication of (lower) triangular Toeplitz matrices
3. polynomial... | H3-main | src/ops/toeplitz.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from distutils.core import setup
ext_modules = []
cmdclass = {}
with open("requirements.txt", "r") as handle:
install_requires = handle.read().splitlines()
setup(
name="cpa",
version="1.0.0",
description="",
url="http://githu... | CPA-main | setup.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from collections import defaultdict
import matplotlib.font_manager
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from adjustText import adjust_text
from sklearn.decomposition import KernelPCA
from skl... | CPA-main | cpa/plotting.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from cpa.api import API
from cpa.plotting import CPAVisuals
| CPA-main | cpa/__init__.py |
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