repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNVLT/cryodrgn/cryodrgn/dataset.py | import numpy as np
import torch
from torch.utils import data
import os
import multiprocessing as mp
from multiprocessing import Pool
from . import fft
from . import mrc
from . import utils
from . import starfile
log = utils.log
def load_particles(mrcs_txt_star, lazy=False, datadir=None):
'''
Load particle st... | 10,068 | 37.876448 | 157 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNVLT/cryodrgn/cryodrgn/pose.py | import torch
import torch.nn as nn
import numpy as np
import pickle
from . import lie_tools
from . import utils
log = utils.log
class PoseTracker(nn.Module):
def __init__(self, rots_np, trans_np=None, D=None, emb_type=None, device=None):
super(PoseTracker, self).__init__()
rots = torch.tensor(rots... | 4,665 | 39.224138 | 123 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNVLT/cryodrgn/cryodrgn/ctf.py | import numpy as np
import torch
from . import utils
log = utils.log
def compute_ctf(freqs, dfu, dfv, dfang, volt, cs, w, phase_shift=0, bfactor=None):
'''
Compute the 2D CTF
Input:
freqs (np.ndarray) Nx2 or BxNx2 tensor of 2D spatial frequencies
dfu (float or Bx1 tensor): DefocusU (Ang... | 3,928 | 34.718182 | 116 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNVLT/cryodrgn/cryodrgn/lie_tools.py | '''
Tools for dealing with SO(3) group and algebra
Adapted from https://github.com/pimdh/lie-vae
All functions are pytorch-ified
'''
import torch
from torch.distributions import Normal
import numpy as np
def map_to_lie_algebra(v):
"""Map a point in R^N to the tangent space at the identity, i.e.
to the Lie Alg... | 7,476 | 33.939252 | 115 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNVLT/cryodrgn/cryodrgn/models.py | '''Pytorch models'''
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import fft
from . import lie_tools
from . import utils
from . import lattice
log = utils.log
class HetOnlyVAE(nn.Module):
# No pose inference
def __init__(self, lattice, # Lattice object
... | 34,008 | 40.934649 | 123 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNVLT/cryodrgn/cryodrgn/z_train.py | import torch
import torch.nn as nn
import pickle
class ZTracker(nn.Module):
def __init__(self, zmu, zvar):
super(ZTracker, self).__init__()
self.zmu = zmu
self.zvar = zvar
# zvals shape: N x Zdim for each
zmu_embed = nn.Embedding(zmu.shape[0], zmu.shape[1], sparse=T... | 988 | 29.90625 | 76 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNVLT/cryodrgn/cryodrgn/commands/graph_traversal.py | '''
Find shortest path along nearest neighbor graph
'''
import torch
import argparse
import pickle
import numpy as np
import os
from heapq import heappush, heappop
def add_args(parser):
parser.add_argument('data', help='Input z.pkl embeddings')
parser.add_argument('--anchors', type=int, nargs='+', required=Tr... | 5,822 | 34.078313 | 136 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNVLT/cryodrgn/cryodrgn/commands/parse_pose_csparc.py | '''Parse image poses from a cryoSPARC .cs metafile'''
import argparse
import numpy as np
import sys, os
import pickle
import torch
from cryodrgn import lie_tools
from cryodrgn import utils
log = utils.log
def add_args(parser):
parser.add_argument('input', help='Cryosparc .cs file')
parser.add_argument('--ab... | 2,097 | 30.313433 | 154 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNVLT/cryodrgn/cryodrgn/commands/train_vae.py | '''
Train a VAE for heterogeneous reconstruction with known pose
'''
import numpy as np
import sys, os
import argparse
import pickle
from datetime import datetime as dt
from copy import deepcopy
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
try:
import a... | 29,929 | 47.905229 | 236 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNVLT/cryodrgn/cryodrgn/commands/backproject_voxel.py | '''
Backproject cryo-EM images
'''
import argparse
import numpy as np
import sys, os
import time
import pickle
import torch
from cryodrgn import utils
from cryodrgn import mrc
from cryodrgn import fft
from cryodrgn import dataset
from cryodrgn import ctf
from cryodrgn.pose import PoseTracker
from cryodrgn.lattice i... | 5,925 | 37.232258 | 146 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNVLT/cryodrgn/cryodrgn/commands/eval_images.py | '''
Evaluate cryoDRGN z and loss for a stack of images
'''
import numpy as np
import sys, os
import argparse
import pickle
from datetime import datetime as dt
import pprint
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from cryodrgn import mrc
from cryodrgn... | 9,661 | 46.831683 | 206 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNVLT/cryodrgn/cryodrgn/commands/eval_vol.py | '''
Evaluate the decoder at specified values of z
'''
import numpy as np
import sys, os
import argparse
import pickle
from datetime import datetime as dt
import matplotlib.pyplot as plt
import pprint
import torch
from cryodrgn import mrc
from cryodrgn import utils
from cryodrgn import fft
from cryodrgn import lie_to... | 6,706 | 43.125 | 152 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNVLT/cryodrgn/cryodrgn/commands/train_nn.py | '''
Train a NN to model a 3D density map given 2D images with pose assignments
'''
import numpy as np
import sys, os
import argparse
import pickle
from datetime import datetime as dt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
try:
import apex.amp as a... | 15,346 | 46.221538 | 207 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/setup.py | #!/usr/bin/env python
from setuptools import setup, find_packages
import os,sys
sys.path.insert(0, f'{os.path.dirname(__file__)}/cryodrgn')
import cryodrgn
version = cryodrgn.__version__
setup(name='cryodrgn',
version=version,
description='cryoDRGN heterogeneous reconstruction',
author='Ellen Zhong... | 899 | 23.324324 | 59 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/testing/test_entropy.py | import numpy as np
import sys, os
import argparse
import pickle
from datetime import datetime as dt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.distributions import Normal
#sys.path.insert(0,os.path.abspath(os.path.dirname(__file__))+'/lib-python')... | 1,128 | 23.021277 | 79 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/testing/test_translate.py | '''
'''
import numpy as np
import sys, os
import argparse
import pickle
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
sys.path.insert(0,'../lib-python')
import fft
import models
import mrc
from lattice import Lattice
imgs,_ = mrc.parse_mrc('data/hand.mrcs')
img = imgs[0]
D = img.shape[0]
ht = f... | 791 | 17.857143 | 53 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/build/lib/cryodrgn/losses.py | """Equivariance loss for Encoder"""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class EquivarianceLoss(nn.Module):
"""Equivariance loss for SO(2) subgroup."""
def __init__(self, model, D):
super().__init__()
self.model = model
self.D = D
... | 1,081 | 30.823529 | 85 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/build/lib/cryodrgn/lattice.py | '''Lattice object'''
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import utils
log = utils.log
class Lattice:
def __init__(self, D, extent=0.5, ignore_DC=True):
assert D % 2 == 1, "Lattice size must be odd"
x0, x1 = np.meshgrid(np.linspace(-extent,... | 6,614 | 37.459302 | 109 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/build/lib/cryodrgn/dataset.py | import numpy as np
import torch
from torch.utils import data
import os
from . import fft
from . import mrc
from . import utils
from . import starfile
log = utils.log
def load_particles(mrcs_txt_star, lazy=False, datadir=None, relion31=False):
'''
Load particle stack from either a .mrcs file, a .star file, a ... | 7,944 | 36.300469 | 141 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/build/lib/cryodrgn/pose.py | import torch
import torch.nn as nn
import numpy as np
import pickle
from . import lie_tools
from . import utils
log = utils.log
class PoseTracker(nn.Module):
def __init__(self, rots_np, trans_np=None, D=None, emb_type=None):
super(PoseTracker, self).__init__()
rots = torch.tensor(rots_np).float()
... | 4,572 | 38.422414 | 123 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/build/lib/cryodrgn/ctf.py | import numpy as np
import torch
from . import utils
log = utils.log
def compute_ctf(freqs, dfu, dfv, dfang, volt, cs, w, phase_shift=0, bfactor=None):
'''
Compute the 2D CTF
Input:
freqs (np.ndarray) Nx2 or BxNx2 tensor of 2D spatial frequencies
dfu (float or Bx1 tensor): DefocusU (Ang... | 3,928 | 34.718182 | 116 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/build/lib/cryodrgn/lie_tools.py | '''
Tools for dealing with SO(3) group and algebra
Adapted from https://github.com/pimdh/lie-vae
All functions are pytorch-ified
'''
import torch
from torch.distributions import Normal
import numpy as np
def map_to_lie_algebra(v):
"""Map a point in R^N to the tangent space at the identity, i.e.
to the Lie Alg... | 7,476 | 33.939252 | 115 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/build/lib/cryodrgn/models.py | '''Pytorch models'''
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import fft
from . import lie_tools
from . import utils
from . import lattice
log = utils.log
class HetOnlyVAE(nn.Module):
# No pose inference
def __init__(self, lattice, # Lattice object
... | 31,767 | 40.097025 | 123 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/build/lib/cryodrgn/commands/graph_traversal.py | '''
Find shortest path along nearest neighbor graph
'''
import torch
import argparse
import pickle
import numpy as np
import os
from heapq import heappush, heappop
def add_args(parser):
parser.add_argument('data', help='Input z.pkl embeddings')
parser.add_argument('--anchors', type=int, nargs='+', required=Tr... | 5,822 | 34.078313 | 136 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/build/lib/cryodrgn/commands/parse_pose_csparc.py | '''Parse image poses from a cryoSPARC .cs metafile'''
import argparse
import numpy as np
import sys, os
import pickle
import torch
from cryodrgn import lie_tools
from cryodrgn import utils
log = utils.log
def add_args(parser):
parser.add_argument('input', help='Cryosparc .cs file')
parser.add_argument('--ab... | 2,097 | 30.313433 | 154 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/build/lib/cryodrgn/commands/train_vae.py | '''
Train a VAE for heterogeneous reconstruction with known pose
'''
import numpy as np
import sys, os
import argparse
import pickle
from datetime import datetime as dt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Subset
try:
import apex.amp as amp
exc... | 24,822 | 48.646 | 206 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/build/lib/cryodrgn/commands/backproject_voxel.py | '''
Backproject cryo-EM images
'''
import argparse
import numpy as np
import sys, os
import time
import pickle
import torch
from cryodrgn import utils
from cryodrgn import mrc
from cryodrgn import fft
from cryodrgn import dataset
from cryodrgn import ctf
from cryodrgn.pose import PoseTracker
from cryodrgn.lattice i... | 5,410 | 34.834437 | 146 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/build/lib/cryodrgn/commands/eval_images.py | '''
Evaluate cryoDRGN z and loss for a stack of images
'''
import numpy as np
import sys, os
import argparse
import pickle
from datetime import datetime as dt
import pprint
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from cryodrgn import mrc
from cryodrgn... | 9,394 | 45.509901 | 182 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/build/lib/cryodrgn/commands/eval_vol.py | '''
Evaluate the decoder at specified values of z
'''
import numpy as np
import sys, os
import argparse
import pickle
from datetime import datetime as dt
import matplotlib.pyplot as plt
import pprint
import torch
from cryodrgn import mrc
from cryodrgn import utils
from cryodrgn import fft
from cryodrgn import lie_to... | 6,408 | 42.598639 | 152 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/build/lib/cryodrgn/commands/train_nn.py | '''
Train a NN to model a 3D density map given 2D images with pose assignments
'''
import numpy as np
import sys, os
import argparse
import pickle
from datetime import datetime as dt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
try:
import apex.amp as a... | 14,117 | 45.594059 | 196 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/cryodrgn/losses.py | """Equivariance loss for Encoder"""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class EquivarianceLoss(nn.Module):
"""Equivariance loss for SO(2) subgroup."""
def __init__(self, model, D):
super().__init__()
self.model = model
self.D = D
... | 1,081 | 30.823529 | 85 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/cryodrgn/lattice.py | '''Lattice object'''
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import utils
log = utils.log
class Lattice:
def __init__(self, D, extent=0.5, ignore_DC=True):
assert D % 2 == 1, "Lattice size must be odd"
x0, x1 = np.meshgrid(np.linspace(-extent,... | 6,614 | 37.459302 | 109 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/cryodrgn/dataset.py | import numpy as np
import torch
from torch.utils import data
import os
from . import fft
from . import mrc
from . import utils
from . import starfile
log = utils.log
def load_particles(mrcs_txt_star, lazy=False, datadir=None, relion31=False):
'''
Load particle stack from either a .mrcs file, a .star file, a ... | 7,944 | 36.300469 | 141 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/cryodrgn/pose.py | import torch
import torch.nn as nn
import numpy as np
import pickle
from . import lie_tools
from . import utils
log = utils.log
class PoseTracker(nn.Module):
def __init__(self, rots_np, trans_np=None, D=None, emb_type=None):
super(PoseTracker, self).__init__()
rots = torch.tensor(rots_np).float()
... | 4,572 | 38.422414 | 123 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/cryodrgn/ctf.py | import numpy as np
import torch
from . import utils
log = utils.log
def compute_ctf(freqs, dfu, dfv, dfang, volt, cs, w, phase_shift=0, bfactor=None):
'''
Compute the 2D CTF
Input:
freqs (np.ndarray) Nx2 or BxNx2 tensor of 2D spatial frequencies
dfu (float or Bx1 tensor): DefocusU (Ang... | 3,928 | 34.718182 | 116 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/cryodrgn/lie_tools.py | '''
Tools for dealing with SO(3) group and algebra
Adapted from https://github.com/pimdh/lie-vae
All functions are pytorch-ified
'''
import torch
from torch.distributions import Normal
import numpy as np
def map_to_lie_algebra(v):
"""Map a point in R^N to the tangent space at the identity, i.e.
to the Lie Alg... | 7,476 | 33.939252 | 115 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/cryodrgn/models.py | '''Pytorch models'''
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import fft
from . import lie_tools
from . import utils
from . import lattice
log = utils.log
class HetOnlyVAE(nn.Module):
# No pose inference
def __init__(self, lattice, # Lattice object
... | 31,767 | 40.097025 | 123 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/cryodrgn/commands/graph_traversal.py | '''
Find shortest path along nearest neighbor graph
'''
import torch
import argparse
import pickle
import numpy as np
import os
from heapq import heappush, heappop
def add_args(parser):
parser.add_argument('data', help='Input z.pkl embeddings')
parser.add_argument('--anchors', type=int, nargs='+', required=Tr... | 5,822 | 34.078313 | 136 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/cryodrgn/commands/parse_pose_csparc.py | '''Parse image poses from a cryoSPARC .cs metafile'''
import argparse
import numpy as np
import sys, os
import pickle
import torch
from cryodrgn import lie_tools
from cryodrgn import utils
log = utils.log
def add_args(parser):
parser.add_argument('input', help='Cryosparc .cs file')
parser.add_argument('--ab... | 2,097 | 30.313433 | 154 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/cryodrgn/commands/train_vae.py | '''
Train a VAE for heterogeneous reconstruction with known pose
'''
import numpy as np
import sys, os
import argparse
import pickle
from datetime import datetime as dt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Subset
try:
import apex.amp as amp
exc... | 24,394 | 48.282828 | 202 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/cryodrgn/commands/backproject_voxel.py | '''
Backproject cryo-EM images
'''
import argparse
import numpy as np
import sys, os
import time
import pickle
import torch
from cryodrgn import utils
from cryodrgn import mrc
from cryodrgn import fft
from cryodrgn import dataset
from cryodrgn import ctf
from cryodrgn.pose import PoseTracker
from cryodrgn.lattice i... | 5,410 | 34.834437 | 146 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/cryodrgn/commands/eval_images.py | '''
Evaluate cryoDRGN z and loss for a stack of images
'''
import numpy as np
import sys, os
import argparse
import pickle
from datetime import datetime as dt
import pprint
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from cryodrgn import mrc
from cryodrgn... | 9,394 | 45.509901 | 182 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/cryodrgn/commands/eval_vol.py | '''
Evaluate the decoder at specified values of z
'''
import numpy as np
import sys, os
import argparse
import pickle
from datetime import datetime as dt
import matplotlib.pyplot as plt
import pprint
import torch
from cryodrgn import mrc
from cryodrgn import utils
from cryodrgn import fft
from cryodrgn import lie_to... | 6,408 | 42.598639 | 152 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNEvilTwin/cryodrgn/cryodrgn/commands/train_nn.py | '''
Train a NN to model a 3D density map given 2D images with pose assignments
'''
import numpy as np
import sys, os
import argparse
import pickle
from datetime import datetime as dt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
try:
import apex.amp as a... | 14,117 | 45.594059 | 196 | py |
modularity | modularity-main/probability.py | import torch
from math import log
def log_normalize(logp):
return logp - torch.logsumexp(logp.flatten(), dim=-1)
def log2prob(logp):
return torch.exp(log_normalize(logp))
def temperature(logp, temp):
return log_normalize(logp/temp)
def discrete_entropy(logp):
logp = log_normalize(logp)
plogp... | 1,443 | 33.380952 | 117 | py |
modularity | modularity-main/modularity.py | import torch
from collections import deque
from probability import entropy_to_temperature, discrete_entropy, log2prob
from tqdm import trange, tqdm
ADJACENCY_EPS = 1e-15
def is_valid_adjacency_matrix(adj:torch.Tensor, enforce_sym=False, enforce_no_self=False, enforce_binary=False) -> bool:
valid = torch.all(adj... | 13,930 | 46.708904 | 124 | py |
modularity | modularity-main/generate_dummy_checkpoints.py | import torch
from models import LitWrapper
from pathlib import Path
import argparse
def create_dummy_checkpoint(dataset, task, uid, save_dir=Path(), extra_model_args={}):
mdl = LitWrapper(dataset=dataset, task=task, l2=0., l1=0., drop=0., run=uid)
the_path = save_dir / mdl.get_uid()
the_path.mkdir(exist_... | 1,800 | 32.981132 | 92 | py |
modularity | modularity-main/associations.py | import torch
from torch.utils.data import DataLoader
from math import ceil, prod
from tqdm import tqdm
from typing import List, Optional
METHODS = ['forward_cov', 'forward_jac', 'backward_jac', 'backward_hess']
METHODS += [m+"_norm" for m in METHODS]
def corrcov(covariance, eps=1e-12):
sigma = covariance.diag()... | 15,292 | 46.493789 | 132 | py |
modularity | modularity-main/analysis.py | import pandas as pd
import torch
from pathlib import Path
from models import LitWrapper
from itertools import product
from util import merge_dicts
from pandas import DataFrame
from typing import Union, Iterable
def last_model(model_dir: Path) -> Path:
return model_dir / 'weights' / 'last.ckpt'
def best_model(mo... | 6,429 | 43.344828 | 142 | py |
modularity | modularity-main/plot_metrics.py | import torch
import numpy as np
import pandas as pd
from analysis import generate_model_specs, load_data_as_table
from pathlib import Path
import matplotlib.pyplot as plt
LOGS_DIR = Path('logs')
DATA_DIR = Path('data')
FIG_SIZE = (6, 4)
def plot_by_hyper(df: pd.DataFrame, x_name, y_name, **kwargs):
fig = plt.fi... | 1,240 | 34.457143 | 107 | py |
modularity | modularity-main/create_best_ckpt.py | #!/usr/bin/env python
import torch
import argparse
import warnings
from pathlib import Path
from typing import Union
from eval import evaluate
def bestify(weights_dir: Union[str, Path],
field: str = "val_loss",
mode: str = "min",
overwrite: bool = False,
data_dir: Union... | 2,822 | 36.64 | 110 | py |
modularity | modularity-main/eval.py | import torch
import torch.nn.functional as F
from models import LitWrapper
from associations import get_similarity_by_layer, get_similarity_combined, corrcov
from associations import METHODS as association_methods
from modularity import monte_carlo_modularity, girvan_newman, soft_num_clusters, is_valid_adjacency_matrix... | 16,355 | 52.980198 | 156 | py |
modularity | modularity-main/train.py | import torch
import argparse
import pytorch_lightning as pl
from pytorch_lightning.loggers import TensorBoardLogger
from models import LitWrapper
from torch.utils.data import DataLoader
from pathlib import Path
from sys import exit
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# Trainer config... | 4,430 | 47.692308 | 133 | py |
modularity | modularity-main/models/lightning_wrapper.py | import torch
import torchvision
from torch.utils.data import random_split
from .mnist import MnistSupervised
from .cifar10 import Cifar10Fast
import pytorch_lightning as pl
import torch.nn.functional as F
class LitWrapper(pl.LightningModule):
def __init__(self, **kwargs):
super().__init__()
# Ass... | 6,477 | 41.900662 | 128 | py |
modularity | modularity-main/models/cifar10.py | import torch
import torch.nn as nn
import torch.nn.functional as F
# Some global CIFAR metadata
INPUT_SIZE = (3, 32, 32)
INPUT_DIM = 3*32*32
CLASSES = 10
def validate_layer_size(layer, in_size):
return layer(torch.randn((1,) + in_size)).size()[1:]
def prod(vals):
out = 1
for v in vals:
out *= ... | 4,477 | 33.713178 | 115 | py |
modularity | modularity-main/models/mnist.py | import torch.nn as nn
import torch.nn.functional as F
# Some global MNIST metadata
INPUT_SIZE = (1, 28, 28)
INPUT_DIM = 1*28*28
CLASSES = 10
class MnistSupervised(nn.Module):
DATASET = 'mnist'
TASK = 'supervised'
def __init__(self, pdrop=0.0, channels=(64, 64)):
super().__init__()
self... | 916 | 24.472222 | 79 | py |
LSA | LSA-main/setup.py | from setuptools import setup
with open("README.md", "r") as fh:
long_description = fh.read()
setup(
name='LayersSustainabilityAnalysis',
version='1.0.3',
url='https://github.com/khalooei/LSA',
license='MIT',
description='A Python library that analyzes the layer sustainability of neural networ... | 1,191 | 31.216216 | 93 | py |
LSA | LSA-main/LayerSustainabilityAnalysis/layersustainabilityanalysis.py |
import os
import random
import time
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
class LayerSustainabilityAnalysis:
def __init__(self,pretrained_model):
self.pretrained_model = pretrained_model
self.pretrained_model.eval()
... | 8,481 | 39.975845 | 136 | py |
LSA | LSA-main/models/dla.py | '''DLA in PyTorch.
Reference:
Deep Layer Aggregation. https://arxiv.org/abs/1707.06484
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
sel... | 4,425 | 31.544118 | 83 | py |
LSA | LSA-main/models/shufflenetv2.py | '''ShuffleNetV2 in PyTorch.
See the paper "ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class ShuffleBlock(nn.Module):
def __init__(self, groups=2):
super(ShuffleBlock, self).__init__()
... | 5,530 | 32.932515 | 107 | py |
LSA | LSA-main/models/regnet.py | '''RegNet in PyTorch.
Paper: "Designing Network Design Spaces".
Reference: https://github.com/keras-team/keras-applications/blob/master/keras_applications/efficientnet.py
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class SE(nn.Module):
'''Squeeze-and-Excitation block.'''
def __in... | 4,548 | 28.160256 | 106 | py |
LSA | LSA-main/models/efficientnet.py | '''EfficientNet in PyTorch.
Paper: "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks".
Reference: https://github.com/keras-team/keras-applications/blob/master/keras_applications/efficientnet.py
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
def swish(x):
return x ... | 5,719 | 31.5 | 106 | py |
LSA | LSA-main/models/pnasnet.py | '''PNASNet in PyTorch.
Paper: Progressive Neural Architecture Search
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class SepConv(nn.Module):
'''Separable Convolution.'''
def __init__(self, in_planes, out_planes, kernel_size, stride):
super(SepConv, self).__init__()
se... | 4,258 | 32.801587 | 105 | py |
LSA | LSA-main/models/resnet.py | '''ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansi... | 4,218 | 30.721805 | 83 | py |
LSA | LSA-main/models/dla_simple.py | '''Simplified version of DLA in PyTorch.
Note this implementation is not identical to the original paper version.
But it seems works fine.
See dla.py for the original paper version.
Reference:
Deep Layer Aggregation. https://arxiv.org/abs/1707.06484
'''
import torch
import torch.nn as nn
import torch.nn.function... | 4,084 | 30.666667 | 83 | py |
LSA | LSA-main/models/mobilenetv2.py | '''MobileNetV2 in PyTorch.
See the paper "Inverted Residuals and Linear Bottlenecks:
Mobile Networks for Classification, Detection and Segmentation" for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class Block(nn.Module):
'''expand + depthwise + pointwise'''
def __init... | 3,092 | 34.551724 | 114 | py |
LSA | LSA-main/models/vgg.py | '''VGG11/13/16/19 in Pytorch.'''
import torch
import torch.nn as nn
cfg = {
'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512... | 3,032 | 31.967391 | 117 | py |
LSA | LSA-main/models/densenet.py | '''DenseNet in PyTorch.'''
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Bottleneck(nn.Module):
def __init__(self, in_planes, growth_rate):
super(Bottleneck, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = nn.Conv2d(in_planes, 4*gr... | 3,542 | 31.805556 | 96 | py |
LSA | LSA-main/models/preact_resnet.py | '''Pre-activation ResNet in PyTorch.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Identity Mappings in Deep Residual Networks. arXiv:1603.05027
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class PreActBlock(nn.Module):
'''Pre-activation version of the BasicBlock.... | 4,078 | 33.277311 | 102 | py |
LSA | LSA-main/models/googlenet.py | '''GoogLeNet with PyTorch.'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class Inception(nn.Module):
def __init__(self, in_planes, n1x1, n3x3red, n3x3, n5x5red, n5x5, pool_planes):
super(Inception, self).__init__()
# 1x1 conv branch
self.b1 = nn.Sequential(
... | 3,221 | 28.833333 | 83 | py |
LSA | LSA-main/models/resnext.py | '''ResNeXt in PyTorch.
See the paper "Aggregated Residual Transformations for Deep Neural Networks" for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class Block(nn.Module):
'''Grouped convolution block.'''
expansion = 2
def __init__(self, in_planes, cardinality=32... | 3,478 | 35.239583 | 129 | py |
LSA | LSA-main/models/senet.py | '''SENet in PyTorch.
SENet is the winner of ImageNet-2017. The paper is not released yet.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(... | 4,027 | 32.016393 | 102 | py |
LSA | LSA-main/models/shufflenet.py | '''ShuffleNet in PyTorch.
See the paper "ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices" for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class ShuffleBlock(nn.Module):
def __init__(self, groups):
super(ShuffleBlock, self).__init... | 3,542 | 31.209091 | 126 | py |
LSA | LSA-main/models/wide_resnet.py | import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
from torch.autograd import Variable
import sys
import numpy as np
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=True)
def conv_init... | 3,079 | 33.222222 | 98 | py |
LSA | LSA-main/models/vggnet.py | import torch
import torch.nn as nn
from torch.autograd import Variable
def conv_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
init.xavier_uniform(m.weight, gain=np.sqrt(2))
init.constant(m.bias, 0)
def cfg(depth):
depth_lst = [11, 13, 16, 19]
assert (depth ... | 2,213 | 26.675 | 95 | py |
LSA | LSA-main/models/lenet.py | '''LeNet in PyTorch.'''
import torch.nn as nn
import torch.nn.functional as F
class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16*5*5, 120)
self.fc2 = nn.Linear... | 699 | 28.166667 | 43 | py |
LSA | LSA-main/models/mobilenet.py | '''MobileNet in PyTorch.
See the paper "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class Block(nn.Module):
'''Depthwise conv + Pointwise conv'''
def __init__(self, in_planes, out_... | 2,025 | 31.677419 | 123 | py |
LSA | LSA-main/models/dpn.py | '''Dual Path Networks in PyTorch.'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class Bottleneck(nn.Module):
def __init__(self, last_planes, in_planes, out_planes, dense_depth, stride, first_layer):
super(Bottleneck, self).__init__()
self.out_planes = out_planes
sel... | 3,562 | 34.989899 | 116 | py |
nn4mc_cpp | nn4mc_cpp-master/examples/example_usage/radhensNN.py | #!/usr/bin/env python3
from keras.models import load_model
from keras.preprocessing import image
import numpy as np
import keras.backend as K
import sys
sample= np.ones((50, 2))
sample= np.reshape(sample, (1, 50, 2))
model = load_model('../data/weights.best.hdf5')
model.compile(optimizer='rmsprop', loss= 'mse')
inp... | 575 | 22.04 | 75 | py |
nn4mc_cpp | nn4mc_cpp-master/data/simpleRNNexample.py | #!/usr/bin/env python
from __future__ import absolute_import, division, print_function, unicode_literals
import pandas as pd
import collections
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras import layers
N = 1000
Tp = 800
t = np.arange(0,N)
x = np.sin(0.02*t)+2*np.r... | 1,735 | 27 | 131 | py |
nn4mc_cpp | nn4mc_cpp-master/data/loadModel.py | #!/usr/bin/env python
import sys, os
import numpy as np
import tensorflow as tf
from tensorflow import keras
import keras.backend as K
print(tf.version.VERSION)
def custom_loss(y_true, y_pred):
r_hat = y_pred[:, 1]
r_true = y_true[:, 1]
th_hat= y_pred[:, 0]
th_true= y_true[:, 0]
coseno= K.cos(th_h... | 2,035 | 24.45 | 82 | py |
nn4mc_cpp | nn4mc_cpp-master/data/lenet.py | #!/usr/bin/env python3
#from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
batch_size = 128
num_classes = 10
epochs = 12
# ... | 2,174 | 31.462687 | 74 | py |
PyLaia | PyLaia-master/benchmarks/common.py | from pytorch_lightning import seed_everything
from laia.common.arguments import CommonArgs, CreateCRNNArgs
from laia.dummies import DummyMNISTLines
from laia.scripts.htr.create_model import run as model
from laia.utils import SymbolsTable
def setup(train_path, fixed_input_height=0):
seed = 31102020
seed_ever... | 1,161 | 27.341463 | 81 | py |
PyLaia | PyLaia-master/tests/nn/mask_image_from_size_test.py | import unittest
import torch
from laia.data import PaddedTensor
from laia.nn import MaskImageFromSize
class MaskImageFromSizeTest(unittest.TestCase):
def test_tensor(self):
x = torch.randn(3, 5, 7, 9, requires_grad=True)
layer = MaskImageFromSize(mask_value=-99)
y = layer(x)
torc... | 1,590 | 32.145833 | 78 | py |
PyLaia | PyLaia-master/tests/nn/image_to_sequence_test.py | import unittest
import torch
from laia.data import PaddedTensor
from laia.nn import ImageToSequence
class ImageToSequenceTest(unittest.TestCase):
def test_forward(self):
x = torch.tensor(
[[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]], dtype=torch.float
)
m = ImageToSequence... | 3,006 | 33.563218 | 87 | py |
PyLaia | PyLaia-master/tests/nn/pyramid_maxpool_2d_test.py | import pytest
import torch
from laia.data import PaddedTensor
from laia.nn import PyramidMaxPool2d
@pytest.mark.parametrize("use_nnutils", [True, False])
def test_tensor(use_nnutils):
x = torch.randn(3, 5, 7, 8, dtype=torch.double, requires_grad=True)
layer = PyramidMaxPool2d(levels=[1, 2], use_nnutils=use_n... | 1,217 | 30.230769 | 71 | py |
PyLaia | PyLaia-master/tests/nn/resnet_test.py | import numpy as np
import pytest
import torch
import laia.nn.resnet as resnet
from laia.data import PaddedTensor
def test_basicblock_forward():
net = resnet.BasicBlock(inplanes=8, planes=8)
y = net(torch.randn(4, 8, 15, 12))
assert y.size() == (4, 8, 15, 12)
net = resnet.BasicBlock(inplanes=3, plane... | 4,199 | 33.42623 | 81 | py |
PyLaia | PyLaia-master/tests/nn/temporal_pyramid_maxpool_2d_test.py | import pytest
import torch
from laia.data import PaddedTensor
from laia.nn import TemporalPyramidMaxPool2d
@pytest.mark.parametrize("use_nnutils", [True, False])
def test_tensor(use_nnutils):
x = torch.randn(3, 5, 7, 8, requires_grad=True)
layer = TemporalPyramidMaxPool2d(levels=[1, 2], use_nnutils=use_nnuti... | 2,083 | 31.5625 | 76 | py |
PyLaia | PyLaia-master/tests/nn/image_pooling_sequencer_test.py | import unittest
import torch
from torch.nn.functional import adaptive_avg_pool2d, adaptive_max_pool2d
from laia.data import PaddedTensor
from laia.nn import ImagePoolingSequencer
class ImagePoolingSequencerTest(unittest.TestCase):
def test_bad_sequencer(self):
self.assertRaises(ValueError, ImagePoolingS... | 3,826 | 30.891667 | 88 | py |
PyLaia | PyLaia-master/tests/nn/adaptive_pool_2d_test.py | import unittest
import torch
from laia.data import PaddedTensor
from laia.nn import AdaptiveAvgPool2d, AdaptiveMaxPool2d
class AdaptiveAvgPool2dTest(unittest.TestCase):
def setUp(self):
self.x = torch.tensor(
[
# n = 0
[
# c = 0
... | 7,673 | 29.212598 | 80 | py |
PyLaia | PyLaia-master/tests/callbacks/training_timer_test.py | import pytest
import pytorch_lightning as pl
from laia.callbacks import TrainingTimer
from laia.dummies import DummyEngine, DummyLoggingPlugin, DummyMNIST, DummyTrainer
# classes outside of test because they need to be pickle-able
class __TestCallback(pl.Callback):
def on_train_epoch_start(self, trainer, *args):... | 1,977 | 35.62963 | 82 | py |
PyLaia | PyLaia-master/tests/callbacks/progress_bar_test.py | import re
import pytorch_lightning as pl
from tqdm import tqdm
from laia.callbacks import ProgressBar
from laia.callbacks.meters import Timer
from laia.dummies import DummyEngine, DummyMNIST, DummyTrainer
class __TestCallback(pl.Callback):
def __init__(self, pbar):
super().__init__()
self.pbar =... | 3,889 | 34.045045 | 80 | py |
PyLaia | PyLaia-master/tests/callbacks/learning_rate_test.py | import pytest
import torch
from laia.callbacks import LearningRate
from laia.dummies import DummyEngine, DummyLoggingPlugin, DummyMNIST, DummyTrainer
def test_learning_rate_warns(tmpdir):
trainer = DummyTrainer(
default_root_dir=tmpdir,
max_epochs=1,
callbacks=[LearningRate()],
)
... | 1,590 | 31.469388 | 84 | py |
PyLaia | PyLaia-master/tests/common/saver_test.py | from pathlib import Path
import pytest
import torch
from laia.common.saver import BasicSaver, ObjectSaver
def test_basic_saver(tmpdir):
saver = BasicSaver()
saver.save(None, tmpdir / "test.pth")
# with extra non-existing dir
saver.save(None, tmpdir / "extra" / "test.pth")
# again to test exists_... | 1,341 | 22.964286 | 88 | py |
PyLaia | PyLaia-master/tests/common/loader_test.py | import os
import shutil
from collections import OrderedDict
from pathlib import Path
import pytest
import pytorch_lightning as pl
import torch
from laia.common.loader import ModelLoader, ObjectLoader
from laia.dummies import DummyEngine, DummyMNIST, DummyTrainer
class Foo:
def __init__(self, arg, *args, kwarg=N... | 7,285 | 28.027888 | 88 | py |
PyLaia | PyLaia-master/tests/common/arguments_test.py | from re import escape
import pytest
import pytorch_lightning as pl
import torch
from laia.common.arguments import CreateCRNNArgs, TrainerArgs
def test_trainer_args():
args = TrainerArgs()
assert not hasattr(args, "callbacks")
# instantiate to check if its valid
pl.Trainer(**vars(args))
def test_tr... | 1,712 | 28.033898 | 80 | py |
PyLaia | PyLaia-master/tests/models/htr/laia_crnn_test.py | import unittest
import pytest
import torch
from torch.nn.utils.rnn import PackedSequence, pad_packed_sequence
from laia.data import PaddedTensor
from laia.models.htr import LaiaCRNN
class LaiaCRNNTest(unittest.TestCase):
def test_get_conv_output_size(self):
ys = LaiaCRNN.get_conv_output_size(
... | 8,829 | 34.461847 | 88 | py |
PyLaia | PyLaia-master/tests/models/htr/conv_block_test.py | import unittest
import pytest
import torch
from laia.data import PaddedTensor
from laia.models.htr import ConvBlock
class ConvBlockTest(unittest.TestCase):
def test_output_size(self):
m = ConvBlock(4, 5, kernel_size=3, stride=1, dilation=1, poolsize=2)
x = torch.randn(3, 4, 11, 13)
y = m... | 5,187 | 38.007519 | 88 | py |
PyLaia | PyLaia-master/tests/decoders/ctc_nbest_decoder_test.py | import unittest
import torch
from laia.decoders import CTCNBestDecoder
class CTCNBestDecoderTest(unittest.TestCase):
def test(self):
x = torch.tensor(
[
[[1.0, 3.0, -1.0, 0.0]],
[[-1.0, 2.0, -2.0, 3.0]],
[[1.0, 5.0, 9.0, 2.0]],
... | 669 | 23.814815 | 83 | py |
PyLaia | PyLaia-master/tests/decoders/ctc_alignment_test.py | import math
import unittest
import torch
from laia.decoders import ctc_alignment
class CTCAlignmentTest(unittest.TestCase):
def setUp(self):
self._logp = torch.tensor(
[[0.3, 0.5, 0.2], [0.4, 0.5, 0.1], [0.5, 0.1, 0.4], [0.1, 0.7, 0.2]]
).log_()
def test_empty_reference(self):
... | 2,259 | 37.965517 | 81 | py |
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