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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PT-MAP-sf | PT-MAP-sf-master/methods/baselinetrain.py | import backbone
import utils
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import torch.nn.functional as F
class BaselineTrain(nn.Module):
def __init__(self, model_func, num_class, loss_type = 'softmax'):
super(BaselineTrain, self).__init__()
self.featur... | 1,927 | 33.428571 | 124 | py |
PT-MAP-sf | PT-MAP-sf-master/methods/baselinefinetune.py | import backbone
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import torch.nn.functional as F
from methods.meta_template import MetaTemplate
class BaselineFinetune(MetaTemplate):
def __init__(self, model_func, n_way, n_support, loss_type = "dist"):
super(Baselin... | 4,407 | 41.796117 | 124 | py |
PT-MAP-sf | PT-MAP-sf-master/methods/matchingnet.py | # This code is modified from https://github.com/facebookresearch/low-shot-shrink-hallucinate
import backbone
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import torch.nn.functional as F
from methods.meta_template import MetaTemplate
import utils
import copy
class MatchingN... | 3,735 | 35.627451 | 199 | py |
PT-MAP-sf | PT-MAP-sf-master/methods/models/utils.py | import torch.nn as nn
import torch
import numpy as np
def constant_init(module, val, bias=0):
nn.init.constant_(module.weight, val)
if hasattr(module, 'bias') and module.bias is not None:
nn.init.constant_(module.bias, bias)
def xavier_init(module, gain=1, bias=0, distribution='normal'):
assert d... | 2,292 | 30.847222 | 73 | py |
PT-MAP-sf | PT-MAP-sf-master/methods/models/imagenet/resnet.py | import torch.nn as nn
# from .utils import load_state_dict_from_url
try:
from torch.hub import load_state_dict_from_url
except ImportError:
from torch.utils.model_zoo import load_url as load_state_dict_from_url
import torch
import numpy as np
from models.utils import *
__all__ = ['ResNet', 'resnet18', 'resnet3... | 19,341 | 38.880412 | 117 | py |
PT-MAP-sf | PT-MAP-sf-master/methods/models/imagenet/mobilenetv2.py | """
Creates a MobileNetV2 Model as defined in:
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. (2018).
MobileNetV2: Inverted Residuals and Linear Bottlenecks
arXiv preprint arXiv:1801.04381.
import from https://github.com/tonylins/pytorch-mobilenet-v2
"""
import math
from models.utils i... | 34,822 | 36.892274 | 130 | py |
PT-MAP-sf | PT-MAP-sf-master/datasets/cvtransforms.py | from __future__ import division
import torch
import math
import random
import cv2
import numpy as np
import numbers
import types
import collections
import matplotlib.pyplot as plt
from sklearn.preprocessing import minmax_scale
import time
from turbojpeg import TurboJPEG
from jpeg2dct.numpy import load, loads
import d... | 57,509 | 36.490222 | 150 | py |
PT-MAP-sf | PT-MAP-sf-master/datasets/cvfunctional.py | from __future__ import division
import torch
import math
import random
from PIL import Image
import cv2
import numpy as np
import numbers
import types
import collections
import warnings
import matplotlib.pyplot as plt
from torchvision.transforms import functional
from numpy import r_
from jpeg2dct.numpy import load, lo... | 36,212 | 36.448811 | 125 | py |
PT-MAP-sf | PT-MAP-sf-master/datasets/dataloader_imagenet_dct2.py | import os
import time
import torch
from datasets.dataset_imagenet_dct import ImageFolderDCT
import datasets.cvtransforms as transforms
from datasets import train_y_mean, train_y_std, train_cb_mean, train_cb_std, \
train_cr_mean, train_cr_std
from datasets import train_y_mean_upscaled, train_y_std_upscaled, train_cb... | 2,237 | 36.3 | 114 | py |
PT-MAP-sf | PT-MAP-sf-master/datasets/dataset_imagenet_dct2.py | # Optimized for DCT
# Upsampling in the compressed domain
import os
import sys
import random
from datasets.vision import VisionDataset
from PIL import Image
import cv2
import os.path
import numpy as np
import torch
from turbojpeg import TurboJPEG
from datasets import train_y_mean_resized, train_y_std_resized, train_cb_... | 12,008 | 39.16388 | 112 | py |
PT-MAP-sf | PT-MAP-sf-master/datasets/vision.py | import os
import torch
import torch.utils.data as data
class VisionDataset(data.Dataset):
_repr_indent = 4
def __init__(self, root, transforms=None, transform=None, target_transform=None):
if isinstance(root, torch._six.string_classes):
root = os.path.expanduser(root)
self.root = ... | 2,950 | 35.432099 | 86 | py |
PT-MAP-sf | PT-MAP-sf-master/data/additional_transforms.py | # Copyright 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 torch
from PIL import ImageEnhance
transformtypedict=dict(Brightness=ImageEnhance.Brightness, Contrast=ImageEnhance.Contrast... | 850 | 24.787879 | 150 | py |
PT-MAP-sf | PT-MAP-sf-master/data/feature_loader.py | import torch
import numpy as np
import h5py
class SimpleHDF5Dataset:
def __init__(self, file_handle = None):
if file_handle == None:
self.f = ''
self.all_feats_dset = []
self.all_labels = []
self.total = 0
else:
self.f = file_handle
... | 1,293 | 27.755556 | 78 | py |
PT-MAP-sf | PT-MAP-sf-master/data/dataset.py | # This code is modified from https://github.com/facebookresearch/low-shot-shrink-hallucinate
import torch
from PIL import Image
import cv2
import json
import numpy as np
import torchvision.transforms as transforms
import os
# Optimized for DCT
# Upsampling in the compressed domain
import sys
import random
from dataset... | 7,665 | 33.223214 | 116 | py |
PT-MAP-sf | PT-MAP-sf-master/data/datamgr.py | # This code is modified from https://github.com/facebookresearch/low-shot-shrink-hallucinate
import torch
from PIL import Image
import numpy as np
import torchvision.transforms as transforms
import datasets.cvtransforms as transforms_dct
import data.additional_transforms as add_transforms
from data.dataset import Simp... | 8,113 | 46.729412 | 152 | py |
SirenMRI | SirenMRI-main/siren.py | # Based on https://github.com/lucidrains/siren-pytorch
import torch
from torch import nn
from math import sqrt
class Sine(nn.Module):
"""Sine activation with scaling.
Args:
w0 (float): Omega_0 parameter from SIREN paper.
"""
def __init__(self, w0=1.):
super().__init__()
self.w... | 4,675 | 31.472222 | 128 | py |
SirenMRI | SirenMRI-main/sirenMRI_2D.py | import argparse
import getpass
import os
import random
import sys
import torch
import util
import numpy as np
import nibabel as nib
from siren import Siren
from siren import MLP
from training import Trainer
from sklearn.preprocessing import MinMaxScaler
import pickle
import py7zr
parser = argparse.ArgumentParser()
pa... | 8,342 | 42.453125 | 137 | py |
SirenMRI | SirenMRI-main/psnr_3d_slicewise.py | import torch
import numpy as np
import nibabel as nib
from sklearn.preprocessing import MinMaxScaler
import pickle
import argparse
import util
parser = argparse.ArgumentParser()
parser.add_argument("-p", "--predicted", help="Predicted volume")
parser.add_argument("-g", "--ground_truth", help="Ground truth")
parser.add... | 1,852 | 31.508772 | 87 | py |
SirenMRI | SirenMRI-main/training.py | import torch
import tqdm
from collections import OrderedDict
from util import get_clamped_psnr
class Trainer():
def __init__(self, representation, lr=1e-3, print_freq=1):
"""Model to learn a representation of a single datapoint.
Args:
representation (siren.Siren): Neural net represent... | 2,858 | 41.671642 | 115 | py |
SirenMRI | SirenMRI-main/util.py | import numpy as np
import torch
from torch._C import dtype
from typing import Dict
DTYPE_BIT_SIZE: Dict[dtype, int] = {
torch.float32: 32,
torch.float: 32,
torch.float64: 64,
torch.double: 64,
torch.float16: 16,
torch.half: 16,
torch.bfloat16: 16,
torch.complex32: 32,
torch.complex... | 4,832 | 29.980769 | 99 | py |
SirenMRI | SirenMRI-main/sirenMRI_3D.py | import scipy.io
import argparse
import getpass
import os
import random
import torch
import util
import numpy as np
import nibabel as nib
from siren import Siren
from training import Trainer
from sklearn.preprocessing import MinMaxScaler
import pickle
parser = argparse.ArgumentParser()
parser.add_argument("-ld", "--lo... | 4,867 | 34.532847 | 122 | py |
pyehr | pyehr-main/test_twostage.py | """
step 1: find the best config combination (outcome/los) of the same model
step 2: get the prediction results
step 3: calculate the metric
"""
import pandas as pd
import numpy as np
import lightning as L
import torch
from datasets.loader.datamodule import EhrDataModule
from datasets.loader.load_los_info import get_... | 5,913 | 44.145038 | 196 | py |
pyehr | pyehr-main/ml_tune.py | import hydra
import lightning as L
from lightning.pytorch.callbacks import EarlyStopping, ModelCheckpoint
from lightning.pytorch.loggers import CSVLogger, WandbLogger
from omegaconf import DictConfig, OmegaConf
import wandb
from datasets.loader.datamodule import EhrDataModule
from datasets.loader.load_los_info import ... | 2,482 | 36.621212 | 134 | py |
pyehr | pyehr-main/dl_tune.py | import hydra
import lightning as L
from lightning.pytorch.callbacks import EarlyStopping, ModelCheckpoint
from lightning.pytorch.loggers import CSVLogger, WandbLogger
from omegaconf import DictConfig, OmegaConf
import wandb
from datasets.loader.datamodule import EhrDataModule
from datasets.loader.load_los_info import ... | 3,058 | 39.786667 | 163 | py |
pyehr | pyehr-main/train.py | import lightning as L
from lightning.pytorch.callbacks import EarlyStopping, ModelCheckpoint
from lightning.pytorch.loggers import CSVLogger
from configs.dl import dl_best_hparams
from configs.experiments import experiments_configs
from configs.ml import ml_best_hparams
from datasets.loader.datamodule import EhrDataMo... | 3,519 | 43.556962 | 157 | py |
pyehr | pyehr-main/models/stagenet.py | from typing import Dict, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.utils.rnn as rnn_utils
from models.utils import get_last_visit
class StageNetLayer(nn.Module):
"""StageNet layer.
Paper: Stagenet: Stage-aware neural networks for health risk pr... | 10,710 | 36.714789 | 207 | py |
pyehr | pyehr-main/models/xgboost.py | from xgboost import XGBClassifier, XGBRegressor
class XGBoost():
def __init__(self, **params):
"""params is a dict
seed: int, random seed
n_estimators: int, number of trees
learning_rate: float, learning rate
max_depth: int, depth of trees
"""
task = params[... | 1,584 | 39.641026 | 195 | py |
pyehr | pyehr-main/models/lstm.py | from torch import nn
class LSTM(nn.Module):
def __init__(self, input_dim, hidden_dim, act_layer=nn.GELU, drop=0.0, **kwargs):
super().__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.proj = nn.Linear(input_dim, hidden_dim)
self.act = act_layer()
... | 503 | 30.5 | 105 | py |
pyehr | pyehr-main/models/adacare.py | from typing import Dict, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.utils.rnn as rnn_utils
from models.utils import get_last_visit
class Sparsemax(nn.Module):
"""Sparsemax function."""
def __init__(self, dim=None):
super(Sparsemax, self).__init__()
self.dim = ... | 10,099 | 33.827586 | 148 | py |
pyehr | pyehr-main/models/mcgru.py | import torch
from torch import nn
class MCGRU(nn.Module):
def __init__(self, lab_dim, demo_dim, hidden_dim: int=32, feat_dim: int=8, act_layer=nn.GELU, drop=0.0, **kwargs):
super().__init__()
self.lab_dim = lab_dim
self.demo_dim = demo_dim
self.hidden_dim = hidden_dim
self.f... | 1,488 | 38.184211 | 118 | py |
pyehr | pyehr-main/models/gru.py | from torch import nn
class GRU(nn.Module):
def __init__(self, input_dim, hidden_dim, act_layer=nn.GELU, drop=0.0, **kwargs):
super().__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.proj = nn.Linear(input_dim, hidden_dim)
self.act = act_layer()
... | 499 | 30.25 | 103 | py |
pyehr | pyehr-main/models/mlp.py | from torch import nn
class MLP(nn.Module):
def __init__(self, input_dim, hidden_dim, act_layer=nn.GELU, drop=0.0, **kwargs):
super().__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.proj = nn.Linear(input_dim, hidden_dim)
self.act = act_layer()
... | 585 | 26.904762 | 85 | py |
pyehr | pyehr-main/models/utils.py | import torch
def generate_mask(seq_lens):
"""Generates a mask for the sequence.
Args:
seq_lens: [batch size]
(max_len: int)
Returns:
mask: [batch size, max_len]
"""
max_len = torch.max(seq_lens).to(seq_lens.device)
mask = torch.arange(max_len).expand(len(seq_lens), ma... | 1,490 | 28.82 | 91 | py |
pyehr | pyehr-main/models/heads.py | import torch
from torch import nn
class MultitaskHead(nn.Module):
def __init__(self, hidden_dim, output_dim, act_layer=nn.GELU, drop=0.0):
super(MultitaskHead, self).__init__()
self.hidden_dim = (hidden_dim,)
self.output_dim = (output_dim,)
self.act = act_layer()
self.outco... | 772 | 29.92 | 76 | py |
pyehr | pyehr-main/models/transformer.py | import math
from typing import Dict, List, Optional, Tuple
import torch
from torch import nn
class Attention(nn.Module):
def forward(self, query, key, value, mask=None, dropout=None):
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(query.size(-1))
if mask is not None:
... | 6,829 | 34.759162 | 92 | py |
pyehr | pyehr-main/models/agent.py | from typing import Dict, List, Optional, Tuple
import torch
import torch.nn as nn
from models.utils import get_last_visit
class AgentLayer(nn.Module):
"""Dr. Agent layer.
Paper: Junyi Gao et al. Dr. Agent: Clinical predictive model via mimicked second opinions. JAMIA.
This layer is used in the Dr. Age... | 11,163 | 35.603279 | 130 | py |
pyehr | pyehr-main/models/retain.py | from typing import Dict, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.utils.rnn as rnn_utils
class RETAINLayer(nn.Module):
"""RETAIN layer.
Paper: Edward Choi et al. RETAIN: An Interpretable Predictive Model for
Healthcare using Reverse Time Attention Mechanism. NIPS 2016.
... | 4,165 | 34.305085 | 88 | py |
pyehr | pyehr-main/models/__init__.py | from .adacare import AdaCare
from .agent import Agent
from .catboost import CatBoost # ML model
from .concare import ConCare
from .dt import DT # ML model
from .gbdt import GBDT # ML model
from .grasp import GRASP
from .gru import GRU
from .heads import MultitaskHead
from .lstm import LSTM
from .mlp import MLP
from ... | 549 | 26.5 | 42 | py |
pyehr | pyehr-main/models/concare.py | import math
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn as nn
from models.utils import generate_mask, get_last_visit
class FinalAttentionQKV(nn.Module):
def __init__(
self,
attention_input_dim: int,
attention_hidden_dim: int,
attention_type: str = ... | 24,095 | 33.720461 | 157 | py |
pyehr | pyehr-main/models/rnn.py | from typing import Dict, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.utils.rnn as rnn_utils
class RNNLayer(nn.Module):
def __init__(
self,
input_size: int,
hidden_size: int,
rnn_type: str = "GRU",
num_layers: int = 1,
dropout: float = 0... | 3,584 | 35.958763 | 103 | py |
pyehr | pyehr-main/models/grasp.py | import copy
import math
import random
from typing import Dict, List, Optional, Tuple
import numpy as np
import torch
import torch.nn as nn
import torch.nn.utils.rnn as rnn_utils
from sklearn.neighbors import kneighbors_graph
from models.concare import ConCareLayer
from models.rnn import RNNLayer
from models.utils imp... | 10,664 | 35.030405 | 162 | py |
pyehr | pyehr-main/models/tcn.py | from typing import Dict, List, Optional, Tuple
import numpy as np
import torch
import torch.nn as nn
import torch.nn.utils.rnn as rnn_utils
from torch.nn.utils import weight_norm
from models.utils import get_last_visit
# From TCN original paper https://github.com/locuslab/TCN
class Chomp1d(nn.Module):
def __ini... | 6,513 | 31.733668 | 154 | py |
pyehr | pyehr-main/metrics/__init__.py | import torch
from .binary_classification_metrics import get_binary_metrics
from .es import es_score
from .osmae import osmae_score
from .regression_metrics import get_regression_metrics
from .utils import check_metric_is_better
def reverse_los(y, los_info):
return y * los_info["los_std"] + los_info["los_mean"]
... | 2,072 | 45.066667 | 255 | py |
pyehr | pyehr-main/metrics/regression_metrics.py | from torchmetrics.regression import MeanAbsoluteError, MeanSquaredError, R2Score
# get regression metrics: mse, mae, rmse, r2
def get_regression_metrics(preds, labels):
mse = MeanSquaredError(squared=True)
rmse = MeanSquaredError(squared=False)
mae = MeanAbsoluteError()
r2 = R2Score()
mse(preds, ... | 590 | 25.863636 | 80 | py |
pyehr | pyehr-main/metrics/binary_classification_metrics.py | import torch
from torchmetrics import AUROC, Accuracy, AveragePrecision
threshold = 0.5
def get_binary_metrics(preds, labels):
accuracy = Accuracy(task="binary", threshold=threshold)
auroc = AUROC(task="binary", threshold=threshold)
auprc = AveragePrecision(task="binary", threshold=threshold)
# conv... | 631 | 26.478261 | 64 | py |
pyehr | pyehr-main/datasets/loader/datamodule.py | import os
import lightning as L
import pandas as pd
import torch
import torch.utils.data as data
class EhrDataset(data.Dataset):
def __init__(self, data_path, mode='train'):
super().__init__()
self.data = pd.read_pickle(os.path.join(data_path,f'{mode}_x.pkl'))
self.label = pd.read_pickle(... | 2,074 | 38.150943 | 137 | py |
pyehr | pyehr-main/datasets/loader/unpad.py | import torch
from torch.nn.utils.rnn import unpad_sequence
def unpad_y(y_pred, y_true, lens):
raw_device = y_pred.device
device = torch.device("cpu")
y_pred, y_true, lens = y_pred.to(device), y_true.to(device), lens.to(device)
y_pred_unpad = unpad_sequence(y_pred, batch_first=True, lengths=lens)
y... | 967 | 41.086957 | 80 | py |
pyehr | pyehr-main/pipelines/dl_pipeline.py | import os
import lightning as L
import torch
import torch.nn as nn
import models
from datasets.loader.unpad import unpad_y
from losses import get_loss
from metrics import get_all_metrics, check_metric_is_better
from models.utils import generate_mask, get_last_visit
class DlPipeline(L.LightningModule):
def __ini... | 6,197 | 44.573529 | 113 | py |
pyehr | pyehr-main/losses/multitask_loss.py | import torch
from torch import nn
class MultitaskLoss(nn.Module):
def __init__(self, task_num=2):
super(MultitaskLoss, self).__init__()
self.task_num = task_num
self.alpha = nn.Parameter(torch.ones((task_num)))
self.mse = nn.MSELoss()
self.bce = nn.BCELoss()
def forwar... | 667 | 32.4 | 61 | py |
pyehr | pyehr-main/losses/time_aware_loss.py | import torch
from torch import nn
class TimeAwareLoss(nn.Module):
def __init__(self, decay_rate=0.1, reward_factor=0.1):
super(TimeAwareLoss, self).__init__()
self.bce = nn.BCELoss(reduction='none')
self.decay_rate = decay_rate
self.reward_factor = reward_factor
def forward(se... | 1,133 | 36.8 | 102 | py |
pyehr | pyehr-main/losses/__init__.py | import torch
import torch.nn.functional as F
from .multitask_loss import get_multitask_loss
from .time_aware_loss import get_time_aware_loss
def get_loss(y_pred, y_true, task, time_aware=False):
if task == "outcome":
loss = F.binary_cross_entropy(y_pred, y_true[:, 0])
elif task == "los":
loss... | 632 | 29.142857 | 85 | py |
CSSL | CSSL-master/backbone/ResNet18.py | # Copyright 2021-present, Lorenzo Bonicelli, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara.
# 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 torch
import torch.nn as nn
import torch.nn.functional a... | 7,191 | 36.264249 | 112 | py |
CSSL | CSSL-master/models/ccic.py | # Copyright 2021-present, Lorenzo Bonicelli, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara.
# 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 import Adam
from utils.ring_buffer import Ring... | 12,414 | 42.409091 | 157 | py |
CSSL | CSSL-master/models/lwf.py | # Copyright 2021-present, Lorenzo Bonicelli, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara.
# 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 torch
from datasets import get_dataset
from torch.optim ... | 4,297 | 41.137255 | 120 | py |
CSSL | CSSL-master/models/ewc_on.py | # Copyright 2021-present, Lorenzo Bonicelli, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara.
# 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 torch
import torch.nn as nn
import torch.nn.functional a... | 3,277 | 34.247312 | 112 | py |
CSSL | CSSL-master/models/der.py | # Copyright 2021-present, Lorenzo Bonicelli, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara.
# 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 utils.buffer import Buffer
from torch.nn import functional... | 1,865 | 34.884615 | 112 | py |
CSSL | CSSL-master/models/joint.py | # Copyright 2021-present, Lorenzo Bonicelli, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara.
# 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 import SGD
from utils.args import *
from mode... | 4,217 | 39.171429 | 112 | py |
CSSL | CSSL-master/models/gdumb.py | # Copyright 2021-present, Lorenzo Bonicelli, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara.
# 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 utils.args import *
from models.utils.continual_model impo... | 1,915 | 34.481481 | 112 | py |
CSSL | CSSL-master/models/pseudoer.py | import torch
from utils.buffer import Buffer
from utils.args import *
from models.utils.continual_model import ContinualModel
from datasets import get_dataset
def get_parser() -> ArgumentParser:
parser = ArgumentParser(description='Continual learning via'
' Experience Replay... | 1,849 | 33.90566 | 111 | py |
CSSL | CSSL-master/models/si.py | # Copyright 2021-present, Lorenzo Bonicelli, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara.
# 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 torch
import torch.nn as nn
from utils.args import *
fro... | 2,544 | 35.357143 | 113 | py |
CSSL | CSSL-master/models/er.py | # Copyright 2021-present, Lorenzo Bonicelli, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara.
# 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 torch
from utils.buffer import Buffer
from utils.args im... | 1,791 | 33.461538 | 112 | py |
CSSL | CSSL-master/models/icarl.py | # Copyright 2021-present, Lorenzo Bonicelli, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara.
# 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 copy import deepcopy
import torch
import torch.nn.function... | 8,773 | 36.982684 | 112 | py |
CSSL | CSSL-master/models/utils/continual_model.py | # Copyright 2021-present, Lorenzo Bonicelli, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara.
# 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 torch.nn as nn
from torch.optim import SGD
import torch
... | 5,508 | 36.993103 | 113 | py |
CSSL | CSSL-master/datasets/seq_cifar10.py | # Copyright 2021-present, Lorenzo Bonicelli, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara.
# 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 torchvision.datasets import CIFAR10
import torchvision.tra... | 5,250 | 37.610294 | 112 | py |
CSSL | CSSL-master/datasets/utils/continual_dataset.py | # Copyright 2021-present, Lorenzo Bonicelli, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara.
# 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 abc import abstractmethod
from argparse import Namespace
f... | 6,677 | 36.1 | 112 | py |
CSSL | CSSL-master/datasets/utils/validation.py | # Copyright 2021-present, Lorenzo Bonicelli, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara.
# 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 torch
from PIL import Image
import numpy as np
import os... | 2,809 | 35.973684 | 112 | py |
CSSL | CSSL-master/utils/main.py | # Copyright 2021-present, Lorenzo Bonicelli, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara.
# 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 importlib
from models import get_all_models
from argpars... | 1,572 | 33.195652 | 112 | py |
CSSL | CSSL-master/utils/training.py | # Copyright 2021-present, Lorenzo Bonicelli, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara.
# 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 torch
from utils.status import ProgressBar
from utils.lo... | 5,448 | 36.840278 | 112 | py |
CSSL | CSSL-master/utils/mixup.py | # Copyright 2021-present, Lorenzo Bonicelli, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara.
# 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 torch
from torch.distributions.beta import Beta
def mix... | 915 | 34.230769 | 112 | py |
CSSL | CSSL-master/utils/buffer.py | # Copyright 2021-present, Lorenzo Bonicelli, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara.
# 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 torch
import numpy as np
from typing import Tuple
from t... | 5,755 | 39.535211 | 112 | py |
CSSL | CSSL-master/utils/ring_buffer.py | # Copyright 2021-present, Lorenzo Bonicelli, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara.
# 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 torch
import numpy as np
from typing import Tuple
from t... | 4,989 | 38.603175 | 112 | py |
CSSL | CSSL-master/utils/triplet.py | # Copyright 2021-present, Lorenzo Bonicelli, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara.
# 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 torch
def negative_only_triplet_loss(labels, embedding... | 4,282 | 36.570175 | 112 | py |
CSSL | CSSL-master/utils/conf.py | # Copyright 2021-present, Lorenzo Bonicelli, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara.
# 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 random
import torch
import numpy as np
def get_device(... | 860 | 24.323529 | 112 | py |
mixture-of-experts | mixture-of-experts-master/setup.py | from setuptools import setup, find_packages
setup(
name = 'mixture-of-experts',
packages = find_packages(),
version = '0.2.1',
license='MIT',
description = 'Sparsely-Gated Mixture of Experts for Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url = 'https://github.com/lucidrains/... | 745 | 30.083333 | 96 | py |
mixture-of-experts | mixture-of-experts-master/mixture_of_experts/mixture_of_experts.py | import torch
from torch import nn
import torch.nn.functional as F
import math
from inspect import isfunction
# constants
MIN_EXPERT_CAPACITY = 4
# helper functions
def default(val, default_val):
default_val = default_val() if isfunction(default_val) else default_val
return val if val is not None else defau... | 12,926 | 38.898148 | 302 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/MNIST/mnist_noencode.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import pickle
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import os
# Custom datasets for train and test sets, used to extract the index with the actua... | 8,959 | 30.660777 | 156 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/MNIST/mnist_vae.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import pickle
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import os
# Custom datasets for train and test sets, used to extract the index with the actua... | 6,465 | 29.074419 | 156 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/MNIST/mnist_vae_shifted.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import pickle
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import os
# Custom datasets for train and test sets, used to extract the index with the actua... | 7,751 | 29.761905 | 156 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/MNIST/mnist_noencode_huge.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import pickle
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import os
# Custom datasets for train and test sets, used to extract the index with the actua... | 9,119 | 30.777003 | 156 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNVLT/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/CryoDRGNVLT/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/CryoDRGNVLT/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/CryoDRGNVLT/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/CryoDRGNVLT/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, device=None):
assert D % 2 == 1, "Lattice size must be odd"
x0, x1 = np.meshgrid(np.lins... | 6,786 | 38.005747 | 109 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNVLT/cryodrgn/build/lib/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/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, device=None):
super(PoseTracker, self).__init__()
rots = torch.tensor(rots... | 4,665 | 39.224138 | 123 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNVLT/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/CryoDRGNVLT/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/CryoDRGNVLT/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
... | 34,008 | 40.934649 | 123 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNVLT/cryodrgn/build/lib/cryodrgn/z_train.py | import torch
import torch.nn as nn
import numpy as np
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.shap... | 1,016 | 28.911765 | 76 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNVLT/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/CryoDRGNVLT/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/CryoDRGNVLT/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
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... | 32,280 | 47.180597 | 236 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNVLT/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... | 5,925 | 37.232258 | 146 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNVLT/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... | 9,661 | 46.831683 | 206 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNVLT/cryodrgn/build/lib/cryodrgn/commands/z_train.py | import torch
import torch.nn as nn
import numpy as np
import pickle
class ZTracker(nn.Module):
def __init__(self, z_vals):
super(ZTracker, self).__init__()
self.zvals = z_vals
def get_z_val(self, ind):
zval = self.zvals[:, ind, :]
return zval
def save(self, out_pkl... | 414 | 23.411765 | 49 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNVLT/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... | 6,706 | 43.125 | 152 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNVLT/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... | 15,346 | 46.221538 | 207 | py |
ExplicitLatentVariables | ExplicitLatentVariables-main/CryoDRGNVLT/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/CryoDRGNVLT/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, device=None):
assert D % 2 == 1, "Lattice size must be odd"
x0, x1 = np.meshgrid(np.lins... | 6,786 | 38.005747 | 109 | py |
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