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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qcsbm | qcsbm-master/autoencoder_example/run_lib.py | import os
import logging
import numpy as np
from models import simple_untied_autoencoder
import torch
import torch.nn
import tensorflow as tf
import torch.optim as optim
from torch.utils import tensorboard
import datetime
def run(config, workdir):
# Create directories for experimental logs.
checkpoint_dir = os.p... | 3,357 | 43.184211 | 166 | py |
qcsbm | qcsbm-master/autoencoder_example/models/simple_untied_autoencoder.py | import torch
import torch.nn as nn
class simple_untied_autoencoder(nn.Module):
"""simple score model"""
def __init__(self):
""" Output = R Sigmoid( W^T Input + b ) + c """
super().__init__()
self.R = nn.Linear(2, 2)
self.Wt = nn.Linear(2, 2)
self.Sig = Swish(2)
self.param = 1.0
# Initia... | 1,119 | 29.27027 | 77 | py |
qcsbm | qcsbm-master/autoencoder_example/configs/ae_config.py | from configs.default_toy_configs import get_default_configs
import torch
def get_config():
config = get_default_configs()
training = config.training
training.n_iters = 10001
optim = config.optim
optim.lr = 1e-4
config.seed = 8
return config
| 262 | 15.4375 | 59 | py |
qcsbm | qcsbm-master/autoencoder_example/configs/default_toy_configs.py | import ml_collections
import torch
def get_default_configs():
config = ml_collections.ConfigDict()
# training
config.training = training = ml_collections.ConfigDict()
training.n_iters = 20000
training.batch_size = 5000
training.snapshot_freq = 500
training.log_freq = 200
training.plot_freq = 1000
tr... | 1,662 | 23.101449 | 94 | py |
qcsbm | qcsbm-master/real_world/likelihood.py | """Various sampling methods."""
import torch
import numpy as np
from scipy import integrate
from models import utils as mutils
def get_div_fn(fn):
"""Create the divergence function of `fn` using the Hutchinson-Skilling trace estimator."""
def div_fn(x, t, eps):
with torch.enable_grad():
x.requires_gra... | 4,113 | 41.412371 | 111 | py |
qcsbm | qcsbm-master/real_world/losses.py | """All functions related to loss computation and optimization.
"""
from re import I
import torch
import torch.optim as optim
import numpy as np
from models import utils as mutils
from sde_lib import VESDE, VPSDE
def get_optimizer(config, params):
"""Returns a flax optimizer object based on `config`."""
if config.... | 9,502 | 42.792627 | 153 | py |
qcsbm | qcsbm-master/real_world/run_lib.py | import os
import numpy as np
import tensorflow as tf
import logging
# Keep the import below for registering all model definitions
from models import ddpm, ncsnv2, ncsnpp, ncsnpp_mod
import losses
import sampling
from models import utils as mutils
from models.ema import ExponentialMovingAverage
import datasets
import li... | 7,048 | 43.898089 | 118 | py |
qcsbm | qcsbm-master/real_world/utils.py | import torch
import logging
import numpy as np
import time
from models import utils as mutils
def restore_checkpoint(ckpt_dir, state, device):
loaded_state = torch.load(ckpt_dir, map_location=device)
state['optimizer'].load_state_dict(loaded_state['optimizer'])
state['model'].load_state_dict(loaded_state['model'... | 2,912 | 44.515625 | 136 | py |
qcsbm | qcsbm-master/real_world/sampling.py | """Various sampling methods."""
import functools
import torch
import numpy as np
import abc
from models.utils import from_flattened_numpy, to_flattened_numpy, get_score_fn
from scipy import integrate
import sde_lib
from models import utils as mutils
_CORRECTORS = {}
_PREDICTORS = {}
def register_predictor(cls=None... | 17,731 | 35.788382 | 131 | py |
qcsbm | qcsbm-master/real_world/controllable_generation.py | from models import utils as mutils
import torch
import numpy as np
from sampling import NoneCorrector, NonePredictor, shared_corrector_update_fn, shared_predictor_update_fn
import functools
def get_pc_inpainter(sde, predictor, corrector, inverse_scaler, snr,
n_steps=1, probability_flow=False, con... | 8,178 | 44.187845 | 107 | py |
qcsbm | qcsbm-master/real_world/datasets.py | """Return training and evaluation/test datasets from config files."""
import jax
import tensorflow as tf
import tensorflow_datasets as tfds
import costum_datasets.imagenet
def get_data_scaler(config):
"""Data normalizer. Assume data are always in [0, 1]."""
if config.data.centered:
# Rescale to [-1, 1]
ret... | 7,784 | 36.071429 | 99 | py |
qcsbm | qcsbm-master/real_world/run_lib_evaluation_asym.py | import logging
import numpy as np
import os
import io
import time
import tensorflow as tf
import torch
from ml_collections.config_flags import config_flags
from absl import flags
from absl import app
import losses
import datasets
import sde_lib
from models import ncsnpp, ncsnpp_mod
from models import utils as mutils
f... | 5,604 | 42.449612 | 129 | py |
qcsbm | qcsbm-master/real_world/run_lib_sampling.py | import numpy as np
import io
import os
import time
import logging
import tensorflow as tf
import torch
from torchvision.utils import make_grid, save_image
from ml_collections.config_flags import config_flags
from absl import flags
from absl import app
import sampling
import datasets
import sde_lib
from models import n... | 5,790 | 41.270073 | 190 | py |
qcsbm | qcsbm-master/real_world/run_lib_evaluation_nll.py | import logging
import numpy as np
import os
import io
import glob
import time
import tensorflow as tf
import torch
from ml_collections.config_flags import config_flags
from absl import flags
from absl import app
import losses
import datasets
import sde_lib
import likelihood
from models import ncsnpp, ncsnpp_mod
from m... | 5,263 | 41.451613 | 165 | py |
qcsbm | qcsbm-master/real_world/evaluation.py | """Utility functions for computing FID/Inception scores."""
import jax
import numpy as np
import six
import tensorflow as tf
import tensorflow_gan as tfgan
import tensorflow_hub as tfhub
INCEPTION_TFHUB = 'https://tfhub.dev/tensorflow/tfgan/eval/inception/1'
INCEPTION_OUTPUT = 'logits'
INCEPTION_FINAL_POOL = 'pool_3'... | 4,344 | 31.916667 | 93 | py |
qcsbm | qcsbm-master/real_world/sde_lib.py | """Abstract SDE classes, Reverse SDE, and VE/VP SDEs."""
import abc
import torch
import numpy as np
# For importance sampling
def scan(f, init, xs):
carry = init
ys = []
for x in xs:
carry, y = f(carry, x)
ys.append(y)
return carry, torch.stack(ys)
class SDE(abc.ABC):
"""SDE abstract class. Function... | 10,270 | 31.606349 | 123 | py |
qcsbm | qcsbm-master/real_world/models/up_or_down_sampling.py | """Layers used for up-sampling or down-sampling images.
Many functions are ported from https://github.com/NVlabs/stylegan2.
"""
import torch.nn as nn
import torch
import torch.nn.functional as F
import numpy as np
from op import upfirdn2d
# Function ported from StyleGAN2
def get_weight(module,
shape,... | 8,900 | 33.5 | 91 | py |
qcsbm | qcsbm-master/real_world/models/utils.py | """All functions and modules related to model definition.
"""
import torch
import sde_lib
import numpy as np
_MODELS = {}
def register_model(cls=None, *, name=None):
"""A decorator for registering model classes."""
def _register(cls):
if name is None:
local_name = cls.__name__
else:
local_... | 5,886 | 29.502591 | 110 | py |
qcsbm | qcsbm-master/real_world/models/layers.py | """Common layers for defining score networks.
"""
import math
import string
from functools import partial
import torch.nn as nn
import torch
import torch.nn.functional as F
import numpy as np
from .normalization import ConditionalInstanceNorm2dPlus
def get_act(config):
"""Get activation functions from the config fi... | 22,059 | 33.148607 | 112 | py |
qcsbm | qcsbm-master/real_world/models/ncsnpp_mod.py | from . import utils, layers, layerspp, normalization
import torch.nn as nn
import functools
import torch
import numpy as np
ResnetBlockDDPM = layerspp.ResnetBlockDDPMpp
ResnetBlockBigGAN = layerspp.ResnetBlockBigGANpp
Combine = layerspp.Combine
conv3x3 = layerspp.conv3x3
conv1x1 = layerspp.conv1x1
get_act = layers.get... | 13,438 | 34.180628 | 101 | py |
qcsbm | qcsbm-master/real_world/models/ddpm.py | """DDPM model.
This code is the pytorch equivalent of:
https://github.com/hojonathanho/diffusion/blob/master/diffusion_tf/models/unet.py
"""
import torch
import torch.nn as nn
import functools
from . import utils, layers, normalization
RefineBlock = layers.RefineBlock
ResidualBlock = layers.ResidualBlock
ResnetBlock... | 5,454 | 31.861446 | 113 | py |
qcsbm | qcsbm-master/real_world/models/ncsnv2.py | """The NCSNv2 model."""
import torch
import torch.nn as nn
import functools
from .utils import get_sigmas, register_model
from .layers import (CondRefineBlock, RefineBlock, ResidualBlock, ncsn_conv3x3,
ConditionalResidualBlock, get_act)
from .normalization import get_normalization
CondResidualBlo... | 15,415 | 37.54 | 120 | py |
qcsbm | qcsbm-master/real_world/models/normalization.py | """Normalization layers."""
import torch.nn as nn
import torch
import functools
def get_normalization(config, conditional=False):
"""Obtain normalization modules from the config file."""
norm = config.model.normalization
if conditional:
if norm == 'InstanceNorm++':
return functools.partial(Conditional... | 7,049 | 34.074627 | 106 | py |
qcsbm | qcsbm-master/real_world/models/ema.py | # Modified from https://raw.githubusercontent.com/fadel/pytorch_ema/master/torch_ema/ema.py
from __future__ import division
from __future__ import unicode_literals
import torch
# Partially based on: https://github.com/tensorflow/tensorflow/blob/r1.13/tensorflow/python/training/moving_averages.py
class ExponentialMo... | 3,414 | 33.846939 | 119 | py |
qcsbm | qcsbm-master/real_world/models/ncsnpp.py | from . import utils, layers, layerspp, normalization
import torch.nn as nn
import functools
import torch
import numpy as np
ResnetBlockDDPM = layerspp.ResnetBlockDDPMpp
ResnetBlockBigGAN = layerspp.ResnetBlockBigGANpp
Combine = layerspp.Combine
conv3x3 = layerspp.conv3x3
conv1x1 = layerspp.conv1x1
get_act = layers.get... | 13,023 | 34.78022 | 113 | py |
qcsbm | qcsbm-master/real_world/models/layerspp.py | """Layers for defining NCSN++.
"""
from . import layers
from . import up_or_down_sampling
import torch.nn as nn
import torch
import torch.nn.functional as F
import numpy as np
conv1x1 = layers.ddpm_conv1x1
conv3x3 = layers.ddpm_conv3x3
NIN = layers.NIN
default_init = layers.default_init
class GaussianFourierProjecti... | 8,373 | 31.332046 | 99 | py |
qcsbm | qcsbm-master/real_world/configs/default_imagenet_configs.py | import ml_collections
import torch
def get_default_configs():
config = ml_collections.ConfigDict()
# training
config.training = training = ml_collections.ConfigDict()
config.training.batch_size = 128
training.n_iters = 1300001
training.snapshot_freq = 10000
training.log_freq = 50
training.eval_freq = ... | 2,256 | 25.869048 | 94 | py |
qcsbm | qcsbm-master/real_world/configs/default_cifar10_configs.py | import ml_collections
import torch
def get_default_configs():
config = ml_collections.ConfigDict()
# training
config.training = training = ml_collections.ConfigDict()
config.training.batch_size = 128
training.n_iters = 1300001
training.snapshot_freq = 10000
training.log_freq = 50
training.eval_freq =... | 2,254 | 25.845238 | 94 | py |
qcsbm | qcsbm-master/real_world/configs/default_svhn_configs.py | import ml_collections
import torch
def get_default_configs():
config = ml_collections.ConfigDict()
# training
config.training = training = ml_collections.ConfigDict()
config.training.batch_size = 128
training.n_iters = 1300001
training.snapshot_freq = 10000
training.log_freq = 50
training.eval_freq =... | 2,251 | 25.809524 | 94 | py |
qcsbm | qcsbm-master/real_world/configs/default_cifar100_configs.py | import ml_collections
import torch
def get_default_configs():
config = ml_collections.ConfigDict()
# training
config.training = training = ml_collections.ConfigDict()
config.training.batch_size = 128
training.n_iters = 1300001
training.snapshot_freq = 10000
training.log_freq = 50
training.eval_freq = ... | 2,254 | 25.845238 | 94 | py |
qcsbm | qcsbm-master/real_world/op/upfirdn2d.py | import os
import torch
from torch.nn import functional as F
from torch.autograd import Function
from torch.utils.cpp_extension import load
module_path = os.path.dirname(__file__)
upfirdn2d_op = load(
"upfirdn2d",
sources=[
os.path.join(module_path, "upfirdn2d.cpp"),
os.path.join(module_path, ... | 5,672 | 27.223881 | 108 | py |
qcsbm | qcsbm-master/real_world/op/fused_act.py | import os
import torch
from torch import nn
from torch.nn import functional as F
from torch.autograd import Function
from torch.utils.cpp_extension import load
module_path = os.path.dirname(__file__)
fused = load(
"fused",
sources=[
os.path.join(module_path, "fused_bias_act.cpp"),
os.path.joi... | 2,690 | 26.459184 | 83 | py |
qcsbm | qcsbm-master/2d_examples/likelihood.py | """
Likelihood function from: https://github.com/yang-song/score_sde_pytorch
"""
import torch
import numpy as np
from scipy import integrate
def from_flattened_numpy(x, shape):
return torch.from_numpy(x.reshape(shape))
def to_flattened_numpy(x):
return x.detach().cpu().numpy().reshape((-1,))
def get_div_fn(fn):
... | 2,330 | 37.213115 | 104 | py |
qcsbm | qcsbm-master/2d_examples/run_lib_eval.py | import torch
from utils import calculate_asymmetricity, get_oracle_score_pointwise, calculate_score_error
from models import simple_score_fn
import likelihood
import datasets
import logging
import sampling_fn
import sde_lib
from prdc import compute_prdc
def eval_sampling(config):
# Load checkpoint.
score_model = ... | 4,911 | 42.469027 | 128 | py |
qcsbm | qcsbm-master/2d_examples/losses.py | import torch
def to_sliced_tensor(batch, slices):
return batch.unsqueeze(0).expand(slices, *batch.shape).contiguous().view(-1, *batch.shape[1:])
def to_sliced_vector(batch, slices):
sliced_batch = batch.unsqueeze(0).expand(slices, *batch.shape).contiguous().view(-1)
return sliced_batch
def get_step_fn(reg_t... | 3,591 | 45.649351 | 121 | py |
qcsbm | qcsbm-master/2d_examples/sampling_fn.py | """
Sampling function from: https://github.com/yang-song/score_sde_pytorch
"""
import abc
import torch
from scipy import integrate
def from_flattened_numpy(x, shape):
return torch.from_numpy(x.reshape(shape))
def to_flattened_numpy(x):
return x.detach().cpu().numpy().reshape((-1,))
class Predictor(abc.ABC):
d... | 2,337 | 30.594595 | 87 | py |
qcsbm | qcsbm-master/2d_examples/run_lib.py | import os
import logging
import datasets
import losses
from models import simple_score_fn
from utils import plot_vector_field, get_oracle_score_pointwise, calculate_score_error, plot_data_points
import sampling_fn
import torch
import tensorflow as tf
import torch.optim as optim
from torch.utils import tensorboard
impo... | 5,756 | 46.975 | 173 | py |
qcsbm | qcsbm-master/2d_examples/utils.py | import logging
import numpy as np
import torch
from torch.autograd import Variable
import pandas as pd
import seaborn as sn
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
import datasets
matplotlib.use('Agg')
def oracle_score_denominator(point, batch, sigma):
return torch.e... | 6,322 | 38.030864 | 133 | py |
qcsbm | qcsbm-master/2d_examples/sde_lib.py | """
SDE library from: https://github.com/yang-song/score_sde_pytorch
"""
import abc
import torch
import numpy as np
def scan(f, init, xs):
carry = init
ys = []
for x in xs:
carry, y = f(carry, x)
ys.append(y)
return carry, torch.stack(ys)
class SDE(abc.ABC):
def __init__(self, N):
super().__init... | 3,173 | 25.672269 | 119 | py |
qcsbm | qcsbm-master/2d_examples/models/simple_score_fn.py | import torch
import torch.nn as nn
class Swish(nn.Module):
def __init__(self, dim=-1):
"""
Swish from: https://github.com/wgrathwohl/LSD/blob/master/networks.py#L299
"""
super().__init__()
if dim > 0:
self.beta = nn.Parameter(torch.ones((dim,)))
else:
self.beta = torch.ones((1,))
... | 3,242 | 38.54878 | 132 | py |
qcsbm | qcsbm-master/2d_examples/configs/default_toy_configs.py | import ml_collections
import torch
def get_default_configs():
config = ml_collections.ConfigDict()
# training
config.training = training = ml_collections.ConfigDict()
training.n_iters = 3000
training.batch_size = 5000
training.snapshot_freq = 500
training.log_freq = 250
training.plot_freq = 1000
# ... | 1,446 | 23.116667 | 94 | py |
enhance | enhance-master/enhance.py | import numpy as np
import platform
import os
import time
import argparse
from astropy.io import fits
import tensorflow as tf
import keras as krs
import models as nn_model
# Using TensorFlow backend
os.environ["KERAS_BACKEND"] = "tensorflow"
print('tensorflow version:',tf.__version__)
print('keras version:',krs.__vers... | 6,637 | 34.881081 | 175 | py |
enhance | enhance-master/models.py | from keras.layers import Input, Conv2D, Activation, BatchNormalization, GaussianNoise, add, UpSampling2D
from keras.models import Model
from keras.regularizers import l2
import tensorflow as tf
from tensorflow.keras.layers import Layer, InputSpec
from keras.utils import conv_utils
import keras.backend as K
import keras... | 10,167 | 39.189723 | 120 | py |
enhance | enhance-master/train.py | import numpy as np
# from astropy.io import fits
import platform
import os
import json
import argparse
import h5py
try:
from contextlib import redirect_stdout
except:
# https://stackoverflow.com/questions/44226221/contextlib-redirect-stdout-in-python2-7
import sys
import contextlib
@contextlib.conte... | 11,900 | 40.179931 | 168 | py |
Reproducing-BowNet | Reproducing-BowNet-main/kmeans_cluster_and_bownet_training.py | '''
2020 ML Reproducibility Challenge
Harry Nguyen, Stone Yun, Hisham Mohammad
Part of our submission for reproducing the CVPR 2020 paper: Learning Representations by Predicting Bags of Visual Words
https://arxiv.org/abs/2002.12247
'''
import numpy as np
import argparse
import time
from tqdm import tqdm
import torch
im... | 8,446 | 43.457895 | 143 | py |
Reproducing-BowNet | Reproducing-BowNet-main/rotation_prediction_training.py | from __future__ import print_function
import argparse
import copy
import os
import imp
from dataloader import DataLoader, GenericDataset, get_dataloader
import matplotlib.pyplot as plt
from model import BowNet
from utils import load_checkpoint, accuracy
from tqdm import tqdm
import torch
import torch.nn as nn
import t... | 4,850 | 33.161972 | 126 | py |
Reproducing-BowNet | Reproducing-BowNet-main/rotnet_linearclf.py | from __future__ import print_function
import argparse
import sys
import os
import imp
from dataloader import DataLoader, GenericDataset, get_dataloader
import matplotlib.pyplot as plt
import copy
from model import LinearClassifier, NonLinearClassifier
from utils import load_checkpoint, accuracy
from model import BowN... | 6,222 | 30.75 | 126 | py |
Reproducing-BowNet | Reproducing-BowNet-main/rotnet_nonlinearclf.py | from __future__ import print_function
import argparse
import sys
import os
import imp
from dataloader import DataLoader, GenericDataset, get_dataloader
import matplotlib.pyplot as plt
import copy
from model import LinearClassifier, NonLinearClassifier
from utils import load_checkpoint, accuracy
from model import BowN... | 6,538 | 29.990521 | 126 | py |
Reproducing-BowNet | Reproducing-BowNet-main/rotnet_linearclf_supervised.py | from __future__ import print_function
import argparse
import os
import imp
from dataloader import get_dataloader,DataLoader, GenericDataset
import matplotlib.pyplot as plt
import copy
from model import BowNet,LinearClassifier, NonLinearClassifier
from utils import load_checkpoint, accuracy
#from model import BowNet3... | 6,680 | 31.275362 | 126 | py |
Reproducing-BowNet | Reproducing-BowNet-main/bownet_plus_linearclf_cifar_training.py | from __future__ import print_function
import argparse
import os
import imp
from dataloader import DataLoader, GenericDataset, get_dataloader
import matplotlib.pyplot as plt
import copy
from model import BowNet, BowNet2, LinearClassifier, NonLinearClassifier
from utils import accuracy, load_checkpoint
from tqdm import... | 6,492 | 33.354497 | 126 | py |
Reproducing-BowNet | Reproducing-BowNet-main/dataloader.py | """
Data-loader from original RotNet paper's code
Gidaris et. al https://arxiv.org/abs/1803.07728
https://github.com/gidariss/FeatureLearningRotNet
"""
from __future__ import print_function
import torch
import torch.utils.data as data
import torchvision
import torchvision.datasets as datasets
import torchvision.trans... | 11,540 | 35.065625 | 140 | py |
Reproducing-BowNet | Reproducing-BowNet-main/utils.py | import numpy as np
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
def load_checkpoint(checkpoint_path, device, bownet_arch, num_classes=4, bow_training=False):
checkpoint = torch.load(checkpoint_path)
bownet = bownet_arch(num_classes, bow_training).t... | 1,171 | 31.555556 | 93 | py |
Reproducing-BowNet | Reproducing-BowNet-main/model.py | '''
2020 ML Reproducibility Challenge
Harry Nguyen, Stone Yun, Hisham Mohammad
Part of our submission for reproducing the CVPR 2020 paper: Learning Representations by Predicting Bags of Visual Words
https://arxiv.org/abs/2002.12247
'''
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as ... | 12,270 | 39.903333 | 132 | py |
Reproducing-BowNet | Reproducing-BowNet-main/layers.py | '''
2020 ML Reproducibility Challenge
Harry Nguyen, Stone Yun, Hisham Mohammad
Part of our submission for reproducing the CVPR 2020 paper: Learning Representations by Predicting Bags of Visual Words
https://arxiv.org/abs/2002.12247
'''
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as ... | 4,469 | 38.910714 | 148 | py |
Reproducing-BowNet | Reproducing-BowNet-main/test_model.py |
from __future__ import print_function
import argparse
import copy
import os
import imp
from dataloader import DataLoader, GenericDataset, get_dataloader
import matplotlib.pyplot as plt
from model import BowNet
from utils import load_checkpoint, accuracy
from tqdm import tqdm
import torch
import torch.nn as nn
import ... | 2,212 | 27.74026 | 122 | py |
Reproducing-BowNet | Reproducing-BowNet-main/rotnet_nonlinearclf_supervised.py | from __future__ import print_function
import argparse
import os
import imp
from dataloader import get_dataloader,DataLoader, GenericDataset
import matplotlib.pyplot as plt
import copy
from model import BowNet, LinearClassifier, NonLinearClassifier
from utils import load_checkpoint, accuracy
#from model import BowNet3... | 6,625 | 31.165049 | 126 | py |
scitldr | scitldr-master/scripts/generate.py | import torch
from fairseq.models.bart import BARTModel
import argparse
from tqdm import tqdm
import os
from os.path import join
import logging
import time
def generate_TLDRs(bsz, count, datadir, outdir,
checkpoint_dir, checkpoint_file, test_fname,
beam, lenpen, max_len_b, min_l... | 4,039 | 38.223301 | 105 | py |
sound-clf-pytorch | sound-clf-pytorch-master/advanced/preprocess_fat2018.py | """Preprocess Freesound Audio Tagging 2018 competition data.
"""
import warnings
warnings.simplefilter('ignore')
from src.libs import *
from tqdm import tqdm
import fire
def convert(config='config.yaml'):
cfg = load_config(config)
print(cfg)
DATA_ROOT = Path(cfg.data_root)
DEST = Path('work')/cfg.typ... | 1,498 | 33.068182 | 87 | py |
sound-clf-pytorch | sound-clf-pytorch-master/advanced/fat2018.py | """Multi-fold Freesound Audio Tagging solution.
"""
from src.libs import *
import datetime
from advanced.metric_fat2018 import eval_fat2018_all_splits, eval_fat2018_by_probas
from src.models import resnetish18, VGGish, AlexNet
def report_result(message):
print(message)
# you might want to report to slack or ... | 6,592 | 44.157534 | 143 | py |
sound-clf-pytorch | sound-clf-pytorch-master/advanced/metric_fat2018.py | # Based on https://github.com/DCASE-REPO/dcase2018_baseline/blob/master/task2/evaluation.py
from src.libs import *
import datetime
import numpy as np
import torch
import multiprocessing
def one_ap(gt, topk):
for i, p in enumerate(topk):
if gt == p:
return 1.0 / (i + 1.0)
return 0.... | 2,603 | 36.2 | 128 | py |
sound-clf-pytorch | sound-clf-pytorch-master/src/multi_label_libs.py | import pytorch_lightning as pl
import datetime
import logging
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import multiprocessing
from dlcliche.torch_utils import IntraBatchMixupBCE
from dlcliche.utils import copy_file
from .lwlrap import Lwlrap
from skmultilearn.model_selection... | 7,547 | 35.463768 | 128 | py |
sound-clf-pytorch | sound-clf-pytorch-master/src/augmentations.py | # Borrowed from https://github.com/pytorch/vision/blob/master/torchvision/transforms/functional.py
import torch
import torch.nn.functional as F
import math
class GenericRandomResizedCrop():
def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.)):
self.size = size
self.scale = scale
... | 1,926 | 34.036364 | 107 | py |
sound-clf-pytorch | sound-clf-pytorch-master/src/models.py | """Audio models based on VGGish [1] paper.
## About
Based on following implementations:
- https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py -- borrowed most of code from this torchvision implementation.
- https://github.com/harritaylor/torchvggish
## Disclaimer
Tried to follow the original... | 14,294 | 36.81746 | 253 | py |
sound-clf-pytorch | sound-clf-pytorch-master/src/libs.py | import warnings
warnings.simplefilter('ignore')
# Essential PyTorch
import torch
import torchaudio
# Other modules used in this notebook
from pathlib import Path
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from IPython.display import Audio
import fire
import yaml
import multiprocessing
from... | 6,724 | 32.292079 | 128 | py |
sound-clf-pytorch | sound-clf-pytorch-master/for_evar/cnn14_decoupled.py | """CNN14 network, decoupled from Spectrogram, LogmelFilterBank, SpecAugmentation, and classifier head.
## Reference
- [1] https://arxiv.org/abs/1912.10211 "PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition"
- [2] https://github.com/qiuqiangkong/audioset_tagging_cnn
"""
import torch
fro... | 6,092 | 35.704819 | 136 | py |
DIA_noDIA | DIA_noDIA-main/Saliency_maps.py | import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
import pandas as pd
import seaborn as sns
from matplotlib import gridspec
import random
import data_utils
import model_utils
import plot_utils
import os
os.environ['PYTHONHASHSEED'] = str(76)
random.seed(45)
np.rando... | 12,932 | 36.59593 | 165 | py |
DIA_noDIA | DIA_noDIA-main/plot_utils.py | import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import tensorflow as tf
from tensorflow import keras
def create_confusion_matrix(model_name, y_pred, targ):
"""
Parameters
- model_name: name of the model trained and specify which data is being predict... | 2,036 | 39.74 | 134 | py |
DIA_noDIA | DIA_noDIA-main/test.py | import data_utils
import model_utils
import plot_utils
import matplotlib.pyplot as plt
import sys
import os
import numpy as np
import random
import tensorflow as tf
import pandas as pd
def multiple_models(data_location, model_name, ddh = 0, checkpoints = 0):
if checkpoints == 1:
comd = "cp model_check... | 5,740 | 39.429577 | 131 | py |
DIA_noDIA | DIA_noDIA-main/model_utils.py | import numpy as np
import time
import pandas as pd
import os
import random
kernel_size = 5
epochs = 40
batch_size = 20
os.environ['PYTHONHASHSEED'] = str(76)
import tensorflow as tf
from tensorflow import keras
def keras_model_small_and_save(train, test, train_targ, test_targ ):
np.random.seed(1)
tf.random.... | 38,695 | 41.4764 | 141 | py |
DIA_noDIA | DIA_noDIA-main/grid_run.py | import sys
import os
import numpy as np
import random
os.environ['PYTHONHASHSEED'] = str(76)
import tensorflow as tf
from tensorflow import keras
import time
import pandas as pd
from config import *
import glob
from astropy.io import fits
from tensorflow.keras.wrappers.scikit_learn import KerasClassifier
from sklearn.m... | 7,361 | 44.165644 | 338 | py |
DIA_noDIA | DIA_noDIA-main/RESNET.py | import data_utils
import model_utils
import plot_utils
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import sys
import os
import tensorflow as tf
import numpy as np
import random
from tensorflow import keras
os.environ['PYTHONHASHSEED'] = str(76)
random.seed(45)
np.random.seed(1)
tf.random... | 5,014 | 39.12 | 128 | py |
DIA_noDIA | DIA_noDIA-main/grid_run_best.py | import sys
import os
import numpy as np
import random
os.environ['PYTHONHASHSEED'] = str(76)
import tensorflow as tf
from tensorflow import keras
import time
import pandas as pd
from config import *
import glob
from astropy.io import fits
import data_utils
import plot_utils
import model_utils
import matplotlib.pyplot a... | 7,241 | 40.62069 | 334 | py |
DIA_noDIA | DIA_noDIA-main/model_utils_kfold.py |
import numpy as np
import time
import pandas as pd
import os
import random
from sklearn.model_selection import KFold
kernel_size = 5
epochs = 40
batch_size = 20
os.environ['PYTHONHASHSEED'] = str(76)
import tensorflow as tf
from tensorflow import keras
def keras_model_small_and_save(train, test, train_targ, test_ta... | 39,816 | 41.53953 | 145 | py |
Inf-Net | Inf-Net-master/MyTest_LungInf.py | # -*- coding: utf-8 -*-
"""Preview
Code for 'Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Scans'
submit to Transactions on Medical Imaging, 2020.
First Version: Created on 2020-05-13 (@author: Ge-Peng Ji)
"""
import torch
import torch.nn.functional as F
import numpy as np
import os
import argparse... | 2,697 | 39.878788 | 117 | py |
Inf-Net | Inf-Net-master/PseudoGenerator.py | # -*- coding: utf-8 -*-
"""Preview
Code for 'Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Scans'
submit to Transactions on Medical Imaging, 2020.
First Version: Created on 2020-05-13 (@author: Ge-Peng Ji)
"""
# ---- base lib -----
import os
import argparse
from datetime import datetime
import cv2
... | 10,492 | 43.84188 | 133 | py |
Inf-Net | Inf-Net-master/MyTrain_LungInf.py | # -*- coding: utf-8 -*-
"""Preview
Code for 'Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Scans'
submit to Transactions on Medical Imaging, 2020.
1st Version: Created on 2020-05-13 (@author: Ge-Peng Ji)
2nd Version: Fix some bugs caused by THOP on 2020-06-10 (@author: Ge-Peng Ji)
"""
import torch
... | 9,425 | 47.338462 | 133 | py |
Inf-Net | Inf-Net-master/MyTest_MulClsLungInf_UNet.py | # -*- coding: utf-8 -*-
"""Preview
Code for 'Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Scans'
submit to Transactions on Medical Imaging, 2020.
First Version: Created on 2020-05-13 (@author: Ge-Peng Ji)
"""
import os
import numpy as np
from Code.utils.dataloader_MulClsLungInf_UNet import LungDat... | 2,735 | 38.652174 | 124 | py |
Inf-Net | Inf-Net-master/MyTrain_MulClsLungInf_UNet.py | # -*- coding: utf-8 -*-
"""Preview
Code for 'Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Scans'
submit to Transactions on Medical Imaging, 2020.
First Version: Created on 2020-05-13 (@author: Ge-Peng Ji)
"""
import os
import numpy as np
import torch.optim as optim
from Code.utils.dataloader_MulCl... | 3,332 | 38.211765 | 130 | py |
Inf-Net | Inf-Net-master/Code/model_lung_infection/InfNet_ResNet.py | # -*- coding: utf-8 -*-
"""Preview
Code for 'Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Scans'
submit to Transactions on Medical Imaging, 2020.
First Version: Created on 2020-05-05 (@author: Ge-Peng Ji)
Second Version: Fix some bugs and edit some parameters on 2020-05-15. (@author: Ge-Peng Ji)
""... | 10,527 | 42.504132 | 114 | py |
Inf-Net | Inf-Net-master/Code/model_lung_infection/InfNet_Res2Net.py | # -*- coding: utf-8 -*-
"""Preview
Code for 'Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Scans'
submit to Transactions on Medical Imaging, 2020.
First Version: Created on 2020-05-05 (@author: Ge-Peng Ji)
Second Version: Fix some bugs and edit some parameters on 2020-05-15. (@author: Ge-Peng Ji)
""... | 9,945 | 43.00885 | 114 | py |
Inf-Net | Inf-Net-master/Code/model_lung_infection/InfNet_VGGNet.py | # -*- coding: utf-8 -*-
"""Preview
Code for 'Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Scans'
submit to Transactions on Medical Imaging, 2020.
First Version: Created on 2020-05-05 (@author: Ge-Peng Ji)
Second Version: Fix some bugs and edit some parameters on 2020-05-15. (@author: Ge-Peng Ji)
""... | 9,869 | 43.660633 | 114 | py |
Inf-Net | Inf-Net-master/Code/model_lung_infection/backbone/ResNet.py | import torch.nn as nn
import math
def conv3x3(in_planes, out_planes, stride=1):
"""
3x3 convolution with padding
"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(... | 4,234 | 28.823944 | 77 | py |
Inf-Net | Inf-Net-master/Code/model_lung_infection/backbone/Res2Net.py | import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torch
import torch.nn.functional as F
__all__ = ['Res2Net', 'res2net50_v1b', 'res2net101_v1b', 'res2net50_v1b_26w_4s']
model_urls = {
'res2net50_v1b_26w_4s': 'https://shanghuagao.oss-cn-beijing.aliyuncs.com/res2net/res2net50_v1b_26... | 8,355 | 35.489083 | 122 | py |
Inf-Net | Inf-Net-master/Code/model_lung_infection/backbone/VGGNet.py | import torch
import torch.nn as nn
class B2_VGG(nn.Module):
def __init__(self):
super(B2_VGG, self).__init__()
conv1 = nn.Sequential()
conv1.add_module('conv1_1', nn.Conv2d(3, 64, 3, 1, 1))
conv1.add_module('relu1_1', nn.ReLU(inplace=True))
conv1.add_module('conv1_2', nn.Co... | 6,220 | 47.984252 | 76 | py |
Inf-Net | Inf-Net-master/Code/model_lung_infection/backbone/DenseNet.py | import re
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from collections import OrderedDict
__all__ = ['DenseNet', 'densenet121', 'densenet169', 'densenet201', 'densenet161']
model_urls = {
'densenet121': 'https://download.pytorch.org/models/densenet... | 10,105 | 43.915556 | 109 | py |
Inf-Net | Inf-Net-master/Code/model_lung_infection/module/grid_attention_layer.py | import torch
from torch import nn
from torch.nn import functional as F
from Code.model_lung_infection import init_weights
class _GridAttentionBlockND(nn.Module):
def __init__(self, in_channels, gating_channels, inter_channels=None, dimension=3, mode='concatenation',
sub_sample_factor=(2, 2, 2)):
... | 16,624 | 40.458853 | 137 | py |
Inf-Net | Inf-Net-master/Code/model_lung_infection/module/unet_parts.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 29 22:32:00 2020
@author: taozhou
"""
""" Parts of the U-Net model """
import torch
import torch.nn as nn
import torch.nn.functional as F
class DoubleConv(nn.Module):
"""(convolution => [BN] => ReLU) * 2"""
def __init__(self, in_channel... | 2,689 | 30.27907 | 122 | py |
Inf-Net | Inf-Net-master/Code/model_lung_infection/module/networks_other.py | import torch
import torch.nn as nn
from torch.nn import init
import functools
from torch.autograd import Variable
from torch.optim import lr_scheduler
import time
import numpy as np
###############################################################################
# Functions
############################################... | 20,744 | 36.786885 | 120 | py |
Inf-Net | Inf-Net-master/Code/utils/dataloader_MulClsLungInf_UNet.py | # -*- coding: utf-8 -*-
"""Preview
Code for 'Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Scans'
submit to Transactions on Medical Imaging, 2020.
First Version: Created on 2020-05-13 (@author: Ge-Peng Ji)
"""
import os
import torch
from torch.utils.data import Dataset
import cv2
from Code.utils.on... | 2,008 | 28.985075 | 90 | py |
Inf-Net | Inf-Net-master/Code/utils/utils.py | import torch
import numpy as np
# `pip install thop`
from thop import profile
from thop import clever_format
def clip_gradient(optimizer, grad_clip):
"""
For calibrating mis-alignment gradient via cliping gradient technique
:param optimizer:
:param grad_clip:
:return:
"""
for group in opti... | 1,740 | 26.634921 | 102 | py |
Inf-Net | Inf-Net-master/Code/utils/loss_function.py | #!/usr/env/bin python3.6
# NOTES: this repository do not used in current release, I will add more loss function in future
from typing import List
import torch
from torch import Tensor, einsum
from utils import simplex, one_hot
class CrossEntropy():
def __init__(self, **kwargs):
# Self.idc is used to fil... | 3,176 | 33.532609 | 108 | py |
Inf-Net | Inf-Net-master/Code/utils/dataloader_LungInf.py | # -*- coding: utf-8 -*-
"""Preview
Code for 'Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Scans'
submit to Transactions on Medical Imaging, 2020.
First Version: Created on 2020-05-13 (@author: Ge-Peng Ji)
"""
import os
from PIL import Image
import torch.utils.data as data
import torchvision.transf... | 5,014 | 34.06993 | 115 | py |
roosterize | roosterize-master/train.py | #!/usr/bin/env python
"""Train models."""
import os
import signal
import torch
import onmt.opts as opts
import onmt.utils.distributed
from onmt.utils.misc import set_random_seed
from onmt.utils.logging import init_logger, logger
from onmt.train_single import main as single_main
from onmt.utils.parse import ArgumentPa... | 6,683 | 32.253731 | 79 | py |
roosterize | roosterize-master/preprocess.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Pre-process Data / features files and build vocabulary
"""
import codecs
import glob
import sys
import gc
import torch
from functools import partial
from collections import Counter, defaultdict
from onmt.utils.logging import init_logger, logger
from onmt.utils.misc... | 8,024 | 35.312217 | 79 | py |
roosterize | roosterize-master/roosterize/main.py | import random
import sys
import time
from pathlib import Path
from seutil import CliUtils, IOUtils, LoggingUtils
from roosterize.Environment import Environment
from roosterize.Macros import Macros
from roosterize.Utils import Utils
logging_file = Macros.this_dir.parent / "experiment.log"
LoggingUtils.setup(filename=... | 9,561 | 33.644928 | 126 | py |
roosterize | roosterize-master/roosterize/interface/CommandLineInterface.py | import warnings
import re
import shutil
import tempfile
import urllib
import urllib.request
from pathlib import Path
from typing import List, NamedTuple, Optional, Tuple
import numpy as np
from roosterize.Macros import Macros
from seutil import BashUtils, IOUtils
from roosterize.data.CoqDocument import CoqDocument
fr... | 14,772 | 38.289894 | 137 | py |
roosterize | roosterize-master/roosterize/ml/naming/MultiSourceSeq2Seq.py | import collections
import gc
import sys
import time
from functools import partial
from pathlib import Path
from typing import Dict, List, NoReturn, Optional, Tuple
import numpy as np
import torch
import torch.nn
import torch.nn.utils
from recordclass import RecordClass
from seutil import IOUtils, LoggingUtils
from ro... | 36,659 | 40.052632 | 240 | py |
roosterize | roosterize-master/roosterize/ml/onmt/MultiSourceModelSaver.py | from typing import *
from copy import deepcopy
from onmt.models.model_saver import ModelSaver
import torch
import torch.nn as nn
from seutil import LoggingUtils
class MultiSourceModelSaver(ModelSaver):
logger = LoggingUtils.get_logger(__name__)
@classmethod
def build_model_saver(cls, src_types, model_... | 2,532 | 34.676056 | 98 | py |
roosterize | roosterize-master/roosterize/ml/onmt/MultiSourceInputFeedRNNDecoder.py | from typing import *
from roosterize.ml.onmt.MultiSourceGlobalAttention import MultiSourceGlobalAttention
from onmt.decoders.decoder import DecoderBase
from onmt.models.stacked_rnn import StackedLSTM, StackedGRU
from onmt.modules import context_gate_factory, GlobalAttention
from onmt.utils.misc import aeq
import torch... | 11,379 | 39.642857 | 133 | py |
roosterize | roosterize-master/roosterize/ml/onmt/MultiSourceTrainer.py | from typing import *
from copy import deepcopy
from roosterize.ml.onmt.CustomReportMgr import CustomReportMgr
from roosterize.ml.onmt.CustomTrainer import CustomTrainer
from roosterize.ml.onmt.MultiSourceCopyGenerator import MultiSourceCopyGeneratorLossCompute
import onmt
from onmt.modules.sparse_losses import Sparsem... | 11,353 | 40.137681 | 344 | py |
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