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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stablediffusion | stablediffusion-main/ldm/modules/ema.py | import torch
from torch import nn
class LitEma(nn.Module):
def __init__(self, model, decay=0.9999, use_num_upates=True):
super().__init__()
if decay < 0.0 or decay > 1.0:
raise ValueError('Decay must be between 0 and 1')
self.m_name2s_name = {}
self.register_buffer('de... | 3,110 | 37.407407 | 102 | py |
stablediffusion | stablediffusion-main/ldm/modules/attention.py | from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from typing import Optional, Any
from ldm.modules.diffusionmodules.util import checkpoint
try:
import xformers
import xformers.ops
XFORMERS_IS_AVAILBLE... | 11,806 | 33.523392 | 143 | py |
stablediffusion | stablediffusion-main/ldm/modules/midas/utils.py | """Utils for monoDepth."""
import sys
import re
import numpy as np
import cv2
import torch
def read_pfm(path):
"""Read pfm file.
Args:
path (str): path to file
Returns:
tuple: (data, scale)
"""
with open(path, "rb") as file:
color = None
width = None
heig... | 4,582 | 23.121053 | 88 | py |
stablediffusion | stablediffusion-main/ldm/modules/midas/api.py | # based on https://github.com/isl-org/MiDaS
import cv2
import torch
import torch.nn as nn
from torchvision.transforms import Compose
from ldm.modules.midas.midas.dpt_depth import DPTDepthModel
from ldm.modules.midas.midas.midas_net import MidasNet
from ldm.modules.midas.midas.midas_net_custom import MidasNet_small
fr... | 5,338 | 30.222222 | 103 | py |
stablediffusion | stablediffusion-main/ldm/modules/midas/midas/base_model.py | import torch
class BaseModel(torch.nn.Module):
def load(self, path):
"""Load model from file.
Args:
path (str): file path
"""
parameters = torch.load(path, map_location=torch.device('cpu'))
if "optimizer" in parameters:
parameters = parameters["mod... | 367 | 20.647059 | 71 | py |
stablediffusion | stablediffusion-main/ldm/modules/midas/midas/midas_net.py | """MidashNet: Network for monocular depth estimation trained by mixing several datasets.
This file contains code that is adapted from
https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
"""
import torch
import torch.nn as nn
from .base_model import BaseModel
f... | 2,709 | 34.194805 | 130 | py |
stablediffusion | stablediffusion-main/ldm/modules/midas/midas/vit.py | import torch
import torch.nn as nn
import timm
import types
import math
import torch.nn.functional as F
class Slice(nn.Module):
def __init__(self, start_index=1):
super(Slice, self).__init__()
self.start_index = start_index
def forward(self, x):
return x[:, self.start_index :]
class... | 14,625 | 28.727642 | 96 | py |
stablediffusion | stablediffusion-main/ldm/modules/midas/midas/dpt_depth.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .base_model import BaseModel
from .blocks import (
FeatureFusionBlock,
FeatureFusionBlock_custom,
Interpolate,
_make_encoder,
forward_vit,
)
def _make_fusion_block(features, use_bn):
return FeatureFusionBlock_custom(
... | 3,154 | 27.681818 | 89 | py |
stablediffusion | stablediffusion-main/ldm/modules/midas/midas/midas_net_custom.py | """MidashNet: Network for monocular depth estimation trained by mixing several datasets.
This file contains code that is adapted from
https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
"""
import torch
import torch.nn as nn
from .base_model import BaseModel
f... | 5,207 | 39.6875 | 168 | py |
stablediffusion | stablediffusion-main/ldm/modules/midas/midas/blocks.py | import torch
import torch.nn as nn
from .vit import (
_make_pretrained_vitb_rn50_384,
_make_pretrained_vitl16_384,
_make_pretrained_vitb16_384,
forward_vit,
)
def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ign... | 9,242 | 25.947522 | 150 | py |
stablediffusion | stablediffusion-main/ldm/modules/distributions/distributions.py | import torch
import numpy as np
class AbstractDistribution:
def sample(self):
raise NotImplementedError()
def mode(self):
raise NotImplementedError()
class DiracDistribution(AbstractDistribution):
def __init__(self, value):
self.value = value
def sample(self):
retur... | 2,970 | 30.946237 | 131 | py |
stablediffusion | stablediffusion-main/ldm/modules/karlo/diffusers_pipeline.py | # Copyright 2022 Kakao Brain and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requi... | 23,391 | 44.6875 | 131 | py |
stablediffusion | stablediffusion-main/ldm/modules/karlo/kakao/sampler.py | # ------------------------------------------------------------------------------------
# Karlo-v1.0.alpha
# Copyright (c) 2022 KakaoBrain. All Rights Reserved.
# source: https://github.com/kakaobrain/karlo/blob/3c68a50a16d76b48a15c181d1c5a5e0879a90f85/karlo/sampler/t2i.py#L15
# ----------------------------------------... | 8,718 | 30.937729 | 116 | py |
stablediffusion | stablediffusion-main/ldm/modules/karlo/kakao/template.py | # ------------------------------------------------------------------------------------
# Karlo-v1.0.alpha
# Copyright (c) 2022 KakaoBrain. All Rights Reserved.
# ------------------------------------------------------------------------------------
import os
import logging
import torch
from omegaconf import OmegaConf
... | 4,288 | 29.41844 | 102 | py |
stablediffusion | stablediffusion-main/ldm/modules/karlo/kakao/modules/resample.py | # ------------------------------------------------------------------------------------
# Modified from Guided-Diffusion (https://github.com/openai/guided-diffusion)
# ------------------------------------------------------------------------------------
from abc import abstractmethod
import torch as th
def create_nam... | 2,237 | 31.434783 | 86 | py |
stablediffusion | stablediffusion-main/ldm/modules/karlo/kakao/modules/nn.py | # ------------------------------------------------------------------------------------
# Adapted from Guided-Diffusion repo (https://github.com/openai/guided-diffusion)
# ------------------------------------------------------------------------------------
import math
import torch as th
import torch.nn as nn
import to... | 3,233 | 27.121739 | 86 | py |
stablediffusion | stablediffusion-main/ldm/modules/karlo/kakao/modules/unet.py | # ------------------------------------------------------------------------------------
# Modified from Guided-Diffusion (https://github.com/openai/guided-diffusion)
# ------------------------------------------------------------------------------------
import math
from abc import abstractmethod
import torch as th
impo... | 28,289 | 34.674653 | 124 | py |
stablediffusion | stablediffusion-main/ldm/modules/karlo/kakao/modules/xf.py | # ------------------------------------------------------------------------------------
# Adapted from the repos below:
# (a) Guided-Diffusion (https://github.com/openai/guided-diffusion)
# (b) CLIP ViT (https://github.com/openai/CLIP/)
# ----------------------------------------------------------------------------------... | 6,535 | 27.172414 | 86 | py |
stablediffusion | stablediffusion-main/ldm/modules/karlo/kakao/modules/diffusion/gaussian_diffusion.py | # ------------------------------------------------------------------------------------
# Adapted from Guided-Diffusion repo (https://github.com/openai/guided-diffusion)
# ------------------------------------------------------------------------------------
import enum
import math
import numpy as np
import torch as th
... | 30,386 | 35.655006 | 129 | py |
stablediffusion | stablediffusion-main/ldm/modules/karlo/kakao/modules/diffusion/respace.py | # ------------------------------------------------------------------------------------
# Adapted from Guided-Diffusion repo (https://github.com/openai/guided-diffusion)
# ------------------------------------------------------------------------------------
import torch as th
from .gaussian_diffusion import GaussianDi... | 4,768 | 41.20354 | 91 | py |
stablediffusion | stablediffusion-main/ldm/modules/karlo/kakao/models/clip.py | # ------------------------------------------------------------------------------------
# Karlo-v1.0.alpha
# Copyright (c) 2022 KakaoBrain. All Rights Reserved.
# ------------------------------------------------------------------------------------
# -----------------------------------------------------------------------... | 6,071 | 32.180328 | 97 | py |
stablediffusion | stablediffusion-main/ldm/modules/karlo/kakao/models/sr_64_256.py | # ------------------------------------------------------------------------------------
# Karlo-v1.0.alpha
# Copyright (c) 2022 KakaoBrain. All Rights Reserved.
# ------------------------------------------------------------------------------------
import copy
import torch
from ldm.modules.karlo.kakao.modules.unet impo... | 3,661 | 40.146067 | 115 | py |
stablediffusion | stablediffusion-main/ldm/modules/karlo/kakao/models/decoder_model.py | # ------------------------------------------------------------------------------------
# Karlo-v1.0.alpha
# Copyright (c) 2022 KakaoBrain. All Rights Reserved.
# ------------------------------------------------------------------------------------
import copy
import torch
from ldm.modules.karlo.kakao.modules import cr... | 6,719 | 33.639175 | 86 | py |
stablediffusion | stablediffusion-main/ldm/modules/karlo/kakao/models/prior_model.py | # ------------------------------------------------------------------------------------
# Karlo-v1.0.alpha
# Copyright (c) 2022 KakaoBrain. All Rights Reserved.
# ------------------------------------------------------------------------------------
import copy
import torch
from ldm.modules.karlo.kakao.modules import cr... | 5,101 | 35.705036 | 90 | py |
stablediffusion | stablediffusion-main/ldm/modules/image_degradation/bsrgan.py | # -*- coding: utf-8 -*-
"""
# --------------------------------------------
# Super-Resolution
# --------------------------------------------
#
# Kai Zhang (cskaizhang@gmail.com)
# https://github.com/cszn
# From 2019/03--2021/08
# --------------------------------------------
"""
import numpy as np
import cv2
import tor... | 25,198 | 33.471956 | 147 | py |
stablediffusion | stablediffusion-main/ldm/modules/image_degradation/bsrgan_light.py | # -*- coding: utf-8 -*-
import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as ... | 22,341 | 33.266871 | 147 | py |
stablediffusion | stablediffusion-main/ldm/modules/image_degradation/utils_image.py | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
#import matplotlib.pyplot as plt # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
'''
# ----------------------... | 29,022 | 30.684498 | 107 | py |
stablediffusion | stablediffusion-main/ldm/modules/encoders/modules.py | import torch
import torch.nn as nn
import kornia
from torch.utils.checkpoint import checkpoint
from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel
import open_clip
from ldm.util import default, count_params, autocast
class AbstractEncoder(nn.Module):
def __init__(self):
su... | 12,694 | 35.168091 | 120 | py |
stablediffusion | stablediffusion-main/ldm/modules/diffusionmodules/upscaling.py | import torch
import torch.nn as nn
import numpy as np
from functools import partial
from ldm.modules.diffusionmodules.util import extract_into_tensor, make_beta_schedule
from ldm.util import default
class AbstractLowScaleModel(nn.Module):
# for concatenating a downsampled image to the latent representation
d... | 3,424 | 40.768293 | 110 | py |
stablediffusion | stablediffusion-main/ldm/modules/diffusionmodules/model.py | # pytorch_diffusion + derived encoder decoder
import math
import torch
import torch.nn as nn
import numpy as np
from einops import rearrange
from typing import Optional, Any
from ldm.modules.attention import MemoryEfficientCrossAttention
try:
import xformers
import xformers.ops
XFORMERS_IS_AVAILBLE = True... | 34,384 | 39.310668 | 138 | py |
stablediffusion | stablediffusion-main/ldm/modules/diffusionmodules/openaimodel.py | from abc import abstractmethod
import math
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from ldm.modules.diffusionmodules.util import (
checkpoint,
conv_nd,
linear,
avg_pool_nd,
zero_module,
normalization,
timestep_embedding,
)
from ldm.module... | 31,050 | 37.429455 | 143 | py |
stablediffusion | stablediffusion-main/ldm/modules/diffusionmodules/util.py | # adopted from
# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
# and
# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
# and
# https://github.com/openai/gu... | 10,102 | 35.21147 | 164 | py |
stablediffusion | stablediffusion-main/ldm/models/autoencoder.py | import torch
import pytorch_lightning as pl
import torch.nn.functional as F
from contextlib import contextmanager
from ldm.modules.diffusionmodules.model import Encoder, Decoder
from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
from ldm.util import instantiate_from_config
from ldm.modul... | 8,560 | 37.913636 | 116 | py |
stablediffusion | stablediffusion-main/ldm/models/diffusion/ddim.py | """SAMPLING ONLY."""
import torch
import numpy as np
from tqdm import tqdm
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor
class DDIMSampler(object):
def __init__(self, model, schedule="linear", device=torch.device("cuda"), **kwar... | 17,344 | 50.468843 | 136 | py |
stablediffusion | stablediffusion-main/ldm/models/diffusion/ddpm.py | """
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https... | 88,698 | 46.331377 | 162 | py |
stablediffusion | stablediffusion-main/ldm/models/diffusion/plms.py | """SAMPLING ONLY."""
import torch
import numpy as np
from tqdm import tqdm
from functools import partial
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
from ldm.models.diffusion.sampling_util import norm_thresholding
class PLMSSampler(object):
def __... | 12,967 | 51.715447 | 131 | py |
stablediffusion | stablediffusion-main/ldm/models/diffusion/sampling_util.py | import torch
import numpy as np
def append_dims(x, target_dims):
"""Appends dimensions to the end of a tensor until it has target_dims dimensions.
From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py"""
dims_to_append = target_dims - x.ndim
if dims_to_append < 0:
rais... | 753 | 33.272727 | 100 | py |
stablediffusion | stablediffusion-main/ldm/models/diffusion/dpm_solver/dpm_solver.py | import torch
import torch.nn.functional as F
import math
from tqdm import tqdm
class NoiseScheduleVP:
def __init__(
self,
schedule='discrete',
betas=None,
alphas_cumprod=None,
continuous_beta_0=0.1,
continuous_beta_1=20.,
):
"""Cr... | 66,500 | 56.180567 | 308 | py |
stablediffusion | stablediffusion-main/ldm/models/diffusion/dpm_solver/sampler.py | """SAMPLING ONLY."""
import torch
from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver
MODEL_TYPES = {
"eps": "noise",
"v": "v"
}
class DPMSolverSampler(object):
def __init__(self, model, device=torch.device("cuda"), **kwargs):
super().__init__()
self.model = model
... | 3,530 | 35.402062 | 115 | py |
stablediffusion | stablediffusion-main/ldm/data/util.py | import torch
from ldm.modules.midas.api import load_midas_transform
class AddMiDaS(object):
def __init__(self, model_type):
super().__init__()
self.transform = load_midas_transform(model_type)
def pt2np(self, x):
x = ((x + 1.0) * .5).detach().cpu().numpy()
return x
def n... | 629 | 25.25 | 62 | py |
Generalization-of-Federated-Learning | Generalization-of-Federated-Learning-main/dataset/generate_mnist.py | import numpy as np
import os
import sys
import random
import torch
import torchvision
import torchvision.transforms as transforms
from utils.dataset_utils import check, separate_data, split_data, save_file
random.seed(1)
np.random.seed(1)
num_clients = 10
num_classes = 10
dir_path = "mnist/"
# Allocate data to user... | 2,976 | 35.304878 | 101 | py |
Generalization-of-Federated-Learning | Generalization-of-Federated-Learning-main/system/main.py | #!/usr/bin/env python
import copy
import torch
import argparse
import os
import time
import warnings
import numpy as np
import torchvision
import logging
from flcore.servers.serveravg import FedAvg
from flcore.servers.serverpFedMe import pFedMe
from flcore.servers.serverperavg import PerAvg
from flcore.servers.serverp... | 20,098 | 43.964206 | 151 | py |
Generalization-of-Federated-Learning | Generalization-of-Federated-Learning-main/system/flcore/clients/clientbase.py | import copy
import torch
import torch.nn as nn
import numpy as np
import os
import torch.nn.functional as F
from torch.utils.data import DataLoader
from sklearn.preprocessing import label_binarize
from sklearn import metrics
from utils.data_utils import read_client_data
class Client(object):
"""
Base class fo... | 6,948 | 33.572139 | 102 | py |
Generalization-of-Federated-Learning | Generalization-of-Federated-Learning-main/system/flcore/servers/serverbase.py | import torch
import os
import numpy as np
import pandas as pd
import copy
import time
import random
from utils.data_utils import read_client_data
from utils.dlg import DLG
class Server(object):
def __init__(self, args, times):
# Set up the main attributes
self.args = args
self.device = ar... | 14,979 | 36.638191 | 173 | py |
Generalization-of-Federated-Learning | Generalization-of-Federated-Learning-main/system/flcore/servers/serverscaffold.py | import copy
import random
import time
import torch
from flcore.clients.clientscaffold import clientSCAFFOLD
from flcore.servers.serverbase import Server
from threading import Thread
class SCAFFOLD(Server):
def __init__(self, args, times):
super().__init__(args, times)
# select slow clients
... | 6,029 | 38.155844 | 116 | py |
cfsl | cfsl-master/frameworks/cfsl/experiment_builder.py | import os
import time
import numpy as np
import torchvision
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import tqdm
from torch.utils.tensorboard import SummaryWriter
from utils.storage import build_experiment_folder, save_statistics, save_to_json, save_config
class ExperimentBuilder(object):
def ... | 24,993 | 50.427984 | 188 | py |
cfsl | cfsl-master/frameworks/cfsl/data.py | import concurrent.futures
from collections import defaultdict
import os
import numpy as np
import torch
import tqdm
from PIL import ImageFile
from torch.utils.data import Dataset
from utils.dataset_tools import get_label_set, load_dataset, load_image, check_download_dataset
import re
ImageFile.LOAD_TRUNCATED_IMAGES =... | 12,561 | 45.354244 | 297 | py |
cfsl | cfsl-master/frameworks/cfsl/standard_neural_network_architectures.py | from collections import OrderedDict
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
# flag
class Conv2dNormLeakyReLU(nn.Module):
def __init__(self, input_shape, num_filters, kernel_size, dilation=1, stride=1, groups=1, padding=0, use_bias=False,
normalization... | 52,387 | 43.737831 | 246 | py |
cfsl | cfsl-master/frameworks/cfsl/meta_neural_network_architectures.py | import logging
import math
from copy import copy
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import _calculate_fan_in_and_fan_out
def extract_top_level_dict(current_dict):
"""
Builds a graph dictionary from the passed depth_keys, value pair. Useful... | 52,354 | 46.85649 | 223 | py |
cfsl | cfsl-master/frameworks/cfsl/meta_optimizer.py | import torch
import torch.nn as nn
class GradientDescentLearningRule(nn.Module):
"""Simple (stochastic) gradient descent learning rule.
For a scalar error function `E(p[0], p_[1] ... )` of some set of
potentially multidimensional parameters this attempts to find a local
minimum of the loss function by... | 5,676 | 48.798246 | 111 | py |
cfsl | cfsl-master/frameworks/cfsl/pytorch_utils.py | import torch
import torch.functional as F
import torch.nn as nn
import numpy as np
def int_to_one_hot(int_labels):
num_output_units = torch.max(int_labels).long() + 1
labels_one_hot = torch.zeros(int_labels.shape[0], num_output_units).long().to(int_labels.device)
labels_one_hot.scatter_(1, int_labels.unsq... | 430 | 27.733333 | 100 | py |
cfsl | cfsl-master/frameworks/cfsl/train_continual_learning_few_shot_system.py | from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from utils.parser_utils import get_args
from utils.dataset_tools import check_download_dataset
from data import ConvertToThreeChannels, FewShotLearningDatasetParallel
from torchvision import transforms
from experiment_builder im... | 6,704 | 55.822034 | 119 | py |
cfsl | cfsl-master/frameworks/cfsl/models/fine_tune_from_pretrained_few_shot_classifier.py | import os
from collections import OrderedDict, defaultdict
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
from torch.optim import AdamW
from meta_neural_network_architectures import VGGActivationNormNetwork, \
VGGActivationNormNetworkWithAttention
fro... | 32,626 | 52.751236 | 163 | py |
cfsl | cfsl-master/frameworks/cfsl/models/matching_network_few_shot_classifier.py | import os
from collections import OrderedDict, defaultdict
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
from torch.optim import AdamW
from meta_neural_network_architectures import VGGActivationNormNetwork, \
VGGActivationNormNetworkWithAttention
fro... | 12,697 | 42.486301 | 131 | py |
cfsl | cfsl-master/frameworks/cfsl/models/vgg_aha_few_shot_classifier.py | from operator import sub
import os
import random
from collections import OrderedDict, defaultdict, deque
import re
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
from torch.optim import AdamW
from meta_neural_network_architectures import VGGActivationNor... | 37,618 | 51.687675 | 163 | py |
cfsl | cfsl-master/frameworks/cfsl/models/embedding_maml_few_shot_classifier.py | import os
from collections import OrderedDict, defaultdict
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
from torch.optim import AdamW
from meta_neural_network_architectures import VGGActivationNormNetwork, \
VGGActivationNormNetworkWithAttention
fro... | 31,000 | 53.292469 | 128 | py |
cfsl | cfsl-master/frameworks/cfsl/models/fine_tune_from_scratch_few_shot_classifier.py | import os
from collections import OrderedDict, defaultdict
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
from torch.optim import AdamW
from meta_neural_network_architectures import VGGActivationNormNetwork, \
VGGActivationNormNetworkWithAttention
fro... | 31,917 | 53.190153 | 149 | py |
cfsl | cfsl-master/frameworks/cfsl/models/vgg_maml_few_shot_classifier.py | import os
from collections import OrderedDict, defaultdict
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
from torch.optim import AdamW
from meta_neural_network_architectures import VGGActivationNormNetwork, \
VGGActivationNormNetworkWithAttention
fro... | 26,266 | 50.503922 | 128 | py |
cfsl | cfsl-master/frameworks/cfsl/models/cls_few_shot_classifier.py | """cls_few_shot_classifier.py"""
import os
import random
from collections import OrderedDict, defaultdict, deque
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter
import torchvision
import matplotlib
i... | 19,502 | 39.378882 | 126 | py |
cfsl | cfsl-master/frameworks/cfsl/models/maml_few_shot_classifier.py | import os
from collections import OrderedDict, defaultdict
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
from torch.optim import AdamW
from meta_neural_network_architectures import VGGActivationNormNetwork, \
VGGActivationNormNetworkWithAttention
fro... | 14,763 | 48.710438 | 120 | py |
cfsl | cfsl-master/frameworks/cfsl/utils/matching.py | """lake/utils.py"""
import os
import math
import random
import datetime
import torch
import numpy as np
import matplotlib.pyplot as plt
def get_summary_dir():
now = datetime.datetime.now()
summary_dir = os.path.join('.', 'runs', now.strftime("%Y%m%d-%H%M%S"))
return summary_dir
def set_seed(seed):
random.... | 10,438 | 31.930599 | 118 | py |
cfsl | cfsl-master/frameworks/cfsl/utils/parser_utils.py | import argparse
import os
import torch
import json
def get_args():
parser = argparse.ArgumentParser(description='Welcome to the meta-learning training and inference system')
parser.add_argument('--batch_size', nargs="?", type=int, default=32, help='Batch_size for experiment')
parser.add_argument('--imag... | 11,506 | 49.030435 | 187 | py |
cfsl | cfsl-master/frameworks/cfsl/utils/generic.py | import torch
import numpy as np
def set_torch_seed(seed):
"""
Sets the pytorch seeds for current experiment run
:param seed: The seed (int)
:return: A random number generator to use
"""
rng = np.random.RandomState(seed=seed)
torch_seed = rng.randint(0, 999999)
torch.manual_seed(seed=torch_seed)
ret... | 1,272 | 30.04878 | 115 | py |
cfsl | cfsl-master/frameworks/cfsl/utils/dataset_tools.py | import os
import json
import shutil
import urllib
import pathlib
import tarfile
import tempfile
import concurrent.futures
import numpy as np
import tqdm
from PIL import Image
from torchvision import transforms
DOWNLOAD_URL = {
'omniglot_dataset': 'https://storage.googleapis.com/project-agi/datasets/omniglot/omnigl... | 15,291 | 45.907975 | 161 | py |
espnet | espnet-master/setup.py | #!/usr/bin/env python3
"""ESPnet setup script."""
import os
from setuptools import find_packages, setup
requirements = {
"install": [
"setuptools>=38.5.1",
"packaging",
"configargparse>=1.2.1",
"typeguard==2.13.3",
"humanfriendly",
"scipy>=1.4.1",
"fileloc... | 5,217 | 30.817073 | 94 | py |
espnet | espnet-master/tools/check_install.py | #!/usr/bin/env python3
"""Script to check whether the installation is done correctly."""
# Copyright 2018 Nagoya University (Tomoki Hayashi)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import importlib
import re
import shutil
import subprocess
import sys
from pathlib import Path
from packaging.versi... | 6,394 | 31.29798 | 83 | py |
espnet | espnet-master/test/test_e2e_compatibility.py | #!/usr/bin/env python3
# coding: utf-8
# Copyright 2019 Tomoki Hayashi
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
from __future__ import print_function
import importlib
import os
import re
import shutil
import subprocess
import tempfile
from os.path import join
import chainer
import numpy as np
imp... | 3,521 | 30.72973 | 111 | py |
espnet | espnet-master/test/test_e2e_asr_conformer.py | import argparse
import pytest
import torch
from espnet.nets.pytorch_backend.e2e_asr_conformer import E2E
from espnet.nets.pytorch_backend.transformer import plot
def make_arg(**kwargs):
defaults = dict(
adim=2,
aheads=1,
dropout_rate=0.0,
transformer_attn_dropout_rate=None,
... | 4,895 | 25.901099 | 83 | py |
espnet | espnet-master/test/test_custom_transducer.py | # coding: utf-8
import argparse
import json
import tempfile
import pytest
import torch
from packaging.version import parse as V
import espnet.lm.pytorch_backend.extlm as extlm_pytorch
import espnet.nets.pytorch_backend.lm.default as lm_pytorch
from espnet.asr.pytorch_backend.asr_init import load_trained_model
from e... | 21,623 | 30.33913 | 87 | py |
espnet | espnet-master/test/test_batch_beam_search.py | import os
from argparse import Namespace
from test.test_beam_search import prepare, transformer_args
import numpy
import pytest
import torch
from espnet.nets.batch_beam_search import BatchBeamSearch, BeamSearch
from espnet.nets.beam_search import Hypothesis
from espnet.nets.lm_interface import dynamic_import_lm
from ... | 5,863 | 30.026455 | 88 | py |
espnet | espnet-master/test/test_e2e_mt.py | # coding: utf-8
# Copyright 2019 Hirofumi Inaguma
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
from __future__ import division
import argparse
import importlib
import os
import tempfile
from test.utils_test import make_dummy_json_mt
import chainer
import numpy as np
import pytest
import torch
from e... | 12,854 | 31.298995 | 88 | py |
espnet | espnet-master/test/test_multi_spkrs.py | # coding: utf-8
# Copyright 2018 Hiroshi Seki
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import argparse
import importlib
import re
import numpy
import pytest
import torch
def make_arg(**kwargs):
defaults = dict(
aconv_chans=2,
aconv_filts=20,
adim=20,
aheads=4,... | 7,289 | 28.51417 | 86 | py |
espnet | espnet-master/test/test_positional_encoding.py | import pytest
import torch
from espnet.nets.pytorch_backend.transformer.embedding import (
LearnableFourierPosEnc,
PositionalEncoding,
ScaledPositionalEncoding,
)
@pytest.mark.parametrize(
"dtype, device",
[(dt, dv) for dt in ("float32", "float64") for dv in ("cpu", "cuda")],
)
def test_pe_extend... | 5,074 | 30.32716 | 87 | py |
espnet | espnet-master/test/test_asr_init.py | # coding: utf-8
import argparse
import json
import os
import tempfile
import numpy as np
import pytest
import torch
import espnet.nets.pytorch_backend.lm.default as lm_pytorch
from espnet.asr.asr_utils import torch_save
from espnet.asr.pytorch_backend.asr_init import freeze_modules, load_trained_modules
from espnet.... | 7,583 | 25.989324 | 84 | py |
espnet | espnet-master/test/test_e2e_mt_transformer.py | # coding: utf-8
# Copyright 2019 Hirofumi Inaguma
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import argparse
import pytest
import torch
from espnet.nets.pytorch_backend.e2e_mt_transformer import E2E
from espnet.nets.pytorch_backend.transformer import plot
def make_arg(**kwargs):
defaults = di... | 3,754 | 26.014388 | 83 | py |
espnet | espnet-master/test/test_e2e_asr_transformer.py | import argparse
import chainer
import numpy
import pytest
import torch
import espnet.nets.chainer_backend.e2e_asr_transformer as ch
import espnet.nets.pytorch_backend.e2e_asr_transformer as th
from espnet.nets.pytorch_backend.nets_utils import rename_state_dict
from espnet.nets.pytorch_backend.transformer import plot... | 8,385 | 29.717949 | 87 | py |
espnet | espnet-master/test/test_beam_search_timesync.py | from argparse import Namespace
import pytest
import torch
from espnet.nets.asr_interface import dynamic_import_asr
from espnet.nets.beam_search_timesync import BeamSearchTimeSync
from espnet.nets.lm_interface import dynamic_import_lm
from espnet.nets.scorers.length_bonus import LengthBonus
rnn_args = Namespace(
... | 5,619 | 26.149758 | 88 | py |
espnet | espnet-master/test/test_e2e_tts_transformer.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
from argparse import Namespace
import numpy as np
import pytest
import torch
from espnet.nets.pytorch_backend.e2e_tts_transformer import Transformer, subsequent_mask
from espnet... | 15,609 | 32.354701 | 88 | py |
espnet | espnet-master/test/test_asr_interface.py | import pytest
from espnet.nets.asr_interface import dynamic_import_asr
@pytest.mark.parametrize(
"name, backend",
[(nn, backend) for nn in ("transformer", "rnn") for backend in ("pytorch",)],
)
def test_asr_build(name, backend):
model = dynamic_import_asr(name, backend).build(
10, 10, mtlalpha=0.... | 415 | 26.733333 | 81 | py |
espnet | espnet-master/test/test_e2e_asr.py | # coding: utf-8
# Copyright 2017 Shigeki Karita
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
from __future__ import division
import argparse
import importlib
import os
import tempfile
from test.utils_test import make_dummy_json
import chainer
import numpy as np
import pytest
import torch
import espn... | 26,215 | 34.331536 | 88 | py |
espnet | espnet-master/test/test_asr_quantize.py | # Copyright 2021 Gaopeng Xu
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import pytest
import torch
from espnet.nets.asr_interface import dynamic_import_asr
@pytest.mark.parametrize(
"name, backend",
[(nn, backend) for nn in ("transformer", "rnn") for backend in ("pytorch",)],
)
def test_asr_... | 642 | 28.227273 | 81 | py |
espnet | espnet-master/test/test_optimizer.py | # coding: utf-8
# Copyright 2017 Shigeki Karita
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import chainer
import numpy
import pytest
import torch
from espnet.optimizer.factory import dynamic_import_optimizer
from espnet.optimizer.pytorch import OPTIMIZER_FACTORY_DICT
class ChModel(chainer.Chain):
... | 3,023 | 29.857143 | 83 | py |
espnet | espnet-master/test/test_loss.py | # Copyright 2017 Shigeki Karita
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import chainer.functions as F
import numpy
import pytest
import torch
from espnet.nets.pytorch_backend.e2e_asr import pad_list
from espnet.nets.pytorch_backend.nets_utils import th_accuracy
@pytest.mark.parametrize("ctc_type"... | 5,405 | 35.527027 | 86 | py |
espnet | espnet-master/test/test_beam_search.py | from argparse import Namespace
import numpy
import pytest
import torch
from espnet.nets.asr_interface import dynamic_import_asr
from espnet.nets.beam_search import BeamSearch
from espnet.nets.lm_interface import dynamic_import_lm
from espnet.nets.scorers.length_bonus import LengthBonus
rnn_args = Namespace(
elay... | 6,366 | 27.55157 | 88 | py |
espnet | espnet-master/test/test_lm.py | from test.test_beam_search import prepare, rnn_args
import chainer
import numpy
import pytest
import torch
import espnet.lm.chainer_backend.lm as lm_chainer
import espnet.nets.pytorch_backend.lm.default as lm_pytorch
from espnet.nets.beam_search import beam_search
from espnet.nets.lm_interface import dynamic_import_l... | 6,647 | 33.268041 | 88 | py |
espnet | espnet-master/test/test_e2e_tts_fastspeech.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import json
import os
import shutil
import tempfile
from argparse import Namespace
import numpy as np
import pytest
import torch
from espnet.nets.pytorch_backend.e2e_tts_fastspe... | 21,140 | 32.398104 | 87 | py |
espnet | espnet-master/test/test_distributed_launch.py | # coding: utf-8
#
# SPDX-FileCopyrightText:
# Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
import argparse
import itertools
import os
import sys
from multiprocessing import Queue
import pytest
from espnet.distributed.pytorch_backend.launch import... | 4,110 | 26.225166 | 76 | py |
espnet | espnet-master/test/test_e2e_st_transformer.py | # coding: utf-8
# Copyright 2019 Hirofumi Inaguma
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import argparse
import pytest
import torch
from espnet.nets.pytorch_backend.e2e_st_transformer import E2E
from espnet.nets.pytorch_backend.transformer import plot
def make_arg(**kwargs):
defaults = dic... | 5,352 | 28.738889 | 85 | py |
espnet | espnet-master/test/test_torch.py | # Copyright 2017 Shigeki Karita
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import torch
from espnet.nets.pytorch_backend.nets_utils import pad_list
def test_pad_list():
xs = [[1, 2, 3], [1, 2], [1, 2, 3, 4]]
xs = list(map(lambda x: torch.LongTensor(x), xs))
xpad = pad_list(xs, -1)
e... | 852 | 26.516129 | 74 | py |
espnet | espnet-master/test/test_initialization.py | # Copyright 2017 Shigeki Karita
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import argparse
import os
import random
import numpy
import torch
args = argparse.Namespace(
elayers=4,
subsample="1_2_2_1_1",
etype="vggblstmp",
eunits=320,
eprojs=320,
dtype="lstm",
dlayers=2,
... | 3,380 | 28.4 | 72 | py |
espnet | espnet-master/test/test_e2e_st.py | # coding: utf-8
# Copyright 2019 Hirofumi Inaguma
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
from __future__ import division
import argparse
import importlib
import os
import tempfile
from test.utils_test import make_dummy_json_st
import chainer
import numpy as np
import pytest
import torch
from e... | 20,465 | 33.629442 | 88 | py |
espnet | espnet-master/test/test_e2e_asr_maskctc.py | import argparse
import pytest
import torch
from espnet.nets.pytorch_backend.e2e_asr_maskctc import E2E
from espnet.nets.pytorch_backend.maskctc.add_mask_token import mask_uniform
from espnet.nets.pytorch_backend.transformer import plot
def make_arg(**kwargs):
defaults = dict(
adim=2,
aheads=2,
... | 3,708 | 27.530769 | 83 | py |
espnet | espnet-master/test/test_e2e_asr_transducer.py | # coding: utf-8
import argparse
import json
import tempfile
import numpy as np
import pytest
import torch
from packaging.version import parse as V
import espnet.lm.pytorch_backend.extlm as extlm_pytorch
import espnet.nets.pytorch_backend.lm.default as lm_pytorch
from espnet.asr.pytorch_backend.asr_init import load_t... | 14,894 | 28.849699 | 88 | py |
espnet | espnet-master/test/test_e2e_vc_transformer.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2020 Wen-Chin Huang
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
from argparse import Namespace
from math import floor
import numpy as np
import pytest
import torch
from espnet.nets.pytorch_backend.e2e_vc_transformer import Transformer, subse... | 16,029 | 32.676471 | 88 | py |
espnet | espnet-master/test/test_e2e_asr_mulenc.py | # coding: utf-8
# Copyright 2019 Ruizhi Li
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
from __future__ import division
import argparse
import importlib
import os
import tempfile
from test.utils_test import make_dummy_json
import numpy as np
import pytest
import torch
from espnet.nets.pytorch_backen... | 21,844 | 35.408333 | 88 | py |
espnet | espnet-master/test/test_e2e_vc_tacotron2.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2020 Wen-Chin Huang
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
from __future__ import division, print_function
from argparse import Namespace
import numpy as np
import pytest
import torch
from espnet.nets.pytorch_backend.e2e_vc_tacotron2 i... | 8,638 | 27.417763 | 88 | py |
espnet | espnet-master/test/test_e2e_tts_tacotron2.py | #!/usr/bin/env python3
# Copyright 2019 Tomoki Hayashi
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
from __future__ import division, print_function
from argparse import Namespace
import numpy as np
import pytest
import torch
from espnet.nets.pytorch_backend.e2e_tts_tacotron2 import Tacotron2
from es... | 8,798 | 27.661238 | 88 | py |
espnet | espnet-master/test/test_train_dtype.py | import pytest
import torch
from espnet.nets.asr_interface import dynamic_import_asr
@pytest.mark.parametrize(
"dtype, device, model, conf",
[
(dtype, device, nn, conf)
for nn, conf in [
(
"transformer",
dict(adim=4, eunits=3, dunits=3, elayers=2, dl... | 2,795 | 28.125 | 85 | py |
espnet | espnet-master/test/test_transformer_decode.py | import numpy
import pytest
import torch
from espnet.nets.pytorch_backend.transformer.decoder import Decoder
from espnet.nets.pytorch_backend.transformer.encoder import Encoder
from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask
RTOL = 1e-4
@pytest.mark.parametrize("normalize_before", [True, Fal... | 4,486 | 30.159722 | 85 | py |
espnet | espnet-master/test/test_recog.py | # coding: utf-8
# Copyright 2018 Hiroshi Seki
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import argparse
import numpy
import pytest
import torch
import espnet.lm.pytorch_backend.extlm as extlm_pytorch
import espnet.nets.pytorch_backend.lm.default as lm_pytorch
from espnet.nets.pytorch_backend impor... | 4,644 | 28.967742 | 87 | py |
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