repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
speechbrain | speechbrain-main/tests/unittests/test_linear.py | import torch
import torch.nn
def test_linear(device):
from speechbrain.nnet.linear import Linear
inputs = torch.rand(1, 2, 4, device=device)
lin_t = Linear(n_neurons=4, input_size=inputs.shape[-1], bias=False)
lin_t.w.weight = torch.nn.Parameter(
torch.eye(inputs.shape[-1], device=device)
... | 443 | 23.666667 | 72 | py |
speechbrain | speechbrain-main/tests/unittests/test_arpa.py | import pytest
def test_read_arpa():
from speechbrain.lm.arpa import read_arpa
import io
with io.StringIO() as f:
print("Anything can be here", file=f)
print("", file=f)
print("\\data\\", file=f)
print("ngram 1=2", file=f)
print("ngram 2=3", file=f)
print(""... | 4,592 | 34.882813 | 75 | py |
speechbrain | speechbrain-main/tests/unittests/test_data_pipeline.py | import pytest
def test_data_pipeline():
from speechbrain.utils.data_pipeline import DataPipeline
pipeline = DataPipeline(
["text"],
dynamic_items=[
{"func": lambda x: x.lower(), "takes": ["text"], "provides": "foo"},
{"func": lambda x: x[::-1], "takes": "foo", "provide... | 5,242 | 32.183544 | 87 | py |
speechbrain | speechbrain-main/tests/unittests/test_losses.py | import torch
import pytest
def test_nll(device):
from speechbrain.nnet.losses import nll_loss
predictions = torch.zeros(4, 10, 8, device=device)
targets = torch.zeros(4, 10, device=device)
lengths = torch.ones(4, device=device)
out_cost = nll_loss(predictions, targets, lengths)
assert torch.a... | 8,520 | 34.210744 | 80 | py |
speechbrain | speechbrain-main/tests/unittests/test_metrics.py | import torch
import torch.nn
import math
def test_metric_stats(device):
from speechbrain.utils.metric_stats import MetricStats
from speechbrain.nnet.losses import l1_loss
l1_stats = MetricStats(metric=l1_loss)
l1_stats.append(
ids=["utterance1", "utterance2"],
predictions=torch.tensor... | 6,686 | 32.268657 | 79 | py |
speechbrain | speechbrain-main/tests/unittests/test_schedulers.py | def test_NewBobScheduler():
from speechbrain.nnet.schedulers import NewBobScheduler
scheduler = NewBobScheduler(initial_value=0.8)
prev_lr, next_lr = scheduler(1.0)
assert prev_lr == 0.8
assert next_lr == 0.8
prev_lr, next_lr = scheduler(1.1)
assert next_lr == 0.4
prev_lr, next_lr =... | 737 | 24.448276 | 61 | py |
speechbrain | speechbrain-main/tests/unittests/test_CNN.py | import torch
import torch.nn
def test_SincConv(device):
from speechbrain.nnet.CNN import SincConv
input = torch.rand([4, 16000], device=device)
convolve = SincConv(
input_shape=input.shape, out_channels=8, kernel_size=65, padding="same"
).to(device)
output = convolve(input)
assert out... | 2,850 | 26.413462 | 80 | py |
speechbrain | speechbrain-main/tests/unittests/test_pooling.py | import torch
import torch.nn
def test_pooling1d(device):
from speechbrain.nnet.pooling import Pooling1d
input = (
torch.tensor([1, 3, 2], device=device)
.unsqueeze(0)
.unsqueeze(-1)
.float()
)
pool = Pooling1d("max", 3).to(device)
output = pool(input)
assert o... | 1,446 | 22.721311 | 80 | py |
speechbrain | speechbrain-main/tests/unittests/test_ctc_segmentation.py | from speechbrain.pretrained import EncoderDecoderASR
import pytest
pytest.importorskip(
"speechbrain.alignment.ctc_segmentation",
reason="These tests require the ctc_segmentation library",
)
@pytest.fixture()
def asr_model():
"""Load model for the CTC segmentation test."""
asr_model = EncoderDecoder... | 2,861 | 30.8 | 79 | py |
speechbrain | speechbrain-main/tests/unittests/test_tokenizer.py | import os
import torch
def test_tokenizer():
from speechbrain.tokenizers.SentencePiece import SentencePiece
gt = [
["HELLO", "MORNING", "MORNING", "HELLO"],
["HELLO", "MORNING", "HELLO"],
]
# Word-level input test
dict_int2lab = {1: "HELLO", 2: "MORNING"}
spm = SentencePiece... | 3,846 | 25.531034 | 72 | py |
speechbrain | speechbrain-main/tests/unittests/test_hpopt.py | import pytest
def test_hpopt_generic():
from io import StringIO
from speechbrain.utils import hpopt as hp
import json
output = StringIO()
reporter = hp.GenericHyperparameterOptimizationReporter(
objective_key="per", output=output
)
result = {"train_loss": 0.9, "valid_loss": 1.2, ... | 1,769 | 25.818182 | 64 | py |
speechbrain | speechbrain-main/tests/unittests/test_dataloader.py | import torch
import pytest
def test_saveable_dataloader(tmpdir, device):
from speechbrain.dataio.dataloader import SaveableDataLoader
save_file = tmpdir + "/dataloader.ckpt"
dataset = torch.randn(10, 1, device=device)
dataloader = SaveableDataLoader(dataset, collate_fn=None)
data_iterator = iter(... | 3,274 | 36.215909 | 80 | py |
speechbrain | speechbrain-main/tests/unittests/test_callchains.py | def test_lengths_arg_exists():
from speechbrain.utils.callchains import lengths_arg_exists
def non_len_func(x):
return x + 1
def len_func(x, lengths):
return x + lengths
assert not lengths_arg_exists(non_len_func)
assert lengths_arg_exists(len_func)
def test_lengths_capable_chai... | 782 | 22.727273 | 64 | py |
speechbrain | speechbrain-main/tests/unittests/test_attention.py | import torch
def test_rel_pos_MHA(device):
from speechbrain.nnet.attention import RelPosMHAXL
bsz = 2
emb_dim = 4
k_len = [12, 10]
q_len = [10, 12]
bias = [True, False]
head_dim = [4, None]
for kl in k_len:
for ql in q_len:
for b in bias:
for h in... | 792 | 27.321429 | 69 | py |
speechbrain | speechbrain-main/tests/unittests/test_data_io.py | import torch
import os
def test_read_audio(tmpdir, device):
from speechbrain.dataio.dataio import read_audio, write_audio
test_waveform = torch.rand(16000, device=device)
wavfile = os.path.join(tmpdir, "wave.wav")
write_audio(wavfile, test_waveform.cpu(), 16000)
# dummy annotation
for i in r... | 3,324 | 37.218391 | 85 | py |
speechbrain | speechbrain-main/tests/unittests/test_g2p.py | import torch
from torch.nn import functional as F
def _fake_probs(idx, count):
result = torch.zeros(count)
result[idx] = 2.0
return F.softmax(result, dim=-1)
def _batch_fake_probs(indexes, count):
p_seq = torch.zeros(indexes.shape + (count,))
for batch_idx in range(len(indexes)):
for it... | 2,629 | 29.229885 | 76 | py |
speechbrain | speechbrain-main/tests/integration/PLDA/example_plda_experiment.py | #!/usr/bin/python
import os
import pickle
import numpy
from numpy import linalg as LA
from speechbrain.processing.PLDA_LDA import StatObject_SB # noqa F401
from speechbrain.processing.PLDA_LDA import PLDA
from speechbrain.processing.PLDA_LDA import Ndx
from speechbrain.processing.PLDA_LDA import fast_PLDA_scoring
# ... | 1,797 | 27.09375 | 70 | py |
speechbrain | speechbrain-main/tests/integration/ASR_alignment_viterbi/example_asr_alignment_viterbi_experiment.py | #!/usr/bin/env/python3
"""This minimal example trains an HMM-based aligner with the Viterbi algorithm.
The encoder is based on a combination of convolutional, recurrent, and
feed-forward networks (CRDNN) that predict phoneme states.
Given the tiny dataset, the expected behavior is to overfit the training data
(with a v... | 5,017 | 32.677852 | 79 | py |
speechbrain | speechbrain-main/tests/integration/separation/example_conv_tasnet.py | #!/usr/bin/env/python3
"""This minimal example trains a speech separation system with on a tiny dataset.
The architecture is based on ConvTasnet and expects in input mixtures of two
speakers.
"""
import torch
import pathlib
import speechbrain as sb
import torch.nn.functional as F
from hyperpyyaml import load_hyperpyya... | 5,334 | 30.755952 | 81 | py |
speechbrain | speechbrain-main/tests/integration/G2P/example_g2p.py | #!/usr/bin/env/python3
"""This minimal example trains a grapheme-to-phoneme (G2P) converter
that turns a sequence of characters into a sequence of phonemes. The system uses
a standard attention-based encoder-decoder pipeline. The encoder is based on an
LSTM, while the decoder is based on a GRU. Greedy search applied o... | 6,166 | 34.854651 | 80 | py |
speechbrain | speechbrain-main/tests/integration/ASR_CTC/example_asr_ctc_experiment_complex_net.py | #!/usr/bin/env/python3
"""This minimal example trains a CTC-based speech recognizer on a tiny dataset.
The encoder is based on a combination of convolutional, recurrent, and
feed-forward networks (CRDNN) that predict phonemes. A greedy search is used on
top of the output probabilities.
Given the tiny dataset, the expe... | 5,107 | 33.053333 | 80 | py |
speechbrain | speechbrain-main/tests/integration/ASR_CTC/example_asr_ctc_experiment.py | #!/usr/bin/env/python3
"""This minimal example trains a CTC-based speech recognizer on a tiny dataset.
The encoder is based on a combination of convolutional, recurrent, and
feed-forward networks (CRDNN) that predict phonemes. A greedy search is used on
top of the output probabilities.
Given the tiny dataset, the expe... | 5,096 | 32.98 | 80 | py |
speechbrain | speechbrain-main/tests/integration/ASR_CTC/example_asr_ctc_experiment_quaternion_net.py | #!/usr/bin/env/python3
"""This minimal example trains a CTC-based speech recognizer on a tiny dataset.
The encoder is based on a combination of convolutional, recurrent, and
feed-forward networks (CRDNN) that predict phonemes. A greedy search is used on
top of the output probabilities.
Given the tiny dataset, the expe... | 5,110 | 33.073333 | 80 | py |
speechbrain | speechbrain-main/tests/integration/ASR_Transducer/example_asr_transducer_experiment.py | #!/usr/bin/env/python3
"""This minimal example trains a RNNT-based speech recognizer on a tiny dataset.
The encoder is based on a combination of convolutional, recurrent, and
feed-forward networks (CRDNN) that predict phonemes. A beamsearch is used on
top of the output probabilities.
Given the tiny dataset, the expect... | 6,161 | 34.011364 | 80 | py |
speechbrain | speechbrain-main/tests/integration/LM_RNN/example_lm_rnn_experiment.py | #!/usr/bin/env/python3
"""This minimal example trains a character-level language model that predicts
the next characters given the previous ones. The system uses a standard
attention-based encoder-decoder pipeline. The encoder is based on a simple LSTM.
Given the tiny dataset, the expected behavior is to overfit the t... | 4,471 | 33.666667 | 80 | py |
speechbrain | speechbrain-main/tests/integration/VAD/example_vad.py | """This minimal example trains a Voice Activity Detector (VAD) on a tiny dataset.
The network is based on a LSTM with a linear transformation on the top of that.
The system is trained with the binary cross-entropy metric.
"""
import os
import torch
import numpy as np
import speechbrain as sb
from hyperpyyaml import lo... | 4,976 | 31.109677 | 81 | py |
speechbrain | speechbrain-main/tests/integration/ASR_alignment_forward/example_asr_alignment_forward_experiment.py | #!/usr/bin/env/python3
"""This minimal example trains an HMM-based aligner with the forward algorithm.
The encoder is based on a combination of convolutional, recurrent, and
feed-forward networks (CRDNN) that predict phoneme states.
Given the tiny dataset, the expected behavior is to overfit the training data
(with a v... | 4,687 | 31.783217 | 79 | py |
speechbrain | speechbrain-main/tests/integration/ASR_seq2seq/example_asr_seq2seq_experiment.py | #!/usr/bin/env/python3
"""This minimal example trains a seq2seq attention-based model for speech
recognition on a tiny dataset. The encoder is based on a combination of
convolutional, recurrent, and feed-forward networks (CRDNN). The decoder is
based on a GRU. A greedy search is used on top of the output probabilitie... | 6,322 | 33.933702 | 80 | py |
speechbrain | speechbrain-main/tests/integration/enhance_GAN/example_enhance_gan_experiment.py | #!/usr/bin/env/python3
"""This minimal example trains a GAN speech enhancement system on a tiny dataset.
The generator and the discriminator are based on convolutional networks.
"""
import torch
import pathlib
import speechbrain as sb
from hyperpyyaml import load_hyperpyyaml
class EnhanceGanBrain(sb.Brain):
def ... | 6,058 | 33.821839 | 81 | py |
speechbrain | speechbrain-main/tests/integration/sampling/example_sorting.py | """This minimal example checks on sampling with ascending/descending sorting and random shuffling; w/ & w/o DDP.
"""
import os
import torch
import pickle
import pathlib
import itertools
import speechbrain as sb
import torch.multiprocessing as mp
from hyperpyyaml import load_hyperpyyaml
class SamplingBrain(sb.Brain):... | 7,867 | 33.358079 | 116 | py |
speechbrain | speechbrain-main/tests/integration/autoencoder/example_auto_experiment.py | #!/usr/bin/env/python3
"""This minimal example trains an autoencoder over speech features. The encoder
is a MLP that transforms the input into a lower-dimensional latent representation.
The decoder is another MLP that predicts the input features. The system is trained
with MSE. Given the tiny dataset, the expected beha... | 4,741 | 33.115108 | 82 | py |
speechbrain | speechbrain-main/tests/integration/speaker_id/example_xvector_experiment.py | #!/usr/bin/env/python3
"""This minimal example trains a speaker identification system based on
x-vectors. The encoder is based on TDNNs. The classifier is a MLP.
"""
import pathlib
import speechbrain as sb
from hyperpyyaml import load_hyperpyyaml
# Trains xvector model
class XvectorBrain(sb.Brain):
def compute_f... | 4,655 | 32.021277 | 80 | py |
speechbrain | speechbrain-main/tests/integration/augmentation/example_do_clip.py | import os
import speechbrain as sb
from hyperpyyaml import load_hyperpyyaml
from speechbrain.dataio.dataio import read_audio, write_audio
output_folder = os.path.join("results", "do_clip")
experiment_dir = os.path.dirname(os.path.abspath(__file__))
hyperparams_file = os.path.join(experiment_dir, "hyperparams.yaml")
... | 1,559 | 30.2 | 80 | py |
speechbrain | speechbrain-main/tests/integration/augmentation/example_speed_perturb.py | import os
import speechbrain as sb
from hyperpyyaml import load_hyperpyyaml
from speechbrain.dataio.dataio import read_audio, write_audio
output_folder = os.path.join("results", "speed_perturb")
experiment_dir = os.path.dirname(os.path.abspath(__file__))
hyperparams_file = os.path.join(experiment_dir, "hyperparams.yam... | 1,576 | 30.54 | 80 | py |
speechbrain | speechbrain-main/tests/integration/augmentation/example_drop_freq.py | import os
import speechbrain as sb
from hyperpyyaml import load_hyperpyyaml
from speechbrain.dataio.dataio import read_audio, write_audio
output_folder = os.path.join("results", "drop_freq")
experiment_dir = os.path.dirname(os.path.abspath(__file__))
hyperparams_file = os.path.join(experiment_dir, "hyperparams.yaml")
... | 1,565 | 30.32 | 80 | py |
speechbrain | speechbrain-main/tests/integration/augmentation/example_add_noise.py | import os
import speechbrain as sb
from hyperpyyaml import load_hyperpyyaml
from speechbrain.dataio.dataio import read_audio, write_audio
output_folder = os.path.join("results", "add_noise")
experiment_dir = os.path.dirname(os.path.abspath(__file__))
hyperparams_file = os.path.join(experiment_dir, "hyperparams.yaml")
... | 1,572 | 30.46 | 80 | py |
speechbrain | speechbrain-main/tests/integration/augmentation/example_add_babble.py | import os
import speechbrain as sb
from hyperpyyaml import load_hyperpyyaml
from speechbrain.dataio.dataio import read_audio, write_audio
output_folder = os.path.join("results", "add_babble")
experiment_dir = os.path.dirname(os.path.abspath(__file__))
hyperparams_file = os.path.join(experiment_dir, "hyperparams.yaml")... | 1,602 | 30.431373 | 80 | py |
speechbrain | speechbrain-main/tests/integration/augmentation/example_drop_chunk.py | import os
import speechbrain as sb
from hyperpyyaml import load_hyperpyyaml
from speechbrain.dataio.dataio import read_audio, write_audio
output_folder = os.path.join("results", "drop_chunk")
experiment_dir = os.path.dirname(os.path.abspath(__file__))
hyperparams_file = os.path.join(experiment_dir, "hyperparams.yaml")... | 1,572 | 30.46 | 80 | py |
speechbrain | speechbrain-main/tests/integration/augmentation/example_add_reverb.py | import os
import speechbrain as sb
from hyperpyyaml import load_hyperpyyaml
from speechbrain.dataio.dataio import read_audio, write_audio
output_folder = os.path.join("results", "add_reverb")
experiment_dir = os.path.dirname(os.path.abspath(__file__))
hyperparams_file = os.path.join(experiment_dir, "hyperparams.yaml")... | 1,577 | 30.56 | 80 | py |
speechbrain | speechbrain-main/tests/utils/recipe_tests.py | """Library for running recipe tests.
Authors
* Mirco Ravanelli 2022
* Andreas Nautsch 2022, 2023
"""
import os
import re
import csv
import sys
import pydoc
from time import time
import subprocess as sp
from hyperpyyaml import load_hyperpyyaml
from tests.consistency.test_recipe import __skip_list
def check_row_for_... | 24,805 | 33.938028 | 232 | py |
speechbrain | speechbrain-main/tests/utils/check_docstrings.py | """This library contains functions that checks the dosctrings
Authors
* Mirco Ravanelli 2022
"""
import re
from speechbrain.utils.data_utils import get_all_files
def extractName(s, search_class=False):
"""Extracts the names of the function or classes in the input string.
Arguments
---------
s: str... | 4,501 | 31.157143 | 103 | py |
speechbrain | speechbrain-main/tests/utils/check_url.py | """Libraries for automatic finding URLs in the files and checking if they are
reachable.
Authors
* Mirco Ravanelli 2022
"""
import os
import re
import time
import requests
from tqdm.contrib import tqdm
from speechbrain.utils.data_utils import get_all_files
def get_url(path):
"""This function searches for the UR... | 3,721 | 23.326797 | 81 | py |
speechbrain | speechbrain-main/tests/utils/check_yaml.py | """Tests for checking consistency between yaml files and their corresponding training scripts.
Authors
* Mirco Ravanelli 2022
* Andreas Nautsch 2022
"""
import os
import re
def get_yaml_var(hparam_file):
"""Extracts from the input yaml file (hparams_file) the list of variables that
should be used in the s... | 11,055 | 33.55 | 108 | py |
speechbrain | speechbrain-main/tests/utils/refactoring_checks.py | #!/usr/bin/env/python3
"""This is a test script for creating a list of expected outcomes (before refactoring);
then, manual editing might change YAMLs and/or code; another test runs to compare results
(after refactoring to before). The target is a list of known HF repos.
The goal is to identify to which extent changes... | 19,156 | 36.489237 | 197 | py |
speechbrain | speechbrain-main/tests/utils/check_HF_repo.py | """Library for the HuggingFace (HF) repositories.
Authors
* Mirco Ravanelli 2022
* Andreas Nautsch 2022, 2023
"""
import os
import csv
from speechbrain.utils.data_utils import download_file
from tests.consistency.test_recipe import __skip_list
def run_HF_check(
recipe_folder="tests/recipes", field="HF_repo", o... | 3,957 | 28.318519 | 95 | py |
speechbrain | speechbrain-main/docs/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | 4,241 | 26.192308 | 79 | py |
Dink-Net | Dink-Net-main/main.py | import os
import wandb
import argparse
from utils import *
from tqdm import tqdm
from model import DinkNet, DinkNet_dgl
def train(args=None):
# setup random seed
setup_seed(args.seed)
# load graph data
if args.dataset in ["cora", "citeseer"]:
x, adj, y, n, k, d = load_data(args.dataset)
... | 3,933 | 32.338983 | 122 | py |
Dink-Net | Dink-Net-main/utils.py | import dgl
import sys
import copy
import torch
import random
import numpy as np
import pickle as pkl
import networkx as nx
import scipy.sparse as sp
from munkres import Munkres
from collections import Counter
from sklearn.metrics import accuracy_score, f1_score
from sklearn.metrics import adjusted_rand_score as ari_sco... | 21,067 | 34.7691 | 124 | py |
Dink-Net | Dink-Net-main/model.py | from utils import *
import torch.nn as nn
import dgl.function as fn
import torch.nn.functional as F
from dgl.nn.pytorch import GraphConv
# ------------------------from scratch------------------------
class GCN(nn.Module):
def __init__(self, in_ft, out_ft, act):
super(GCN, self).__init__()
self.fc =... | 8,030 | 34.852679 | 124 | py |
ice-ice | ice-ice/legacy.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 16,502 | 50.411215 | 154 | py |
ice-ice | ice-ice/style_mixing.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 4,891 | 40.109244 | 132 | py |
ice-ice | ice-ice/projector.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 8,990 | 41.211268 | 136 | py |
ice-ice | ice-ice/generate.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 5,338 | 40.069231 | 132 | py |
ice-ice | ice-ice/dataset_tool.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 17,876 | 39.173034 | 174 | py |
ice-ice | ice-ice/train.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 24,067 | 43.487985 | 192 | py |
ice-ice | ice-ice/calc_metrics.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 8,336 | 42.649215 | 142 | py |
ice-ice | ice-ice/training/loss.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 7,297 | 53.462687 | 160 | py |
ice-ice | ice-ice/training/augment.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 26,373 | 60.050926 | 366 | py |
ice-ice | ice-ice/training/dataset.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 8,551 | 35.084388 | 158 | py |
ice-ice | ice-ice/training/networks.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 39,286 | 49.23913 | 164 | py |
ice-ice | ice-ice/training/__init__.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 435 | 42.6 | 76 | py |
ice-ice | ice-ice/training/training_loop.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 21,596 | 50.177725 | 168 | py |
ice-ice | ice-ice/training/networks_old.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 37,392 | 50.223288 | 164 | py |
ice-ice | ice-ice/torch_utils/custom_ops.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 5,644 | 43.448819 | 146 | py |
ice-ice | ice-ice/torch_utils/training_stats.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 10,707 | 38.806691 | 118 | py |
ice-ice | ice-ice/torch_utils/persistence.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 9,708 | 37.527778 | 144 | py |
ice-ice | ice-ice/torch_utils/misc.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 10,992 | 40.798479 | 133 | py |
ice-ice | ice-ice/torch_utils/__init__.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 436 | 42.7 | 76 | py |
ice-ice | ice-ice/torch_utils/ops/bias_act.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 10,047 | 46.173709 | 185 | py |
ice-ice | ice-ice/torch_utils/ops/grid_sample_gradfix.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 3,299 | 38.285714 | 138 | py |
ice-ice | ice-ice/torch_utils/ops/conv2d_gradfix.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 7,677 | 43.900585 | 197 | py |
ice-ice | ice-ice/torch_utils/ops/upfirdn2d.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 16,287 | 41.306494 | 157 | py |
ice-ice | ice-ice/torch_utils/ops/conv2d_resample.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 7,591 | 47.356688 | 130 | py |
ice-ice | ice-ice/torch_utils/ops/fma.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 2,034 | 32.360656 | 105 | py |
ice-ice | ice-ice/torch_utils/ops/__init__.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 436 | 42.7 | 76 | py |
ice-ice | ice-ice/metrics/metric_utils.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 11,806 | 41.778986 | 167 | py |
ice-ice | ice-ice/metrics/kernel_inception_distance.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 2,302 | 48 | 118 | py |
ice-ice | ice-ice/metrics/frechet_inception_distance.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 2,040 | 47.595238 | 118 | py |
ice-ice | ice-ice/metrics/perceptual_path_length.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 5,538 | 40.962121 | 131 | py |
ice-ice | ice-ice/metrics/inception_score.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 1,874 | 47.076923 | 126 | py |
ice-ice | ice-ice/metrics/metric_main.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 5,715 | 36.359477 | 147 | py |
ice-ice | ice-ice/metrics/__init__.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 435 | 42.6 | 76 | py |
ice-ice | ice-ice/metrics/precision_recall.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 3,617 | 56.428571 | 159 | py |
ice-ice | ice-ice/dnnlib/util.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 16,625 | 33.782427 | 151 | py |
ice-ice | ice-ice/dnnlib/__init__.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 476 | 46.7 | 76 | py |
ice-ice | ice-ice/ice/landmark_interpolation.py | import numpy as np
import scipy.spatial
import skimage.draw
import torch
from torchvision import io
import face_alignment
import matplotlib.pyplot as plt
def interpolate_from_landmarks(image, landmarks, vertex_indices=None, weights=None, mask=None):
H, W = image.shape[-2:]
step = 4
rect = landmarks.new_... | 4,861 | 37.283465 | 136 | py |
ice-ice | ice-ice/ice/resnet.py | import torch.nn as nn
import torch.utils.model_zoo as model_zoo
__all__ = ['ResNet', 'resnet50']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org/mode... | 7,115 | 31.792627 | 116 | py |
ice-ice | ice-ice/ice/wrapper.py | import matplotlib.pyplot as plt
import face_alignment
import kornia
import torch
from torch import nn
import torchvision.transforms as transforms
import torch.nn.functional as F
from torch_utils import misc
import dnnlib
import legacy
from external.identity.iresnet import iresnet50, iresnet100
from external.landmark.... | 10,977 | 36.986159 | 106 | py |
ice-ice | ice-ice/ice/criterions.py | import matplotlib.pyplot as plt
import kornia
import torch
from torch import nn
import torch.nn.functional as F
import torchvision.transforms as transforms
from wrapper import StyleGanWrapper, FaceSegmenter, KeyPointDetector
from landmark_interpolation import interpolate_from_landmarks
def masked_mean(x, mask):
... | 9,823 | 34.338129 | 94 | py |
ice-ice | ice-ice/ice/jtj_analysis.py | import functools
import itertools
import numpy as np
from pathlib import Path
import pickle
import matplotlib.pyplot as plt
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
import torchvision.transforms as transforms
from torch.utils.data import DataLoader, Dataset
from tqdm import t... | 7,312 | 32.240909 | 118 | py |
ice-ice | ice-ice/ice/external/identity/iresnet.py | import torch
from torch import nn
__all__ = ['iresnet18', 'iresnet34', 'iresnet50', 'iresnet100', 'iresnet200']
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes,
out_planes,
kernel_size=... | 7,401 | 36.383838 | 97 | py |
ice-ice | ice-ice/ice/external/parsing/model.py | #!/usr/bin/python
# -*- encoding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
# from resnet import Resnet18
# from modules.bn import InPlaceABNSync as BatchNorm2d
# ---------------------------------------------------
import torch
import torch.nn as nn
import torch... | 14,108 | 35.742188 | 91 | py |
ice-ice | ice-ice/ice/external/attribution/resnet.py | import torch.nn as nn
import torch.utils.model_zoo as model_zoo
__all__ = ['ResNet', 'resnet50']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org/mode... | 7,115 | 31.792627 | 116 | py |
MrMustard-develop | MrMustard-develop/setup.py | # Copyright 2021 Xanadu Quantum Technologies Inc.
# 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 required by applicable law or agre... | 2,308 | 31.521127 | 90 | py |
MrMustard-develop | MrMustard-develop/mrmustard/typing.py | # Copyright 2021 Xanadu Quantum Technologies Inc.
# 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 required by applicable law or agre... | 2,400 | 24.010417 | 74 | py |
MrMustard-develop | MrMustard-develop/mrmustard/logger.py | # Copyright 2010 Pallets
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
# 2. Redist... | 4,353 | 36.86087 | 84 | py |
MrMustard-develop | MrMustard-develop/mrmustard/_version.py | # Copyright 2021 Xanadu Quantum Technologies Inc.
# 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 required by applicable law or agre... | 695 | 33.8 | 74 | py |
MrMustard-develop | MrMustard-develop/mrmustard/__init__.py | # Copyright 2022 Xanadu Quantum Technologies Inc.
# 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 required by applicable law or agre... | 5,726 | 33.089286 | 100 | py |
MrMustard-develop | MrMustard-develop/mrmustard/physics/fock.py | # Copyright 2021 Xanadu Quantum Technologies Inc.
# 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 required by applicable law or agre... | 35,463 | 36.528042 | 166 | py |
MrMustard-develop | MrMustard-develop/mrmustard/physics/bargmann.py | # Copyright 2023 Xanadu Quantum Technologies Inc.
# 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 required by applicable law or agre... | 5,185 | 43.706897 | 147 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.