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
value |
|---|---|---|---|---|---|---|
CLMR | CLMR-master/clmr/datasets/magnatagatune.py | import os
import warnings
import subprocess
import torch
import numpy as np
import zipfile
from collections import defaultdict
from typing import Any, Tuple, Optional
from tqdm import tqdm
import soundfile as sf
import torchaudio
torchaudio.set_audio_backend("soundfile")
from torch import Tensor, FloatTensor
from tor... | 6,744 | 35.069519 | 99 | py |
CLMR | CLMR-master/clmr/datasets/million_song_dataset.py | import os
import pickle
import torch
import torchaudio
from collections import defaultdict
from pathlib import Path
from torch import Tensor, FloatTensor
from tqdm import tqdm
from typing import Any, Tuple, Optional
from clmr.datasets import Dataset
def load_id2gt(gt_file, msd_7d):
ids = []
with open(gt_file... | 4,169 | 29.661765 | 89 | py |
CLMR | CLMR-master/clmr/datasets/gtzan.py | import torchaudio
from torchaudio.datasets.gtzan import gtzan_genres
from torch.utils.data import Dataset
class GTZAN(Dataset):
subset_map = {"train": "training", "valid": "validation", "test": "testing"}
def __init__(self, root, download, subset):
self.dataset = torchaudio.datasets.GTZAN(
... | 802 | 26.689655 | 80 | py |
CLMR | CLMR-master/clmr/datasets/audio.py | import os
from glob import glob
from torch import Tensor
from typing import Tuple
from clmr.datasets import Dataset
class AUDIO(Dataset):
"""Create a Dataset for any folder of audio files.
Args:
root (str): Path to the directory where the dataset is found or downloaded.
src_ext_audio (str): ... | 1,506 | 24.116667 | 91 | py |
CLMR | CLMR-master/clmr/datasets/dataset.py | import os
import subprocess
import torchaudio
from torch.utils.data import Dataset as TorchDataset
from abc import abstractmethod
def preprocess_audio(source, target, sample_rate):
p = subprocess.Popen(
["ffmpeg", "-i", source, "-ar", str(sample_rate), target, "-loglevel", "quiet"]
)
p.wait()
cl... | 1,201 | 25.130435 | 87 | py |
CLMR | CLMR-master/clmr/datasets/librispeech.py | import os
import torchaudio
from torch.utils.data import Dataset
class LIBRISPEECH(Dataset):
subset_map = {"train": "train-clean-100", "test": "test-clean"}
def __init__(self, root, download, subset):
self.dataset = torchaudio.datasets.LIBRISPEECH(
root=root, download=download, url=self.... | 1,147 | 26.333333 | 79 | py |
CLMR | CLMR-master/clmr/utils/checkpoint.py | import torch
from collections import OrderedDict
def load_encoder_checkpoint(checkpoint_path: str, output_dim: int) -> OrderedDict:
state_dict = torch.load(checkpoint_path, map_location=torch.device("cpu"))
if "pytorch-lightning_version" in state_dict.keys():
new_state_dict = OrderedDict(
... | 1,265 | 33.216216 | 82 | py |
RSFormer | RSFormer-master/utils.py | import torch.nn.functional as F
# pad
def pad(x, factor=16, mode='reflect'):
_, _, h_even, w_even = x.shape
padh_left = (factor - h_even % factor) // 2
padw_top = (factor - w_even % factor) // 2
padh_right = padh_left if h_even % 2 == 0 else padh_left + 1
padw_bottom = padw_top if w_even % 2 == 0 ... | 776 | 31.375 | 79 | py |
RSFormer | RSFormer-master/datasets.py | import os
from PIL import Image
from torch.utils.data import Dataset
import torchvision.transforms.functional as ttf
class MyTestDataSet(Dataset):
def __init__(self, inputPathTest):
super(MyTestDataSet, self).__init__()
self.inputPath = inputPathTest
self.inputImages = os.listdir(inputPath... | 702 | 28.291667 | 78 | py |
RSFormer | RSFormer-master/demo.py | import sys
import time
import torch
import torch.nn as nn
from tqdm import tqdm
from torch.utils.data import DataLoader
from torchvision.utils import save_image
from RSFormer import RSFormer
from datasets import *
from config import Options
from utils import pad, unpad
if __name__ == '__main__':
opt = Options()
... | 1,721 | 30.888889 | 101 | py |
RSFormer | RSFormer-master/RSFormer.py | import torch
import torch.nn as nn
class FeedForward(nn.Module):
def __init__(self, dim, mlp_ratio=4):
super().__init__()
hidden_features = int(dim * mlp_ratio)
self.norm = LayerNorm(dim)
self.fc1 = nn.Conv2d(dim, hidden_features, 1)
self.dwconv = nn.Conv2d(hidden_features,... | 8,508 | 34.016461 | 146 | py |
DRT | DRT-master/external_libs/matconvnet/matconvnet/utils/layers.py | # file: layers.py
# brief: A number of objects to wrap caffe layers for conversion
# author: Andrea Vedaldi
from collections import OrderedDict
from math import floor, ceil
from operator import mul
import numpy as np
from numpy import array
import scipy
import scipy.io
import scipy.misc
import copy
import collections
... | 43,791 | 36.493151 | 156 | py |
DRT | DRT-master/external_libs/matconvnet/matconvnet/utils/import-caffe.py | #! /usr/bin/python
# file: import-caffe.py
# brief: Caffe importer for DagNN and SimpleNN
# author: Karel Lenc and Andrea Vedaldi
# Requires Google Protobuf for Python and SciPy
import sys
import os
import argparse
import code
import re
import numpy as np
from math import floor, ceil
import numpy
from numpy import ar... | 33,156 | 36.213244 | 114 | py |
DRT | DRT-master/external_libs/matconvnet/matconvnet/utils/proto/caffe_0115_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
from google.protobuf import descriptor
from google.protobuf import message
from google.protobuf import reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
DESCRIPTOR = descriptor.FileDescriptor(
name='caffe.proto',
... | 148,708 | 41.163028 | 17,413 | py |
DRT | DRT-master/external_libs/matconvnet/matconvnet/utils/proto/caffe_fastrcnn_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
# source: caffe_fastrcnn.proto
from google.protobuf.internal import enum_type_wrapper
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protob... | 194,370 | 42.777252 | 22,943 | py |
DRT | DRT-master/external_libs/matconvnet/matconvnet/utils/proto/caffe_6e3916_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
from google.protobuf import descriptor
from google.protobuf import message
from google.protobuf import reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
DESCRIPTOR = descriptor.FileDescriptor(
name='caffe_6e3916.pro... | 218,004 | 42.349572 | 26,073 | py |
DRT | DRT-master/external_libs/matconvnet/matconvnet/utils/proto/caffe_old_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
from google.protobuf import descriptor
from google.protobuf import message
from google.protobuf import reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
DESCRIPTOR = descriptor.FileDescriptor(
name='caffe-old.proto'... | 39,691 | 43.348603 | 4,364 | py |
DRT | DRT-master/external_libs/matconvnet/matconvnet/utils/proto/caffe_b590f1d_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
from google.protobuf import descriptor
from google.protobuf import message
from google.protobuf import reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
DESCRIPTOR = descriptor.FileDescriptor(
name='caffe_b590f1d.pr... | 232,112 | 42.264306 | 27,801 | py |
DRT | DRT-master/external_libs/matconvnet/matconvnet/utils/proto/caffe_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
from google.protobuf import descriptor
from google.protobuf import message
from google.protobuf import reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
DESCRIPTOR = descriptor.FileDescriptor(
name='caffe.proto',
... | 91,458 | 42.407214 | 10,562 | py |
DRT | DRT-master/external_libs/matconvnet/matconvnet/utils/proto/vgg_caffe_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
from google.protobuf import descriptor
from google.protobuf import message
from google.protobuf import reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
DESCRIPTOR = descriptor.FileDescriptor(
name='vgg_caffe.proto'... | 44,873 | 42.865103 | 4,761 | py |
DRT | DRT-master/external_libs/matconvnet/utils/layers.py | # file: layers.py
# brief: A number of objects to wrap caffe layers for conversion
# author: Andrea Vedaldi
from collections import OrderedDict
from math import floor, ceil
from operator import mul
import numpy as np
from numpy import array
import scipy
import scipy.io
import scipy.misc
import copy
import collections
... | 43,791 | 36.493151 | 156 | py |
DRT | DRT-master/external_libs/matconvnet/utils/import-caffe.py | #! /usr/bin/python
# file: import-caffe.py
# brief: Caffe importer for DagNN and SimpleNN
# author: Karel Lenc and Andrea Vedaldi
# Requires Google Protobuf for Python and SciPy
import sys
import os
import argparse
import code
import re
import numpy as np
from math import floor, ceil
import numpy
from numpy import ar... | 33,156 | 36.213244 | 114 | py |
DRT | DRT-master/external_libs/matconvnet/utils/proto/caffe_0115_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
from google.protobuf import descriptor
from google.protobuf import message
from google.protobuf import reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
DESCRIPTOR = descriptor.FileDescriptor(
name='caffe.proto',
... | 148,708 | 41.163028 | 17,413 | py |
DRT | DRT-master/external_libs/matconvnet/utils/proto/caffe_fastrcnn_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
# source: caffe_fastrcnn.proto
from google.protobuf.internal import enum_type_wrapper
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protob... | 194,370 | 42.777252 | 22,943 | py |
DRT | DRT-master/external_libs/matconvnet/utils/proto/caffe_6e3916_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
from google.protobuf import descriptor
from google.protobuf import message
from google.protobuf import reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
DESCRIPTOR = descriptor.FileDescriptor(
name='caffe_6e3916.pro... | 218,004 | 42.349572 | 26,073 | py |
DRT | DRT-master/external_libs/matconvnet/utils/proto/caffe_old_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
from google.protobuf import descriptor
from google.protobuf import message
from google.protobuf import reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
DESCRIPTOR = descriptor.FileDescriptor(
name='caffe-old.proto'... | 39,691 | 43.348603 | 4,364 | py |
DRT | DRT-master/external_libs/matconvnet/utils/proto/caffe_b590f1d_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
from google.protobuf import descriptor
from google.protobuf import message
from google.protobuf import reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
DESCRIPTOR = descriptor.FileDescriptor(
name='caffe_b590f1d.pr... | 232,112 | 42.264306 | 27,801 | py |
DRT | DRT-master/external_libs/matconvnet/utils/proto/caffe_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
from google.protobuf import descriptor
from google.protobuf import message
from google.protobuf import reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
DESCRIPTOR = descriptor.FileDescriptor(
name='caffe.proto',
... | 91,458 | 42.407214 | 10,562 | py |
DRT | DRT-master/external_libs/matconvnet/utils/proto/vgg_caffe_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
from google.protobuf import descriptor
from google.protobuf import message
from google.protobuf import reflection
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
DESCRIPTOR = descriptor.FileDescriptor(
name='vgg_caffe.proto'... | 44,873 | 42.865103 | 4,761 | py |
DRT | DRT-master/caffe/tools/extra/parse_log.py | #!/usr/bin/env python
"""
Parse training log
Evolved from parse_log.sh
"""
import os
import re
import extract_seconds
import argparse
import csv
from collections import OrderedDict
def parse_log(path_to_log):
"""Parse log file
Returns (train_dict_list, train_dict_names, test_dict_list, test_dict_names)
... | 6,700 | 33.015228 | 86 | py |
DRT | DRT-master/caffe/examples/web_demo/app.py | import os
import time
import cPickle
import datetime
import logging
import flask
import werkzeug
import optparse
import tornado.wsgi
import tornado.httpserver
import numpy as np
import pandas as pd
from PIL import Image
import cStringIO as StringIO
import urllib
import exifutil
import caffe
REPO_DIRNAME = os.path.abs... | 7,793 | 33.184211 | 105 | py |
DRT | DRT-master/caffe/examples/pycaffe/caffenet.py | from __future__ import print_function
from caffe import layers as L, params as P, to_proto
from caffe.proto import caffe_pb2
# helper function for common structures
def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):
conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
... | 2,112 | 36.732143 | 91 | py |
DRT | DRT-master/caffe/examples/pycaffe/layers/pyloss.py | import caffe
import numpy as np
class EuclideanLossLayer(caffe.Layer):
"""
Compute the Euclidean Loss in the same manner as the C++ EuclideanLossLayer
to demonstrate the class interface for developing layers in Python.
"""
def setup(self, bottom, top):
# check input pair
if len(bo... | 1,223 | 31.210526 | 79 | py |
DRT | DRT-master/caffe/examples/finetune_flickr_style/assemble_data.py | #!/usr/bin/env python
"""
Form a subset of the Flickr Style data, download images to dirname, and write
Caffe ImagesDataLayer training file.
"""
import os
import urllib
import hashlib
import argparse
import numpy as np
import pandas as pd
from skimage import io
import multiprocessing
# Flickr returns a special image i... | 3,636 | 35.737374 | 94 | py |
DRT | DRT-master/caffe/examples/coco_caption/captioner.py | #!/usr/bin/env python
from collections import OrderedDict
import h5py
import math
import matplotlib.pyplot as plt
import numpy as np
import os
import random
import sys
sys.path.append('./python/')
import caffe
class Captioner():
def __init__(self, weights_path, image_net_proto, lstm_net_proto,
vocab... | 16,658 | 40.337469 | 88 | py |
DRT | DRT-master/caffe/examples/coco_caption/retrieval_experiment.py | #!/usr/bin/env python
from collections import OrderedDict
import json
import numpy as np
import pprint
import cPickle as pickle
import string
import sys
# seed the RNG so we evaluate on the same subset each time
np.random.seed(seed=0)
from coco_to_hdf5_data import *
from captioner import Captioner
COCO_EVAL_PATH = ... | 15,281 | 41.099174 | 89 | py |
DRT | DRT-master/caffe/python/draw_net.py | #!/usr/bin/env python
"""
Draw a graph of the net architecture.
"""
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
from google.protobuf import text_format
import caffe
import caffe.draw
from caffe.proto import caffe_pb2
def parse_args():
"""Parse input arguments
"""
parser = Argument... | 1,389 | 29.217391 | 78 | py |
DRT | DRT-master/caffe/python/detect.py | #!/usr/bin/env python
"""
detector.py is an out-of-the-box windowed detector
callable from the command line.
By default it configures and runs the Caffe reference ImageNet model.
Note that this model was trained for image classification and not detection,
and finetuning for detection can be expected to improve results... | 5,743 | 32.011494 | 88 | py |
DRT | DRT-master/caffe/python/classify.py | #!/usr/bin/env python
"""
classify.py is an out-of-the-box image classifer callable from the command line.
By default it configures and runs the Caffe reference ImageNet model.
"""
import numpy as np
import os
import sys
import argparse
import glob
import time
import caffe
def main(argv):
pycaffe_dir = os.path.... | 4,262 | 29.669065 | 88 | py |
DRT | DRT-master/caffe/python/caffe/net_spec.py | """Python net specification.
This module provides a way to write nets directly in Python, using a natural,
functional style. See examples/pycaffe/caffenet.py for an example.
Currently this works as a thin wrapper around the Python protobuf interface,
with layers and parameters automatically generated for the "layers"... | 7,876 | 34.642534 | 82 | py |
DRT | DRT-master/caffe/python/caffe/classifier.py | #!/usr/bin/env python
"""
Classifier is an image classifier specialization of Net.
"""
import numpy as np
import caffe
class Classifier(caffe.Net):
"""
Classifier extends Net for image class prediction
by scaling, center cropping, or oversampling.
Parameters
----------
image_dims : dimensio... | 3,501 | 34.734694 | 78 | py |
DRT | DRT-master/caffe/python/caffe/detector.py | #!/usr/bin/env python
"""
Do windowed detection by classifying a number of images/crops at once,
optionally using the selective search window proposal method.
This implementation follows ideas in
Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik.
Rich feature hierarchies for accurate object detection... | 8,562 | 38.460829 | 80 | py |
DRT | DRT-master/caffe/python/caffe/__init__.py | from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver
from ._caffe import set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list
from .proto.caffe_pb2 import TRAIN, TEST
from .classifier import Classifier
from .detector import Detector
from . i... | 385 | 47.25 | 109 | py |
DRT | DRT-master/caffe/python/caffe/pycaffe.py | """
Wrap the internal caffe C++ module (_caffe.so) with a clean, Pythonic
interface.
"""
from collections import OrderedDict
try:
from itertools import izip_longest
except:
from itertools import zip_longest as izip_longest
import numpy as np
from ._caffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, \... | 9,706 | 32.129693 | 80 | py |
DRT | DRT-master/caffe/python/caffe/draw.py | """
Caffe network visualization: draw the NetParameter protobuffer.
.. note::
This requires pydot>=1.0.2, which is not included in requirements.txt since
it requires graphviz and other prerequisites outside the scope of the
Caffe.
"""
from caffe.proto import caffe_pb2
import pydot
# Internal layer and ... | 7,216 | 32.724299 | 79 | py |
DRT | DRT-master/caffe/python/caffe/io.py | import numpy as np
import skimage.io
from scipy.ndimage import zoom
from skimage.transform import resize
try:
# Python3 will most likely not be able to load protobuf
from caffe.proto import caffe_pb2
except:
import sys
if sys.version_info >= (3, 0):
print("Failed to include caffe_pb2, things mi... | 12,575 | 32.094737 | 79 | py |
DRT | DRT-master/caffe/python/caffe/test/test_python_layer_with_param_str.py | import unittest
import tempfile
import os
import six
import caffe
class SimpleParamLayer(caffe.Layer):
"""A layer that just multiplies by the numeric value of its param string"""
def setup(self, bottom, top):
try:
self.value = float(self.param_str)
except ValueError:
... | 1,925 | 31.1 | 79 | py |
DRT | DRT-master/caffe/python/caffe/test/test_solver.py | import unittest
import tempfile
import os
import numpy as np
import six
import caffe
from test_net import simple_net_file
class TestSolver(unittest.TestCase):
def setUp(self):
self.num_output = 13
net_f = simple_net_file(self.num_output)
f = tempfile.NamedTemporaryFile(mode='w+', delete=F... | 1,849 | 33.259259 | 76 | py |
DRT | DRT-master/caffe/python/caffe/test/test_layer_type_list.py | import unittest
import caffe
class TestLayerTypeList(unittest.TestCase):
def test_standard_types(self):
for type_name in ['Data', 'Convolution', 'InnerProduct']:
self.assertIn(type_name, caffe.layer_type_list(),
'%s not in layer_type_list()' % type_name)
| 302 | 26.545455 | 65 | py |
DRT | DRT-master/caffe/python/caffe/test/test_net.py | import unittest
import tempfile
import os
import numpy as np
import six
import caffe
def simple_net_file(num_output):
"""Make a simple net prototxt, based on test_net.cpp, returning the name
of the (temporary) file."""
f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
f.write("""name: 'testne... | 2,927 | 34.707317 | 78 | py |
DRT | DRT-master/caffe/python/caffe/test/test_net_spec.py | import unittest
import tempfile
import caffe
from caffe import layers as L
from caffe import params as P
def lenet(batch_size):
n = caffe.NetSpec()
n.data, n.label = L.DummyData(shape=[dict(dim=[batch_size, 1, 28, 28]),
dict(dim=[batch_size, 1, 1, 1])],
... | 3,287 | 39.097561 | 77 | py |
DRT | DRT-master/caffe/python/caffe/test/test_python_layer.py | import unittest
import tempfile
import os
import six
import caffe
class SimpleLayer(caffe.Layer):
"""A layer that just multiplies by ten"""
def setup(self, bottom, top):
pass
def reshape(self, bottom, top):
top[0].reshape(*bottom[0].data.shape)
def forward(self, bottom, top):
... | 4,604 | 31.659574 | 81 | py |
DRT | DRT-master/caffe/scripts/cpp_lint.py | #!/usr/bin/python2
#
# Copyright (c) 2009 Google Inc. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of... | 187,464 | 37.501746 | 93 | py |
DRT | DRT-master/caffe/scripts/download_model_binary.py | #!/usr/bin/env python
import os
import sys
import time
import yaml
import urllib
import hashlib
import argparse
required_keys = ['caffemodel', 'caffemodel_url', 'sha1']
def reporthook(count, block_size, total_size):
"""
From http://blog.moleculea.com/2012/10/04/urlretrieve-progres-indicator/
"""
glob... | 2,496 | 31.428571 | 78 | py |
TraceLinkExplanation | TraceLinkExplanation-master/sentence_classifier/predict.py | from collections import defaultdict
import os
import sys
sys.path.append(".")
sys.path.append("..")
from torch import nn
from tqdm import tqdm
from transformers.models.auto.tokenization_auto import AutoTokenizer
from transformers import AutoModelForSequenceClassification
from torch.utils.data import DataLoader
import ... | 3,821 | 31.389831 | 87 | py |
TraceLinkExplanation | TraceLinkExplanation-master/sentence_classifier/train.py | from transformers import TrainingArguments, AutoTokenizer
from transformers import AutoModelForSequenceClassification, Trainer
import torch
from datasets import load_metric
import numpy as np
import sys
sys.path.append("..")
sys.path.append(".")
from evaluation import utils
from nltk.tokenize import sent_tokenize
imp... | 3,452 | 28.512821 | 85 | py |
Opportunistic | Opportunistic-master/src/utils.py | import gc
import numpy as np
import torch
class ExperienceBuffer():
def __init__(self, buffer_size):
self.buffer = []
self.buffer_size = buffer_size
def push(self,experience):
if len(self.buffer) + 1 >= self.buffer_size:
self.buffer[0:(1+len(self.buffer))-self.buffer_s... | 2,645 | 33.363636 | 95 | py |
DROO | DROO-master/memoryPyTorch.py | # #################################################################
# This file contains the main DROO operations, including building DNN,
# Storing data sample, Training DNN, and generating quantized binary offloading decisions.
# version 1.0 -- February 2020. Written based on Tensorflow 2 by Weijian Pan and
# ... | 5,082 | 31.583333 | 109 | py |
DROO | DROO-master/memoryTF2.py | # #################################################################
# This file contains the main DROO operations, including building DNN,
# Storing data sample, Training DNN, and generating quantized binary offloading decisions.
# version 1.0 -- January 2020. Written based on Tensorflow 2 by Weijian Pan and
# ... | 5,117 | 33.816327 | 129 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/exps/der_womask/cifar100/b0/10steps/main.py | '''
@Author : Yan Shipeng, Xie Jiangwei
@Contact: yanshp@shanghaitech.edu.cn, xiejw@shanghaitech.edu.cn
'''
import sys
import os
import os.path as osp
import copy
import time
import shutil
import cProfile
import logging
from pathlib import Path
import numpy as np
import random
from easydict import EasyDict as edict
fr... | 8,825 | 37.710526 | 119 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/exps/der_womask/cifar100/b50/10steps/main.py | '''
@Author : Yan Shipeng, Xie Jiangwei
@Contact: yanshp@shanghaitech.edu.cn, xiejw@shanghaitech.edu.cn
'''
import sys
import os
import os.path as osp
import copy
import time
import shutil
import cProfile
import logging
from pathlib import Path
import numpy as np
import random
from easydict import EasyDict as edict
fr... | 8,825 | 37.710526 | 119 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/exps/der_womask/imagenet-100/b0_10s/main.py | '''
@Author : Yan Shipeng, Xie Jiangwei
@Contact: yanshp@shanghaitech.edu.cn, xiejw@shanghaitech.edu.cn
'''
import sys
import os
import os.path as osp
import copy
import time
import shutil
import cProfile
import logging
from pathlib import Path
import numpy as np
import random
from easydict import EasyDict as edict
fr... | 8,777 | 37.840708 | 119 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/exps/weight_align/cifar100/b0/10steps/main.py | '''
@Author : Yan Shipeng, Xie Jiangwei
@Contact: yanshp@shanghaitech.edu.cn, xiejw@shanghaitech.edu.cn
'''
import sys
import os
import os.path as osp
import copy
import time
import shutil
import cProfile
import logging
from pathlib import Path
import numpy as np
import random
from easydict import EasyDict as edict
fr... | 8,825 | 37.710526 | 119 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/learn/pretrain.py | import os.path as osp
import torch
import torch.nn.functional as F
from inclearn.tools import factory, utils
from inclearn.tools.metrics import ClassErrorMeter, AverageValueMeter
# import line_profiler
# import atexit
# profile = line_profiler.LineProfiler()
# atexit.register(profile.print_stats)
def _compute_loss(... | 3,886 | 36.019048 | 118 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/tools/memory.py | import numpy as np
from copy import deepcopy
import torch
from torch.nn import functional as F
from inclearn.tools.utils import get_class_loss
from inclearn.convnet.utils import extract_features
class MemorySize:
def __init__(self, mode, inc_dataset, total_memory=None, fixed_memory_per_cls=None):
self.mo... | 6,375 | 41.791946 | 120 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/tools/utils.py | import random
from copy import deepcopy
import numpy as np
import datetime
import torch
from inclearn.tools.metrics import ClassErrorMeter
def get_date():
return datetime.datetime.now().strftime("%Y%m%d")
def to_onehot(targets, n_classes):
if not hasattr(targets, "device"):
targets = torch.from_nu... | 6,969 | 33.85 | 97 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/tools/scheduler.py | import math
from torch.optim.lr_scheduler import _LRScheduler
from torch.optim.lr_scheduler import ReduceLROnPlateau
class ConstantTaskLR:
def __init__(self, lr):
self._lr = lr
def get_lr(self, task_i):
return self._lr
class CosineAnnealTaskLR:
def __init__(self, lr_max, lr_min, task_ma... | 3,749 | 41.134831 | 152 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/tools/factory.py | import torch
from torch import nn
from torch import optim
from inclearn import models
from inclearn.convnet import resnet, cifar_resnet, modified_resnet_cifar, preact_resnet
from inclearn.datasets import data
def get_optimizer(params, optimizer, lr, weight_decay=0.0):
if optimizer == "adam":
return optim... | 2,205 | 30.971014 | 87 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/tools/metrics.py | import numpy as np
import torch
import numbers
import math
class IncConfusionMeter:
"""Maintains a confusion matrix for a given calssification problem.
The ConfusionMeter constructs a confusion matrix for a multi-class
classification problems. It does not support multi-label, multi-class problems:
for... | 7,415 | 37.625 | 107 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/convnet/resnet.py | """Taken & slightly modified from:
* https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
"""
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from torch.nn import functional as F
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152']
model_urls = {
... | 8,130 | 32.460905 | 109 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/convnet/network.py | import copy
import pdb
import torch
from torch import nn
import torch.nn.functional as F
from inclearn.tools import factory
from inclearn.convnet.imbalance import BiC, WA
from inclearn.convnet.classifier import CosineClassifier
class BasicNet(nn.Module):
def __init__(
self,
convnet_type,
... | 6,100 | 35.532934 | 119 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/convnet/imbalance.py | import torch
import torch.nn.functional as F
from torch import nn
import numpy as np
from torch.optim.lr_scheduler import CosineAnnealingLR
class BiC(nn.Module):
def __init__(self, lr, scheduling, lr_decay_factor, weight_decay, batch_size, epochs):
super(BiC, self).__init__()
self.beta = torch.nn.... | 5,074 | 40.260163 | 117 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/convnet/utils.py | import numpy as np
import torch
from torch import nn
from torch.optim import SGD
import torch.nn.functional as F
from inclearn.tools.metrics import ClassErrorMeter, AverageValueMeter
def finetune_last_layer(
logger,
network,
loader,
n_class,
nepoch=30,
lr=0.1,
scheduling=[15, 35],
lr_d... | 4,496 | 35.266129 | 118 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/convnet/classifier.py | import math
import torch
from torch.nn.parameter import Parameter
from torch.nn import functional as F
from torch.nn import Module
class CosineClassifier(Module):
def __init__(self, in_features, n_classes, sigma=True):
super(CosineClassifier, self).__init__()
self.in_features = in_features
... | 1,035 | 31.375 | 92 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/convnet/preact_resnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class PreActBlock(nn.Module):
'''Pre-activation version of the BasicBlock.'''
expansion = 1
def __init__(self, in_planes, planes, stride=1, remove_last_relu=False):
super(PreActBlock, self).__init__()
self.bn1 = nn.BatchNo... | 5,859 | 37.552632 | 113 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/convnet/cifar_resnet.py | ''' Incremental-Classifier Learning
Authors : Khurram Javed, Muhammad Talha Paracha
Maintainer : Khurram Javed
Lab : TUKL-SEECS R&D Lab
Email : 14besekjaved@seecs.edu.pk '''
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
class DownsampleA(nn.Module):
... | 5,944 | 29.331633 | 102 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/convnet/modified_resnet_cifar.py | """Taken & slightly modified from:
* https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
"""
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from torch.nn import functional as F
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152']
model_urls = {
... | 4,519 | 32.731343 | 109 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/models/base.py | import abc
import logging
import torch
import torch.nn.functional as F
import numpy as np
from inclearn.tools.metrics import ClassErrorMeter
LOGGER = logging.Logger("IncLearn", level="INFO")
class IncrementalLearner(abc.ABC):
"""Base incremental learner.
Methods are called in this order (& repeated for each... | 6,449 | 40.883117 | 120 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/models/align.py | import numpy as np
import random
import time
import math
import os
from copy import deepcopy
from scipy.spatial.distance import cdist
import torch
from torch.nn import DataParallel
from torch.nn import functional as F
from inclearn.convnet import network
from inclearn.models.base import IncrementalLearner
from inclea... | 14,420 | 42.436747 | 137 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/models/incmodel.py | import numpy as np
import random
import time
import math
import os
from copy import deepcopy
from scipy.spatial.distance import cdist
import torch
from torch.nn import DataParallel
from torch.nn import functional as F
from inclearn.convnet import network
from inclearn.models.base import IncrementalLearner
from inclea... | 19,955 | 43.445434 | 137 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/datasets/dataset.py | import os.path as osp
import numpy as np
import glob
import albumentations as A
from albumentations.pytorch import ToTensorV2
from torchvision import datasets, transforms
import torch
def get_datasets(dataset_names):
return [get_dataset(dataset_name) for dataset_name in dataset_names.split("-")]
def get_datas... | 16,245 | 51.918567 | 120 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/datasets/data.py | import random
import cv2
import numpy as np
import os.path as osp
from copy import deepcopy
from PIL import Image
import multiprocessing as mp
from multiprocessing import Pool
import albumentations as A
from albumentations.pytorch import ToTensorV2
import warnings
warnings.filterwarnings("ignore", "Corrupt EXIF data",... | 14,954 | 37.44473 | 119 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/codes/base/main.py | '''
@Author : Yan Shipeng, Xie Jiangwei
@Contact: yanshp@shanghaitech.edu.cn, xiejw@shanghaitech.edu.cn
'''
import sys
import os
import os.path as osp
import copy
import time
import shutil
import cProfile
import logging
from pathlib import Path
import numpy as np
import random
from easydict import EasyDict as edict
fr... | 8,825 | 37.710526 | 119 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/setup.py | from setuptools import setup, find_packages
setup(
name='pareto',
version='0.1',
packages=find_packages(),
zip_safe=False,
install_requires=[
'numpy',
'scipy',
'torch',
'torchvision',
'tqdm',
],
)
| 262 | 15.4375 | 43 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/pareto/metrics.py | from typing import Iterable
from torch import Tensor
__all__ = ['topk_accuracies', 'topk_accuracy']
def topk_accuracies(
output: Tensor,
label: Tensor,
ks: Iterable[int] = (1,),
):
assert output.dim() == 2
assert label.dim() == 1
assert output.size(0) == label.size(0)
m... | 771 | 19.864865 | 65 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/pareto/networks/multi_lenet.py | from typing import Tuple, List
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
class MultiLeNet(nn.Module):
def __init__(self) -> None:
super(MultiLeNet, self).__init__()
self.conv1 = nn.Conv2d(1, 10, (5, 5))
self.conv2 = nn.Conv2d(10, 20, (5, 5... | 927 | 27.121212 | 81 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/pareto/optim/hvp_solver.py | from functools import partial
from typing import Tuple, List, Iterable, Callable
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn.utils import parameters_to_vector
__all__ = ['HVPSolver', 'AutogradHVPSolver', 'VisionHVPSolver']
class HVPSolver(object):
"""
Hessian-Vector product ca... | 7,426 | 26.712687 | 101 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/pareto/optim/linalg_solver.py | from contextlib import contextmanager
from functools import partial
from typing import Tuple
import numpy as np
from scipy.sparse.linalg import LinearOperator, minres
import torch
import torch.nn as nn
from torch import Tensor
from .hvp_solver import HVPSolver
__all__ = ['PDError', 'HVPLinearOperator', 'KrylovSol... | 8,325 | 26.66113 | 104 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/pareto/optim/min_norm_solver.py | # This code is from
# Multi-Task Learning as Multi-Objective Optimization
# Ozan Sener, Vladlen Koltun
# Neural Information Processing Systems (NeurIPS) 2018
# https://github.com/intel-isl/MultiObjectiveOptimization
from itertools import combinations
import numpy as np
import torch
__all__ = ['find_min_norm_elem... | 4,953 | 29.9625 | 109 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/pareto/optim/kkt_solver.py | from typing import Tuple, Mapping
import numpy as np
import torch
import torch.nn as nn
from torch import Tensor
from .hvp_solver import HVPSolver
from .min_norm_solver import find_min_norm_element
from .linalg_solver import KrylovSolver, MINRESSolver, CGSolver
__all__ = ['KKTSolver', 'KrylovKKTSolver', 'CGKKTSolv... | 8,382 | 29.483636 | 111 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/pareto/datasets/multi_mnist.py | from pathlib import Path
import codecs
import gzip
import urllib
import random
import numpy as np
from scipy import ndimage
from PIL import Image
import torch
class MultiMNIST(torch.utils.data.Dataset):
urls = [
'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz',
'http://yann.lecun.c... | 8,297 | 42.904762 | 105 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/submission/min_norm_solver.py | import sys
from itertools import combinations
import numpy as np
import torch
def _min_norm_element_from2(v1v1, v1v2, v2v2):
"""
Analytical solution for min_{c} |cx_1 + (1-c)x_2|_2^2
d is the distance (objective) optimzed
v1v1 = <x1,x1>
v1v2 = <x1,x2>
v2v2 = <x2,x2>
"""
if v1v2 >= v1v... | 4,675 | 29.966887 | 109 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/multi_mnist/weighted_sum.py | import random
from pathlib import Path
from termcolor import colored
import numpy as np
import torch
import torch.nn.functional as F
from torch.optim import SGD
from torch.optim.lr_scheduler import CosineAnnealingLR
from torchvision import transforms
from pareto.metrics import topk_accuracy
from pareto.datasets imp... | 5,501 | 26.928934 | 117 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/multi_mnist/cpmtl.py | import random
from pathlib import Path
from termcolor import colored
import numpy as np
import torch
import torch.nn.functional as F
from torch.optim import SGD
from torchvision import transforms
from pareto.metrics import topk_accuracy
from pareto.optim import VisionHVPSolver, MINRESKKTSolver
from pareto.datasets i... | 5,661 | 25.092166 | 117 | py |
DeepAA | DeepAA-master/resnet_imagenet.py | import os
import tensorflow as tf
# ref: https://github.com/gahaalt/resnets-in-tensorflow2/blob/master/Models/Resnets.py
_bn_momentum = 0.9
def regularized_padded_conv(*args, **kwargs):
return tf.keras.layers.Conv2D(*args, **kwargs, padding='same', kernel_regularizer=_regularizer, bias_regularizer=_regularizer,
... | 6,826 | 46.082759 | 151 | py |
DeepAA | DeepAA-master/lr_scheduler.py | import tensorflow as tf
from tensorflow.keras.optimizers.schedules import LearningRateSchedule
from tensorflow.python.framework import ops
from tensorflow.python.ops import math_ops, control_flow_ops
class GradualWarmup_Cosine_Scheduler(LearningRateSchedule):
def __init__(self, starting_lr, initial_lr, ending_lr, ... | 2,824 | 46.083333 | 133 | py |
DeepAA | DeepAA-master/DeepAA_utils.py | import os
import logging
import numpy as np
import copy
import random
import datetime
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
import tensorflow as tf
tf.get_logger().setLevel(logging.ERROR)
from data_generator import DataGenerator, DataAugmentation
from utils import CTLHistory
from lr_scheduler import GradualWarmup... | 13,983 | 42.7 | 161 | py |
DeepAA | DeepAA-master/imagenet_data_utils.py | import numpy as np
import tensorflow as tf
from torchvision.datasets.imagenet import *
from torch import randperm, default_generator
from torch._utils import _accumulate
from torch.utils.data.dataset import Subset
_DATA_TYPE = tf.float32
CMYK_IMAGES = [
'n01739381_1309.JPEG',
'n02077923_14822.JPEG',
'n02... | 7,325 | 40.625 | 129 | py |
DeepAA | DeepAA-master/augmentation.py | # code in this file is adpated from rpmcruz/autoaugment
# https://github.com/rpmcruz/autoaugment/blob/master/transformations.py
# https://github.com/ildoonet/pytorch-randaugment/blob/master/RandAugment/augmentations.py
import random
import PIL, PIL.ImageOps, PIL.ImageEnhance, PIL.ImageDraw
import numpy as np
from PIL... | 11,099 | 31.840237 | 103 | py |
DeepAA | DeepAA-master/data_generator.py | import os
import copy
import logging
import numpy as np
import math
from PIL import Image
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
import tensorflow as tf
tf.get_logger().setLevel(logging.ERROR)
from tensorflow.keras.utils import Sequence
from augmentation import IMAGENET_SIZE, centerCrop_imagenet
CIFAR_MEANS = np.ar... | 8,476 | 41.174129 | 235 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.