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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TESTR | TESTR-main/adet/modeling/transformer_detector.py | from typing import List
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from detectron2.modeling.meta_arch.build import META_ARCH_REGISTRY
from detectron2.modeling import build_backbone
from detectron2.modeling.postprocessing import detector_postprocess as d2_postprocesss
from dete... | 11,075 | 43.481928 | 167 | py |
TESTR | TESTR-main/adet/modeling/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from .transformer_detector import TransformerDetector
_EXCLUDE = {"torch", "ShapeSpec"}
__all__ = [k for k in globals().keys() if k not in _EXCLUDE and not k.startswith("_")]
| 247 | 40.333333 | 86 | py |
TESTR | TESTR-main/adet/modeling/testr/losses.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import copy
from adet.utils.misc import accuracy, generalized_box_iou, box_cxcywh_to_xyxy, box_xyxy_to_cxcywh, is_dist_avail_and_initialized
from detectron2.utils.comm import get_world_size
def sigmoid_focal_loss(inputs, targets, num_inst, alpha: floa... | 11,216 | 45.7375 | 151 | py |
TESTR | TESTR-main/adet/modeling/testr/matcher.py | """
Modules to compute the matching cost and solve the corresponding LSAP.
"""
import torch
from scipy.optimize import linear_sum_assignment
from torch import nn
from adet.utils.misc import box_cxcywh_to_xyxy, generalized_box_iou
class CtrlPointHungarianMatcher(nn.Module):
"""This class computes an assignment bet... | 8,962 | 51.415205 | 122 | py |
TESTR | TESTR-main/adet/modeling/testr/models.py | import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from adet.layers.deformable_transformer import DeformableTransformer
from adet.layers.pos_encoding import PositionalEncoding1D
from adet.utils.misc import NestedTensor, inverse_sigmoid_offset, nested_tensor_from_tensor_list, sigmoid_... | 11,024 | 47.783186 | 117 | py |
TESTR | TESTR-main/demo/predictor.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import numpy as np
import atexit
import bisect
import multiprocessing as mp
from collections import deque
import cv2
import torch
import matplotlib.pyplot as plt
from detectron2.data import MetadataCatalog
from detectron2.engine.defaults import Def... | 9,092 | 35.518072 | 109 | py |
Linly | Linly-main/scripts/generate_chatllama.py | """
This script provides an example to wrap TencentPretrain for generation.
Given the beginning of a text, language model generates the rest.
"""
import sys
import os
import argparse
import torch
import torch.nn.functional as F
tencentpretrain_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
sy... | 4,245 | 35.921739 | 99 | py |
Linly | Linly-main/scripts/convert_llama_from_tencentpretrain_to_hf.py | import argparse
import gc
import json
import math
import os
import shutil
import torch
from transformers import LlamaConfig, LlamaForCausalLM
"""
Sample usage:
```
python llm_model/scripts/convert_llama_tencentpretrain_to_hf.py \
--tp_model_dir /path/to/downloaded_tencentpretrain_model \
--input_dir /path/... | 8,748 | 34.421053 | 150 | py |
attention-transfer | attention-transfer-master/utils.py | from nested_dict import nested_dict
from functools import partial
import torch
from torch.nn.init import kaiming_normal_
from torch.nn.parallel._functions import Broadcast
from torch.nn.parallel import scatter, parallel_apply, gather
import torch.nn.functional as F
def distillation(y, teacher_scores, labels, T, alpha... | 2,861 | 31.157303 | 118 | py |
attention-transfer | attention-transfer-master/cifar.py | """
PyTorch training code for
"Paying More Attention to Attention: Improving the Performance of
Convolutional Neural Networks via Attention Transfer"
https://arxiv.org/abs/1612.03928
This file includes:
* CIFAR ResNet and Wide ResNet training code which exactly reproduces
... | 11,171 | 36.489933 | 104 | py |
attention-transfer | attention-transfer-master/imagenet.py | """
PyTorch training code for
"Paying More Attention to Attention: Improving the Performance of
Convolutional Neural Networks via Attention Transfer"
https://arxiv.org/abs/1612.03928
This file includes:
* ImageNet ResNet training code that follows
https://github.com/face... | 12,607 | 35.973607 | 107 | py |
lru | lru-master/src/pytorchUtils.py | import torch
import pickle as pkl
import pdb
def save_model(model, file_path):
f = open(file_path, 'wb')
pkl.dump(model.state_dict(), f)
f.close()
def load_model(model, file_path):
model.load_state_dict(pkl.load(open(file_path, 'rb')))
def accumulate_grads(model, grads_list):
if grads_list:
grads_... | 652 | 25.12 | 110 | py |
lru | lru-master/src/generate.py | ## Generate tokens using the trained neural language model
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn.utils.rnn import PackedSequence
from time import time
from datetime import datetime
from dataUtils import Data, Sample
from model import lang... | 4,928 | 35.783582 | 130 | py |
lru | lru-master/src/model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn.utils.rnn import PackedSequence
import pdb
from time import time
import math
class RGLRUCell(nn.Module):
'''
Decoupled Projected state and Reset Gate
LRU is 2-dimensional, hence it has 2 inputs a... | 17,457 | 34.848049 | 151 | py |
lru | lru-master/src/char.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn.utils.rnn import PackedSequence
from time import time
from datetime import datetime
from dataUtils import Data
from model import langModel
from utils import Name
from pytorchUtils import save_model, lo... | 11,089 | 36.979452 | 237 | py |
hivemind | hivemind-master/setup.py | import codecs
import glob
import hashlib
import os
import re
import subprocess
import tarfile
import tempfile
import urllib.request
from pkg_resources import parse_requirements, parse_version
from setuptools import find_packages, setup
from setuptools.command.build_py import build_py
from setuptools.command.develop im... | 6,803 | 35.191489 | 125 | py |
hivemind | hivemind-master/benchmarks/benchmark_averaging.py | import argparse
import math
import threading
import time
import torch
import hivemind
from hivemind.compression import Float16Compression
from hivemind.utils.limits import increase_file_limit
from hivemind.utils.logging import get_logger, use_hivemind_log_handler
use_hivemind_log_handler("in_root_logger")
logger = g... | 4,519 | 36.355372 | 117 | py |
hivemind | hivemind-master/benchmarks/benchmark_throughput.py | import argparse
import multiprocessing as mp
import random
import sys
import time
import torch
from hivemind.dht import DHT
from hivemind.moe.client.expert import RemoteExpert
from hivemind.moe.client.remote_expert_worker import RemoteExpertWorker
from hivemind.moe.expert_uid import ExpertInfo
from hivemind.moe.serve... | 8,908 | 33.800781 | 116 | py |
hivemind | hivemind-master/benchmarks/benchmark_tensor_compression.py | import argparse
import time
import torch
from hivemind.compression import deserialize_torch_tensor, serialize_torch_tensor
from hivemind.proto.runtime_pb2 import CompressionType
from hivemind.utils.logging import get_logger, use_hivemind_log_handler
use_hivemind_log_handler("in_root_logger")
logger = get_logger(__na... | 1,755 | 35.583333 | 109 | py |
hivemind | hivemind-master/benchmarks/benchmark_optimizer.py | import multiprocessing as mp
import random
import time
from contextlib import nullcontext
from dataclasses import dataclass
from functools import partial
from typing import Callable
import torch
import torchvision
from torch import nn as nn
from torch.nn import functional as F
from torch.utils.data import Dataset
imp... | 5,519 | 32.865031 | 113 | py |
hivemind | hivemind-master/examples/albert/run_trainer.py | #!/usr/bin/env python3
import os
import pickle
import sys
from dataclasses import asdict
from pathlib import Path
import torch
import transformers
from datasets import load_from_disk
from torch.utils.data import DataLoader
from torch_optimizer import Lamb
from transformers import DataCollatorForLanguageModeling, HfAr... | 12,269 | 36.638037 | 119 | py |
hivemind | hivemind-master/examples/albert/run_training_monitor.py | #!/usr/bin/env python3
import time
from dataclasses import asdict, dataclass, field
from ipaddress import ip_address
from typing import Optional
import requests
import torch
import wandb
from torch_optimizer import Lamb
from transformers import AlbertConfig, AlbertForPreTraining, HfArgumentParser, get_linear_schedule... | 8,868 | 37.899123 | 121 | py |
hivemind | hivemind-master/tests/test_util_modules.py | import asyncio
import concurrent.futures
import multiprocessing as mp
import random
import time
from concurrent.futures import ThreadPoolExecutor
import numpy as np
import pytest
import torch
import hivemind
from hivemind.compression import deserialize_torch_tensor, serialize_torch_tensor
from hivemind.proto.runtime_... | 19,217 | 32.422609 | 119 | py |
hivemind | hivemind-master/tests/test_training.py | from functools import partial
import pytest
import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.datasets import load_digits
from hivemind import DHT
from hivemind.moe.client import RemoteMixtureOfExperts, RemoteSwitchMixtureOfExperts
from hivemind.moe.client.expert import create_remote_exp... | 4,987 | 36.223881 | 119 | py |
hivemind | hivemind-master/tests/test_optimizer.py | import ctypes
import multiprocessing as mp
import time
from functools import partial
import numpy as np
import pytest
import torch
import torch.nn as nn
import torch.nn.functional as F
import hivemind
from hivemind.averaging.control import AveragingStage
from hivemind.optim.grad_averager import GradientAverager, Grad... | 17,305 | 36.297414 | 118 | py |
hivemind | hivemind-master/tests/test_allreduce.py | import asyncio
import random
import time
from typing import Sequence
import pytest
import torch
from hivemind import Quantile8BitQuantization, aenumerate
from hivemind.averaging.allreduce import AllReduceRunner, AveragingMode
from hivemind.averaging.partition import TensorPartContainer, TensorPartReducer
from hivemin... | 9,981 | 42.58952 | 119 | py |
hivemind | hivemind-master/tests/test_connection_handler.py | from __future__ import annotations
import asyncio
import math
from typing import Any, Dict
import pytest
import torch
from hivemind.compression import deserialize_tensor_stream, deserialize_torch_tensor, serialize_torch_tensor
from hivemind.dht import DHT
from hivemind.moe.server.connection_handler import Connection... | 7,574 | 38.248705 | 117 | py |
hivemind | hivemind-master/tests/test_expert_backend.py | from pathlib import Path
from tempfile import TemporaryDirectory
import pytest
import torch
from torch.nn import Linear
from hivemind import BatchTensorDescriptor, ModuleBackend
from hivemind.moe.server.checkpoints import load_experts, store_experts
from hivemind.moe.server.layers.lr_schedule import get_linear_schedu... | 3,531 | 29.982456 | 102 | py |
hivemind | hivemind-master/tests/test_custom_experts.py | import os
import pytest
import torch
from hivemind.dht import DHT
from hivemind.moe.client.expert import create_remote_experts
from hivemind.moe.expert_uid import ExpertInfo
from hivemind.moe.server import background_server
CUSTOM_EXPERTS_PATH = os.path.join(os.path.dirname(__file__), "test_utils", "custom_networks.... | 2,292 | 28.397436 | 97 | py |
hivemind | hivemind-master/tests/test_averaging.py | import random
import time
import numpy as np
import pytest
import torch
import hivemind
from hivemind.averaging import DecentralizedAverager
from hivemind.averaging.allreduce import AveragingMode
from hivemind.averaging.control import AveragingStage
from hivemind.averaging.key_manager import GroupKeyManager
from hive... | 18,064 | 32.268877 | 118 | py |
hivemind | hivemind-master/tests/test_allreduce_fault_tolerance.py | from __future__ import annotations
from enum import Enum, auto
import pytest
import hivemind
from hivemind.averaging.averager import *
from hivemind.averaging.group_info import GroupInfo
from hivemind.averaging.load_balancing import load_balance_peers
from hivemind.averaging.matchmaking import MatchmakingException
f... | 8,978 | 41.353774 | 119 | py |
hivemind | hivemind-master/tests/test_moe.py | import asyncio
import ctypes
import multiprocessing as mp
import threading
import time
import numpy as np
import pytest
import torch
from hivemind.dht import DHT
from hivemind.moe.client.expert import RemoteExpert, create_remote_experts
from hivemind.moe.client.moe import DUMMY, RemoteMixtureOfExperts, _RemoteCallMan... | 13,210 | 36.109551 | 119 | py |
hivemind | hivemind-master/tests/test_compression.py | import multiprocessing as mp
from ctypes import c_int32
import pytest
import torch
import torch.nn as nn
import hivemind
from hivemind.compression import (
CompressionBase,
CompressionInfo,
Float16Compression,
NoCompression,
PerTensorCompression,
RoleAdaptiveCompression,
SizeAdaptiveCompre... | 9,410 | 39.564655 | 115 | py |
hivemind | hivemind-master/tests/test_utils/custom_networks.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from hivemind.moe import register_expert_class
sample_input = lambda batch_size, hidden_dim: torch.empty((batch_size, hidden_dim))
@register_expert_class("perceptron", sample_input)
class MultilayerPerceptron(nn.Module):
def __init__(self, hidde... | 1,368 | 30.837209 | 83 | py |
hivemind | hivemind-master/hivemind/compression/base.py | import dataclasses
import os
import warnings
from abc import ABC, abstractmethod
from enum import Enum, auto
from typing import Any, Optional
import numpy as np
import torch
from hivemind.proto import runtime_pb2
from hivemind.utils.tensor_descr import TensorDescriptor
# While converting read-only NumPy arrays into ... | 5,178 | 41.45082 | 119 | py |
hivemind | hivemind-master/hivemind/compression/floating.py | import math
import numpy as np
import torch
from hivemind.compression.base import CompressionBase, CompressionInfo
from hivemind.proto import runtime_pb2
class Float16Compression(CompressionBase):
compression_type = runtime_pb2.CompressionType.FLOAT16
FP16_MIN, FP16_MAX = torch.finfo(torch.float16).min, tor... | 4,067 | 42.741935 | 119 | py |
hivemind | hivemind-master/hivemind/compression/quantization.py | import math
import os
import warnings
from abc import ABC, abstractmethod
from concurrent.futures import ThreadPoolExecutor
from typing import Tuple
import numpy as np
import torch
from hivemind.compression.base import CompressionBase, CompressionInfo
from hivemind.proto import runtime_pb2
warnings.filterwarnings("i... | 8,300 | 43.390374 | 119 | py |
hivemind | hivemind-master/hivemind/compression/adaptive.py | from abc import ABC, abstractmethod
from typing import Mapping, Sequence, Union
import torch
from hivemind.compression.base import CompressionBase, CompressionInfo, Key, NoCompression, TensorRole
from hivemind.compression.serialization import deserialize_torch_tensor
from hivemind.proto import runtime_pb2
class Ada... | 2,823 | 40.529412 | 119 | py |
hivemind | hivemind-master/hivemind/compression/__init__.py | """
Compression strategies that reduce the network communication in .averaging, .optim and .moe
"""
from hivemind.compression.adaptive import PerTensorCompression, RoleAdaptiveCompression, SizeAdaptiveCompression
from hivemind.compression.base import CompressionBase, CompressionInfo, NoCompression, TensorRole
from hiv... | 658 | 46.071429 | 118 | py |
hivemind | hivemind-master/hivemind/compression/serialization.py | from __future__ import annotations
from typing import AsyncIterator, Dict, Iterable, List, Optional
import torch
from hivemind.compression.base import CompressionBase, CompressionInfo, NoCompression
from hivemind.compression.floating import Float16Compression, ScaledFloat16Compression
from hivemind.compression.quant... | 2,846 | 40.26087 | 118 | py |
hivemind | hivemind-master/hivemind/averaging/averager.py | """ A background process that averages your tensors with peers """
from __future__ import annotations
import asyncio
import contextlib
import ctypes
import multiprocessing as mp
import os
import random
import signal
import threading
import weakref
from dataclasses import asdict
from typing import Any, AsyncIterator, ... | 39,698 | 47.472527 | 130 | py |
hivemind | hivemind-master/hivemind/averaging/control.py | import os
import struct
from enum import Enum
from typing import Optional
import numpy as np
import torch
from hivemind.utils import DHTExpiration, MPFuture, get_dht_time, get_logger
logger = get_logger(__name__)
class AveragingStage(Enum):
IDLE = 0 # still initializing
LOOKING_FOR_GROUP = 1 # running de... | 6,511 | 38.228916 | 119 | py |
hivemind | hivemind-master/hivemind/averaging/allreduce.py | import asyncio
from enum import Enum
from typing import AsyncIterator, Optional, Sequence, Set, Tuple, Type
import torch
from hivemind.averaging.partition import AllreduceException, BannedException, TensorPartContainer, TensorPartReducer
from hivemind.compression import deserialize_torch_tensor, serialize_torch_tenso... | 18,412 | 47.71164 | 120 | py |
hivemind | hivemind-master/hivemind/averaging/partition.py | """
Auxiliary data structures for AllReduceRunner
"""
import asyncio
from collections import deque
from typing import AsyncIterable, AsyncIterator, Optional, Sequence, Tuple, TypeVar
import numpy as np
import torch
from hivemind.compression import CompressionBase, CompressionInfo, NoCompression
from hivemind.proto im... | 14,877 | 51.758865 | 118 | py |
hivemind | hivemind-master/hivemind/hivemind_cli/run_server.py | from functools import partial
from pathlib import Path
import configargparse
import torch
from hivemind.moe import Server
from hivemind.moe.server.layers import schedule_name_to_scheduler
from hivemind.proto.runtime_pb2 import CompressionType
from hivemind.utils.limits import increase_file_limit
from hivemind.utils.l... | 6,331 | 54.06087 | 149 | py |
hivemind | hivemind-master/hivemind/moe/client/moe.py | from __future__ import annotations
import time
from concurrent.futures import Future
from queue import Empty, Queue
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
from torch.autograd.function import once_differentiable
from hivemind.compression import serialize_torch_tensor
fr... | 20,442 | 46.652681 | 119 | py |
hivemind | hivemind-master/hivemind/moe/client/switch_moe.py | from __future__ import annotations
from typing import List, Tuple
import torch
from hivemind.moe.client.expert import DUMMY, RemoteExpert
from hivemind.moe.client.moe import RemoteMixtureOfExperts, _RemoteCallMany
from hivemind.moe.expert_uid import UID_DELIMITER
from hivemind.p2p.p2p_daemon_bindings.control import ... | 10,484 | 45.393805 | 118 | py |
hivemind | hivemind-master/hivemind/moe/client/expert.py | from __future__ import annotations
from concurrent.futures import Future
from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
import torch.nn as nn
from torch.autograd.function import once_differentiable
from hivemind import moe
from hivemind.compression import deserialize_tens... | 9,758 | 40.705128 | 118 | py |
hivemind | hivemind-master/hivemind/moe/server/module_backend.py | from typing import Any, Dict, Optional, Sequence, Tuple, Union
import torch
from torch import nn
from hivemind.moe.server.task_pool import TaskPool
from hivemind.utils.logging import get_logger
from hivemind.utils.nested import nested_compare, nested_flatten, nested_map, nested_pack
from hivemind.utils.tensor_descr i... | 9,743 | 47.477612 | 120 | py |
hivemind | hivemind-master/hivemind/moe/server/checkpoints.py | import os
import threading
from datetime import datetime
from pathlib import Path
from shutil import copy2
from tempfile import TemporaryDirectory
from typing import Dict
import torch
from hivemind.moe.server.module_backend import ModuleBackend
from hivemind.utils.logging import get_logger
logger = get_logger(__name... | 2,731 | 34.947368 | 110 | py |
hivemind | hivemind-master/hivemind/moe/server/server.py | from __future__ import annotations
import multiprocessing as mp
import random
import threading
from contextlib import contextmanager
from functools import partial
from pathlib import Path
from typing import Dict, List, Optional
import torch
from hivemind.dht import DHT
from hivemind.moe.expert_uid import UID_DELIMIT... | 17,792 | 42.082324 | 119 | py |
hivemind | hivemind-master/hivemind/moe/server/runtime.py | import multiprocessing as mp
import multiprocessing.pool
import threading
from collections import defaultdict
from itertools import chain
from queue import SimpleQueue
from selectors import EVENT_READ, DefaultSelector
from statistics import mean
from time import time
from typing import Dict, NamedTuple, Optional
impor... | 8,835 | 43.852792 | 119 | py |
hivemind | hivemind-master/hivemind/moe/server/task_pool.py | """
Task pool is responsible for receiving tasks and grouping them together for processing (but not processing itself)
"""
import ctypes
import multiprocessing as mp
import os
import threading
import time
from abc import ABCMeta, abstractmethod
from collections import namedtuple
from concurrent.futures import Future
fr... | 10,951 | 41.78125 | 119 | py |
hivemind | hivemind-master/hivemind/moe/server/connection_handler.py | import asyncio
import multiprocessing as mp
from typing import AsyncIterator, Dict, Iterable, List, Optional, Tuple, Union
import torch
from hivemind.compression import deserialize_tensor_stream, deserialize_torch_tensor, serialize_torch_tensor
from hivemind.dht import DHT
from hivemind.moe.server.module_backend impo... | 7,267 | 40.062147 | 119 | py |
hivemind | hivemind-master/hivemind/moe/server/layers/custom_experts.py | import importlib
import os
from typing import Callable, Type
import torch
import torch.nn as nn
from hivemind.moe.server.layers import name_to_block, name_to_input
def add_custom_models_from_file(path: str):
spec = importlib.util.spec_from_file_location("custom_module", os.path.abspath(path))
foo = importli... | 1,182 | 31.861111 | 89 | py |
hivemind | hivemind-master/hivemind/moe/server/layers/optim.py | import torch
class OptimizerWrapper(torch.optim.Optimizer):
"""A wrapper for pytorch.optim.Optimizer that forwards all methods to the wrapped optimizer"""
def __init__(self, optim: torch.optim.Optimizer):
super().__init__(optim.param_groups, optim.defaults)
self.optim = optim
@property
... | 1,775 | 29.101695 | 98 | py |
hivemind | hivemind-master/hivemind/moe/server/layers/lr_schedule.py | from torch.optim.lr_scheduler import LambdaLR
# https://github.com/huggingface/transformers/blob/master/src/transformers/optimization.py
def get_linear_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps):
"""
Create a schedule with a learning rate that decreases linearly from the initial lr ... | 1,209 | 42.214286 | 119 | py |
hivemind | hivemind-master/hivemind/moe/server/layers/dropout.py | import torch.autograd
from torch import nn as nn
from hivemind.moe.server.layers.custom_experts import register_expert_class
class DeterministicDropoutFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, x, keep_prob, mask):
ctx.keep_prob = keep_prob
ctx.save_for_backward(mask)
... | 1,732 | 31.092593 | 119 | py |
hivemind | hivemind-master/hivemind/moe/server/layers/common.py | import time
import torch
from torch import nn as nn
from hivemind.moe.server.layers.custom_experts import register_expert_class
# https://github.com/huggingface/transformers/blob/master/src/transformers/activations.py
@torch.jit.script
def gelu_fast(x):
return 0.5 * x * (1.0 + torch.tanh(x * 0.7978845608 * (1.0... | 3,283 | 30.576923 | 98 | py |
hivemind | hivemind-master/hivemind/optim/progress_tracker.py | import asyncio
import contextlib
import logging
import threading
from dataclasses import dataclass
from typing import Dict, Optional
import numpy as np
from pydantic import BaseModel, StrictBool, StrictFloat, confloat, conint
from hivemind.dht import DHT
from hivemind.dht.schema import BytesWithPublicKey, RSASignatur... | 17,452 | 46.947802 | 119 | py |
hivemind | hivemind-master/hivemind/optim/grad_averager.py | import contextlib
from typing import Callable, Iterable, Iterator, Optional, Sequence, TypeVar
import torch
from hivemind.averaging import DecentralizedAverager
from hivemind.averaging.control import StepControl
from hivemind.dht import DHT
from hivemind.utils import DHTExpiration, get_logger
logger = get_logger(__n... | 12,669 | 51.791667 | 119 | py |
hivemind | hivemind-master/hivemind/optim/power_sgd_averager.py | import asyncio
import contextlib
from enum import Enum
from typing import Any, Iterable, Optional, Sequence
import torch
from hivemind.averaging.allreduce import AveragingMode
from hivemind.averaging.group_info import GroupInfo
from hivemind.averaging.load_balancing import load_balance_peers
from hivemind.averaging.m... | 10,944 | 48.080717 | 119 | py |
hivemind | hivemind-master/hivemind/optim/grad_scaler.py | import contextlib
import threading
from copy import deepcopy
from typing import Dict, Optional
import torch
from torch.cuda.amp import GradScaler as TorchGradScaler
from torch.cuda.amp.grad_scaler import OptState, _refresh_per_optimizer_state
from torch.optim import Optimizer as TorchOptimizer
import hivemind
from hi... | 6,239 | 48.52381 | 119 | py |
hivemind | hivemind-master/hivemind/optim/state_averager.py | """ An extension of averager that supports common optimization use cases. """
import logging
import threading
import time
from concurrent.futures import ThreadPoolExecutor
from contextlib import nullcontext
from itertools import chain
from typing import Any, Callable, Dict, Iterable, Iterator, Optional, Sequence, Tuple... | 40,626 | 53.901351 | 119 | py |
hivemind | hivemind-master/hivemind/optim/training_averager.py | """ An extension of averager that supports common optimization use cases. """
from concurrent.futures import ThreadPoolExecutor
from contextlib import nullcontext
from itertools import chain
from threading import Event, Lock
from typing import Dict, Iterator, Optional, Sequence
import torch
from hivemind.averaging im... | 12,900 | 50.194444 | 120 | py |
hivemind | hivemind-master/hivemind/optim/optimizer.py | from __future__ import annotations
import logging
import os
import time
from functools import partial
from typing import Callable, Optional, Sequence, Union
import torch
from hivemind.averaging.control import AveragingStage, StepControl
from hivemind.compression import CompressionBase, NoCompression
from hivemind.dh... | 45,413 | 56.413401 | 123 | py |
hivemind | hivemind-master/hivemind/utils/math.py | import torch
import torch.nn.functional as F
@torch.jit.script
def orthogonalize_(matrix, eps: float = 1e-8):
"""Orthogonalize a 2d tensor in-place over the last dimension"""
n, m = matrix.shape
for i in range(m):
col = matrix[:, i]
F.normalize(col, dim=0, eps=eps, out=col)
if i + ... | 849 | 33 | 93 | py |
hivemind | hivemind-master/hivemind/utils/tensor_descr.py | from __future__ import annotations
import warnings
from dataclasses import asdict, dataclass
from typing import Tuple
import numpy as np
import torch
from hivemind.proto.runtime_pb2 import CompressionType
from hivemind.utils.serializer import MSGPackSerializer
DUMMY_BATCH_SIZE = 3 # used for dummy runs only
warni... | 4,657 | 33.25 | 118 | py |
hivemind | hivemind-master/hivemind/utils/mpfuture.py | from __future__ import annotations
import asyncio
import concurrent.futures._base as base
import multiprocessing as mp
import os
import threading
import uuid
from contextlib import nullcontext
from enum import Enum, auto
from multiprocessing.reduction import ForkingPickler
from typing import Any, Callable, Dict, Gener... | 15,185 | 44.062315 | 119 | py |
hivemind | hivemind-master/docs/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... | 7,815 | 30.389558 | 103 | py |
torchsat | torchsat-master/setup.py | from setuptools import setup, find_packages
import re
with open('README.md', encoding='utf-8') as f:
readme = f.read()
with open("torchsat/__init__.py", encoding="utf-8") as f:
version = re.search(r'__version__ = "(.*?)"', f.read()).group(1)
requirements = [x.strip() for x in open("requirements.txt").readlin... | 903 | 29.133333 | 95 | py |
torchsat | torchsat-master/torchsat/models/classification/efficientnet.py | """model.py - Model and module class for EfficientNet.
They are built to mirror those in the official TensorFlow implementation.
"""
# Author: lukemelas (github username)
# Github repo: https://github.com/lukemelas/EfficientNet-PyTorch
# With adjustments and added comments by workingcoder (github username).
import... | 17,604 | 43.682741 | 125 | py |
torchsat | torchsat-master/torchsat/models/classification/inception.py | from collections import namedtuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.hub import load_state_dict_from_url
__all__ = ['Inception3', 'inception_v3']
model_urls = {
# Inception v3 ported from TensorFlow
'inception_v3_google': 'https://download.pytorch.org/models/incep... | 14,485 | 38.045822 | 118 | py |
torchsat | torchsat-master/torchsat/models/classification/resnet.py | import torch
import torch.nn as nn
from torch.hub import load_state_dict_from_url
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
'wide_resnet50_2', 'wide_resnet101_2']
""" This script was taken from https://github.com/pyto... | 15,502 | 42.063889 | 118 | py |
torchsat | torchsat-master/torchsat/models/classification/vgg.py | import torch
import torch.nn as nn
from torch.hub import load_state_dict_from_url
__all__ = [
'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
'vgg19_bn', 'vgg19',
]
model_urls = {
'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
'vgg13': 'https://download.pytor... | 8,773 | 44.226804 | 118 | py |
torchsat | torchsat-master/torchsat/models/classification/utils.py | """utils.py - Helper functions for building the model and for loading model parameters.
These helper functions are built to mirror those in the official TensorFlow implementation.
"""
# Author: lukemelas (github username)
# Github repo: https://github.com/lukemelas/EfficientNet-PyTorch
# With adjustments and added ... | 24,938 | 41.19797 | 130 | py |
torchsat | torchsat-master/torchsat/models/classification/densenet.py | import re
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from collections import OrderedDict
from torch.hub import load_state_dict_from_url
__all__ = ['DenseNet', 'densenet121', 'densenet169', 'densenet201', 'densenet161']
model_urls = {
'densenet121': 'http... | 12,159 | 45.949807 | 118 | py |
torchsat | torchsat-master/torchsat/models/classification/mobilenet.py | import torch
from torch import nn
from torch.hub import load_state_dict_from_url
__all__ = ['MobileNetV2', 'mobilenet_v2']
model_urls = {
'mobilenet_v2': 'https://download.pytorch.org/models/mobilenet_v2-b0353104.pth',
}
def _make_divisible(v, divisor, min_value=None):
"""
This function is taken from ... | 6,956 | 37.436464 | 121 | py |
torchsat | torchsat-master/torchsat/models/classification/resnest/ablation.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang
## Email: zhanghang0704@gmail.com
## Copyright (c) 2020
##
## LICENSE file in the root directory of this source tree
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
"""ResNeSt ablation stud... | 4,850 | 44.336449 | 91 | py |
torchsat | torchsat-master/torchsat/models/classification/resnest/resnet.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang
## Email: zhanghang0704@gmail.com
## Copyright (c) 2020
##
## LICENSE file in the root directory of this source tree
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
"""ResNet variants"""
im... | 13,205 | 42.156863 | 162 | py |
torchsat | torchsat-master/torchsat/models/classification/resnest/resnest.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang
## Email: zhanghang0704@gmail.com
## Copyright (c) 2020
##
## LICENSE file in the root directory of this source tree
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
"""ResNeSt models"""
im... | 3,669 | 43.756098 | 93 | py |
torchsat | torchsat-master/torchsat/models/classification/resnest/splat.py | """Split-Attention"""
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import Conv2d, Module, Linear, BatchNorm2d, ReLU
from torch.nn.modules.utils import _pair
__all__ = ['SplAtConv2d']
class SplAtConv2d(Module):
"""Split-Attention Conv2d
"""
def __init__(self, in_channels... | 3,620 | 35.21 | 101 | py |
torchsat | torchsat-master/torchsat/models/segmentation/unet.py | from torch import nn
from torch.nn import functional as F
import torch
from torchvision import models
import torchvision
from ..classification import resnet
__all__ = ['UNetResNet','unet34', 'unet101', 'unet152']
"""
This script has been taken (and modified) from :
https://github.com/ternaus/TernausNet
@ARTICLE{arXi... | 7,219 | 35.464646 | 122 | py |
torchsat | torchsat-master/torchsat/models/segmentation/unet_v2.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from ..classification import resnet
class DecoderBlock(nn.Module):
def __init__(self, in_channels, mid_channels, out_channels):
super(DecoderBlock, self).__init__()
self.conv1 = nn.Sequential(nn.Conv2d(in_channels, mid_channels, ke... | 3,539 | 42.170732 | 106 | py |
torchsat | torchsat-master/torchsat/datasets/utils.py | import os
import six
from PIL import Image
import numpy as np
from torch.utils.model_zoo import tqdm
def gen_bar_updater():
pbar = tqdm(total=None)
def bar_update(count, block_size, total_size):
if pbar.total is None and total_size:
pbar.total = total_size
progress_bytes = count *... | 2,969 | 29 | 109 | py |
torchsat | torchsat-master/torchsat/datasets/folder.py | # original source code from https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py
import os
import os.path
import sys
from pathlib import Path
import torch.utils.data as data
from .utils import default_loader, image_loader
IMG_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.ti... | 10,659 | 33.723127 | 121 | py |
torchsat | torchsat-master/torchsat/datasets/sat.py | import os
import numpy as np
import torch.utils.data as data
from scipy.io import loadmat
class SAT(data.Dataset):
"""SAT-4 and SAT-6 datasets
Arguments:
data {root} -- [description]
Raises:
ValueError -- [description]
ValueError -- [description]
Returns:
... | 2,134 | 23.825581 | 78 | py |
torchsat | torchsat-master/torchsat/transforms/transforms_cls.py | import collections
import numbers
import random
import cv2
import numpy as np
from PIL import Image
from . import functional as F
__all__ = [
"Compose",
"Lambda",
"ToTensor",
"Normalize",
"ToGray",
"GaussianBlur",
"RandomNoise",
"RandomBrightness",
"RandomContrast",
"RandomShi... | 14,708 | 27.841176 | 118 | py |
torchsat | torchsat-master/torchsat/transforms/transforms_det.py | import collections
import numbers
import random
import torch
import cv2
import numpy as np
from PIL import Image
from . import functional as F
__all__ = [
"Compose",
"Lambda",
"ToTensor",
"Normalize",
"ToGray",
"GaussianBlur",
"RandomNoise",
"RandomBrightness",
"RandomContrast",
... | 19,389 | 31.753378 | 118 | py |
torchsat | torchsat-master/torchsat/transforms/functional.py | import collections
import numbers
from functools import wraps
import cv2
import numpy as np
import torch
from PIL import Image
from scipy.ndimage.filters import gaussian_filter
__numpy_type_map = {
"float64": torch.DoubleTensor,
"float32": torch.FloatTensor,
"float16": torch.HalfTensor,
"int64": torch... | 20,150 | 28.76514 | 111 | py |
torchsat | torchsat-master/torchsat/transforms/transforms_cd.py | import collections
import numbers
import random
from PIL import Image
import numpy as np
import cv2
import torch
from . import functional as F
__all__ = [
"Compose",
"Lambda",
"ToTensor",
"Normalize",
"ToGray",
"GaussianBlur",
"RandomNoise",
"RandomBrightness",
"RandomContrast",
... | 16,096 | 31.001988 | 119 | py |
torchsat | torchsat-master/torchsat/transforms/transforms_seg.py | import collections
import numbers
import random
from PIL import Image
import numpy as np
import cv2
import torch
from . import functional as F
__all__ = [
"Compose",
"Lambda",
"ToTensor",
"Normalize",
"ToGray",
"GaussianBlur",
"RandomNoise",
"RandomBrightness",
"RandomContrast",
... | 14,500 | 28.533605 | 118 | py |
torchsat | torchsat-master/scripts/train_cd.py | import argparse
import os
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from ignite.metrics import IoU, Precision, Recall
import torchsat.transforms.transforms_cd as T
from torchsat.datasets.f... | 6,995 | 41.4 | 115 | py |
torchsat | torchsat-master/scripts/train_seg.py | import argparse
import os
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from ignite.metrics import IoU, Precision, Recall
import torchsat.transforms.transforms_seg as T_seg
from torchsat.datas... | 6,322 | 38.767296 | 112 | py |
torchsat | torchsat-master/scripts/train_cls.py | import argparse
import os
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import torchsat.transforms.transforms_cls as T_cls
from torchsat.datasets.folder import ImageFolder
from torchsat.models.utils import get_m... | 5,282 | 39.328244 | 111 | py |
torchsat | torchsat-master/tests/test_transform_cls.py | from pathlib import Path
import math
import numpy as np
import pytest
import tifffile
import torch
from PIL import Image
from torchsat.transforms import transforms_cls
tiff_files = [
'./tests/fixtures/different-types/tiff_1channel_float.tif',
'./tests/fixtures/different-types/tiff_1channel_uint16.tif',
'... | 10,987 | 30.757225 | 107 | py |
torchsat | torchsat-master/tests/test_models.py | import pytest
import torch
# from torchsat.models.classification import resnet, densenet, vgg, inception, mobilenet
import torchsat.models.classification as clss
import torchsat.models.segmentation as seg
IN_CHANNELS = [1, 3, 8]
cls_models = [k for k, v in clss.__dict__.items() if callable(v) and k.lower()==k and k[0... | 2,348 | 40.210526 | 96 | py |
torchsat | torchsat-master/tests/test_transform_det.py | from pathlib import Path
import math
import numpy as np
import pytest
import tifffile
import torch
from PIL import Image
from torchsat.transforms import transforms_det
tiff_files = [
'./tests/fixtures/different-types/tiff_1channel_float.tif',
'./tests/fixtures/different-types/tiff_1channel_uint16.tif',
'... | 3,221 | 32.216495 | 84 | py |
torchsat | torchsat-master/tests/test_transform_seg.py | from pathlib import Path
import math
import numpy as np
import pytest
import tifffile
import torch
from PIL import Image
from torchsat.transforms import transforms_seg
tiff_files = [
'./tests/fixtures/different-types/tiff_1channel_float.tif',
'./tests/fixtures/different-types/tiff_1channel_uint16.tif',
'... | 14,188 | 35.104326 | 110 | py |
torchsat | torchsat-master/docs/source/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... | 2,126 | 31.723077 | 79 | py |
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