id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
165,895 | import contextlib
import math
import os
from copy import copy
from pathlib import Path
from urllib.error import URLError
import cv2
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sn
import torch
from PIL import Image, ImageDraw, ImageFont
from utils import Try... | null |
165,899 | import contextlib
import glob
import hashlib
import json
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import Pool, ThreadPool
from pathlib import Path
from threading import Thread
from urllib.parse import urlparse
import numpy as np
import psutil
i... | null |
165,903 | import contextlib
import glob
import hashlib
import json
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import Pool, ThreadPool
from pathlib import Path
from threading import Thread
from urllib.parse import urlparse
import numpy as np
import psutil
i... | null |
165,904 | import contextlib
import glob
import hashlib
import json
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import Pool, ThreadPool
from pathlib import Path
from threading import Thread
from urllib.parse import urlparse
import numpy as np
import psutil
i... | null |
165,905 | import glob
import re
from pathlib import Path
import numpy as np
import yaml
from utils.plots import Annotator, colors
The provided code snippet includes necessary dependencies for implementing the `construct_dataset` function. Write a Python function `def construct_dataset(clearml_info_string)` to solve the followin... | Load in a clearml dataset and fill the internal data_dict with its contents. |
165,906 | import argparse
import json
import logging
import os
import sys
from pathlib import Path
import comet_ml
ROOT = FILE.parents[3]
from train import train
from utils.callbacks import Callbacks
from utils.general import increment_path
from utils.torch_utils import select_device
def get_args(known=False):
parser = arg... | null |
165,907 | import argparse
import json
import logging
import os
import sys
from pathlib import Path
import comet_ml
from train import train
from utils.callbacks import Callbacks
from utils.general import increment_path
from utils.torch_utils import select_device
def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml ... | null |
165,908 | import logging
import os
from urllib.parse import urlparse
try:
import comet_ml
except (ModuleNotFoundError, ImportError):
comet_ml = None
import yaml
COMET_PREFIX = 'comet://'
def download_model_checkpoint(opt, experiment):
model_dir = f'{opt.project}/{experiment.name}'
os.makedirs(model_dir, exist_ok=... | Downloads model weights from Comet and updates the weights path to point to saved weights location Args: opt (argparse.Namespace): Command Line arguments passed to YOLOv5 training script Returns: None/bool: Return True if weights are successfully downloaded else return None |
165,909 | import logging
import os
from urllib.parse import urlparse
try:
import comet_ml
except (ModuleNotFoundError, ImportError):
comet_ml = None
import yaml
COMET_PREFIX = 'comet://'
def download_model_checkpoint(opt, experiment):
model_dir = f'{opt.project}/{experiment.name}'
os.makedirs(model_dir, exist_ok=... | Restores run parameters to its original state based on the model checkpoint and logged Experiment parameters. Args: opt (argparse.Namespace): Command Line arguments passed to YOLOv5 training script Returns: None/bool: Return True if the run is restored successfully else return None |
165,910 | import logging
import os
import sys
from contextlib import contextmanager
from pathlib import Path
from utils.general import LOGGER, colorstr
The provided code snippet includes necessary dependencies for implementing the `all_logging_disabled` function. Write a Python function `def all_logging_disabled(highest_level=l... | source - https://gist.github.com/simon-weber/7853144 A context manager that will prevent any logging messages triggered during the body from being processed. :param highest_level: the maximum logging level in use. This would only need to be changed if a custom level greater than CRITICAL is defined. |
165,911 | import argparse
import io
import torch
from flask import Flask, request
from PIL import Image
models = {}
def predict(model):
if request.method != 'POST':
return
if request.files.get('image'):
# Method 1
# with request.files["image"] as f:
# im = Image.open(io.BytesIO(f.rea... | null |
165,912 | import contextlib
import glob
import inspect
import logging
import logging.config
import math
import os
import platform
import random
import re
import signal
import subprocess
import sys
import time
import urllib
from copy import deepcopy
from datetime import datetime
from itertools import repeat
from multiprocessing.p... | null |
165,913 | import contextlib
import glob
import inspect
import logging
import logging.config
import math
import os
import platform
import random
import re
import signal
import subprocess
import sys
import time
import urllib
from copy import deepcopy
from datetime import datetime
from itertools import repeat
from multiprocessing.p... | null |
165,914 | import contextlib
import glob
import inspect
import logging
import logging.config
import math
import os
import platform
import random
import re
import signal
import subprocess
import sys
import time
import urllib
from copy import deepcopy
from datetime import datetime
from itertools import repeat
from multiprocessing.p... | null |
165,915 | import contextlib
import glob
import inspect
import logging
import logging.config
import math
import os
import platform
import random
import re
import signal
import subprocess
import sys
import time
import urllib
from copy import deepcopy
from datetime import datetime
from itertools import repeat
from multiprocessing.p... | null |
165,916 | import contextlib
import glob
import inspect
import logging
import logging.config
import math
import os
import platform
import random
import re
import signal
import subprocess
import sys
import time
import urllib
from copy import deepcopy
from datetime import datetime
from itertools import repeat
from multiprocessing.p... | null |
165,917 | import contextlib
import glob
import inspect
import logging
import logging.config
import math
import os
import platform
import random
import re
import signal
import subprocess
import sys
import time
import urllib
from copy import deepcopy
from datetime import datetime
from itertools import repeat
from multiprocessing.p... | null |
165,918 | import contextlib
import glob
import inspect
import logging
import logging.config
import math
import os
import platform
import random
import re
import signal
import subprocess
import sys
import time
import urllib
from copy import deepcopy
from datetime import datetime
from itertools import repeat
from multiprocessing.p... | null |
165,919 | import contextlib
import glob
import inspect
import logging
import logging.config
import math
import os
import platform
import random
import re
import signal
import subprocess
import sys
import time
import urllib
from copy import deepcopy
from datetime import datetime
from itertools import repeat
from multiprocessing.p... | null |
165,920 | import contextlib
import glob
import inspect
import logging
import logging.config
import math
import os
import platform
import random
import re
import signal
import subprocess
import sys
import time
import urllib
from copy import deepcopy
from datetime import datetime
from itertools import repeat
from multiprocessing.p... | null |
165,921 | import contextlib
import glob
import inspect
import logging
import logging.config
import math
import os
import platform
import random
import re
import signal
import subprocess
import sys
import time
import urllib
from copy import deepcopy
from datetime import datetime
from itertools import repeat
from multiprocessing.p... | null |
165,922 | import contextlib
import glob
import inspect
import logging
import logging.config
import math
import os
import platform
import random
import re
import signal
import subprocess
import sys
import time
import urllib
from copy import deepcopy
from datetime import datetime
from itertools import repeat
from multiprocessing.p... | null |
165,923 | import contextlib
import glob
import inspect
import logging
import logging.config
import math
import os
import platform
import random
import re
import signal
import subprocess
import sys
import time
import urllib
from copy import deepcopy
from datetime import datetime
from itertools import repeat
from multiprocessing.p... | null |
165,924 | import contextlib
import glob
import inspect
import logging
import logging.config
import math
import os
import platform
import random
import re
import signal
import subprocess
import sys
import time
import urllib
from copy import deepcopy
from datetime import datetime
from itertools import repeat
from multiprocessing.p... | null |
165,925 | import contextlib
import glob
import inspect
import logging
import logging.config
import math
import os
import platform
import random
import re
import signal
import subprocess
import sys
import time
import urllib
from copy import deepcopy
from datetime import datetime
from itertools import repeat
from multiprocessing.p... | null |
165,926 | import contextlib
import glob
import inspect
import logging
import logging.config
import math
import os
import platform
import random
import re
import signal
import subprocess
import sys
import time
import urllib
from copy import deepcopy
from datetime import datetime
from itertools import repeat
from multiprocessing.p... | null |
165,927 | import math
import warnings
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import torch
from utils import TryExcept, threaded
def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7):
# Returns Intersection over Union (IoU) of box1(1,4) to box2(n,4)
# Get ... | null |
165,929 | iou = bbox_mpdiou(pbox.T, tbox[i], x1y1x2y2=False, mpdiou_hw=pi.size(2) ** 2 + pi.size(3) ** 2, grid=torch.stack([gj, gi]))
iou = bbox_mpdiou(pbox.T, selected_tbox, x1y1x2y2=False, mpdiou_hw=pi.size(2) ** 2 + pi.size(3) ** 2, grid=torch.stack([gj, gi]))
def bbox_mpdiou(box1, box2, x1y1x2y2=True, mpdiou_hw=None, grid=... | null |
165,932 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import numpy as np
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
class SwinTransformer(nn.Module):
def __init__(self,
pretrain_img_size=224,
p... | null |
165,933 | import torch, yaml
import torch.nn as nn
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from functools import partial
from typing import List
from torch import Tensor
import copy
import os
import numpy as np
class FasterNet(nn.Module):
def __init__(self,
in_chans=3,
... | null |
165,934 | import torch, yaml
import torch.nn as nn
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from functools import partial
from typing import List
from torch import Tensor
import copy
import os
import numpy as np
class FasterNet(nn.Module):
def __init__(self,
in_chans=3,
... | null |
165,935 | import torch, yaml
import torch.nn as nn
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from functools import partial
from typing import List
from torch import Tensor
import copy
import os
import numpy as np
class FasterNet(nn.Module):
def __init__(self,
in_chans=3,
... | null |
165,936 | import torch, yaml
import torch.nn as nn
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from functools import partial
from typing import List
from torch import Tensor
import copy
import os
import numpy as np
class FasterNet(nn.Module):
def __init__(self,
in_chans=3,
... | null |
165,937 | import torch, yaml
import torch.nn as nn
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from functools import partial
from typing import List
from torch import Tensor
import copy
import os
import numpy as np
class FasterNet(nn.Module):
def __init__(self,
in_chans=3,
... | null |
165,938 | import torch, yaml
import torch.nn as nn
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from functools import partial
from typing import List
from torch import Tensor
import copy
import os
import numpy as np
class FasterNet(nn.Module):
def __init__(self,
in_chans=3,
... | null |
165,939 | import torch.nn as nn
import numpy as np
from timm.models.layers import SqueezeExcite
import torch
def replace_batchnorm(net):
for child_name, child in net.named_children():
if hasattr(child, 'fuse_self'):
fused = child.fuse_self()
setattr(net, child_name, fused)
replace... | null |
165,940 | import torch.nn as nn
import numpy as np
from timm.models.layers import SqueezeExcite
import torch
The provided code snippet includes necessary dependencies for implementing the `_make_divisible` function. Write a Python function `def _make_divisible(v, divisor, min_value=None)` to solve the following problem:
This fu... | This function is taken from the original tf repo. It ensures that all layers have a channel number that is divisible by 8 It can be seen here: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py :param v: :param divisor: :param min_value: :return: |
165,941 | import torch.nn as nn
import numpy as np
from timm.models.layers import SqueezeExcite
import torch
class RepViT(nn.Module):
def __init__(self, cfgs):
super(RepViT, self).__init__()
# setting of inverted residual blocks
self.cfgs = cfgs
# building first layer
input_channel = s... | Constructs a MobileNetV3-Large model |
165,942 | import torch.nn as nn
import numpy as np
from timm.models.layers import SqueezeExcite
import torch
class RepViT(nn.Module):
def __init__(self, cfgs):
super(RepViT, self).__init__()
# setting of inverted residual blocks
self.cfgs = cfgs
# building first layer
input_channel = s... | Constructs a MobileNetV3-Large model |
165,943 | import torch.nn as nn
import numpy as np
from timm.models.layers import SqueezeExcite
import torch
class RepViT(nn.Module):
def __init__(self, cfgs):
super(RepViT, self).__init__()
# setting of inverted residual blocks
self.cfgs = cfgs
# building first layer
input_channel = s... | Constructs a MobileNetV3-Large model |
165,944 | import torch.nn as nn
import numpy as np
from timm.models.layers import SqueezeExcite
import torch
class RepViT(nn.Module):
def __init__(self, cfgs):
super(RepViT, self).__init__()
# setting of inverted residual blocks
self.cfgs = cfgs
# building first layer
input_channel = s... | Constructs a MobileNetV3-Large model |
165,945 | import torch.nn as nn
import numpy as np
from timm.models.layers import SqueezeExcite
import torch
class RepViT(nn.Module):
def __init__(self, cfgs):
super(RepViT, self).__init__()
# setting of inverted residual blocks
self.cfgs = cfgs
# building first layer
input_channel = s... | Constructs a MobileNetV3-Large model |
165,946 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.autograd
def fuse_conv_bn(conv, bn):
# Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/
fusedconv = (
nn.Conv2d(
conv.in_channels,
conv.out_channels,
... | null |
165,947 | import torch
from torch import nn
import numpy as np
from models.ODConv.odconv import ODConv2d
def fuse_conv_bn(conv, bn):
# Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/
fusedconv = (
nn.Conv2d(
conv.in_channels,
conv.out_channels,
... | null |
165,948 | import torch
from torch import nn
import numpy as np
from models.ODConv.odconv import ODConv2d
The provided code snippet includes necessary dependencies for implementing the `_make_divisible` function. Write a Python function `def _make_divisible(v, divisor, min_value=None)` to solve the following problem:
This functi... | This function is taken from the original tf repo. It ensures that all layers have a channel number that is divisible by 8 It can be seen here: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py :param v: :param divisor: :param min_value: :return: |
165,949 | import torch
from torch import nn
import numpy as np
from models.ODConv.odconv import ODConv2d
class OD_MobileNetV2(nn.Module):
def __init__(self,
num_classes=1000,
width_mult=1.0,
inverted_residual_setting=None,
round_nearest=8,
... | null |
165,950 | import torch
from torch import nn
import numpy as np
from models.ODConv.odconv import ODConv2d
class OD_MobileNetV2(nn.Module):
def __init__(self,
num_classes=1000,
width_mult=1.0,
inverted_residual_setting=None,
round_nearest=8,
b... | null |
165,951 | import torch
from torch import nn
import numpy as np
from models.ODConv.odconv import ODConv2d
class OD_MobileNetV2(nn.Module):
def __init__(self,
num_classes=1000,
width_mult=1.0,
inverted_residual_setting=None,
round_nearest=8,
b... | null |
165,952 | import torch
import torch.nn as nn
from models.ODConv.odconv import ODConv2d
import numpy as np
def odconv3x3(in_planes, out_planes, stride=1, reduction=0.0625, kernel_num=1):
return ODConv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1,
reduction=reduction, kernel_num=kernel_... | null |
165,953 | import torch
import torch.nn as nn
from models.ODConv.odconv import ODConv2d
import numpy as np
def odconv1x1(in_planes, out_planes, stride=1, reduction=0.0625, kernel_num=1):
return ODConv2d(in_planes, out_planes, kernel_size=1, stride=stride, padding=0,
reduction=reduction, kernel_num=kernel_... | null |
165,954 | import torch
import torch.nn as nn
from models.ODConv.odconv import ODConv2d
import numpy as np
class BasicBlock(nn.Module):
def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=0.0625, kernel_num=1):
def forward(self, x):
class OD_ResNet(nn.Module):
def __init__(self, block, layers,... | null |
165,955 | import torch
import torch.nn as nn
from models.ODConv.odconv import ODConv2d
import numpy as np
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=0.0625, kernel_num=1):
super(BasicBlock, self).__init__()
self.conv1 = odconv3x3(in... | null |
165,956 | import torch
import torch.nn as nn
from models.ODConv.odconv import ODConv2d
import numpy as np
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=0.0625, kernel_num=1):
super(Bottleneck, self).__init__()
self.conv1 = odconv1x1(in... | null |
165,957 | import torch
import torch.nn as nn
from models.ODConv.odconv import ODConv2d
import numpy as np
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=0.0625, kernel_num=1):
super(Bottleneck, self).__init__()
self.conv1 = odconv1x1(in... | null |
165,958 | import math
import numpy as np
import torch.nn as nn
from einops import rearrange, reduce
from timm.models.layers.activations import *
from timm.models.layers import DropPath, trunc_normal_, create_attn
from timm.models.efficientnet_blocks import num_groups, SqueezeExcite as SE
from functools import partial
def get_ac... | null |
165,959 | import math
import numpy as np
import torch.nn as nn
from einops import rearrange, reduce
from timm.models.layers.activations import *
from timm.models.layers import DropPath, trunc_normal_, create_attn
from timm.models.efficientnet_blocks import num_groups, SqueezeExcite as SE
from functools import partial
class Layer... | null |
165,960 | import math
import numpy as np
import torch.nn as nn
from einops import rearrange, reduce
from timm.models.layers.activations import *
from timm.models.layers import DropPath, trunc_normal_, create_attn
from timm.models.efficientnet_blocks import num_groups, SqueezeExcite as SE
from functools import partial
class EMO(n... | null |
165,961 | import math
import numpy as np
import torch.nn as nn
from einops import rearrange, reduce
from timm.models.layers.activations import *
from timm.models.layers import DropPath, trunc_normal_, create_attn
from timm.models.efficientnet_blocks import num_groups, SqueezeExcite as SE
from functools import partial
class EMO(n... | null |
165,962 | import math
import numpy as np
import torch.nn as nn
from einops import rearrange, reduce
from timm.models.layers.activations import *
from timm.models.layers import DropPath, trunc_normal_, create_attn
from timm.models.efficientnet_blocks import num_groups, SqueezeExcite as SE
from functools import partial
class EMO(n... | null |
165,963 | import math
import numpy as np
import torch.nn as nn
from einops import rearrange, reduce
from timm.models.layers.activations import *
from timm.models.layers import DropPath, trunc_normal_, create_attn
from timm.models.efficientnet_blocks import num_groups, SqueezeExcite as SE
from functools import partial
class EMO(n... | null |
165,964 | from typing import Sequence
import torch
import torch.nn as nn
import numpy as np
from mmcv.cnn.bricks import DropPath, build_activation_layer, build_norm_layer
from mmengine.model import BaseModule
class RIFormerBlock(BaseModule):
"""RIFormer Block.
Args:
dim (int): Embedding dim.
mlp_ratio (fl... | generate RIFormer blocks for a stage. |
165,965 | from typing import Sequence
import torch
import torch.nn as nn
import numpy as np
from mmcv.cnn.bricks import DropPath, build_activation_layer, build_norm_layer
from mmengine.model import BaseModule
def update_weight(model_dict, weight_dict):
idx, temp_dict = 0, {}
for k, v in weight_dict.items():
k = ... | null |
165,966 | from typing import Dict, List, Tuple, Union, Optional, Type, Callable, Any
from inspect import signature
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def build_kwargs_from_config(config: Dict, target_func: Callable) -> Dict[str, Any]:
valid_keys = list(signature(target_func)... | null |
165,967 | from typing import Dict, List, Tuple, Union, Optional, Type, Callable, Any
from inspect import signature
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def build_kwargs_from_config(config: Dict, target_func: Callable) -> Dict[str, Any]:
valid_keys = list(signature(target_func)... | null |
165,968 | from typing import Dict, List, Tuple, Union, Optional, Type, Callable, Any
from inspect import signature
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def get_same_padding(kernel_size: Union[int, Tuple[int, ...]]) -> Union[int, Tuple[int, ...]]:
if isinstance(kernel_size, tu... | null |
165,969 | from typing import Dict, List, Tuple, Union, Optional, Type, Callable, Any
from inspect import signature
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def list_sum(x: List) -> Any:
return x[0] if len(x) == 1 else x[0] + list_sum(x[1:])
def merge_tensor(x: List[torch.Tensor],... | null |
165,970 | from typing import Dict, List, Tuple, Union, Optional, Type, Callable, Any
from inspect import signature
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def resize(
x: torch.Tensor,
size: Optional[Any] = None,
scale_factor: Optional[List[float]] = None,
mode: str =... | null |
165,971 | from typing import Dict, List, Tuple, Union, Optional, Type, Callable, Any
from inspect import signature
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def val2list(x: Union[List, Tuple, Any], repeat_time=1) -> List:
if isinstance(x, (list, tuple)):
return list(x)
... | null |
165,972 | from typing import Dict, List, Tuple, Union, Optional, Type, Callable, Any
from inspect import signature
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def build_kwargs_from_config(config: Dict, target_func: Callable) -> Dict[str, Any]:
valid_keys = list(signature(target_func)... | null |
165,973 | from typing import Dict, List, Tuple, Union, Optional, Type, Callable, Any
from inspect import signature
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def build_kwargs_from_config(config: Dict, target_func: Callable) -> Dict[str, Any]:
class EfficientViTBackbone(nn.Module):
... | null |
165,974 | from typing import Dict, List, Tuple, Union, Optional, Type, Callable, Any
from inspect import signature
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def build_kwargs_from_config(config: Dict, target_func: Callable) -> Dict[str, Any]:
valid_keys = list(signature(target_func)... | null |
165,975 | from typing import Dict, List, Tuple, Union, Optional, Type, Callable, Any
from inspect import signature
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def build_kwargs_from_config(config: Dict, target_func: Callable) -> Dict[str, Any]:
valid_keys = list(signature(target_func)... | null |
165,982 | import torch
import torch.nn as nn
from models.ODConv.odconv import ODConv2d
import numpy as np
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=0.0625, kernel_num=1):
super(BasicBlock, self).__init__()
self.conv1 = odconv3x3(in... | null |
165,985 | import torch
import torch.nn as nn
from models.ODConv.odconv import ODConv2d
import numpy as np
class Bottleneck(nn.Module):
def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=0.0625, kernel_num=1):
def forward(self, x):
class OD_ResNet(nn.Module):
def __init__(self, block, layers,... | null |
165,986 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import weight_init, DropPath
import numpy as np
class VanillaNet(nn.Module):
def __init__(self, in_chans=3, num_classes=1000, dims=[96, 192, 384, 768],
drop_rate=0, act_num=3, strides=[2,2,2,1], deploy=False... | null |
165,987 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import weight_init, DropPath
import numpy as np
class VanillaNet(nn.Module):
def __init__(self, in_chans=3, num_classes=1000, dims=[96, 192, 384, 768],
drop_rate=0, act_num=3, strides=[2,2,2,1], deploy=False... | null |
165,988 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import weight_init, DropPath
import numpy as np
class VanillaNet(nn.Module):
def __init__(self, in_chans=3, num_classes=1000, dims=[96, 192, 384, 768],
drop_rate=0, act_num=3, strides=[2,2,2,1], deploy=False... | null |
165,989 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import weight_init, DropPath
import numpy as np
class VanillaNet(nn.Module):
def __init__(self, in_chans=3, num_classes=1000, dims=[96, 192, 384, 768],
drop_rate=0, act_num=3, strides=[2,2,2,1], deploy=False... | null |
165,990 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import weight_init, DropPath
import numpy as np
class VanillaNet(nn.Module):
def __init__(self, in_chans=3, num_classes=1000, dims=[96, 192, 384, 768],
drop_rate=0, act_num=3, strides=[2,2,2,1], deploy=False... | null |
165,991 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import weight_init, DropPath
import numpy as np
class VanillaNet(nn.Module):
def __init__(self, in_chans=3, num_classes=1000, dims=[96, 192, 384, 768],
drop_rate=0, act_num=3, strides=[2,2,2,1], deploy=... | null |
165,992 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import weight_init, DropPath
import numpy as np
class VanillaNet(nn.Module):
def __init__(self, in_chans=3, num_classes=1000, dims=[96, 192, 384, 768],
drop_rate=0, act_num=3, strides=[2,2,2,1], deploy=False... | null |
165,993 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import weight_init, DropPath
import numpy as np
class VanillaNet(nn.Module):
def __init__(self, in_chans=3, num_classes=1000, dims=[96, 192, 384, 768],
drop_rate=0, act_num=3, strides=[2,2,2,1], deploy=False... | null |
165,994 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import weight_init, DropPath
import numpy as np
class VanillaNet(nn.Module):
def __init__(self, in_chans=3, num_classes=1000, dims=[96, 192, 384, 768],
drop_rate=0, act_num=3, strides=[2,2,2,1], deploy=... | null |
165,995 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import weight_init, DropPath
import numpy as np
class VanillaNet(nn.Module):
def __init__(self, in_chans=3, num_classes=1000, dims=[96, 192, 384, 768],
drop_rate=0, act_num=3, strides=[2,2,2,1], deploy=... | null |
165,996 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import weight_init, DropPath
import numpy as np
class VanillaNet(nn.Module):
def __init__(self, in_chans=3, num_classes=1000, dims=[96, 192, 384, 768],
drop_rate=0, act_num=3, strides=[2,2,2,1], deploy=False... | null |
165,997 | import os
import copy
import torch
import torch.nn as nn
import numpy as np
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.layers import DropPath, trunc_normal_, to_2tuple
from timm.models.registry import register_model
def _cfg(url='', **kwargs):
return {
'url': url,
... | null |
165,998 | import os
import copy
import torch
import torch.nn as nn
import numpy as np
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.layers import DropPath, trunc_normal_, to_2tuple
from timm.models.registry import register_model
class GroupNorm(nn.GroupNorm):
"""
Group Normalization w... | generate PoolFormer blocks for a stage return: PoolFormer blocks |
165,999 | import os
import copy
import torch
import torch.nn as nn
import numpy as np
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.layers import DropPath, trunc_normal_, to_2tuple
from timm.models.registry import register_model
default_cfgs = {
'poolformer_s': _cfg(crop_pct=0.9),
'po... | PoolFormer-S12 model, Params: 12M --layers: [x,x,x,x], numbers of layers for the four stages --embed_dims, --mlp_ratios: embedding dims and mlp ratios for the four stages --downsamples: flags to apply downsampling or not in four blocks |
166,000 | import os
import copy
import torch
import torch.nn as nn
import numpy as np
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.layers import DropPath, trunc_normal_, to_2tuple
from timm.models.registry import register_model
default_cfgs = {
'poolformer_s': _cfg(crop_pct=0.9),
'po... | PoolFormer-S24 model, Params: 21M |
166,001 | import os
import copy
import torch
import torch.nn as nn
import numpy as np
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.layers import DropPath, trunc_normal_, to_2tuple
from timm.models.registry import register_model
default_cfgs = {
'poolformer_s': _cfg(crop_pct=0.9),
'po... | PoolFormer-S36 model, Params: 31M |
166,002 | import os
import copy
import torch
import torch.nn as nn
import numpy as np
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.layers import DropPath, trunc_normal_, to_2tuple
from timm.models.registry import register_model
default_cfgs = {
'poolformer_s': _cfg(crop_pct=0.9),
'po... | PoolFormer-M36 model, Params: 56M |
166,003 | import os
import copy
import torch
import torch.nn as nn
import numpy as np
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.layers import DropPath, trunc_normal_, to_2tuple
from timm.models.registry import register_model
default_cfgs = {
'poolformer_s': _cfg(crop_pct=0.9),
'po... | PoolFormer-M48 model, Params: 73M |
166,004 | from functools import partial
import torch
import torch.nn as nn
import numpy as np
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models import checkpoint_seq, to_2tuple
from timm.models.layers import trunc_normal_, DropPath
from timm.models.registry import register_model
def _cfg(url='',... | null |
166,005 | from functools import partial
import torch
import torch.nn as nn
import numpy as np
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models import checkpoint_seq, to_2tuple
from timm.models.layers import trunc_normal_, DropPath
from timm.models.registry import register_model
def update_weigh... | null |
166,006 | from functools import partial
import torch
import torch.nn as nn
import numpy as np
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models import checkpoint_seq, to_2tuple
from timm.models.layers import trunc_normal_, DropPath
from timm.models.registry import register_model
class InceptionDW... | null |
166,007 | from functools import partial
import torch
import torch.nn as nn
import numpy as np
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models import checkpoint_seq, to_2tuple
from timm.models.layers import trunc_normal_, DropPath
from timm.models.registry import register_model
class InceptionDW... | null |
166,008 | from functools import partial
import torch
import torch.nn as nn
import numpy as np
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models import checkpoint_seq, to_2tuple
from timm.models.layers import trunc_normal_, DropPath
from timm.models.registry import register_model
class InceptionDW... | null |
166,009 | from functools import partial
import torch
import torch.nn as nn
import numpy as np
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models import checkpoint_seq, to_2tuple
from timm.models.layers import trunc_normal_, DropPath
from timm.models.registry import register_model
class InceptionDW... | null |
166,010 | import os
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Dict
import itertools
import numpy as np
from timm.models.layers import DropPath, trunc_normal_, to_2tuple
def stem(in_chs, out_chs, act_layer=nn.ReLU):
return nn.Sequential(
nn.Conv2d(in... | null |
166,011 | import os
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Dict
import itertools
import numpy as np
from timm.models.layers import DropPath, trunc_normal_, to_2tuple
class AttnFFN(nn.Module):
def __init__(self, dim, mlp_ratio=4.,
act_laye... | null |
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