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34,545 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B7 RandAug. Tensorflow compatible variant |
34,546 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B8 RandAug. Tensorflow compatible variant |
34,547 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B0 AdvProp. Tensorflow compatible variant Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665) |
34,548 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B1 AdvProp. Tensorflow compatible variant Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665) |
34,549 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B2 AdvProp. Tensorflow compatible variant Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665) |
34,550 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B3 AdvProp. Tensorflow compatible variant Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665) |
34,551 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B4 AdvProp. Tensorflow compatible variant Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665) |
34,552 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B5 AdvProp. Tensorflow compatible variant Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665) |
34,553 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B6 AdvProp. Tensorflow compatible variant Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665) |
34,554 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B7 AdvProp. Tensorflow compatible variant Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665) |
34,555 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B8 AdvProp. Tensorflow compatible variant Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665) |
34,556 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B0 NoisyStudent. Tensorflow compatible variant Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252) |
34,557 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B1 NoisyStudent. Tensorflow compatible variant Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252) |
34,558 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B2 NoisyStudent. Tensorflow compatible variant Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252) |
34,559 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B3 NoisyStudent. Tensorflow compatible variant Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252) |
34,560 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B4 NoisyStudent. Tensorflow compatible variant Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252) |
34,561 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B5 NoisyStudent. Tensorflow compatible variant Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252) |
34,562 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B6 NoisyStudent. Tensorflow compatible variant Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252) |
34,563 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B7 NoisyStudent. Tensorflow compatible variant Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252) |
34,564 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-L2 NoisyStudent @ 475x475. Tensorflow compatible variant Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252) |
34,565 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-L2 NoisyStudent. Tensorflow compatible variant Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252) |
34,566 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet_edge(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=Fals... | EfficientNet-Edge Small. Tensorflow compatible variant |
34,567 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet_edge(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=Fals... | EfficientNet-Edge-Medium. Tensorflow compatible variant |
34,568 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet_edge(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=Fals... | EfficientNet-Edge-Large. Tensorflow compatible variant |
34,569 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet_condconv(
variant, channel_multiplier=1.0, depth_multiplier=1.0, ex... | EfficientNet-CondConv-B0 w/ 4 Experts |
34,570 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet_condconv(
variant, channel_multiplier=1.0, depth_multiplier=1.0, ex... | EfficientNet-CondConv-B0 w/ 8 Experts |
34,571 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet_condconv(
variant, channel_multiplier=1.0, depth_multiplier=1.0, ex... | EfficientNet-CondConv-B1 w/ 8 Experts |
34,572 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet_lite(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=Fals... | EfficientNet-Lite0. Tensorflow compatible variant |
34,573 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet_lite(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=Fals... | EfficientNet-Lite1. Tensorflow compatible variant |
34,574 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet_lite(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=Fals... | EfficientNet-Lite2. Tensorflow compatible variant |
34,575 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet_lite(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=Fals... | EfficientNet-Lite3. Tensorflow compatible variant |
34,576 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet_lite(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=Fals... | EfficientNet-Lite4. Tensorflow compatible variant |
34,577 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mixnet_s(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
"""Creates a ... | Creates a MixNet Small model. |
34,578 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mixnet_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwar... | Creates a MixNet Medium model. |
34,579 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mixnet_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwar... | Creates a MixNet Large model. |
34,580 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mixnet_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwar... | Creates a MixNet Extra-Large model. Not a paper spec, experimental def by RW w/ depth scaling. |
34,581 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mixnet_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwar... | Creates a MixNet Double Extra Large model. Not a paper spec, experimental def by RW w/ depth scaling. |
34,582 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mixnet_s(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
"""Creates a ... | Creates a MixNet Small model. Tensorflow compatible variant |
34,583 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mixnet_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwar... | Creates a MixNet Medium model. Tensorflow compatible variant |
34,584 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mixnet_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwar... | Creates a MixNet Large model. Tensorflow compatible variant |
34,585 | import torch
from torch import nn as nn
from torch.nn import functional as F
def swish_jit_fwd(x):
return x.mul(torch.sigmoid(x)) | null |
34,586 | import torch
from torch import nn as nn
from torch.nn import functional as F
def swish_jit_bwd(x, grad_output):
x_sigmoid = torch.sigmoid(x)
return grad_output * (x_sigmoid * (1 + x * (1 - x_sigmoid))) | null |
34,587 | import torch
from torch import nn as nn
from torch.nn import functional as F
class SwishJitAutoFn(torch.autograd.Function):
def forward(ctx, x):
def backward(ctx, grad_output):
def swish_me(x, inplace=False):
return SwishJitAutoFn.apply(x) | null |
34,588 | import torch
from torch import nn as nn
from torch.nn import functional as F
def mish_jit_fwd(x):
return x.mul(torch.tanh(F.softplus(x))) | null |
34,589 | import torch
from torch import nn as nn
from torch.nn import functional as F
def mish_jit_bwd(x, grad_output):
x_sigmoid = torch.sigmoid(x)
x_tanh_sp = F.softplus(x).tanh()
return grad_output.mul(x_tanh_sp + x * x_sigmoid * (1 - x_tanh_sp * x_tanh_sp)) | null |
34,590 | import torch
from torch import nn as nn
from torch.nn import functional as F
class MishJitAutoFn(torch.autograd.Function):
""" Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
A memory efficient, jit scripted variant of Mish
"""
def forward(ctx, x):
... | null |
34,591 | import torch
from torch import nn as nn
from torch.nn import functional as F
def hard_sigmoid_jit_fwd(x, inplace: bool = False):
return (x + 3).clamp(min=0, max=6).div(6.) | null |
34,592 | import torch
from torch import nn as nn
from torch.nn import functional as F
def hard_sigmoid_jit_bwd(x, grad_output):
m = torch.ones_like(x) * ((x >= -3.) & (x <= 3.)) / 6.
return grad_output * m | null |
34,593 | import torch
from torch import nn as nn
from torch.nn import functional as F
class HardSigmoidJitAutoFn(torch.autograd.Function):
def forward(ctx, x):
ctx.save_for_backward(x)
return hard_sigmoid_jit_fwd(x)
def backward(ctx, grad_output):
x = ctx.saved_tensors[0]
return hard_sigm... | null |
34,594 | import torch
from torch import nn as nn
from torch.nn import functional as F
def hard_swish_jit_fwd(x):
return x * (x + 3).clamp(min=0, max=6).div(6.) | null |
34,595 | import torch
from torch import nn as nn
from torch.nn import functional as F
def hard_swish_jit_bwd(x, grad_output):
m = torch.ones_like(x) * (x >= 3.)
m = torch.where((x >= -3.) & (x <= 3.), x / 3. + .5, m)
return grad_output * m | null |
34,596 | import torch
from torch import nn as nn
from torch.nn import functional as F
class HardSwishJitAutoFn(torch.autograd.Function):
"""A memory efficient, jit-scripted HardSwish activation"""
def forward(ctx, x):
ctx.save_for_backward(x)
return hard_swish_jit_fwd(x)
def backward(ctx, grad_output... | null |
34,597 | import torch
from torch import nn as nn
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `swish_jit` function. Write a Python function `def swish_jit(x, inplace: bool = False)` to solve the following problem:
Swish - Described originally as SiLU (https... | Swish - Described originally as SiLU (https://arxiv.org/abs/1702.03118v3) and also as Swish (https://arxiv.org/abs/1710.05941). TODO Rename to SiLU with addition to PyTorch |
34,598 | import torch
from torch import nn as nn
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `mish_jit` function. Write a Python function `def mish_jit(x, _inplace: bool = False)` to solve the following problem:
Mish: A Self Regularized Non-Monotonic Neura... | Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681 |
34,599 | import torch
from torch import nn as nn
from torch.nn import functional as F
def hard_sigmoid_jit(x, inplace: bool = False):
# return F.relu6(x + 3.) / 6.
return (x + 3).clamp(min=0, max=6).div(6.) # clamp seems ever so slightly faster? | null |
34,600 | import torch
from torch import nn as nn
from torch.nn import functional as F
def hard_swish_jit(x, inplace: bool = False):
# return x * (F.relu6(x + 3.) / 6)
return x * (x + 3).clamp(min=0, max=6).div(6.) # clamp seems ever so slightly faster? | null |
34,601 | from torch import nn as nn
from torch.nn import functional as F
def sigmoid(x, inplace: bool = False):
return x.sigmoid_() if inplace else x.sigmoid()
The provided code snippet includes necessary dependencies for implementing the `swish` function. Write a Python function `def swish(x, inplace: bool = False)` to so... | Swish - Described originally as SiLU (https://arxiv.org/abs/1702.03118v3) and also as Swish (https://arxiv.org/abs/1710.05941). TODO Rename to SiLU with addition to PyTorch |
34,602 | from torch import nn as nn
from torch.nn import functional as F
def tanh(x, inplace: bool = False):
return x.tanh_() if inplace else x.tanh()
The provided code snippet includes necessary dependencies for implementing the `mish` function. Write a Python function `def mish(x, inplace: bool = False)` to solve the fol... | Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681 |
34,603 | from torch import nn as nn
from torch.nn import functional as F
def hard_swish(x, inplace: bool = False):
inner = F.relu6(x + 3.).div_(6.)
return x.mul_(inner) if inplace else x.mul(inner) | null |
34,604 | from torch import nn as nn
from torch.nn import functional as F
def hard_sigmoid(x, inplace: bool = False):
if inplace:
return x.add_(3.).clamp_(0., 6.).div_(6.)
else:
return F.relu6(x + 3.) / 6. | null |
34,605 | from typing import Any, Optional
_NO_JIT = False
def is_no_jit():
return _NO_JIT | null |
34,606 | import torch.nn as nn
import torch.nn.functional as F
from .activations import get_act_fn, get_act_layer, HardSwish
from .config import layer_config_kwargs
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mobilenet_v3_rw(variant, channel_multipli... | MobileNet-V3 RW Attn: See note in gen function for this variant. |
34,607 | import torch.nn as nn
import torch.nn.functional as F
from .activations import get_act_fn, get_act_layer, HardSwish
from .config import layer_config_kwargs
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mobilenet_v3(variant, channel_multiplier=... | MobileNet V3 Large 0.75 |
34,608 | import torch.nn as nn
import torch.nn.functional as F
from .activations import get_act_fn, get_act_layer, HardSwish
from .config import layer_config_kwargs
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mobilenet_v3(variant, channel_multiplier=... | MobileNet V3 Large 1.0 |
34,609 | import torch.nn as nn
import torch.nn.functional as F
from .activations import get_act_fn, get_act_layer, HardSwish
from .config import layer_config_kwargs
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mobilenet_v3(variant, channel_multiplier=... | MobileNet V3 Large (Minimalistic) 1.0 |
34,610 | import torch.nn as nn
import torch.nn.functional as F
from .activations import get_act_fn, get_act_layer, HardSwish
from .config import layer_config_kwargs
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mobilenet_v3(variant, channel_multiplier=... | MobileNet V3 Small 0.75 |
34,611 | import torch.nn as nn
import torch.nn.functional as F
from .activations import get_act_fn, get_act_layer, HardSwish
from .config import layer_config_kwargs
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mobilenet_v3(variant, channel_multiplier=... | MobileNet V3 Small 1.0 |
34,612 | import torch.nn as nn
import torch.nn.functional as F
from .activations import get_act_fn, get_act_layer, HardSwish
from .config import layer_config_kwargs
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mobilenet_v3(variant, channel_multiplier=... | MobileNet V3 Small (Minimalistic) 1.0 |
34,613 | import torch.nn as nn
import torch.nn.functional as F
from .activations import get_act_fn, get_act_layer, HardSwish
from .config import layer_config_kwargs
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mobilenet_v3(variant, channel_multiplier=... | MobileNet V3 Large 0.75. Tensorflow compat variant. |
34,614 | import torch.nn as nn
import torch.nn.functional as F
from .activations import get_act_fn, get_act_layer, HardSwish
from .config import layer_config_kwargs
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mobilenet_v3(variant, channel_multiplier=... | MobileNet V3 Large 1.0. Tensorflow compat variant. |
34,615 | import torch.nn as nn
import torch.nn.functional as F
from .activations import get_act_fn, get_act_layer, HardSwish
from .config import layer_config_kwargs
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mobilenet_v3(variant, channel_multiplier=... | MobileNet V3 Large Minimalistic 1.0. Tensorflow compat variant. |
34,616 | import torch.nn as nn
import torch.nn.functional as F
from .activations import get_act_fn, get_act_layer, HardSwish
from .config import layer_config_kwargs
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mobilenet_v3(variant, channel_multiplier=... | MobileNet V3 Small 0.75. Tensorflow compat variant. |
34,617 | import torch.nn as nn
import torch.nn.functional as F
from .activations import get_act_fn, get_act_layer, HardSwish
from .config import layer_config_kwargs
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mobilenet_v3(variant, channel_multiplier=... | MobileNet V3 Small 1.0. Tensorflow compat variant. |
34,618 | import torch.nn as nn
import torch.nn.functional as F
from .activations import get_act_fn, get_act_layer, HardSwish
from .config import layer_config_kwargs
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mobilenet_v3(variant, channel_multiplier=... | MobileNet V3 Small Minimalistic 1.0. Tensorflow compat variant. |
34,619 | import collections.abc
import math
from functools import partial
from itertools import repeat
from typing import Tuple, Optional
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from .config import *
def _ntuple(n):
def parse(x):
if isinstance(x, collections.abc.Iterabl... | null |
34,620 | import collections.abc
import math
from functools import partial
from itertools import repeat
from typing import Tuple, Optional
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from .config import *
def _calc_same_pad(i: int, k: int, s: int, d: int):
return max((-(i // -s) - 1)... | null |
34,621 | import collections.abc
import math
from functools import partial
from itertools import repeat
from typing import Tuple, Optional
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from .config import *
def _split_channels(num_chan, num_groups):
split = [num_chan // num_groups for... | null |
34,622 | import collections.abc
import math
from functools import partial
from itertools import repeat
from typing import Tuple, Optional
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from .config import *
def _calc_same_pad(i: int, k: int, s: int, d: int):
return max((-(i // -s) - 1)... | null |
34,623 | import collections.abc
import math
from functools import partial
from itertools import repeat
from typing import Tuple, Optional
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from .config import *
def create_conv2d_pad(in_chs, out_chs, kernel_size, **kwargs):
padding = kwargs... | null |
34,624 | import re
from copy import deepcopy
from .conv2d_layers import *
from geffnet.activations import *
_SE_ARGS_DEFAULT = dict(
gate_fn=sigmoid,
act_layer=None, # None == use containing block's activation layer
reduce_mid=False,
divisor=1)
def resolve_se_args(kwargs, in_chs, act_layer=None):
se_kwargs... | null |
34,625 | import re
from copy import deepcopy
from .conv2d_layers import *
from geffnet.activations import *
import torch
import torch.nn as nn
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `drop_connect` function. Write a Python function `def drop_connect(inputs... | Apply drop connect. |
34,626 | import re
from copy import deepcopy
from .conv2d_layers import *
from geffnet.activations import *
import math
def get_condconv_initializer(initializer, num_experts, expert_shape):
def condconv_initializer(weight):
"""CondConv initializer function."""
num_params = np.p... | null |
34,627 | import re
from copy import deepcopy
from .conv2d_layers import *
from geffnet.activations import *
def get_condconv_initializer(initializer, num_experts, expert_shape):
def condconv_initializer(weight):
"""CondConv initializer function."""
num_params = np.prod(expert_shape)
if (len(weight.s... | null |
34,628 | from typing import Tuple
import math
import numpy as np
from enum import IntEnum
from typing import List, Tuple, Union
import torch
from torch.nn import functional as F
import logging
import cv2
def _extract_i_from_iuvarr(iuv_arr):
return iuv_arr[0, :, :] | null |
34,629 | from typing import Tuple
import math
import numpy as np
from enum import IntEnum
from typing import List, Tuple, Union
import torch
from torch.nn import functional as F
import logging
import cv2
def _extract_u_from_iuvarr(iuv_arr):
return iuv_arr[1, :, :] | null |
34,630 | from typing import Tuple
import math
import numpy as np
from enum import IntEnum
from typing import List, Tuple, Union
import torch
from torch.nn import functional as F
import logging
import cv2
def _extract_v_from_iuvarr(iuv_arr):
return iuv_arr[2, :, :] | null |
34,631 | from typing import Tuple
import math
import numpy as np
from enum import IntEnum
from typing import List, Tuple, Union
import torch
from torch.nn import functional as F
import logging
import cv2
Boxes = torch.Tensor
class BoxMode(IntEnum):
"""
Enum of different ways to represent a box.
"""
XYXY_ABS = 0
... | null |
34,632 | from typing import List, Optional
import numpy as np
from fastapi import FastAPI, Body
from fastapi.exceptions import HTTPException
from pydantic import BaseModel
from PIL import Image
import gradio as gr
from modules.api.models import *
from modules.api import api
from scripts import external_code, global_state
from ... | null |
34,633 | import torch
import os
import functools
import time
import base64
import numpy as np
import safetensors.torch
import cv2
import logging
from typing import Any, Callable, Dict, List
from modules.safe import unsafe_torch_load
from modules.modelloader import load_file_from_url
from scripts.logging import logger
def get_s... | null |
34,634 | import torch
import os
import functools
import time
import base64
import numpy as np
import safetensors.torch
import cv2
import logging
from typing import Any, Callable, Dict, List
from modules.safe import unsafe_torch_load
from modules.modelloader import load_file_from_url
from scripts.logging import logger
import l... | Time the decorated function and output the result to debug logger. |
34,635 | import torch
import os
import functools
import time
import base64
import numpy as np
import safetensors.torch
import cv2
import logging
from typing import Any, Callable, Dict, List
from modules.safe import unsafe_torch_load
from modules.modelloader import load_file_from_url
from scripts.logging import logger
svgsuppor... | null |
34,636 | import torch
import os
import functools
import time
import base64
import numpy as np
import safetensors.torch
import cv2
import logging
from typing import Any, Callable, Dict, List
from modules.safe import unsafe_torch_load
from modules.modelloader import load_file_from_url
from scripts.logging import logger
def get_... | null |
34,637 | import torch
import os
import functools
import time
import base64
import numpy as np
import safetensors.torch
import cv2
import logging
from typing import Any, Callable, Dict, List
from modules.safe import unsafe_torch_load
from modules.modelloader import load_file_from_url
from scripts.logging import logger
def read_... | Try read all images in given img_dir. |
34,638 | import torch
import os
import functools
import time
import base64
import numpy as np
import safetensors.torch
import cv2
import logging
from typing import Any, Callable, Dict, List
from modules.safe import unsafe_torch_load
from modules.modelloader import load_file_from_url
from scripts.logging import logger
The prov... | Align the pixel dimension (w/h) to latent dimension. Stable diffusion 1:8 ratio for latent/pixel, i.e., 1 latent unit == 8 pixel unit. |
34,639 | import re
import numpy as np
from modules import scripts, shared
def find_module(module_names):
if isinstance(module_names, str):
module_names = [s.strip() for s in module_names.split(",")]
for data in scripts.scripts_data:
if data.script_class.__module__ in module_names and hasattr(data, "modul... | null |
34,640 | import copy
import os
import shutil
import cv2
import gradio as gr
import modules.scripts as scripts
from modules import images
from modules.processing import process_images
from modules.shared import opts
from PIL import Image
import numpy as np
def get_all_frames(video_path):
if video_path is None:
retur... | null |
34,641 | import copy
import os
import shutil
import cv2
import gradio as gr
import modules.scripts as scripts
from modules import images
from modules.processing import process_images
from modules.shared import opts
from PIL import Image
import numpy as np
def get_min_frame_num(video_list):
min_frame_num = -1
for video ... | null |
34,642 | import copy
import os
import shutil
import cv2
import gradio as gr
import modules.scripts as scripts
from modules import images
from modules.processing import process_images
from modules.shared import opts
from PIL import Image
import numpy as np
def pil2cv(image):
new_image = np.array(image, dtype=np.uint8)
if ne... | null |
34,643 | import copy
import os
import shutil
import cv2
import gradio as gr
import modules.scripts as scripts
from modules import images
from modules.processing import process_images
from modules.shared import opts
from PIL import Image
import numpy as np
_BASEDIR = "/controlnet-m2m"
def save_gif(path, image_list, name, durati... | null |
34,644 | import base64
import gradio as gr
import json
from typing import List, Dict, Any, Tuple
from annotator.openpose import decode_json_as_poses, draw_poses
from annotator.openpose.animalpose import draw_animalposes
from scripts.controlnet_ui.modal import ModalInterface
from modules import shared
from scripts.logging import... | null |
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