credit.models.crossformer

Contents

credit.models.crossformer#

Attributes#

logger

image_height

Classes#

CubeEmbedding

UpBlock

Base class for all neural network modules.

CrossEmbedLayer

Base class for all neural network modules.

DynamicPositionBias

Base class for all neural network modules.

LayerNorm

Base class for all neural network modules.

FeedForward

Base class for all neural network modules.

Attention

Base class for all neural network modules.

Transformer

Base class for all neural network modules.

CrossFormer

Base class for all neural network modules.

Functions#

cast_tuple(val[, length])

apply_spectral_norm(model)

Module Contents#

credit.models.crossformer.logger#
credit.models.crossformer.cast_tuple(val, length=1)#
credit.models.crossformer.apply_spectral_norm(model)#
class credit.models.crossformer.CubeEmbedding(img_size, patch_size, in_chans, embed_dim, norm_layer=nn.LayerNorm)#

Bases: torch.nn.Module

Parameters:

  • img_size – T, Lat, Lon

  • patch_size – T, Lat, Lon

img_size#
patches_resolution#
embed_dim#
proj#
forward(x: torch.Tensor)#
class credit.models.crossformer.UpBlock(in_chans, out_chans, num_groups, num_residuals=2, upsample_v_conv=False, attention_type=None, reduction=32, spatial_kernel=7)#

Bases: torch.nn.Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F


class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

upsample_v_conv = False#
output_channels#
b#
attention = None#
forward(x)#
class credit.models.crossformer.CrossEmbedLayer(dim_in, dim_out, kernel_sizes, stride=2)#

Bases: torch.nn.Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F


class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

convs#
forward(x)#
class credit.models.crossformer.DynamicPositionBias(dim)#

Bases: torch.nn.Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F


class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

layers#
forward(x)#
class credit.models.crossformer.LayerNorm(dim, eps=1e-05)#

Bases: torch.nn.Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F


class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

eps = 1e-05#
g#
b#
forward(x)#
class credit.models.crossformer.FeedForward(dim, mult=4, dropout=0.0)#

Bases: torch.nn.Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F


class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

layers#
forward(x)#
class credit.models.crossformer.Attention(dim, attn_type, window_size, dim_head=32, dropout=0.0)#

Bases: torch.nn.Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F


class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

heads#
scale = 0.1767766952966369#
attn_type#
window_size#
norm#
dropout#
to_qkv#
to_out#
dpb#
forward(x)#
class credit.models.crossformer.Transformer(dim, *, local_window_size, global_window_size, depth=4, dim_head=32, attn_dropout=0.0, ff_dropout=0.0)#

Bases: torch.nn.Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F


class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

layers#
forward(x)#
class credit.models.crossformer.CrossFormer(image_height: int = 640, patch_height: int = 1, image_width: int = 1280, patch_width: int = 1, frames: int = 2, output_frames: int = 1, channels: int = 4, surface_channels: int = 7, input_only_channels: int = 3, output_only_channels: int = 0, levels: int = 15, dim: tuple = (64, 128, 256, 512), depth: tuple = (2, 2, 8, 2), dim_head: int = 32, global_window_size: tuple = (5, 5, 2, 1), local_window_size: int = 10, cross_embed_kernel_sizes: tuple = ((4, 8, 16, 32), (2, 4), (2, 4), (2, 4)), cross_embed_strides: tuple = (4, 2, 2, 2), attn_dropout: float = 0.0, ff_dropout: float = 0.0, use_spectral_norm: bool = True, attention_type: str = None, interp: bool = True, upsample_v_conv: bool = False, padding_conf: dict = None, post_conf: dict = None, **kwargs)#

Bases: credit.models.base_model.BaseModel

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F


class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

image_height = 640#
image_width = 1280#
patch_height = 1#
patch_width = 1#
upsample_v_conv = False#
frames = 2#
output_frames = 1#
channels = 4#
surface_channels = 7#
levels = 15#
use_spectral_norm = True#
use_interp = True#
use_padding#
use_post_block#
input_only_channels = 3#
base_input_channels = 70#
input_channels = 140#
base_output_channels = 67#
output_channels = 67#
layers#
cube_embedding#
up_block1#
up_block2#
up_block3#
forward(x)#
rk4(x)#
credit.models.crossformer.image_height = 180#
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