minerva.models.nets.image.setr¶

Classes¶

`ConvModule`	Base class for all neural network modules.
`MMDropPath`	Base class for all neural network modules.
`SETR_PUP`	A modular Lightning model wrapper for supervised learning tasks.
`_SETRUPHead`	Base class for all neural network modules.
`_SetR_PUP`	Base class for all neural network modules.

Module Contents¶

class minerva.models.nets.image.setr.ConvModule(in_channels, out_channels, kernel_size, padding, norm_type, act_type, norm_params=None, act_params=None)[source]¶

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.

Parameters:

in_channels (int)
out_channels (int)
kernel_size (int)
padding (int)
norm_type (type)
act_type (type)
norm_params (Optional[dict])
act_params (Optional[dict])

Convolutional module with normalization and activation.

Parameters¶

in_channelsint: Number of input channels.
out_channelsint: Number of output channels.
kernel_sizeint: Size of the convolution kernel.
paddingint: Padding added to both sides of the input.
norm_typetype: Type of normalization layer (e.g., nn.BatchNorm2d).
act_typetype: Type of activation function (e.g., nn.ReLU).
norm_paramsdict, optional: Optional parameters for normalization.
act_paramsdict, optional: Optional parameters for activation.

activate¶

bn¶

conv¶

forward(x)[source]¶

Forward pass of the ConvModule.

Parameters:: x (torch.Tensor)
Return type:: torch.Tensor

init_weights()[source]¶: Initialize convolution weights.

class minerva.models.nets.image.setr.MMDropPath(drop_prob)[source]¶

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.
Parameters:: drop_prob (float)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

drop_prob¶

forward(x)[source]¶

class minerva.models.nets.image.setr.SETR_PUP(original_resolution=None, img_size=(512, 512), patch_size=16, in_channels=3, embed_dims=1024, num_layers=24, num_heads=16, out_indices=(9, 14, 19, 23), encoder_stride=None, patch_norm=False, dilatation=1, bias=True, padding_type='corner', mlp_ratio=4, attn_drop_rate=0.0, drop_path_rate=0.0, num_fcs=2, qkv_bias=True, output_cls_token=False, act_type=nn.GELU, with_cp=False, encoder_dropout=0.0, encoder_norm_type=nn.LayerNorm, dropout_type=MMDropPath, cls_token=True, interpolate_mode='bilinear', act_params=None, dropout_params=None, encoder_norm_params=None, decoder_channels=256, decoder_in_index=3, num_classes=6, decoder_dropout=0.0, decoder_norm_type=nn.SyncBatchNorm, decoder_num_convs=4, decoder_up_scale=2, decoder_kernel_size=3, decoder_align_corners=False, decoder_norm_params=None, aux_heads_in_index=(0, 1, 2), aux_head_num_convs=2, aux_head_up_scale=4, aux_weights=None, loss_fn=None, optimizer=Adam, optimizer_kwargs=None, train_metrics=None, val_metrics=None, test_metrics=None, freeze_backbone=False, learning_rate=0.001, loss_weights=None, lr_scheduler=None, lr_scheduler_kwargs=None, head_lr_factor=1.0, use_sliding_inference=True, sliding_window_stride=(341, 341))[source]¶

Bases: minerva.models.nets.base.SimpleSupervisedModel

A modular Lightning model wrapper for supervised learning tasks.

This class enables the construction of supervised models by combining a backbone (feature extractor), an optional adapter, and a fully connected (FC) head. It provides a clean interface for setting up custom training, validation, and testing pipelines with pluggable loss functions, metrics, optimizers, and learning rate schedulers.

The architecture is structured as follows:

Backbone Model

v

Adapter (Optional)

(Flatten if needed)
v

Fully Connected Head

v

Loss Function

Training and validation steps comprise the following steps:

Forward pass input through the backbone.
Pass through adapter (if provided).
Flatten the output (if flatten is True) before the FC head.
Forward through the FC head.
Compute loss with respect to targets.
Backpropagate and update parameters.
Compute metrics and log them.
Return loss. train_loss, val_loss, and test_loss are always logged, along with any additional metrics specified in the train_metrics, val_metrics, and test_metrics dictionaries.

This wrapper is especially useful to quickly set up supervised models for various tasks, such as image classification, object detection, and segmentation. It is designed to be flexible and extensible, allowing users to easily swap out components like the backbone, adapter, and FC head as needed. The model is built with a focus on simplicity and modularity, making it easy to adapt to different use cases and requirements. The model is designed to be used with PyTorch Lightning and is compatible with its training loop.

Note: For more complex architectures that does not follow the above structure should not inherit from this class.

Note: Input batches must be tuples (input_tensor, target_tensor).

LightningModule implementation for SETR_PUP (SEgmentation TRansformer with Progressive UPsampling).

Parameters¶

original_resolutiontuple: Resolution of the original images used to pretrain the backbone.
img_sizetuple: Input image size (height, width) used during training and patch embedding.
patch_sizeint: Size of each image patch extracted in the ViT encoder.
in_channelsint: Number of input channels (usually 3 for RGB).
embed_dimsint: Embedding dimension for each patch.
num_layersint: Number of transformer encoder layers.
num_headsint: Number of attention heads in each transformer layer.
out_indicestuple, optional: Indices of the encoder layers to use as features for decoding.
encoder_strideint: Stride used in patch embedding.
patch_normbool: Whether to apply normalization to patch embeddings.
dilatationint: Dilation factor for patch embedding.
biasbool: Whether to include bias in the projection layers.
padding_typestr: Padding mode used in patch embedding (“same” or “corner”).
mlp_ratioint: Expansion ratio for the MLP block inside transformer layers.
attn_drop_ratefloat: Dropout rate applied to attention weights.
drop_path_ratefloat: Probability of dropping entire residual paths (stochastic depth).
num_fcsint: Number of linear layers in the feed-forward MLP of the transformer.
qkv_biasbool: Whether to include bias in QKV projections.
output_cls_tokenbool: Whether to include class token in encoder output.
act_typetype: Activation function class to use (e.g., nn.GELU).
with_cpbool: Whether to enable checkpointing to save memory.
encoder_dropoutfloat: Dropout rate after positional embedding in the encoder.
encoder_norm_typetype: Normalization type used in the encoder.
dropout_typetype: Type of stochastic path dropout layer.
cls_tokenbool: Whether to use a class token in the ViT.
interpolate_modestr: Interpolation mode used for resizing positional embeddings.
act_paramsdict, optional: Additional parameters for the activation function.
dropout_paramsdict, optional: Additional parameters for the dropout layer.
encoder_norm_paramsdict, optional: Additional parameters for the encoder normalization layer.
decoder_channelsint: Number of channels in intermediate layers of the decoder.
decoder_in_indexint: Index into encoder outputs to be used as decoder input.
num_classesint: Number of segmentation classes.
decoder_dropoutfloat: Dropout probability in the decoder.
decoder_norm_typetype: Type of normalization in decoder conv blocks.
decoder_num_convsint: Number of conv+upsample blocks in the decoder.
decoder_up_scaleint: Upsample scale factor for decoder blocks.
decoder_kernel_sizeint: Convolution kernel size in decoder blocks.
decoder_align_cornersbool: Whether to align corners when using bilinear interpolation.
decoder_norm_paramsdict, optional: Additional arguments for decoder normalization.
aux_heads_in_indextuple of int: Indices of encoder layers to feed into each auxiliary decoder head.
aux_head_num_convsint: Number of conv blocks in each auxiliary head.
aux_head_up_scaleint: Upsample factor for auxiliary heads.
aux_weightslist of float, optional: Weights for auxiliary losses [aux1, aux2, aux3].
loss_fnnn.Module, optional: Loss function module (defaults to CrossEntropy).
optimizer_typetype, optional: Optimizer class (e.g., torch.optim.Adam).
optimizer_paramsdict, optional: Parameters to pass to the optimizer.
train_metricsdict, optional: Dictionary of training metrics.
val_metricsdict, optional: Dictionary of validation metrics.
test_metricsdict, optional: Dictionary of test metrics.
learning_ratefloat: Learning rate for training.
loss_weightslist of float, optional: Class-wise weights for the loss function.
head_lr_factorfloat: Learning rate multiplier for decoder heads.
lr_schedulertype, optional: Learning rate scheduler class to be instantiated. By default, it is set to None, which means no scheduler will be used. Should be a subclass of torch.optim.lr_scheduler.LRScheduler (e.g., torch.optim.lr_scheduler.StepLR).
lr_scheduler_kwargsdict, optional: Additional kwargs passed to the scheduler constructor.
use_sliding_inferencebool: Whether to use sliding window inference for large images on validation and test.
sliding_window_stridetuple of int: Stride for sliding window inference (height, width).

_compute_metrics(y_hat, y, step_name)[source]¶

Calculate the metrics for the given step.

Parameters¶

y_hattorch.Tensor: The output data from the forward pass.
ytorch.Tensor: The input data/label.
step_namestr: Name of the step. It will be used to get the metrics from the self.metrics attribute.

Returns¶

Dict[str, torch.Tensor]: A dictionary with the metrics values.

Parameters:

y_hat (torch.Tensor)
y (torch.Tensor)
step_name (str)

Return type:

Dict[str, torch.Tensor]

_eval_step_with_slide(batch, step_name)[source]¶

Parameters:: step_name (str)

_loss_func(y_hat, y)[source]¶

Calculate the loss between the output and the input data.

Parameters¶

y_hattorch.Tensor: The output data from the forward pass.
ytorch.Tensor: The input data/label.

Returns¶

torch.Tensor: The loss value.

Parameters:

y_hat (Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]])
y (torch.Tensor)

Return type:

torch.Tensor

_slide_inference(image, crop_size=(512, 512), stride=(341, 341), ori_shape=None)[source]¶

Realiza inferência por janelamento (sliding window) com reconstrução e resize final opcional.

Parameters:

image (numpy.ndarray)
ori_shape (Optional[Tuple[int, int]])

aux_head1¶

aux_head2¶

aux_head3¶

aux_weights = None¶

configure_optimizers()[source]¶

Choose what optimizers and learning-rate schedulers to use in your optimization. Normally you’d need one. But in the case of GANs or similar you might have multiple. Optimization with multiple optimizers only works in the manual optimization mode.

Return:

Any of these 6 options.

Single optimizer.
List or Tuple of optimizers.
Two lists - The first list has multiple optimizers, and the second has multiple LR schedulers (or multiple lr_scheduler_config).
Dictionary, with an "optimizer" key, and (optionally) a "lr_scheduler" key whose value is a single LR scheduler or lr_scheduler_config.
None - Fit will run without any optimizer.

The lr_scheduler_config is a dictionary which contains the scheduler and its associated configuration. The default configuration is shown below.

lr_scheduler_config = {
    # REQUIRED: The scheduler instance
    "scheduler": lr_scheduler,
    # The unit of the scheduler's step size, could also be 'step'.
    # 'epoch' updates the scheduler on epoch end whereas 'step'
    # updates it after a optimizer update.
    "interval": "epoch",
    # How many epochs/steps should pass between calls to
    # `scheduler.step()`. 1 corresponds to updating the learning
    # rate after every epoch/step.
    "frequency": 1,
    # Metric to monitor for schedulers like `ReduceLROnPlateau`
    "monitor": "val_loss",
    # If set to `True`, will enforce that the value specified 'monitor'
    # is available when the scheduler is updated, thus stopping
    # training if not found. If set to `False`, it will only produce a warning
    "strict": True,
    # If using the `LearningRateMonitor` callback to monitor the
    # learning rate progress, this keyword can be used to specify
    # a custom logged name
    "name": None,
}

When there are schedulers in which the .step() method is conditioned on a value, such as the torch.optim.lr_scheduler.ReduceLROnPlateau scheduler, Lightning requires that the lr_scheduler_config contains the keyword "monitor" set to the metric name that the scheduler should be conditioned on.

Metrics can be made available to monitor by simply logging it using self.log('metric_to_track', metric_val) in your LightningModule.

Note:

Some things to know:

Lightning calls .backward() and .step() automatically in case of automatic optimization.
If a learning rate scheduler is specified in configure_optimizers() with key "interval" (default “epoch”) in the scheduler configuration, Lightning will call the scheduler’s .step() method automatically in case of automatic optimization.
If you use 16-bit precision (precision=16), Lightning will automatically handle the optimizer.
If you use torch.optim.LBFGS, Lightning handles the closure function automatically for you.
If you use multiple optimizers, you will have to switch to ‘manual optimization’ mode and step them yourself.
If you need to control how often the optimizer steps, override the optimizer_step() hook.

static create_from_dict(config)[source]¶

Parameters:: config (Dict)
Return type:: SETR_PUP

decoder_num_classes = 6¶

encoder_out_indices = (9, 14, 19, 23)¶

forward(x)[source]¶

Perform a forward pass with the input data on the backbone model.

Parameters¶

xtorch.Tensor: The input data.

Returns¶

torch.Tensor: The output data from the forward pass.

Parameters:: x (torch.Tensor)

head_lr_factor = 1.0¶

img_size = (512, 512)¶

load_backbone(path, freeze=False)[source]¶

Loads pretrained ViT backbone optionally freezing its weights.

Parameters:

path (str)
freeze (bool)

num_classes = 6¶

predict_step(batch, batch_idx, dataloader_idx=None)[source]¶

Step function called during predict(). By default, it calls forward(). Override to add any processing logic.

The predict_step() is used to scale inference on multi-devices.

To prevent an OOM error, it is possible to use BasePredictionWriter callback to write the predictions to disk or database after each batch or on epoch end.

The BasePredictionWriter should be used while using a spawn based accelerator. This happens for Trainer(strategy="ddp_spawn") or training on 8 TPU cores with Trainer(accelerator="tpu", devices=8) as predictions won’t be returned.

Args:: batch: The output of your data iterable, normally a DataLoader. batch_idx: The index of this batch. dataloader_idx: The index of the dataloader that produced this batch.

(only if multiple dataloaders used)
Return:: Predicted output (optional).

Example

class MyModel(LightningModule):

    def predict_step(self, batch, batch_idx, dataloader_idx=0):
        return self(batch)

dm = ...
model = MyModel()
trainer = Trainer(accelerator="gpu", devices=2)
predictions = trainer.predict(model, dm)

Parameters:

batch (torch.Tensor)
batch_idx (int)
dataloader_idx (Optional[int])

test_step(batch, batch_idx)[source]¶

Operates on a single batch of data from the test set. In this step you’d normally generate examples or calculate anything of interest such as accuracy.

Args:

batch: The output of your data iterable, normally a DataLoader. batch_idx: The index of this batch. dataloader_idx: The index of the dataloader that produced this batch.

(only if multiple dataloaders used)

Return:

Tensor - The loss tensor
dict - A dictionary. Can include any keys, but must include the key 'loss'.
None - Skip to the next batch.

# if you have one test dataloader:
def test_step(self, batch, batch_idx): ...


# if you have multiple test dataloaders:
def test_step(self, batch, batch_idx, dataloader_idx=0): ...

Examples:

# CASE 1: A single test dataset
def test_step(self, batch, batch_idx):
    x, y = batch

    # implement your own
    out = self(x)
    loss = self.loss(out, y)

    # log 6 example images
    # or generated text... or whatever
    sample_imgs = x[:6]
    grid = torchvision.utils.make_grid(sample_imgs)
    self.logger.experiment.add_image('example_images', grid, 0)

    # calculate acc
    labels_hat = torch.argmax(out, dim=1)
    test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)

    # log the outputs!
    self.log_dict({'test_loss': loss, 'test_acc': test_acc})

If you pass in multiple test dataloaders, test_step() will have an additional argument. We recommend setting the default value of 0 so that you can quickly switch between single and multiple dataloaders.

# CASE 2: multiple test dataloaders
def test_step(self, batch, batch_idx, dataloader_idx=0):
    # dataloader_idx tells you which dataset this is.
    x, y = batch

    # implement your own
    out = self(x)

    if dataloader_idx == 0:
        loss = self.loss0(out, y)
    else:
        loss = self.loss1(out, y)

    # calculate acc
    labels_hat = torch.argmax(out, dim=1)
    acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)

    # log the outputs separately for each dataloader
    self.log_dict({f"test_loss_{dataloader_idx}": loss, f"test_acc_{dataloader_idx}": acc})

Note:: If you don’t need to test you don’t need to implement this method.
Note:: When the test_step() is called, the model has been put in eval mode and PyTorch gradients have been disabled. At the end of the test epoch, the model goes back to training mode and gradients are enabled.

Parameters:

batch (torch.Tensor)
batch_idx (int)

training_step(batch, batch_idx)[source]¶

Here you compute and return the training loss and some additional metrics for e.g. the progress bar or logger.

Args:

batch: The output of your data iterable, normally a DataLoader. batch_idx: The index of this batch. dataloader_idx: The index of the dataloader that produced this batch.

(only if multiple dataloaders used)

Return:

Tensor - The loss tensor
dict - A dictionary which can include any keys, but must include the key 'loss' in the case of automatic optimization.
None - In automatic optimization, this will skip to the next batch (but is not supported for multi-GPU, TPU, or DeepSpeed). For manual optimization, this has no special meaning, as returning the loss is not required.

In this step you’d normally do the forward pass and calculate the loss for a batch. You can also do fancier things like multiple forward passes or something model specific.

Example:

def training_step(self, batch, batch_idx):
    x, y, z = batch
    out = self.encoder(x)
    loss = self.loss(out, x)
    return loss

To use multiple optimizers, you can switch to ‘manual optimization’ and control their stepping:

def __init__(self):
    super().__init__()
    self.automatic_optimization = False


# Multiple optimizers (e.g.: GANs)
def training_step(self, batch, batch_idx):
    opt1, opt2 = self.optimizers()

    # do training_step with encoder
    ...
    opt1.step()
    # do training_step with decoder
    ...
    opt2.step()

Note:: When accumulate_grad_batches > 1, the loss returned here will be automatically normalized by accumulate_grad_batches internally.

Parameters:

batch (torch.Tensor)
batch_idx (int)

use_sliding_inference = True¶

validation_step(batch, batch_idx)[source]¶

Operates on a single batch of data from the validation set. In this step you’d might generate examples or calculate anything of interest like accuracy.

Args:

batch: The output of your data iterable, normally a DataLoader. batch_idx: The index of this batch. dataloader_idx: The index of the dataloader that produced this batch.

(only if multiple dataloaders used)

Return:

Tensor - The loss tensor
dict - A dictionary. Can include any keys, but must include the key 'loss'.
None - Skip to the next batch.

# if you have one val dataloader:
def validation_step(self, batch, batch_idx): ...


# if you have multiple val dataloaders:
def validation_step(self, batch, batch_idx, dataloader_idx=0): ...

Examples:

# CASE 1: A single validation dataset
def validation_step(self, batch, batch_idx):
    x, y = batch

    # implement your own
    out = self(x)
    loss = self.loss(out, y)

    # log 6 example images
    # or generated text... or whatever
    sample_imgs = x[:6]
    grid = torchvision.utils.make_grid(sample_imgs)
    self.logger.experiment.add_image('example_images', grid, 0)

    # calculate acc
    labels_hat = torch.argmax(out, dim=1)
    val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)

    # log the outputs!
    self.log_dict({'val_loss': loss, 'val_acc': val_acc})

If you pass in multiple val dataloaders, validation_step() will have an additional argument. We recommend setting the default value of 0 so that you can quickly switch between single and multiple dataloaders.

# CASE 2: multiple validation dataloaders
def validation_step(self, batch, batch_idx, dataloader_idx=0):
    # dataloader_idx tells you which dataset this is.
    x, y = batch

    # implement your own
    out = self(x)

    if dataloader_idx == 0:
        loss = self.loss0(out, y)
    else:
        loss = self.loss1(out, y)

    # calculate acc
    labels_hat = torch.argmax(out, dim=1)
    acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)

    # log the outputs separately for each dataloader
    self.log_dict({f"val_loss_{dataloader_idx}": loss, f"val_acc_{dataloader_idx}": acc})

Note:: If you don’t need to validate you don’t need to implement this method.
Note:: When the validation_step() is called, the model has been put in eval mode and PyTorch gradients have been disabled. At the end of validation, the model goes back to training mode and gradients are enabled.

Parameters:

batch (torch.Tensor)
batch_idx (int)

Parameters:

original_resolution (Optional[tuple])
img_size (tuple)
patch_size (int)
in_channels (int)
embed_dims (int)
num_layers (int)
num_heads (int)
out_indices (Optional[tuple])
encoder_stride (Optional[int])
patch_norm (bool)
dilatation (int)
bias (bool)
padding_type (str)
mlp_ratio (int)
attn_drop_rate (float)
drop_path_rate (float)
num_fcs (int)
qkv_bias (bool)
output_cls_token (bool)
act_type (type)
with_cp (bool)
encoder_dropout (float)
encoder_norm_type (type)
dropout_type (type)
cls_token (bool)
interpolate_mode (str)
act_params (Optional[dict])
dropout_params (Optional[dict])
encoder_norm_params (Optional[dict])
decoder_channels (int)
decoder_in_index (int)
num_classes (int)
decoder_dropout (float)
decoder_norm_type (type)
decoder_num_convs (int)
decoder_up_scale (int)
decoder_kernel_size (int)
decoder_align_corners (bool)
decoder_norm_params (Optional[dict])
aux_heads_in_index (tuple[int, int, int])
aux_head_num_convs (int)
aux_head_up_scale (int)
aux_weights (Optional[list[float]])
loss_fn (Optional[torch.nn.Module])
optimizer (type)
optimizer_kwargs (Optional[Dict])
train_metrics (Optional[Dict[str, torchmetrics.Metric]])
val_metrics (Optional[Dict[str, torchmetrics.Metric]])
test_metrics (Optional[Dict[str, torchmetrics.Metric]])
freeze_backbone (bool)
learning_rate (float)
loss_weights (Optional[list[float]])
lr_scheduler (Optional[type])
lr_scheduler_kwargs (Optional[Dict[str, Any]])
head_lr_factor (float)
use_sliding_inference (bool)
sliding_window_stride (Tuple[int, int])

class minerva.models.nets.image.setr._SETRUPHead(in_channels, channels, num_classes, in_index, num_convs, up_scale, kernel_size, align_corners, dropout, norm_type, act_type, norm_params=None, act_params=None, interpolate_mode='bilinear')[source]¶

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.

Parameters:

in_channels (int)
channels (int)
num_classes (int)
in_index (int)
num_convs (int)
up_scale (int)
kernel_size (int)
align_corners (bool)
dropout (float)
norm_type (type)
act_type (type)
norm_params (Optional[dict])
act_params (Optional[dict])
interpolate_mode (str)

Lightweight decoder head with LayerNorm and upsampling for SETR.

Parameters¶

in_channelsint: Number of input channels from encoder.
channelsint: Number of internal intermediate channels.
num_classesint: Number of target output classes.
in_indexint: Index to select feature from encoder outputs.
num_convsint: Number of upsampling convolutional layers.
up_scaleint: Upsample factor per layer.
kernel_sizeint: Convolution kernel size.
align_cornersbool: Align corners in bilinear upsampling.
dropoutfloat: Dropout probability.
norm_typetype: Normalization layer type.
act_typetype: Activation function type.
norm_paramsdict, optional: Additional parameters for normalization.
act_paramsdict, optional: Additional parameters for activation.
interpolate_modestr, default=”bilinear”: Interpolation mode for upsampling.

align_corners¶

conv_seg¶

forward(xs)[source]¶

Forward pass of SETR decoder head.

Parameters:: xs (List[torch.Tensor])
Return type:: torch.Tensor

in_index¶

norm¶

up_convs¶

class minerva.models.nets.image.setr._SetR_PUP(original_resolution, img_size, patch_size, in_channels, embed_dims, num_layers, num_heads, out_indices, stride, patch_norm, dilatation, bias, padding_type, mlp_ratio, attn_drop_rate, drop_path_rate, num_fcs, qkv_bias, output_cls_token, act_type, with_cp, encoder_dropout, encoder_norm_type, dropout_type, cls_token, interpolate_mode, act_params, dropout_params, encoder_norm_params, decoder_channels, decoder_in_index, num_classes, decoder_dropout, decoder_norm_type, decoder_num_convs, decoder_up_scale, decoder_kernel_size, decoder_align_corners, decoder_norm_params, aux_heads_in_index, aux_head_num_convs, aux_head_up_scale)[source]¶

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.

Parameters:

original_resolution (Optional[tuple])
img_size (tuple)
patch_size (int)
in_channels (int)
embed_dims (int)
num_layers (int)
num_heads (int)
out_indices (Optional[tuple])
stride (int)
patch_norm (bool)
dilatation (int)
bias (bool)
padding_type (str)
mlp_ratio (int)
attn_drop_rate (float)
drop_path_rate (float)
num_fcs (int)
qkv_bias (bool)
output_cls_token (bool)
act_type (type)
with_cp (bool)
encoder_dropout (float)
encoder_norm_type (type)
dropout_type (type)
cls_token (bool)
interpolate_mode (str)
act_params (Optional[dict])
dropout_params (Optional[dict])
encoder_norm_params (Optional[dict])
decoder_channels (int)
decoder_in_index (int)
num_classes (int)
decoder_dropout (float)
decoder_norm_type (type)
decoder_num_convs (int)
decoder_up_scale (int)
decoder_kernel_size (int)
decoder_align_corners (bool)
decoder_norm_params (Optional[dict])
aux_heads_in_index (tuple[int, int, int])
aux_head_num_convs (int)
aux_head_up_scale (int)

Full SETR_PUP model with encoder and decoder.

Parameters¶

original_resolutiontuple: Resolution of original input image.
img_sizetuple: Input image size used for patch embedding.
patch_sizeint: Patch size for Vision Transformer.
in_channelsint: Number of input image channels.
embed_dimsint: Dimensionality of embeddings.
num_layersint: Number of transformer encoder layers.
num_headsint: Number of attention heads.
out_indicestuple or None: Indices of intermediate outputs for decoding.
strideint: Patch stride.
patch_normbool: Apply normalization to patches.
dilatationint: Dilation for patch embedding.
biasbool: Use bias in conv layers.
padding_typestr: Padding type used for patch embedding.
mlp_ratioint: MLP expansion ratio.
attn_drop_ratefloat: Attention dropout rate.
drop_path_ratefloat: Stochastic depth dropout rate.
num_fcsint: Number of fully connected layers in FFN.
qkv_biasbool: Use bias in QKV projections.
output_cls_tokenbool: Output class token with final features.
act_typetype: Activation function type.
with_cpbool: Use gradient checkpointing.
encoder_dropoutfloat: Dropout rate after patch embedding.
encoder_norm_typetype: Type of normalization used in encoder.
dropout_typetype: Type of residual dropout layer.
cls_tokenbool: Use class token in transformer.
interpolate_modestr: Mode for interpolating positional embeddings.
act_paramsdict, optional: Params for activation function.
dropout_paramsdict, optional: Params for dropout module.
encoder_norm_paramsdict, optional: Params for encoder normalization.
decoder_channelsint: Number of intermediate decoder channels.
decoder_in_indexint: Which encoder layer to use in decoder.
num_classesint: Number of classes for segmentation.
decoder_dropoutfloat: Dropout rate in decoder.
decoder_norm_typetype: Normalization type in decoder.
decoder_num_convsint: Number of conv blocks in decoder.
decoder_up_scaleint: Upsample scale factor.
decoder_kernel_sizeint: Decoder conv kernel size.
decoder_align_cornersbool: Use align_corners in bilinear upsample.
decoder_norm_paramsdict, optional: Parameters for decoder normalization.
aux_heads_in_indextuple of int: Which layers to use in auxiliary decoders.
aux_head_num_convsint: Number of convs in each auxiliary head.
aux_head_up_scaleint: Upsample factor in each auxiliary head.

aux_head1¶

aux_head2¶

aux_head3¶

decoder¶

encoder¶

encoder_out_indices¶

forward(x)[source]¶

Parameters:: x (torch.Tensor)