### Install MLP-Mixer

Source: https://github.com/lucidrains/mlp-mixer-pytorch/blob/main/README.md

Use pip to install the package.

```bash
$ pip install mlp-mixer-pytorch
```

--------------------------------

### Train MLPMixer model

Source: https://context7.com/lucidrains/mlp-mixer-pytorch/llms.txt

Demonstrates a complete training loop including loss computation, optimization, and accuracy calculation.

```python
import torch
import torch.nn as nn
from mlp_mixer_pytorch import MLPMixer

# Model configuration
model = MLPMixer(
    image_size=224,
    channels=3,
    patch_size=16,
    dim=512,
    depth=8,
    num_classes=10,
    expansion_factor=4,
    expansion_factor_token=0.5,
    dropout=0.1
)

# Setup training components
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4, weight_decay=0.1)

# Training step example
model.train()

# Simulated batch of CIFAR-10 style data (resized to 224x224)
batch_images = torch.randn(32, 3, 224, 224)
batch_labels = torch.randint(0, 10, (32,))

# Forward pass
logits = model(batch_images)
loss = criterion(logits, batch_labels)

# Backward pass
optimizer.zero_grad()
loss.backward()
optimizer.step()

# Compute accuracy
predictions = logits.argmax(dim=-1)
accuracy = (predictions == batch_labels).float().mean()

print(f"Loss: {loss.item():.4f}")
print(f"Accuracy: {accuracy.item():.4f}")
```

--------------------------------

### Configure and compare model sizes

Source: https://context7.com/lucidrains/mlp-mixer-pytorch/llms.txt

Compares parameter counts between small and large MLPMixer configurations.

```python
import torch
from mlp_mixer_pytorch import MLPMixer, MLPMixer3D, Permutator

# Small model configuration (fewer parameters, faster training)
small_model = MLPMixer(
    image_size=224,
    channels=3,
    patch_size=32,            # Larger patches = fewer tokens
    dim=256,                  # Smaller hidden dimension
    depth=4,                  # Fewer layers
    num_classes=100,
    expansion_factor=2,       # Smaller expansion
    expansion_factor_token=0.25,
    dropout=0.0
)

# Large model configuration (more capacity, slower training)
large_model = MLPMixer(
    image_size=384,           # Higher resolution
    channels=3,
    patch_size=16,            # Smaller patches = more tokens
    dim=1024,                 # Larger hidden dimension
    depth=24,                 # More layers
    num_classes=1000,
    expansion_factor=4,       # Full expansion
    expansion_factor_token=0.5,
    dropout=0.1
)

# Calculate model sizes
small_params = sum(p.numel() for p in small_model.parameters())
large_params = sum(p.numel() for p in large_model.parameters())

print(f"Small model parameters: {small_params:,}")
print(f"Large model parameters: {large_params:,}")

# Example output:
# Small model parameters: ~1.5M
# Large model parameters: ~60M+
```

--------------------------------

### Initialize and Run MLP-Mixer

Source: https://github.com/lucidrains/mlp-mixer-pytorch/blob/main/README.md

Standard usage for processing square images with the MLPMixer model.

```python
import torch
from mlp_mixer_pytorch import MLPMixer

model = MLPMixer(
    image_size = 256,
    channels = 3,
    patch_size = 16,
    dim = 512,
    depth = 12,
    num_classes = 1000
)

img = torch.randn(1, 3, 256, 256)
pred = model(img) # (1, 1000)
```

--------------------------------

### Initialize and run MLPMixer for 2D images

Source: https://context7.com/lucidrains/mlp-mixer-pytorch/llms.txt

Configure the MLPMixer model for standard 2D image classification and perform a forward pass.

```python
import torch
from mlp_mixer_pytorch import MLPMixer

# Create MLPMixer model for ImageNet-style classification
model = MLPMixer(
    image_size=256,           # Input image size (height and width)
    channels=3,               # Number of input channels (RGB)
    patch_size=16,            # Size of each image patch
    dim=512,                  # Hidden dimension for patch embeddings
    depth=12,                 # Number of mixer layers
    num_classes=1000,         # Number of output classes
    expansion_factor=4,       # Expansion factor for token-mixing MLP
    expansion_factor_token=0.5,  # Expansion factor for channel-mixing MLP
    dropout=0.1               # Dropout rate
)

# Create sample input batch (batch_size=4, channels=3, height=256, width=256)
images = torch.randn(4, 3, 256, 256)

# Forward pass
predictions = model(images)
print(f"Output shape: {predictions.shape}")  # Output shape: torch.Size([4, 1000])

# Get predicted classes
predicted_classes = predictions.argmax(dim=-1)
print(f"Predicted classes: {predicted_classes}")  # Predicted classes: tensor([...])
```

--------------------------------

### Initialize and run MLPMixer3D for video classification

Source: https://context7.com/lucidrains/mlp-mixer-pytorch/llms.txt

Use MLPMixer3D to process video data by defining temporal dimensions and patch sizes.

```python
import torch
from mlp_mixer_pytorch import MLPMixer3D

# Create 3D model for video classification
model = MLPMixer3D(
    image_size=(256, 128),    # Spatial dimensions (height, width)
    time_size=4,              # Number of frames in video
    time_patch_size=2,        # Temporal patch size
    channels=3,               # RGB video
    patch_size=16,            # Spatial patch size
    dim=512,                  # Hidden dimension
    depth=12,                 # Number of mixer layers
    num_classes=1000,         # Number of action classes
    expansion_factor=4,       # Token-mixing expansion
    expansion_factor_token=0.5,  # Channel-mixing expansion
    dropout=0.1               # Dropout rate
)

# Create video input (batch, channels, time, height, width)
video_batch = torch.randn(2, 3, 4, 256, 128)

# Forward pass for action recognition
predictions = model(video_batch)
print(f"Output shape: {predictions.shape}")  # Output shape: torch.Size([2, 1000])
```

--------------------------------

### Initialize and run Vision Permutator

Source: https://context7.com/lucidrains/mlp-mixer-pytorch/llms.txt

Configures a Permutator model with parallel spatial branches and performs a forward pass.

```python
import torch
from mlp_mixer_pytorch import Permutator

# Create Permutator model
model = Permutator(
    image_size=256,           # Input image size (must be square)
    patch_size=16,            # Patch size
    dim=512,                  # Hidden dimension (must be divisible by segments)
    depth=12,                 # Number of permutator layers
    num_classes=1000,         # Number of output classes
    segments=8,               # Number of segments for permutation
    expansion_factor=4,       # MLP expansion factor
    dropout=0.1               # Dropout rate
)

# Create input batch
images = torch.randn(4, 3, 256, 256)

# Forward pass
predictions = model(images)
print(f"Output shape: {predictions.shape}")  # Output shape: torch.Size([4, 1000])

# Model uses parallel branches for height and width mixing
# Segment dimension: 512 / 8 = 64 per segment
print(f"Channels per segment: {512 // 8}")  # Channels per segment: 64
```

--------------------------------

### Process Video Data

Source: https://github.com/lucidrains/mlp-mixer-pytorch/blob/main/README.md

Use MLPMixer3D to handle video inputs with temporal dimensions.

```python
import torch
from mlp_mixer_pytorch import MLPMixer3D

model = MLPMixer3D(
    image_size = (256, 128),
    time_size = 4,
    time_patch_size = 2,
    channels = 3,
    patch_size = 16,
    dim = 512,
    depth = 12,
    num_classes = 1000
)

video = torch.randn(1, 3, 4, 256, 128)
pred = model(video) # (1, 1000)
```

--------------------------------

### Citations

Source: https://github.com/lucidrains/mlp-mixer-pytorch/blob/main/README.md

BibTeX entries for referencing the MLP-Mixer and Vision Permutator architectures.

```bibtex
@misc{tolstikhin2021mlpmixer,
    title   = {MLP-Mixer: An all-MLP Architecture for Vision},
    author  = {Ilya Tolstikhin and Neil Houlsby and Alexander Kolesnikov and Lucas Beyer and Xiaohua Zhai and Thomas Unterthiner and Jessica Yung and Daniel Keysers and Jakob Uszkoreit and Mario Lucic and Alexey Dosovitskiy},
    year    = {2021},
    eprint  = {2105.01601},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}
```

```bibtex
@misc{hou2021vision,
    title   = {Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition},
    author  = {Qibin Hou and Zihang Jiang and Li Yuan and Ming-Ming Cheng and Shuicheng Yan and Jiashi Feng},
    year    = {2021},
    eprint  = {2106.12368},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}
```

--------------------------------

### Configure MLPMixer for rectangular images

Source: https://context7.com/lucidrains/mlp-mixer-pytorch/llms.txt

Pass a tuple to image_size to support non-square input dimensions.

```python
import torch
from mlp_mixer_pytorch import MLPMixer

# Create model for rectangular images (256 height, 128 width)
model = MLPMixer(
    image_size=(256, 128),    # Rectangular image size (height, width)
    channels=3,               # RGB input
    patch_size=16,            # Patch size must divide both dimensions
    dim=512,                  # Hidden dimension
    depth=12,                 # Number of layers
    num_classes=1000          # Number of classes
)

# Create rectangular input tensor
rectangular_images = torch.randn(2, 3, 256, 128)

# Forward pass
predictions = model(rectangular_images)
print(f"Output shape: {predictions.shape}")  # Output shape: torch.Size([2, 1000])

# Calculate number of patches
num_patches = (256 // 16) * (128 // 16)
print(f"Number of patches: {num_patches}")  # Number of patches: 128
```

--------------------------------

### Process Rectangular Images

Source: https://github.com/lucidrains/mlp-mixer-pytorch/blob/main/README.md

Configure the model to accept rectangular image dimensions by passing a tuple to image_size.

```python
import torch
from mlp_mixer_pytorch import MLPMixer

model = MLPMixer(
    image_size = (256, 128),
    channels = 3,
    patch_size = 16,
    dim = 512,
    depth = 12,
    num_classes = 1000
)

img = torch.randn(1, 3, 256, 128)
pred = model(img) # (1, 1000)
```

--------------------------------

### Calculate spatiotemporal patches

Source: https://context7.com/lucidrains/mlp-mixer-pytorch/llms.txt

Calculates the total number of 3D patches based on spatial and temporal dimensions.

```python
num_spatial_patches = (256 // 16) * (128 // 16)
num_temporal_patches = 4 // 2
total_patches = num_spatial_patches * num_temporal_patches
print(f"Total 3D patches: {total_patches}")  # Total 3D patches: 256
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.