Examples

Let’s take a look at what we have here!

• Build a Segmentation Model

• Build a Decoder

• Customize a Decoder

Build a Segmentation Model

Let’s say you want to build a segmentation model. You can do it like this:

import mmit
import torch

model = mmit.create_model('resnet18', 'unet', num_classes=2)

x = torch.randn(2, 3, 256, 256)
out = model(x)

Build a Decoder

Let’s say you want to build a decoder for a given encoder. You can do it like this:

import mmit
import torch

encoder = mmit.create_encoder('resnet18', out_indices=(0, 1, 4), output_stride=8)
decoder = mmit.create_decoder('unet') # automatically matches encoder characteristics!

x = torch.randn(2, 3, 256, 256)
features = encoder(x)
out = decoder(*features)

Written like this, it feels like a lot of magic is going on. The explicit way to do this is:

encoder = mmit.create_encoder('resnet18', out_indices=(0, 1, 4), output_stride=8)
decoder = mmit.create_decoder('unetplusplus', encoder.out_channels, encoder.out_reductions)

Customize a Decoder

The available customization options are a lot, but here are an example:

import mmit
import torch

encoder = mmit.create_encoder('resnet18', out_indices=(0, 1, 4), output_stride=8)
decoder = mmit.create_decoder('unetplusplus', upsample_layer='interpolate', activation_layer='leaky-relu')

x = torch.randn(2, 3, 256, 256)
features = encoder(x)
out = decoder(*features)