Getting Started with Instance Segmentation using IceVision

Introduction

This tutorial walk you through the different steps of training the fridge dataset. the IceVision Framework is an agnostic framework. As an illustration, we will train our model using both the fastai library, and pytorch-lightning libraries.

For more information about how the fridge dataset as well as its corresponding parser check out the pennfudan folder in icedata.

Installing IceVision and IceData

If on Colab run the following cell, else check the installation instructions

# IceVision - IceData - MMDetection - YOLO v5 Installation
!wget https://raw.githubusercontent.com/airctic/icevision/master/install_colab.sh
!chmod +x install_colab.sh && ./install_colab.sh

Imports

from icevision.all import *

Model

To create a model, we need to:

Choose one of the models supported by IceVision
Choose one of the backbones corresponding to a chosen model
Determine the number of the object classes: This will be done after parsing a dataset. Check out the Parsing Section

Choose a model and backbone

TorchVision

model_type = models.torchvision.mask_rcnn
backbone = model_type.backbones.resnet34_fpn()

Datasets : Pennfudan

Fridge Objects dataset is tiny dataset that contains 134 images of 4 classes: - can, - carton, - milk bottle, - water bottle.

IceVision provides very handy methods such as loading a dataset, parsing annotations, and more.

# Loading Data
data_dir = icedata.pennfudan.load_data()

train_ds, valid_ds = icedata.pennfudan.dataset(data_dir)

Displaying the same image with different transforms

Note:

Transforms are applied lazily, meaning they are only applied when we grab (get) an item. This means that, if you have augmentation (random) transforms, each time you get the same item from the dataset you will get a slightly different version of it.

samples = [train_ds[0] for _ in range(3)]
show_samples(samples, ncols=3)

png

DataLoader

# DataLoaders
train_dl = model_type.train_dl(train_ds, batch_size=8, num_workers=4, shuffle=True)
valid_dl = model_type.valid_dl(valid_ds, batch_size=8, num_workers=4, shuffle=False)

# show batch
model_type.show_batch(first(valid_dl), ncols=4)

png

Model

Now that we determined the number of classes (num_classes), we can create our model object.

# TODO: Better flow for train_ds
model = model_type.model(backbone=backbone, num_classes=icedata.pennfudan.NUM_CLASSES)

Metrics

metrics = [COCOMetric(metric_type=COCOMetricType.mask)]

Training

IceVision is an agnostic framework meaning it can be plugged to other DL framework such as fastai2, and pytorch-lightning.

You could also plug to oth DL framework using your own custom code.

Training using fastai

learn = model_type.fastai.learner(dls=[train_dl, valid_dl], model=model, metrics=metrics)

learn.lr_find()

SuggestedLRs(lr_min=0.00010000000474974513, lr_steep=1.737800812406931e-05)

png

learn.fine_tune(20, 1e-4, freeze_epochs=1)

epoch	train_loss	valid_loss	COCOMetric	time
0	2.361193	1.672817	0.000040	00:16

epoch	train_loss	valid_loss	COCOMetric	time
0	1.536968	1.494403	0.000147	00:19
1	1.516644	1.430333	0.000538	00:23
2	1.496924	1.343002	0.001926	00:22
3	1.435968	1.183041	0.002488	00:21
4	1.343802	0.962206	0.000928	00:20
5	1.199133	0.734787	0.004951	00:20
6	1.071547	0.682442	0.005912	00:20
7	0.973078	0.692099	0.006344	00:20
8	0.892403	0.649406	0.006396	00:21
9	0.837712	0.690478	0.007804	00:22
10	0.790692	0.647615	0.007775	00:23
11	0.758785	0.626387	0.002885	00:20
12	0.727605	0.609429	0.010509	00:20
13	0.706678	0.611301	0.012746	00:20
14	0.684067	0.616399	0.008784	00:20
15	0.668815	0.621261	0.011861	00:19
16	0.652843	0.615020	0.008686	00:19
17	0.649229	0.612351	0.008146	00:20
18	0.641258	0.609879	0.007551	00:22
19	0.640558	0.608780	0.007924	00:19

Training using Lightning

class LightModel(model_type.lightning.ModelAdapter):
    def configure_optimizers(self):
        return SGD(self.parameters(), lr=1e-4)

light_model = LightModel(model, metrics=metrics)

trainer = pl.Trainer(max_epochs=20, gpus=1)
trainer.fit(light_model, train_dl, valid_dl)

Show Results

model_type.show_results(model, valid_ds, detection_threshold=.5)

png

Inference

Predicting a batch of images

Instead of predicting a whole list of images at one, we can process small batches at the time: This option is more memory efficient.

NOTE: For a more detailed look at inference check out the inference tutorial

infer_dl = model_type.infer_dl(valid_ds, batch_size=4, shuffle=False)
preds = model_type.predict_from_dl(model, infer_dl, keep_images=True)

show_preds(preds=preds[:4], ncols=3)

png

Happy Learning!

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