dnn-cool 0.4.0

DNN.Cool: Multi-task learning for Deep Neural Networks (DNN).

dnn_cool: Deep Neural Networks for Conditional objective oriented learning

WARNING: API is not yet stable, expect breaking changes in 0.x versions!

To install, just do:

pip install dnn_cool

• Introduction: What is dnn_cool in a nutshell?
• Examples: a simple step-by-step example.
• Features: a list of the utilities that dnn_cool provides for you
• Customization: Learn how to add new tasks, modify them, etc.
• Inspiration: list of papers and videos which inspired this library

To see the predefined tasks for this release, see list of predefined tasks

Introduction

A framework for multi-task learning in Pytorch, where you may precondition tasks and compose them into bigger tasks. Many complex neural networks can be trivially implemented with dnn_cool. For example, creating a neural network that does classification and localization is as simple as:

@project.add_flow
def localize_flow(flow, x, out):
    out += flow.obj_exists(x.features)
    out += flow.obj_x(x.features) | out.obj_exists
    out += flow.obj_y(x.features) | out.obj_exists
    out += flow.obj_w(x.features) | out.obj_exists
    out += flow.obj_h(x.features) | out.obj_exists
    out += flow.obj_class(x.features) | out.obj_exists
    return out

If for example you want to classify first if the camera is blocked and then do localization given that the camera is not blocked, you could do:

@project.add_flow
def full_flow(flow, x, out):
    out += flow.camera_blocked(x.cam_features)
    out += flow.localize_flow(x.localization_features) | (~out.camera_blocked)
    return out

Based on these "task flows" as we call them, dnn_cool provides a bunch of features. Currently, this is the list of the predefined tasks (they are all located in dnn_cool.task_flow):

List of predefined tasks

In the current release, the following tasks are available out of the box:

• BinaryClassificationTask - sigmoid activation, thresholding decoder, binary cross entropy loss function. In the examples above, camera_blocked and obj_exists are BinaryClassificationTasks.
• ClassificationTask - softmax activation, sorting classes decoder, categorical cross entropy loss. In the example above, obj_class is a ClassificationTask
• MultilabelClassificationTask - sigmoid activation, thresholding decoder, binary cross entropy loss function.
• BoundedRegressionTask - sigmoid activation, rescaling decoder, mean squared error loss function. In the examples above, obj_x, obj_y, obj_w, obj_h are bounded regression tasks.
• MaskedLanguageModelingTask - softmax activation, sorting decoder, cross entropy per token loss.
• TaskFlow - a composite task, that contains a list of children tasks. We saw 2 task flows above.

Examples

Quick Imagenet example

We just have to add a ClassificationTask named classifier and add the flow below:

@project.add_flow()
def imagenet_model(flow, x, out):
    out += flow.classifier(x.features)
    return out

That's great! But what if there is not an object always? Then we have to first check if an object exists. Let's add a BinaryClassificationTask and use it as a precondition to classifier.

@project.add_flow()
def imagenet_model(flow, x, out):
    out += flow.object_exists(x.features)
    out += flow.classifier(x.features) | out.object_exists
    return out

But what if we also want to localize the object? Then we have to add new tasks that regress the bounding box. Let's call them object_x, object_y, object_w, object_h and make them a BoundedRegressionTask. To avoid preconditioning all tasks on object_exists, let's group them first. Then we modify the flow:

@project.add_flow()
def object_flow(flow, x, out):
    out += flow.classifier(x.features)
    out += flow.object_x(x.features)
    out += flow.object_y(x.features)
    out += flow.object_w(x.features)
    out += flow.object_h(x.features)
    return out 

@project.add_flow()
def imagenet_flow(flow, x, out):
    out += flow.object_exists(x.features)
    out += flow.object_flow(x.features) | out.object_exists
    return out

But what if the camera is blocked? Then there is no need to do anything, so let's create a new flow that executes our imagenet_flow only when the camera is not blocked.

def full_flow(flow, x, out):
    out += flow.camera_blocked(x.features)
    out += flow.imagenet_flow(x.features) | (~out.camera_blocked)
    return out

But what if for example we want to check if the object is a kite, and if it is, to classify its color? Then we would have to modify our object_flow as follows:

@project.add_flow()
def object_flow(flow, x, out):
    out += flow.classifier(x.features)
    out += flow.object_x(x.features)
    out += flow.object_y(x.features)
    out += flow.object_w(x.features)
    out += flow.object_h(x.features)
    out += flow.is_kite(x.features)
    out += flow.color(x.features) | out.is_kite
    return out 

I think you can see what dnn_cool is meant to do! :)

To see a full walkthrough on a synthetic dataset, check out the Colab notebook or the markdown write-up.

Features

Main features are:

• Task precondition
• Missing values handling
• Task composition
• Tensorboard metrics logging
• Task interpretations
• Task evaluation
• Task threshold tuning
• Dataset generation
• Tree explanations
• Memory balancing for dataparallel

Task preconditioning

Use the | for task preconditioning (think of P(A|B) notation). Preconditioning - A | B means that:

• Include the ground truth for B in the input batch when training
• When training, update the weights of the A only when B is satisfied in the ground truth.
• When training, compute the loss function for A only when B is satisfied in the ground truth
• When training, compute the metrics for A only when B is satisfied in the ground truth.
• When tuning threshold for A, optimize only on values for which B is satisfied in the ground truth.
• When doing inference, compute the metrics for A only when the precondition is satisfied according to the decoded result of the B task
• When generating tree explanation in inference mode, do not show the branch for A if B is not satisfied.
• When computing results interpretation, include only loss terms when the precondition is satisfied.

Usually, you have to keep track of all this stuff manually, which makes adding new preconditions very difficult. dnn_cool makes this stuff easy, so that you can chain a long list of preconditions without worrying you forgot something.

Missing values

Sometimes for an input you don't have labels for all tasks. With dnn_cool, you can just mark the missing label and dnn_cool will update only the weights of the tasks for which labels are available.

This feature has the awesome property that you don't need a single dataset with all tasks labeled, you can have different datasets for different tasks and it will work. For example, you can train a single object detection neural network that trains its classifier head on ImageNet, and its detection head on COCO.

Task composition

You can group tasks in a task flow (we already saw 2 above - localize_flow and full_flow). You can use this to organize things better, for example when you want to precondition a whole task flow. For example:

@project.add_flow
def face_regression(flow, x, out):
    out += flow.face_x1(x.face_localization)
    out += flow.face_y1(x.face_localization)
    out += flow.face_w(x.face_localization)
    out += flow.face_h(x.face_localization)
    out += flow.facial_characteristics(x.features)
    return out

Tensorboard logging

dnn_cool logs the metrics per task in Tensorboard, e.g:

Task interpretation

Also, the best and worst inputs per task are logged in the Tensorboard, for example if the input is an image:

Task evaluation

Per-task evaluation information is available, to pinpoint the exact problem in your network. An example evaluation dataframe:

	task_path	metric_name	metric_res	num_samples
0	camera_blocked	accuracy	0.980326	996
1	camera_blocked	f1_score	0.974368	996
2	camera_blocked	precision	0.946635	996
3	camera_blocked	recall	0.960107	996
4	door_open	accuracy	0.921215	902
5	door_open	f1_score	0.966859	902
6	door_open	precision	0.976749	902
7	door_open	recall	0.939038	902
8	door_locked	accuracy	0.983039	201
9	door_locked	f1_score	0.948372	201
10	door_locked	precision	0.982583	201
11	door_locked	recall	0.934788	201
12	person_present	accuracy	0.999166	701
13	person_present	f1_score	0.937541	701
14	person_present	precision	0.927337	701
15	person_present	recall	0.963428	701
16	person_regression.face_regression.face_x1	mean_absolute_error	0.0137292	611
17	person_regression.face_regression.face_y1	mean_absolute_error	0.0232761	611
18	person_regression.face_regression.face_w	mean_absolute_error	0.00740503	611
19	person_regression.face_regression.face_h	mean_absolute_error	0.0101	611
20	person_regression.face_regression.facial_characteristics	accuracy	0.932624	611
21	person_regression.body_regression.body_x1	mean_absolute_error	0.00830785	611
22	person_regression.body_regression.body_y1	mean_absolute_error	0.0151234	611
23	person_regression.body_regression.body_w	mean_absolute_error	0.0130214	611
24	person_regression.body_regression.body_h	mean_absolute_error	0.0101	611
25	person_regression.body_regression.shirt_type	accuracy_1	0.979934	611
26	person_regression.body_regression.shirt_type	accuracy_3	0.993334	611
27	person_regression.body_regression.shirt_type	accuracy_5	0.990526	611
28	person_regression.body_regression.shirt_type	f1_score	0.928516	611
29	person_regression.body_regression.shirt_type	precision	0.959826	611
30	person_regression.body_regression.shirt_type	recall	0.968146	611

Task threshold tuning

Many tasks need to tune their threshold. Just call flow.get_decoder().tune() and you will get optimized thresholds for the metric you define.

Dataset generation

As noted above, dnn_cool will automatically trace the tasks used as a precondition and include the ground truth for them under the key gt.

Tree explanations

Examples:

├── inp 1
│   └── camera_blocked | decoded: [False], activated: [0.], logits: [-117.757324]
│       └── door_open | decoded: [ True], activated: [1.], logits: [41.11258]
│           └── person_present | decoded: [ True], activated: [1.], logits: [60.38873]
│               └── person_regression
│                   ├── body_regression
│                   │   ├── body_h | decoded: [29.672623], activated: [0.46363473], logits: [-0.14571853]
│                   │   ├── body_w | decoded: [12.86382], activated: [0.20099719], logits: [-1.3800735]
│                   │   ├── body_x1 | decoded: [21.34288], activated: [0.3334825], logits: [-0.69247603]
│                   │   ├── body_y1 | decoded: [18.468979], activated: [0.2885778], logits: [-0.9023013]
│                   │   └── shirt_type | decoded: [6 1 0 4 2 5 3], activated: [4.1331367e-23 3.5493638e-17 3.1328378e-26 5.6903808e-30 2.4471377e-25
 2.8071076e-29 1.0000000e+00], logits: [-20.549513  -6.88627  -27.734364 -36.34787  -25.6788   -34.751904
  30.990908]
│                   └── face_regression
│                       ├── face_h | decoded: [11.265154], activated: [0.17601803], logits: [-1.5435623]
│                       ├── face_w | decoded: [12.225838], activated: [0.19102871], logits: [-1.4433397]
│                       ├── face_x1 | decoded: [21.98834], activated: [0.34356782], logits: [-0.64743483]
│                       ├── face_y1 | decoded: [3.2855165], activated: [0.0513362], logits: [-2.9166584]
│                       └── facial_characteristics | decoded: [ True False  True], activated: [9.9999940e-01 1.2074912e-12 9.9999833e-01], logits: [ 14.240071 -27.442476  13.27557 ]

but if the model thinks the camera is blocked, then the explanation would be:

├── inp 2
│   └── camera_blocked | decoded: [ True], activated: [1.], logits: [76.367676]

Memory balancing

When using nn.DataParallel, the computation of the loss function is done on the main GPU, which leads to dramatically unbalanced memory usage if your outputs are big and you have a lot of metrics (e.g segmentation masks, language modeling, etc). dnn_cool gives you a convenient way to balance the memory in such situations - just a single balance_dataparallel_memory = True handles this case for you by first reducing all metrics on their respective device, and then additionally aggregating the results that were reduced on each device automatically. Here's an example memory usage:

Before:

After:

Customization

Since flow.torch() returns a normal nn.Module, you can use any library you are used to. If you use Catalyst, dnn_cool provides a bunch of useful callbacks. Creating a new task is as simple as creating a new instance of this dataclass:

@dataclass
class Task(ITask):
    name: str
    labels: Any
    loss: nn.Module
    per_sample_loss: nn.Module
    available_func: Callable
    inputs: Any
    activation: Optional[nn.Module]
    decoder: Decoder
    module: nn.Module
    metrics: Tuple[str, TorchMetric]

Alternatively, you can subclass ITask and implement its inferface.

Inspiration

• Andrej Karpathy: Tesla Autopilot and Multi-Task Learning for Perception and Prediction
• PyTorch at Tesla - Andrej Karpathy, Tesla
• Multitask learning - Andrew Ng