Resnet Variants (#9)

* Added ResNet variants

* ResNet50 example

* Simplified example

* fixed resnet config

* [Automated] Updated coverage badge

---------

Co-authored-by: Martin Kozlovsky <[email protected]>
Co-authored-by: GitHub Actions <[email protected]>

configs/resnet_model.yaml

@@ -0,0 +1,57 @@
+
+model:
+  name: resnet50_classification
+  nodes:
+    - name: ResNet
+      variant: "50"
+      download_weights: True
+
+    - name: ClassificationHead
+      inputs:
+        - ResNet
+
+  losses:
+    - name: CrossEntropyLoss
+      attached_to: ClassificationHead
+
+  metrics:
+    - name: Accuracy
+      is_main_metric: true
+      attached_to: ClassificationHead
+
+  visualizers:
+    - name: ClassificationVisualizer
+      attached_to: ClassificationHead
+      params:
+        font_scale: 0.5
+        color: [255, 0, 0]
+        thickness: 2
+        include_plot: True
+
+dataset:
+  name: cifar10_test
+
+trainer:
+  batch_size: 4
+  epochs: &epochs 200
+  num_workers: 4
+  validation_interval: 10
+  num_log_images: 8
+
+  preprocessing:
+    train_image_size: [&height 224, &width 224]
+    keep_aspect_ratio: False
+    normalize:
+      active: True
+
+  callbacks:
+    - name: ExportOnTrainEnd
+    - name: TestOnTrainEnd
+
+  optimizer:
+    name: SGD
+    params:
+      lr: 0.02
+
+  scheduler:
+    name: ConstantLR

luxonis_train/nodes/README.md

@@ -5,7 +5,7 @@ arbitrarily as long as the two nodes are compatible with each other.
 
 ## Table Of Contents
 
-- [ResNet18](#resnet18)
+- [ResNet](#resnet)
 - [MicroNet](#micronet)
 - [RepVGG](#repvgg)
 - [EfficientRep](#efficientrep)
@@ -30,15 +30,16 @@ Every node takes these parameters:
 
 Additional parameters for specific nodes are listed below.
 
-## ResNet18
+## ResNet
 
-Adapted from [here](https://pytorch.org/vision/main/models/generated/torchvision.models.resnet18.html).
+Adapted from [here](https://pytorch.org/vision/main/models/resnet.html).
 
 **Params**
 
-| Key              | Type | Default value | Description                            |
-| ---------------- | ---- | ------------- | -------------------------------------- |
-| download_weights | bool | False         | If True download weights from imagenet |
+| Key              | Type                                      | Default value | Description                            |
+| ---------------- | ----------------------------------------- | ------------- | -------------------------------------- |
+| variant          | Literal\["18", "34", "50", "101", "152"\] | "18"          | Variant of the network.                |
+| download_weights | bool                                      | False         | If True download weights from imagenet |
 
 ## MicroNet
 

luxonis_train/nodes/__init__.py

@@ -10,7 +10,7 @@
 from .mobileone import MobileOne
 from .reppan_neck import RepPANNeck
 from .repvgg import RepVGG
-from .resnet18 import ResNet18
+from .resnet import ResNet
 from .rexnetv1 import ReXNetV1_lite
 from .segmentation_head import SegmentationHead
 
@@ -28,6 +28,6 @@
     "ReXNetV1_lite",
     "RepPANNeck",
     "RepVGG",
-    "ResNet18",
+    "ResNet",
     "SegmentationHead",
 ]

luxonis_train/nodes/resnet18.py → luxonis_train/nodes/resnet.py

@@ -1,30 +1,32 @@
-"""ResNet18 backbone.
+"""ResNet backbone.
 
-Source: U{https://pytorch.org/vision/main/models/generated/
-torchvision.models.resnet18.html}
+Source: U{https://pytorch.org/vision/main/models/resnet.html}
 @license: U{PyTorch<https://github.com/pytorch/pytorch/blob/master/LICENSE>}
 """
-
+from typing import Literal
 
 import torchvision
 from torch import Tensor
 
 from .base_node import BaseNode
 
 
-class ResNet18(BaseNode[Tensor, list[Tensor]]):
+class ResNet(BaseNode[Tensor, list[Tensor]]):
     attach_index: int = -1
 
     def __init__(
         self,
+        variant: Literal["18", "34", "50", "101", "152"] = "18",
         channels_list: list[int] | None = None,
         download_weights: bool = False,
         **kwargs,
     ):
-        """Implementation of the ResNet18 backbone.
+        """Implementation of the ResNetX backbone.
 
         TODO: add more info
 
+        @type variant: Literal["18", "34", "50", "101", "152"]
+        @param variant: ResNet variant. Defaults to "18".
         @type channels_list: list[int] | None
         @param channels_list: List of channels to return.
             If unset, defaults to [64, 128, 256, 512].
@@ -35,7 +37,12 @@ def __init__(
         """
         super().__init__(**kwargs)
 
-        self.backbone = torchvision.models.resnet18(
+        if variant not in RESNET_VARIANTS:
+            raise ValueError(
+                f"ResNet model variant should be in {list(RESNET_VARIANTS.keys())}"
+            )
+
+        self.backbone = RESNET_VARIANTS[variant](
             weights="DEFAULT" if download_weights else None
         )
         self.channels_list = channels_list or [64, 128, 256, 512]
@@ -57,3 +64,12 @@ def forward(self, x: Tensor) -> list[Tensor]:
         outs.append(x)
 
         return outs
+
+
+RESNET_VARIANTS = {
+    "18": torchvision.models.resnet18,
+    "34": torchvision.models.resnet34,
+    "50": torchvision.models.resnet50,
+    "101": torchvision.models.resnet101,
+    "152": torchvision.models.resnet152,
+}