Update from dev branch: v0.2.1 release

.github/PULL_REQUEST_TEMPLATE.md

@@ -23,4 +23,6 @@ For example,
 
 ```
 - Added a new feature by `@myusername` in [PR 2023](https://github.com/Nota-NetsPresso/netspresso-trainer/pull/2023)
-```
+```
+
+Please enable **Allow edits and access to secrets by maintainers** so that our maintainers can update the `CHANGELOG.md`.

CHANGELOG.md

@@ -16,6 +16,30 @@ No changes to highlight.
 
 No changes to highlight.
 
+# v0.2.1
+
+## New Features:
+
+- Add dataset validation step and refactoring data modules by `@illian01` in [PR 417](https://github.com/Nota-NetsPresso/netspresso-trainer/pull/417), [PR 419](https://github.com/Nota-NetsPresso/netspresso-trainer/pull/419)
+- Add various dataset examples including automatic open dataset format converter by `@illian01` in [PR 430](https://github.com/Nota-NetsPresso/netspresso-trainer/pull/430)
+- Allow using text file path for the `id_mapping` field by `@illian01` in [PR 432](https://github.com/Nota-NetsPresso/netspresso-trainer/pull/432), [PR 435](https://github.com/Nota-NetsPresso/netspresso-trainer/pull/435)
+
+## Bug Fixes:
+
+- Fix test directory check line by `@illian01` in [PR 428](https://github.com/Nota-NetsPresso/netspresso-trainer/pull/428)
+- Fix Dockerfile installation commandline `@cbpark-nota` in [PR 434](https://github.com/Nota-NetsPresso/netspresso-trainer/pull/434)
+
+## Breaking Changes:
+
+No changes to highlight.
+
+## Other Changes:
+
+- Save training summary at every end of epochs by `@illian01` in [PR 420](https://github.com/Nota-NetsPresso/netspresso-trainer/pull/420)
+- Refacotring: rename postprocessors/register.py to registry.py by `@aychun` in [PR 424](https://github.com/Nota-NetsPresso/netspresso-trainer/pull/424)
+- Add example configuration set by `@illian01` in [PR 438](https://github.com/Nota-NetsPresso/netspresso-trainer/pull/438)
+- Documentation: fix simple use config file path by `@cbpark-nota` in [PR 437](https://github.com/Nota-NetsPresso/netspresso-trainer/pull/437)
+
 # v0.2.0
 
 ## New Features:

Dockerfile

@@ -30,3 +30,4 @@ COPY . /home/appuser/netspresso-trainer
 
 RUN pip install -r requirements.txt && rm -rf /root/.cache/pip
 RUN pip install -r requirements-optional.txt && rm -rf /root/.cache/pip
+RUN python3 -m pip install -e .

README.md

@@ -100,6 +100,37 @@ Please refer to [`scripts/example_train.sh`](./scripts/example_train.sh).
 
 NetsPresso Trainer is compatible with [NetsPresso](https://netspresso.ai/) service. We provide NetsPresso Trainer tutorial that contains whole procedure from model train to model compression and benchmark. Please refer to our [colab tutorial](https://colab.research.google.com/drive/1RBKMCPEa4x-4X31zqzTS8WgQI9TQt3e-?usp=sharing).
 
+## Dataset preparation (Local)
+
+NetsPresso Trainer is designed to accommodate a variety of tasks, each requiring different dataset formats. You can find the specific dataset formats for each task in our [documentation](https://nota-netspresso.github.io/netspresso-trainer/components/data/).
+
+If you are interested in utilizing open datasets, you can use them by following the [instructions](https://nota-netspresso.github.io/netspresso-trainer/getting_started/dataset_preparation/local/#open-datasets).
+
+### Image classification
+
+- [CIFAR100](https://github.com/Nota-NetsPresso/netspresso-trainer/blob/dev/tools/open_dataset_tool/cifar100.py)
+- [ImageNet1K](https://github.com/Nota-NetsPresso/netspresso-trainer/blob/dev/tools/open_dataset_tool/imagenet1k.py)
+
+### Semantic segmentation
+
+- [PascalVOC 2012](https://github.com/Nota-NetsPresso/netspresso-trainer/blob/dev/tools/open_dataset_tool/voc2012_seg.py)
+
+### Object detection
+
+- [COCO 2017](https://github.com/Nota-NetsPresso/netspresso-trainer/blob/dev/tools/open_dataset_tool/coco2017.py)
+
+### Pose estimation
+
+- [WFLW](https://github.com/Nota-NetsPresso/netspresso-trainer/blob/dev/tools/open_dataset_tool/wflw.py)
+
+## Dataset preparation (Huggingface)
+
+NetsPresso Trainer is also compatible with huggingface dataset. To use datasets of huggingface, please check [instructions in our documentations](https://nota-netspresso.github.io/netspresso-trainer/getting_started/dataset_preparation/huggingface/). This enables to utilize a wide range of pre-built datasets which are beneficial for various training scenarios.
+
+## Pretrained weights
+
+Please refer to our [official documentation](https://nota-netspresso.github.io/netspresso-trainer/) for pretrained weights supported by NetsPresso Trainer.
+
 ## Tensorboard
 
 We provide basic tensorboard to track your training status. Run the tensorboard with the following command: 
@@ -109,9 +140,4 @@ tensorboard --logdir ./outputs --port 50001 --bind_all
 ```
 
 where `PORT` for tensorboard is 50001.  
-Note that the default directory of saving result will be `./outputs` directory.
-
-
-## Pretrained weights
-
-Please refer to our [official documentation](https://nota-netspresso.github.io/netspresso-trainer/) for pretrained weights supported by NetsPresso Trainer.
+Note that the default directory of saving result will be `./outputs` directory.

config/augmentation/pose_estimation.yaml

@@ -1,5 +1,4 @@
 augmentation:
-  img_size: &img_size 256
   train:
     -
       name: randomhorizontalflip

config/augmentation/segmentation.yaml

@@ -1,5 +1,4 @@
 augmentation:
-  img_size: &img_size 512
   train:
     - 
       name: randomresizedcrop

config/augmentation/template/common.yaml

@@ -1,32 +1,3 @@
 augmentation:
-  train:
-    - 
-      name: randomresizedcrop
-      size: ~
-      sclale: ~
-      ratio: ~
-      interpolation: bilinear
-    -
-      name: randomhorizontalflip
-      p: ~
-    -
-      name: randomverticalflip
-      p: ~
-    -
-      name: colorjitter
-      brightness: ~
-      contrast: ~
-      saturation: ~
-      hue: ~
-      p: ~
-    -
-      name: resize
-      size: ~
-      interpolation: ~
-      max_size: ~
-    -
-      name: pad
-      padding: ~
-      fill: ~
-      padding_mode: ~
+  train: ~
   inference: ~

config/benchmark_examples/classification-imagenet1k-resnet18/augmentation.yaml

@@ -0,0 +1,21 @@
+augmentation:
+  train:
+    - 
+      name: randomresizedcrop
+      size: 256
+      scale: [0.08, 1.0]
+      ratio: [0.75, 1.33]
+      interpolation: bilinear
+    - 
+      name: randomhorizontalflip
+      p: 0.5
+  inference:
+    - 
+      name: resize
+      size: [256, 256]
+      interpolation: bilinear
+      max_size: ~
+      resize_criteria: ~
+    - 
+      name: centercrop
+      size: 224

config/benchmark_examples/classification-imagenet1k-resnet18/data.yaml

@@ -0,0 +1,16 @@
+data:
+  name: IMAGENET1K
+  task: classification
+  format: local # local, huggingface
+  path:
+    root: ./data/imagenet1k # dataset root
+    train:
+      image: images/train # directory for training images
+      label: labels/imagenet_train.csv # directory for training labels or csv file
+    valid:
+      image: images/valid # directory for valid images
+      label: labels/imagenet_valid.csv # directory for valid labels or csv file
+    test:
+      image: ~
+      label: ~
+  id_mapping: id_mapping.json

config/benchmark_examples/classification-imagenet1k-resnet18/environment.yaml

@@ -0,0 +1,5 @@
+environment: 
+  seed: 1
+  num_workers: 4
+  gpus: 0, 1, 2, 3, 4, 5, 6, 7
+  batch_size: 32 # Batch size per gpu

config/benchmark_examples/classification-imagenet1k-resnet18/logging.yaml

@@ -0,0 +1,10 @@
+logging:
+  project_id: ~
+  output_dir: ./outputs
+  tensorboard: true
+  image: true
+  stdout: true
+  save_optimizer_state: true
+  onnx_input_size: [224, 224]
+  validation_epoch: &validation_epoch 5
+  save_checkpoint_epoch: *validation_epoch  # Multiplier of `validation_epoch`.

config/benchmark_examples/classification-imagenet1k-resnet18/model.yaml

@@ -0,0 +1,44 @@
+model:
+  task: classification
+  name: resnet18
+  checkpoint:
+    use_pretrained: False
+    load_head: False
+    path: ~
+    fx_model_path: ~
+    optimizer_path: ~
+  freeze_backbone: False
+  architecture:
+    full: ~ # auto
+    backbone:
+      name: resnet
+      params:
+        block_type: basicblock
+        norm_type: batch_norm
+      stage_params:
+        - 
+          channels: 64
+          num_blocks: 2
+        - 
+          channels: 128
+          num_blocks: 2
+          replace_stride_with_dilation: False
+        - 
+          channels: 256
+          num_blocks: 2
+          replace_stride_with_dilation: False
+        - 
+          channels: 512
+          num_blocks: 2
+          replace_stride_with_dilation: False
+    head:
+      name: fc
+      params:
+        num_layers: 1
+        intermediate_channels: ~
+        act_type: ~
+        dropout_prob: 0.
+  losses:
+    - criterion: cross_entropy
+      label_smoothing: 0.
+      weight: ~

config/benchmark_examples/classification-imagenet1k-resnet18/training.yaml

@@ -0,0 +1,15 @@
+training:
+  epochs: 90
+  mixed_precision: False
+  ema: ~
+  optimizer:
+    name: sgd
+    lr: 0.1
+    momentum: 0.9
+    weight_decay: 0.0001
+    nesterov: False
+  scheduler:
+    name: step
+    iters_per_phase: 30
+    gamma: 0.1
+    end_epoch: 90

config/benchmark_examples/classification-imagenet1k-resnet34/augmentation.yaml

@@ -0,0 +1,21 @@
+augmentation:
+  train:
+    - 
+      name: randomresizedcrop
+      size: 256
+      scale: [0.08, 1.0]
+      ratio: [0.75, 1.33]
+      interpolation: bilinear
+    - 
+      name: randomhorizontalflip
+      p: 0.5
+  inference:
+    - 
+      name: resize
+      size: [256, 256]
+      interpolation: bilinear
+      max_size: ~
+      resize_criteria: ~
+    - 
+      name: centercrop
+      size: 224

config/benchmark_examples/classification-imagenet1k-resnet34/data.yaml

@@ -0,0 +1,16 @@
+data:
+  name: IMAGENET1K
+  task: classification
+  format: local # local, huggingface
+  path:
+    root: ./data/imagenet1k # dataset root
+    train:
+      image: images/train # directory for training images
+      label: labels/imagenet_train.csv # directory for training labels or csv file
+    valid:
+      image: images/valid # directory for valid images
+      label: labels/imagenet_valid.csv # directory for valid labels or csv file
+    test:
+      image: ~
+      label: ~
+  id_mapping: id_mapping.json

config/benchmark_examples/classification-imagenet1k-resnet34/environment.yaml

@@ -0,0 +1,5 @@
+environment: 
+  seed: 1
+  num_workers: 4
+  gpus: 0, 1, 2, 3, 4, 5, 6, 7
+  batch_size: 32 # Batch size per gpu

config/benchmark_examples/classification-imagenet1k-resnet34/logging.yaml

@@ -0,0 +1,10 @@
+logging:
+  project_id: ~
+  output_dir: ./outputs
+  tensorboard: true
+  image: true
+  stdout: true
+  save_optimizer_state: true
+  onnx_input_size: [224, 224]
+  validation_epoch: &validation_epoch 5
+  save_checkpoint_epoch: *validation_epoch  # Multiplier of `validation_epoch`.

config/benchmark_examples/classification-imagenet1k-resnet34/model.yaml

@@ -0,0 +1,44 @@
+model:
+  task: classification
+  name: resnet34
+  checkpoint:
+    use_pretrained: False
+    load_head: False
+    path: ~
+    fx_model_path: ~
+    optimizer_path: ~
+  freeze_backbone: False
+  architecture:
+    full: ~ # auto
+    backbone:
+      name: resnet
+      params:
+        block_type: basicblock
+        norm_type: batch_norm
+      stage_params:
+        - 
+          channels: 64
+          num_blocks: 3
+        - 
+          channels: 128
+          num_blocks: 4
+          replace_stride_with_dilation: False
+        - 
+          channels: 256
+          num_blocks: 6
+          replace_stride_with_dilation: False
+        - 
+          channels: 512
+          num_blocks: 3
+          replace_stride_with_dilation: False
+    head:
+      name: fc
+      params:
+        num_layers: 1
+        intermediate_channels: ~
+        act_type: ~
+        dropout_prob: 0.
+  losses:
+    - criterion: cross_entropy
+      label_smoothing: 0.
+      weight: ~

config/benchmark_examples/classification-imagenet1k-resnet34/training.yaml

@@ -0,0 +1,15 @@
+training:
+  epochs: 90
+  mixed_precision: False
+  ema: ~
+  optimizer:
+    name: sgd
+    lr: 0.1
+    momentum: 0.9
+    weight_decay: 0.0001
+    nesterov: False
+  scheduler:
+    name: step
+    iters_per_phase: 30
+    gamma: 0.1
+    end_epoch: 90