Add Self-SL configs for SegNext (#2215)

* add fit selfsl configs to segnext

* Move selfsl recipes into src

* Remove unused parameters

* Update batch & lr

* Remove skip for segnext

* Update CHANGELOG

* Update licenses

* Update docstring

CHANGELOG.md

@@ -16,6 +16,7 @@ All notable changes to this project will be documented in this file.
 - Add Semi-SL Mean Teacher algorithm for Instance Segmentation task(<https://github.com/openvinotoolkit/training_extensions/pull/2444>)
 - Official supports for YOLOX-X, YOLOX-L, YOLOX-S, ResNeXt101-ATSS (<https://github.com/openvinotoolkit/training_extensions/pull/2485>)
 - Add new argument to track resource usage in train command(<https://github.com/openvinotoolkit/training_extensions/pull/2500>)
+- Add Self-SL for semantic segmentation of SegNext families (<https://github.com/openvinotoolkit/training_extensions/pull/2215>)
 
 ### Enhancements
 

src/otx/algorithms/segmentation/configs/ham_segnext_b/selfsl/__init__.py

@@ -0,0 +1,4 @@
+"""Initialization of SegNext-B model for Self-SL Segmentation Task."""
+
+# Copyright (C) 2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0

src/otx/algorithms/segmentation/configs/ham_segnext_b/selfsl/data_pipeline.py

@@ -0,0 +1,7 @@
+"""Data Pipeline of SegNext model for Segmentation Task."""
+
+# Copyright (C) 2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+# pylint: disable=invalid-name
+_base_ = ["../../base/data/selfsl/data_pipeline.py"]

src/otx/algorithms/segmentation/configs/ham_segnext_b/selfsl/hparam.yaml

@@ -0,0 +1,17 @@
+# Hyperparameters.
+hyper_parameters:
+  parameter_overrides:
+    learning_parameters:
+      batch_size:
+        default_value: 32
+      learning_rate:
+        default_value: 0.0001
+      learning_rate_warmup_iters:
+        default_value: 0
+      num_iters:
+        default_value: 10
+      enable_early_stopping:
+        default_value: false
+    algo_backend:
+      train_type:
+        default_value: Selfsupervised

src/otx/algorithms/segmentation/configs/ham_segnext_b/selfsl/model.py

@@ -0,0 +1,53 @@
+"""Model configuration of SegNext-B model for Self-SL Segmentation Task."""
+
+# Copyright (C) 2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+# pylint: disable=invalid-name
+
+_base_ = [
+    "../../../../../recipes/stages/segmentation/selfsl.py",
+    "../../../../common/adapters/mmcv/configs/backbones/segnext.py",
+]
+
+
+model = dict(
+    type="DetConB",
+    pretrained="https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_b_20230227-3ab7d230.pth",
+    num_classes=256,
+    num_samples=16,
+    downsample=8,
+    input_transform="resize_concat",
+    in_index=[1, 2, 3],
+    backbone=dict(
+        embed_dims=[64, 128, 320, 512],
+        depths=[3, 3, 12, 3],
+        drop_path_rate=0.1,
+        norm_cfg=dict(type="BN", requires_grad=True),
+    ),
+    neck=dict(
+        type="SelfSLMLP",
+        in_channels=960,
+        hid_channels=1920,
+        out_channels=256,
+        norm_cfg=dict(type="BN1d", requires_grad=True),
+        with_avg_pool=False,
+    ),
+    head=dict(
+        type="DetConHead",
+        predictor=dict(
+            type="SelfSLMLP",
+            in_channels=256,
+            hid_channels=1920,
+            out_channels=256,
+            norm_cfg=dict(type="BN1d", requires_grad=True),
+            with_avg_pool=False,
+        ),
+        loss_cfg=dict(type="DetConLoss", temperature=0.1),
+    ),
+)
+
+optimizer = dict(paramwise_cfg=dict(custom_keys={"pos_block": dict(decay_mult=0.0), "norm": dict(decay_mult=0.0)}))
+load_from = None
+resume_from = None
+fp16 = None

src/otx/algorithms/segmentation/configs/ham_segnext_s/selfsl/__init__.py

@@ -0,0 +1,4 @@
+"""Initialization of SegNext-S model for Self-SL Segmentation Task."""
+
+# Copyright (C) 2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0

src/otx/algorithms/segmentation/configs/ham_segnext_s/selfsl/data_pipeline.py

@@ -0,0 +1,7 @@
+"""Data Pipeline of SegNext model for Segmentation Task."""
+
+# Copyright (C) 2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+# pylint: disable=invalid-name
+_base_ = ["../../base/data/selfsl/data_pipeline.py"]

src/otx/algorithms/segmentation/configs/ham_segnext_s/selfsl/hparam.yaml

@@ -0,0 +1,17 @@
+# Hyperparameters.
+hyper_parameters:
+  parameter_overrides:
+    learning_parameters:
+      batch_size:
+        default_value: 32
+      learning_rate:
+        default_value: 0.0001
+      learning_rate_warmup_iters:
+        default_value: 0
+      num_iters:
+        default_value: 10
+      enable_early_stopping:
+        default_value: false
+    algo_backend:
+      train_type:
+        default_value: Selfsupervised

src/otx/algorithms/segmentation/configs/ham_segnext_s/selfsl/model.py

@@ -0,0 +1,52 @@
+"""Model configuration of SegNext-S model for Self-SL Segmentation Task."""
+
+# Copyright (C) 2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+# pylint: disable=invalid-name
+
+_base_ = [
+    "../../../../../recipes/stages/segmentation/selfsl.py",
+    "../../../../common/adapters/mmcv/configs/backbones/segnext.py",
+]
+
+
+model = dict(
+    type="DetConB",
+    pretrained="https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_s_20230227-f33ccdf2.pth",
+    num_classes=256,
+    num_samples=16,
+    downsample=8,
+    input_transform="resize_concat",
+    in_index=[1, 2, 3],
+    backbone=dict(
+        embed_dims=[64, 128, 320, 512],
+        depths=[2, 2, 4, 2],
+        norm_cfg=dict(type="BN", requires_grad=True),
+    ),
+    neck=dict(
+        type="SelfSLMLP",
+        in_channels=960,
+        hid_channels=1920,
+        out_channels=256,
+        norm_cfg=dict(type="BN1d", requires_grad=True),
+        with_avg_pool=False,
+    ),
+    head=dict(
+        type="DetConHead",
+        predictor=dict(
+            type="SelfSLMLP",
+            in_channels=256,
+            hid_channels=1920,
+            out_channels=256,
+            norm_cfg=dict(type="BN1d", requires_grad=True),
+            with_avg_pool=False,
+        ),
+        loss_cfg=dict(type="DetConLoss", temperature=0.1),
+    ),
+)
+
+optimizer = dict(paramwise_cfg=dict(custom_keys={"pos_block": dict(decay_mult=0.0), "norm": dict(decay_mult=0.0)}))
+load_from = None
+resume_from = None
+fp16 = None

src/otx/algorithms/segmentation/configs/ham_segnext_t/selfsl/__init__.py

@@ -0,0 +1,4 @@
+"""Initialization of SegNext-T model for Self-SL Segmentation Task."""
+
+# Copyright (C) 2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0

src/otx/algorithms/segmentation/configs/ham_segnext_t/selfsl/data_pipeline.py

@@ -0,0 +1,7 @@
+"""Data Pipeline of SegNext model for Segmentation Task."""
+
+# Copyright (C) 2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+# pylint: disable=invalid-name
+_base_ = ["../../base/data/selfsl/data_pipeline.py"]

src/otx/algorithms/segmentation/configs/ham_segnext_t/selfsl/hparam.yaml

@@ -0,0 +1,17 @@
+# Hyperparameters.
+hyper_parameters:
+  parameter_overrides:
+    learning_parameters:
+      batch_size:
+        default_value: 32
+      learning_rate:
+        default_value: 0.0001
+      learning_rate_warmup_iters:
+        default_value: 0
+      num_iters:
+        default_value: 10
+      enable_early_stopping:
+        default_value: false
+    algo_backend:
+      train_type:
+        default_value: Selfsupervised

src/otx/algorithms/segmentation/configs/ham_segnext_t/selfsl/model.py

@@ -0,0 +1,47 @@
+"""Model configuration of SegNext-T model for Self-SL Segmentation Task."""
+
+# Copyright (C) 2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+# pylint: disable=invalid-name
+
+_base_ = [
+    "../../../../../recipes/stages/segmentation/selfsl.py",
+    "../../../../common/adapters/mmcv/configs/backbones/segnext.py",
+]
+
+
+model = dict(
+    type="DetConB",
+    pretrained="https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth",
+    num_classes=256,
+    num_samples=16,
+    downsample=8,
+    input_transform="resize_concat",
+    in_index=[1, 2, 3],
+    neck=dict(
+        type="SelfSLMLP",
+        in_channels=480,
+        hid_channels=960,
+        out_channels=256,
+        norm_cfg=dict(type="BN1d", requires_grad=True),
+        with_avg_pool=False,
+    ),
+    head=dict(
+        type="DetConHead",
+        predictor=dict(
+            type="SelfSLMLP",
+            in_channels=256,
+            hid_channels=960,
+            out_channels=256,
+            norm_cfg=dict(type="BN1d", requires_grad=True),
+            with_avg_pool=False,
+        ),
+        loss_cfg=dict(type="DetConLoss", temperature=0.1),
+    ),
+)
+
+optimizer = dict(paramwise_cfg=dict(custom_keys={"pos_block": dict(decay_mult=0.0), "norm": dict(decay_mult=0.0)}))
+load_from = None
+resume_from = None
+fp16 = None

tests/e2e/cli/semantic_segmentation/test_segmentation.py

@@ -318,8 +318,6 @@ class TestToolsOTXSelfSLSegmentation:
     @pytest.mark.parametrize("template", templates, ids=templates_ids)
     def test_otx_train(self, template, tmp_dir_path):
         tmp_dir_path_1 = tmp_dir_path / "segmentation/test_selfsl"
-        if not (Path(template.model_template_path).parent / "selfsl").is_dir():
-            pytest.skip("Self-SL training type isn't available for this template")
         otx_train_testing(template, tmp_dir_path_1, otx_dir, args_selfsl)
         template_work_dir = get_template_dir(template, tmp_dir_path_1)
         assert (Path(template_work_dir) / "selfsl").is_dir()
@@ -332,8 +330,6 @@ def test_otx_train(self, template, tmp_dir_path):
     @pytest.mark.skipif(TT_STABILITY_TESTS, reason="This is TT_STABILITY_TESTS")
     @pytest.mark.parametrize("template", templates, ids=templates_ids)
     def test_otx_eval(self, template, tmp_dir_path):
-        if not (Path(template.model_template_path).parent / "selfsl").is_dir():
-            pytest.skip("Self-SL training type isn't available for this template")
         tmp_dir_path = tmp_dir_path / "segmentation/test_selfsl_sl"
         otx_eval_testing(template, tmp_dir_path, otx_dir, args)
 
@@ -342,8 +338,6 @@ def test_otx_eval(self, template, tmp_dir_path):
     @pytest.mark.skipif(MULTI_GPU_UNAVAILABLE, reason="The number of gpu is insufficient")
     @pytest.mark.parametrize("template", templates, ids=templates_ids)
     def test_otx_multi_gpu_train_selfsl(self, template, tmp_dir_path):
-        if not (Path(template.model_template_path).parent / "selfsl").is_dir():
-            pytest.skip("Self-SL training type isn't available for this template")
         tmp_dir_path = tmp_dir_path / "segmentation/test_multi_gpu_selfsl"
         args_selfsl_multigpu = copy.deepcopy(args_selfsl)
         args_selfsl_multigpu["--gpus"] = "0,1"

tests/integration/cli/semantic_segmentation/test_segmentation.py

@@ -203,17 +203,13 @@ def test_otx_multi_gpu_train_semisl(self, template, tmp_dir_path):
     @e2e_pytest_component
     @pytest.mark.parametrize("template", default_templates, ids=default_templates_ids)
     def test_otx_train_selfsl(self, template, tmp_dir_path):
-        if not (Path(template.model_template_path).parent / "selfsl").is_dir():
-            pytest.skip("Self-SL training type isn't available for this template")
         tmp_dir_path = tmp_dir_path / "segmentation/test_selfsl"
         otx_train_testing(template, tmp_dir_path, otx_dir, args_selfsl)
 
     @e2e_pytest_component
     @pytest.mark.skipif(MULTI_GPU_UNAVAILABLE, reason="The number of gpu is insufficient")
     @pytest.mark.parametrize("template", default_templates, ids=default_templates_ids)
     def test_otx_multi_gpu_train_selfsl(self, template, tmp_dir_path):
-        if not (Path(template.model_template_path).parent / "selfsl").is_dir():
-            pytest.skip("Self-SL training type isn't available for this template")
         tmp_dir_path = tmp_dir_path / "segmentation/test_multi_gpu_selfsl"
         args_selfsl_multigpu = copy.deepcopy(args_selfsl)
         args_selfsl_multigpu["--gpus"] = "0,1"

tests/regression/semantic_segmentation/test_segmentation.py

@@ -244,8 +244,6 @@ def test_otx_train_semisl_kpi_test(self, reg_cfg, template):
     @e2e_pytest_component
     @pytest.mark.parametrize("template", templates, ids=templates_ids)
     def test_otx_train_selfsl(self, reg_cfg, template, tmp_dir_path):
-        if not (Path(template.model_template_path).parent / "selfsl").is_dir():
-            pytest.skip("Self-SL training type isn't available for this template")
         train_type = "self_supervised"
         self.performance[template.name] = {}
 
@@ -297,8 +295,6 @@ def test_otx_train_selfsl(self, reg_cfg, template, tmp_dir_path):
     @e2e_pytest_component
     @pytest.mark.parametrize("template", templates, ids=templates_ids)
     def test_otx_train_selfsl_kpi_test(self, reg_cfg, template):
-        if not (Path(template.model_template_path).parent / "selfsl").is_dir():
-            pytest.skip("Self-SL training type isn't available for this template")
         train_type = "self_supervised"
         config_selfsl = reg_cfg.load_config(train_type=train_type)
         performance = reg_cfg.get_template_performance(template, train_type=train_type)