feat: doc options auto update

.github/workflows/github-actions-doc-options-update.yml

@@ -0,0 +1,33 @@
+name: GitHub Actions Demo
+on: 
+  push:
+    branches: [master]
+jobs:
+  Explore-GitHub-Actions:
+    runs-on: ubuntu-latest
+    steps:
+      - name: setup python
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.8 #install the python needed
+      - name : checkout
+        uses: actions/checkout@v3
+        with:
+          ref: ${{ github.ref }}
+      - name: Update submodules
+        run: git submodule update --init --recursive
+      - name : install req
+        run: pip install -r  ./.github/workflows/requirements_github_actions.txt	
+      - name : update
+        uses: actions/checkout@v3
+        with:
+          ref: ${{ github.ref }}
+      - name : run script
+        run: python ./scripts/generate_doc.py
+      - name : push
+        run : |
+          git config user.name github-actions-jg
+          git config user.email contact@jolibrain.com
+          git add .
+          git diff-index --quiet HEAD || (git commit -m "doc: options auto update" && git push)
+

.github/workflows/requirements_github_actions.txt

@@ -0,0 +1,9 @@
+-f https://download.pytorch.org/whl/cu113/torch_stable.html
+torch==1.10.1+cu113
+numpy==1.19.5
+Pillow==8.4.0
+torchvision==0.11.2+cu113 
+timm==0.5.4
+visdom==0.1.8.9
+torchviz
+imgaug

models/modules/segformer/segformer_generator.py

@@ -1,7 +1,5 @@
 import os
-from mmseg.models import build_segmentor
 from torch import nn
-import mmcv
 
 from models.modules.resnet_architecture.resnet_generator import ResnetDecoder
 from models.modules.attn_network import BaseGenerator_attn
@@ -11,10 +9,12 @@
 class Segformer(nn.Module):
     def __init__(self,jg_dir,G_config_segformer,img_size,num_classes=10,final_conv=False):
         super().__init__()
+        import mmcv
         cfg = mmcv.Config.fromfile(os.path.join(jg_dir,G_config_segformer))
         cfg.model.pretrained = None
         cfg.model.train_cfg = None
         cfg.model.decode_head.num_classes = num_classes
+        from mmseg.models import build_segmentor
         self.net = build_segmentor(cfg.model, train_cfg=None, test_cfg=cfg.get('test_cfg'))
 
         configure_encoder_decoder(self.net)
@@ -48,6 +48,7 @@ def __init__(self,jg_dir,G_config_segformer,img_size,nb_mask_attn,nb_mask_input,
         self.use_final_conv = final_conv
         self.tanh = nn.Tanh()
 
+        import mmcv
         cfg = mmcv.Config.fromfile(os.path.join(jg_dir,G_config_segformer))
         cfg.model.pretrained = None
         cfg.model.train_cfg = None
@@ -58,6 +59,7 @@ def __init__(self,jg_dir,G_config_segformer,img_size,nb_mask_attn,nb_mask_input,
             num_cls = 3*(self.nb_mask_attn-self.nb_mask_input)
         cfg.model.decode_head.num_classes = num_cls
         cfg.model.auxiliary_head.num_classes = self.nb_mask_attn
+        from mmseg.models import build_segmentor
         self.segformer = build_segmentor(cfg.model, train_cfg=None, test_cfg=cfg.get('test_cfg'))
         self.segformer.train()
         self.softmax_ = nn.Softmax(dim=1)

models/modules/segformer/utils.py

@@ -1,6 +1,3 @@
-from mmseg.models.utils import nlc_to_nchw
-from mmseg.ops import resize
-
 def configure_compute_feat_mit(obj):
     def compute_feat_mit(x, extract_layer_ids=[]):
         outs = []
@@ -11,6 +8,7 @@ def compute_feat_mit(x, extract_layer_ids=[]):
             for block in layer[1]:
                 x = block(x, hw_shape)
             x = layer[2](x)
+            from mmseg.models.utils import nlc_to_nchw
             x = nlc_to_nchw(x, hw_shape)
             if i in obj.out_indices:
                 outs.append(x)
@@ -38,6 +36,7 @@ def decode_encoder_encoder(outs,use_resize=True):
         map of the same size as input."""
         out = obj._decode_head_forward_test(outs, img_metas=None)
         if use_resize:
+            from mmseg.ops import resize
             out = resize(
                 input=out,
                 size=obj.img_size,
@@ -53,6 +52,7 @@ def decode_2_encoder_encoder(outs,use_resize=True):
         """Encode images with backbone and decode into a semantic segmentation
         map of the same size as input."""
         out2 = obj._auxiliary_head_forward_test(outs, img_metas=None)
+        from mmseg.ops import resize
         out2 = resize(
             input=out2,
             size=obj.img_size,

scripts/generate_doc.py

@@ -6,7 +6,9 @@
 from argparse import _HelpAction, _SubParsersAction, _StoreConstAction
 import logging
 
-sys.path.append('../')
+jg_dir=os.path.join("/".join(os.path.abspath(__file__).split("/")[:-2]))
+sys.path.append(jg_dir)
+
 import options as opt
 
 def main():
@@ -36,6 +38,10 @@ def main():
 
     print(options_md)
 
+    path_sv = os.path.join(jg_dir,'docs','options.md')
+    with open(path_sv,'w+') as file:
+        file.writelines(options_md)
+
 # ====
 
 # inspired by https://github.com/alex-rudakov/sphinx-argparse/blob/master/sphinxarg/parser.py (v0.2.5)