Feat/GitHub action

.github/workflows/black.yml

@@ -1,33 +1,10 @@
-# This is a basic workflow to help you get started with Actions
+name: Lint
 
-name: PyLinter-Black
+on: [pull_request]
 
-# Controls when the workflow will run
-on:
-  # Triggers the workflow on push or pull request events but only for the main branch
-  push:
-    branches: [main, DEV]
-  pull_request:
-    branches: [main, DEV]
-
-  # Allows you to run this workflow manually from the Actions tab
-  workflow_dispatch:
-
-# A workflow run is made up of one or more jobs that can run sequentially or in parallel
 jobs:
-  # This workflow contains a single job called "build"
-  build:
-    # The type of runner that the job will run on
+  lint:
     runs-on: ubuntu-latest
-
-    # Steps represent a sequence of tasks that will be executed as part of the job
     steps:
-      # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
-      - uses: actions/checkout@v3
-
-      - name: Black Code Formatter
-        # You may pin to the exact commit or the version.
-        # uses: lgeiger/black-action@4379f39aa4b6a3bb1cceb46a7665b9c26647d82d
-        uses: lgeiger/black-action@v1.0.1
-        with:
-          args: ". --check"
+      - uses: actions/checkout@v2
+      - uses: psf/black@stable

.github/workflows/isort.yml

@@ -0,0 +1,13 @@
+name: Run isort
+on:
+  - push
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: isort/isort-action@v1.0.0
+        with:
+            requirements-files: "requirements.txt requirements-test.txt"
+            configuration: "--check-only --diff --profile black"

.pre-commit-config.yaml

@@ -0,0 +1,20 @@
+# See https://pre-commit.com for more information
+# See https://pre-commit.com/hooks.html for more hooks
+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.4.0
+    hooks:
+    -   id: trailing-whitespace
+    -   id: check-yaml
+    -   id: check-json
+-   repo: https://github.com/psf/black
+    rev: 22.12.0
+    hooks:
+    - id: black
+      language_version: python3.9
+-   repo: https://github.com/pycqa/isort
+    rev: 5.12.0
+    hooks:
+    - id: isort
+      name: isort (python)
+      args: ["--profile", "black", "--filter-files"]

Pipfile

@@ -13,14 +13,12 @@ scikit-learn = "*"
 torchvision = "*"
 pandas = "*"
 psycopg2-binary = "*"
-<<<<<<< HEAD
 aiofiles = "*"
-=======
 minio = "*"
 ipywidgets = "*"
->>>>>>> 2a36b7d23d3e3f03ad041b5f0381232a0aff7e99
 
 [dev-packages]
+pre-commit = "*"
 
 [requires]
 python_version = "3.9"

backend/PaintTransformer/inference.py

@@ -1,13 +1,14 @@
-import numpy as np
 import math
-import torch.nn.functional as F
+import os
+
+import numpy as np
 import torch
+import torch.nn.functional as F
 from PIL import Image
-import os
 
+from .utils.miscellaneous import *
 from .utils.morphology import *
 from .utils.network import *
-from .utils.miscellaneous import *
 
 
 def get_path_from_current_file(path):

backend/PaintTransformer/inference_only_final.py

@@ -1,23 +1,25 @@
 import math
 import os
+from argparse import ArgumentParser
 
 import numpy as np
 import PaintTransformer.morphology as morphology
 import PaintTransformer.network as network
+import PIL
 import torch
 import torch.nn.functional as F
 from PIL import Image
-import PIL
-from argparse import ArgumentParser
 
 idx = 0
 
+
 def save_img(img, output_path):
     result = Image.fromarray(
         (img.data.cpu().numpy().transpose((1, 2, 0)) * 255).astype(np.uint8)
     )
     result.save(output_path)
 
+
 def tensor_to_pil_image(image_tensor):
     pil_image = Image.fromarray(
         (image_tensor.data.cpu().numpy().transpose((1, 2, 0)) * 255).astype(np.uint8)
@@ -562,21 +564,26 @@ def partial_render(this_canvas, patch_coord_y, patch_coord_x):
     return cur_canvas
 
 
-def read_img(img, img_type='RGB', l=None):
+def read_img(img, img_type="RGB", l=None):
     img = img.convert(img_type)
     if l is not None:
         original_w, original_h = img.size
         if original_w > original_h:
-            img = img.resize((l, int(l/original_w*original_h)), resample=Image.NEAREST)
+            img = img.resize(
+                (l, int(l / original_w * original_h)), resample=Image.NEAREST
+            )
         else:
-            img = img.resize((int(l/original_h*original_w), l), resample=Image.NEAREST)
+            img = img.resize(
+                (int(l / original_h * original_w), l), resample=Image.NEAREST
+            )
     img = np.array(img)
     if img.ndim == 2:
         img = np.expand_dims(img, axis=-1)
     img = img.transpose((2, 0, 1))
-    img = torch.from_numpy(img).unsqueeze(0).float() / 255.
+    img = torch.from_numpy(img).unsqueeze(0).float() / 255.0
     return img
 
+
 def read_img_file(img_path, img_type="RGB", h=None, w=None):
     img = Image.open(img_path).convert(img_type)
     if h is not None and w is not None:
@@ -627,27 +634,28 @@ def crop(img, h, w):
 def init(stroke_num: int, model_path: str):
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     model = prepare_infer_model(model_path, stroke_num, device)
-    meta_brushes = make_meta_brushes(device, mode="small")    
+    meta_brushes = make_meta_brushes(device, mode="small")
     return model, meta_brushes, device
 
+
 def inference(
     model,
     device,
     meta_brushes,
     image,
     stroke_num: int,
     patch_size: int,
-    K: int=None,
-    resize_l: int=None,
-    serial: bool=False,
+    K: int = None,
+    resize_l: int = None,
+    serial: bool = False,
 ):
     print(image)
     with torch.no_grad():
-        frame_dir ='/output'
+        frame_dir = "/output"
         frame_list = []
         original_img = read_img(image, "RGB", resize_l).to(device)
         original_h, original_w = original_img.shape[-2:]
-        if K==None:
+        if K == None:
             K = max(math.ceil(math.log2(max(original_h, original_w) / patch_size)), 0)
         original_img_pad_size = patch_size * (2**K)
         original_img_pad = pad(
@@ -705,7 +713,7 @@ def inference(
                     decision=decision,
                     meta_brushes=meta_brushes,
                     cur_canvas=final_result,
-                    frame_dir='/output',
+                    frame_dir="/output",
                     has_border=False,
                     original_h=original_h,
                     original_w=original_w,
@@ -823,8 +831,12 @@ def make_meta_brushes(device: torch, mode: str = "large"):
     Returns:
         torch: meta_brushes
     """
-    brush_L_vertical = read_img_file(f"PaintTransformer/brush/brush_{mode}_vertical.png", "L")
-    brush_L_horizontal = read_img_file(f"PaintTransformer/brush/brush_{mode}_horizontal.png", "L")
+    brush_L_vertical = read_img_file(
+        f"PaintTransformer/brush/brush_{mode}_vertical.png", "L"
+    )
+    brush_L_horizontal = read_img_file(
+        f"PaintTransformer/brush/brush_{mode}_horizontal.png", "L"
+    )
     return torch.cat([brush_L_vertical, brush_L_horizontal], dim=0).to(device)
 
 
@@ -868,19 +880,26 @@ def make_path(input_path, output_dir):
     parser.add_argument("--input", dest="input", type=str, default="iu")
     args = parser.parse_args()
 
+    output_dir_name = (
+        os.path.splitext(args.input)[0]
+        + "_resize_"
+        + "{:04d}".format(args.resize_l)
+        + "_K_"
+        + "{:01d}".format(args.K)
+    )
 
-    output_dir_name = os.path.splitext(args.input)[0] + '_resize_' + '{:04d}'.format(args.resize_l) + '_K_' + '{:01d}'.format(args.K)
-
-    output_dir_root = os.path.join('output', output_dir_name)
+    output_dir_root = os.path.join("output", output_dir_name)
 
-    input_path = os.path.join('input', args.input)
+    input_path = os.path.join("input", args.input)
     resize_l = args.resize_l
     K = args.K
 
     stroke_num = 8
     patch_size = 32
 
-    model, meta_brushes, device = init(stroke_num, model_path="PaintTransformer/model.pth")
+    model, meta_brushes, device = init(
+        stroke_num, model_path="PaintTransformer/model.pth"
+    )
 
     inference(
         model,
@@ -889,7 +908,7 @@ def make_path(input_path, output_dir):
         stroke_num=stroke_num,
         patch_size=32,
         K=K,
-        need_animation=True,        # whether need intermediate results for animation.
-        resize_l=resize_l,          # resize original input to this size. (max(w, h) = resize_l)
-        serial=True,                # if need animation, serial must be True.
+        need_animation=True,  # whether need intermediate results for animation.
+        resize_l=resize_l,  # resize original input to this size. (max(w, h) = resize_l)
+        serial=True,  # if need animation, serial must be True.
     )

backend/PaintTransformer/utils/miscellaneous.py

@@ -1,7 +1,8 @@
 import os
-from PIL import Image
-import torch
+
 import numpy as np
+import torch
+from PIL import Image
 
 
 def set_image(img, img_type="RGB", l=None):

backend/api/api_v1/game/game.py

@@ -1,6 +1,5 @@
 from fastapi import APIRouter, HTTPException
-from fastapi.responses import StreamingResponse, FileResponse
-
+from fastapi.responses import FileResponse, StreamingResponse
 
 router = APIRouter()
 

backend/api/api_v1/inference/inference.py

@@ -1,12 +1,18 @@
-import aiofiles
 from io import BytesIO
-from PIL import Image
 
-from utils import predict_by_img, from_image_to_bytes, from_image_to_str, save_img
-from fastapi import APIRouter, HTTPException, BackgroundTasks
-from fastapi import FastAPI, File, UploadFile, Response
+import aiofiles
+from fastapi import (
+    APIRouter,
+    BackgroundTasks,
+    FastAPI,
+    File,
+    HTTPException,
+    Response,
+    UploadFile,
+)
 from fastapi.responses import FileResponse
-
+from PIL import Image
+from utils import from_image_to_bytes, from_image_to_str, predict_by_img, save_img
 
 router = APIRouter()
 

backend/app_only_final.py

@@ -1,12 +1,11 @@
 # app.py
-from fastapi import FastAPI
-from fastapi import File
-from fastapi import FastAPI
-from PaintTransformer.inference_only_final import init, inference
-from PIL import Image
 import base64
 import io
 
+from fastapi import FastAPI, File
+from PaintTransformer.inference_only_final import inference, init
+from PIL import Image
+
 # Create a FastAPI instance
 app = FastAPI()
 

backend/batch_inference_process/inference_only_final_process.py

@@ -1,8 +1,9 @@
-from PIL import Image
-import requests
 import base64
 import io
 
+import requests
+from PIL import Image
+
 
 def from_image_to_bytes(img):
     """

backend/inference_main.py

@@ -1,10 +1,9 @@
+import os
 import sys
 
 from PaintTransformer.inference import *
 from PIL import Image
 
-import os
-
 resize_l = 1024
 K = 5
 stroke_num = 8

backend/main.py

@@ -1,10 +1,8 @@
 # main.py
+from api.api_v1 import api_router
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 
-from api.api_v1 import api_router
-
-
 app = FastAPI(
     docs_url="/api/docs", redoc_url="/api/redoc", openapi_url="/api/openapi.json"
 )

backend/storage_minio/dummy_data_to_storage.py

@@ -1,6 +1,7 @@
-from minio import Minio
-from glob import glob
 import os
+from glob import glob
+
+from minio import Minio
 
 BUCKET_NAME = "savepaint-bucket"