Merge branch 'release/0.1.22'

requirements.txt

@@ -13,3 +13,4 @@ clean-fid >= 0.1.12, < 0.1.14
 pandas >= 1.3.1
 ipython >= 7.26.0
 pygments >= 2.9.0
+pysoundfile >= 0.9.0; platform_system == "Windows"

src/tensorneko/neko_trainer.py

@@ -12,7 +12,11 @@
 from pytorch_lightning.accelerators import Accelerator
 from pytorch_lightning.callbacks import ModelCheckpoint
 from pytorch_lightning.loggers import TensorBoardLogger
-from pytorch_lightning.plugins import Plugin
+try:
+    from pytorch_lightning.plugins import Plugin  # for pytorch_lightning < 1.5.x
+except ImportError:
+    from pytorch_lightning.plugins import PLUGIN as Plugin  # for pytorch_lightning >= 1.5.x
+
 from pytorch_lightning.plugins.environments import ClusterEnvironment
 from pytorch_lightning.profiler import BaseProfiler
 

src/tensorneko/preprocess/func.py

@@ -71,7 +71,8 @@ def resize_image(tensor: Tensor, size: Tuple[int, int], resize_method: Union[Res
     return f(tensor)
 
 
-def resize_video(tensor: Tensor, size: Tuple[int, int], resize_method: Union[ResizeMethod, str] = ResizeMethod.BICUBIC
+def resize_video(tensor: Tensor, size: Tuple[int, int], resize_method: Union[ResizeMethod, str] = ResizeMethod.BICUBIC,
+    fast = False
 ) -> Tensor:
     """
     Resizing a video to determined size.
@@ -80,17 +81,21 @@ def resize_video(tensor: Tensor, size: Tuple[int, int], resize_method: Union[Res
         tensor (:class:`~torch.Tensor`): Video tensor (T, C, H, W)
         size ((``int``, ``int``)): Target size (H, W)
         resize_method (:class:`ResizeMethod`, optional): Resize method. Default bicubic.
+        fast (bool, optional): If True, use fast mode (pytorch F.interpolate). Default False.
 
     Returns:
         :class:`~torch.Tensor`: The resized video.
     """
-    resize_method = _get_enum_value(resize_method, ResizeMethod)
-    image_resizer = F(resize_image, size=size, resize_method=resize_method) \
-                    >> F(rearrange, pattern="c h w -> 1 c h w")
-    f = F(map, image_resizer) \
-        >> list \
-        >> torch.vstack
-    return f(tensor)
+    if not fast:
+        resize_method = _get_enum_value(resize_method, ResizeMethod)
+        image_resizer = F(resize_image, size=size, resize_method=resize_method) \
+                        >> F(rearrange, pattern="c h w -> 1 c h w")
+        f = F(map, image_resizer) \
+            >> list \
+            >> torch.vstack
+        return f(tensor)
+    else:
+        return func.interpolate(tensor, size, mode=_get_enum_value(resize_method, ResizeMethod), align_corners=False)
 
 
 def _get_padding_pair(padding_size: int, padding_position: Union[PaddingPosition, str]) -> List[int]:

src/tensorneko/visualization/color.py

@@ -0,0 +1,24 @@
+from enum import Enum
+
+
+class Colors(Enum):
+    GREEN = "#C3E88D"
+    YELLOW = "#FFCB6B"
+    RED = "#F07178"
+    BLUE = "#82AAFF"
+    PURPLE = "#C792EA"
+    ORANGE = "#F78C6C"
+    AZURE = "#467CDA"
+    SKY = "#89DDFF"
+    BLACK = "#2B2B2B"
+    GRAY = "#616161"
+    WHITE = "#EEFFFF"
+    BLANK = "#FFFFFF"
+
+
+class ContinuousColors(Enum):
+    RED = "#d11141",
+    YELLOW = "#ffc425",
+    GREEN = "#00b159",
+    BLACK = "#2B2B2B",
+    WHITE = "#EEFFFF"

src/tensorneko/visualization/seaborn.py

@@ -1,4 +1,16 @@
 import seaborn as sns
 
+from .color import Colors
+
+palette = sns.color_palette([
+    Colors.BLUE.value,
+    Colors.ORANGE.value,
+    Colors.GREEN.value,
+    Colors.RED.value,
+    Colors.PURPLE.value,
+    Colors.AZURE.value,
+    Colors.GRAY.value,
+    Colors.YELLOW.value
+])
 
 barplot = sns.barplot

src/tensorneko/visualization/watcher/server.py

@@ -108,12 +108,12 @@ def _run(self) -> None:
             stdout=subprocess.DEVNULL,
             stderr=subprocess.STDOUT
         )
-        print(f"Server started at port {self.port}, view \"{self.view_name}\".")
+        print(f"Server started at http://127.0.0.1:{self.port}/, view \"{self.view_name}\".")
 
     def _run_blocking(self):
         self.process = subprocess.run(["python", "-m", "http.server", "--directory",
             os.path.join("watcher", self.view_name), str(self.port)])
-        print(f"Server started at port {self.port}, view \"{self.view_name}\".")
+        print(f"Server started at http://127.0.0.1:{self.port}/, view \"{self.view_name}\".")
 
     def stop(self) -> None:
         """

src/tensorneko/visualization/watcher/view.py

@@ -51,7 +51,10 @@ def _to_json(self) -> None:
             os.mkdir(view_path)
 
         write.text.to_json(os.path.join("watcher", self.name, "data.json"),
-            list(map(lambda comp: comp.to_dict(), self.components))
+            {
+                "view": self.name,
+                "data": list(map(lambda comp: comp.to_dict(), self.components))
+            }
         )
 
     def add(self, *components: Component) -> View:

src/tensorneko/visualization/watcher/web/src/App.vue

@@ -1,5 +1,5 @@
 <template>
-  <h2 class="display-2">Tensorneko Web Watcher</h2>
+  <h2 class="display-2">Tensorneko Web Watcher::{{view}}</h2>
   <div class="row mb-3">
     <label class="col-sm-2 col-form-label" style="width: 200px">Update Rate (ms)</label>
     <div class="col-sm-10" style="width: 200px;"><input type="number" class="form-control" placeholder="1000"
@@ -40,6 +40,7 @@ export default class App extends Vue {
   readonly logs = logs
   readonly lineCharts = lineCharts
   updateDuration = 5000
+  view: string = null
 
   $refs!: {
     progressbar: ProgressBarTable,
@@ -65,7 +66,9 @@ export default class App extends Vue {
             }
           }
 
-          json.forEach((d: ComponentData) => {
+          this.view = json.view;
+
+          json.data.forEach((d: ComponentData) => {
             if (d.type === "Variable") {
               updateArray(d, this.variables);
             } else if (d.type === "ProgressBar") {

src/tensorneko/visualization/watcher/web/src/components/plot/LineChartCard.vue

@@ -74,7 +74,7 @@ export default class LineChartCard extends Vue {
         type: "line"
       },
       formatter: (params: CallbackDataParams) => {
-        return `${this.xLabel}: ${Math.round(params[0].data[0] * 10000) / 10000}\n` +
+        return `${this.xLabel}: ${Math.round(params[0].data[0] * 10000) / 10000}<br/>` +
             `${this.yLabel}: ${Math.round(params[0].data[1] * 10000) / 10000}`
       }
     };

src/tensorneko/visualization/watcher/web/src/main.ts

@@ -2,20 +2,10 @@ import { createApp } from 'vue'
 import BootstrapVue3 from "bootstrap-vue-3";
 import App from './App.vue'
 import { App as AppRuntime } from "vue"
-import { ComponentData, ProgressBarData, progressbars, VariableData, variables } from "@/data";
 import "../node_modules/bootstrap/dist/css/bootstrap.min.css";
 
 
 async function initApp(): Promise<AppRuntime<Element>> {
-    const response = await fetch(`data.json`);
-    const json = await response.json().catch(() => []);
-    json.forEach((d: ComponentData) => {
-        if (d.type === "Variable") {
-            variables[d.name] = d as VariableData;
-        } else if (d.type === "ProgressBar") {
-            progressbars[d.name] = d as ProgressBarData;
-        }
-    })
     return createApp(App)
 }
 

version.txt

@@ -1 +1 @@
-0.1.21
+0.1.22