Prep for release

fastxtend/__init__.py

@@ -1 +1 @@
-__version__ = "0.0.15"
+__version__ = "0.0.16"

fastxtend/callback/progresize.py

@@ -143,8 +143,14 @@ def before_fit(self):
 
         if self.resize_batch:
             # Set when the progressive resizing step is applied in training percent
-            self.start  = self.start/self.n_epoch  if isinstance(self.start, int)  else self.start
-            self.finish = self.finish/self.n_epoch if isinstance(self.finish, int) else self.finish
+            if self.start >= 1 and isinstance(self.start, int):
+                self.start = self.start/self.n_epoch
+            if self.finish >= 1 and isinstance(self.finish, int):
+                self.finish = self.finish/self.n_epoch
+            if self.start >= 1:
+                warn(f'ProgressiveResize start {self.start} is equal or greater than one and will not start in this training run')
+            if self.finish >= 1:
+                warn(f'ProgressiveResize finish {self.finish} is equal or greater than one and will not finish in this training run')
             n_steps = ((self.final_size-self.current_size) / self.increase_by).int()
             if sum(n_steps.shape)==2:
                 n_steps = n_steps[0].item()

nbs/callback.progresize.ipynb

@@ -216,8 +216,14 @@
     "\n",
     "        if self.resize_batch:\n",
     "            # Set when the progressive resizing step is applied in training percent\n",
-    "            self.start  = self.start/self.n_epoch  if isinstance(self.start, int)  else self.start\n",
-    "            self.finish = self.finish/self.n_epoch if isinstance(self.finish, int) else self.finish\n",
+    "            if self.start >= 1 and isinstance(self.start, int):\n",
+    "                self.start = self.start/self.n_epoch\n",
+    "            if self.finish >= 1 and isinstance(self.finish, int):\n",
+    "                self.finish = self.finish/self.n_epoch\n",
+    "            if self.start >= 1:\n",
+    "                warn(f'ProgressiveResize start {self.start} is equal or greater than one and will not start in this training run')\n",
+    "            if self.finish >= 1:\n",
+    "                warn(f'ProgressiveResize finish {self.finish} is equal or greater than one and will not finish in this training run')\n",
     "            n_steps = ((self.final_size-self.current_size) / self.increase_by).int()\n",
     "            if sum(n_steps.shape)==2:\n",
     "                n_steps = n_steps[0].item()\n",

nbs/optimizer.adan.ipynb

@@ -551,7 +551,7 @@
     "AdamW vs Adan Training Speed\n",
     ":::\n",
     "\n",
-    "[^faster]: Benchmarked on a GeForce 3080 Ti using PyTorch 1.13.1 on Cuda 11.7, Mixed Precision, [Channels Last](callback.channelslast.html) (except DeBERTa and ViT), and fastxtend's [Simple Profiler Callback](callback.simpleprofiler.html). Results may differ on other models, hardware, and across benchmarking runs. Speedup and slowdown are calculated from the total time spent on the optimization step."
+    "[^faster]: Benchmarked on a GeForce 3080 Ti using PyTorch 1.13.1 Cuda 11.7, Mixed Precision, [Channels Last](callback.channelslast.html) (except DeBERTa and ViT), and fastxtend's [Simple Profiler Callback](callback.simpleprofiler.html). Results may differ on other models, hardware, and across benchmarking runs. Speedup and slowdown are calculated from the total time spent on the optimization step."
    ]
   }
  ],

nbs/optimizer.fused.ipynb

@@ -80,7 +80,7 @@
     "\n",
     ": Increase in [AdamW](#adam-optimizer) `opt_step` Speed With Multiple Param Groups vs fastai Native Optimizer {#tbl-layers} {tbl-colwidths=\"[10,2,8,8,8,8,8]\"}\n",
     "\n",
-    "[^faster]: All optimizers benchmarked on a GeForce 3080 Ti using PyTorch 1.12.1 on Cuda 11.6, Mixed Precision, [Channels Last](callback.channelslast.html) (except ViT and DeBERTa), and fastxtend's [Simple Profiler Callback](callback.simpleprofiler.html). Results may differ with other optimizers, models, hardware, and across benchmarking runs. Speedup is calculated from the total time spent on the optimization step.\n",
+    "[^faster]: All optimizers benchmarked on a GeForce 3080 Ti using PyTorch 1.12.1 Cuda 11.6, Mixed Precision, [Channels Last](callback.channelslast.html) (except ViT and DeBERTa), and fastxtend's [Simple Profiler Callback](callback.simpleprofiler.html). Results may differ with other optimizers, models, hardware, and across benchmarking runs. Speedup is calculated from the total time spent on the optimization step.\n",
     "\n",
     "## Examples\n",
     "\n",

settings.ini

@@ -8,7 +8,7 @@ author = Benjamin Warner
 author_email = me@benjaminwarner.dev
 copyright = Benjamin Warner
 branch = main
-version = 0.0.15
+version = 0.0.16
 min_python = 3.8
 audience = Developers
 language = English