fix and setup python publishing

.github/workflows/python-publish.yaml

@@ -0,0 +1,32 @@
+name: Upload Python Package
+
+on:
+  push:
+    tags:
+      - "v*"
+
+permissions:
+  contents: read
+
+jobs:
+  deploy:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python
+      uses: actions/setup-python@v3
+      with:
+        python-version: '3.10'
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip setuptools wheel build
+        python -m pip install -r requirements.txt
+    - name: Build package
+      run: NN_TILDE_VERSION=${{  github.ref_name }} python -m build
+    - name: Publish package
+      uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
+      with:
+        user: __token__
+        password: ${{ secrets.PYPI_TOKEN }}

.gitignore

@@ -6,4 +6,6 @@ src/tests
 *libtorch
 *DS_Store*
 src/docs
-*.ts
+*.ts
+dist/
+*.egg-info*

MANIFEST.in

@@ -0,0 +1 @@
+include requirements.txt

python_tools/__init__.py

@@ -2,6 +2,7 @@
 import logging
 from typing import Any, Callable, Optional, Sequence, Tuple, Union
 
+import cached_conv as cc
 import torch
 
 TYPE_HASH = {bool: 0, int: 1, float: 2, str: 3, torch.Tensor: 4}
@@ -93,27 +94,33 @@ def register_method(
                      f"expected {test_buffer_size//out_ratio} "
                      f"got {y.shape[2]}"))
 
-            logging.info(f"Testing method {method_name} with mc.nn~ API")
-            x = torch.zeros(4, in_channels, test_buffer_size // in_ratio)
-            y = getattr(self, method_name)(x)
-
-            if len(y.shape) != 3:
-                raise ValueError(
-                    ("Output tensor must have exactly 3 dimensions, "
-                     f"got {len(y.shape)}"))
-            if y.shape[0] != 4:
-                raise ValueError(f"Expecting 4 batch output, got {y.shape[0]}")
-            if y.shape[1] != out_channels:
-                raise ValueError((
-                    f"Wrong number of output channels for method \"{method_name}\", "
-                    f"expected {out_channels} got {y.shape[1]}"))
-            if y.shape[2] != test_buffer_size // out_ratio:
-                raise ValueError(
-                    (f"Wrong output length for method \"{method_name}\", "
-                     f"expected {test_buffer_size//out_ratio} "
-                     f"got {y.shape[2]}"))
-            logging.info((f"Added method \"{method_name}\" "
-                          f"tested with buffer size {test_buffer_size}"))
+            if cc.MAX_BATCH_SIZE > 1:
+                logging.info(f"Testing method {method_name} with mc.nn~ API")
+                x = torch.zeros(4, in_channels, test_buffer_size // in_ratio)
+                y = getattr(self, method_name)(x)
+
+                if len(y.shape) != 3:
+                    raise ValueError(
+                        ("Output tensor must have exactly 3 dimensions, "
+                         f"got {len(y.shape)}"))
+                if y.shape[0] != 4:
+                    raise ValueError(
+                        f"Expecting 4 batch output, got {y.shape[0]}")
+                if y.shape[1] != out_channels:
+                    raise ValueError((
+                        f"Wrong number of output channels for method \"{method_name}\", "
+                        f"expected {out_channels} got {y.shape[1]}"))
+                if y.shape[2] != test_buffer_size // out_ratio:
+                    raise ValueError(
+                        (f"Wrong output length for method \"{method_name}\", "
+                         f"expected {test_buffer_size//out_ratio} "
+                         f"got {y.shape[2]}"))
+                logging.info((f"Added method \"{method_name}\" "
+                              f"tested with buffer size {test_buffer_size}"))
+            else:
+                logging.info(
+                    f"Skiping method {method_name} with mc.nn~ API as cc.MAX_BATCH_SIZE={cc.MAX_BATCH_SIZE}"
+                )
         else:
             logging.warn(f"Added method \"{method_name}\" without testing it.")
 

setup.py

@@ -1,7 +1,9 @@
-import subprocess
+import os
 
 import setuptools
 
+VERSION = os.environ["NN_TILDE_VERSION"]
+
 with open("README.md", "r") as readme:
     readme = readme.read()
 
@@ -10,11 +12,7 @@
 
 setuptools.setup(
     name="nn_tilde",
-    version=subprocess.check_output([
-        "git",
-        "describe",
-        "--abbrev=0",
-    ]).strip().decode(),
+    version=VERSION,
     author="Antoine CAILLON",
     author_email="caillon@ircam.fr",
     description="Set of tools to create nn_tilde compatible models.",