Extended unit tests to classifier and fixed pooling

.github/workflows/doctest.yaml

@@ -1,9 +1,7 @@
 name: Verify docs generation
 
 # Runs on pushes to master and all pull requests
-on:    # yamllint disable-line rule:truthy
-    push:
-    pull_request:
+on: pull_request
 
 jobs:
     docs:
@@ -24,4 +22,3 @@ jobs:
                   cd docs
                   make doctest
                   make html
-

.pre-commit-config.yaml

@@ -0,0 +1,15 @@
+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v2.3.0
+    hooks:
+    -   id: check-yaml
+    -   id: end-of-file-fixer
+    -   id: trailing-whitespace
+-   repo: https://github.com/psf/black
+    rev: 22.10.0
+    hooks:
+    -   id: black
+-   repo: https://github.com/pycqa/flake8
+    rev: '6.0.0'  # pick a git hash / tag to point to
+    hooks:
+    -   id: flake8

micromind/conversion/convert.py

@@ -25,10 +25,10 @@
 def convert_to_onnx(net: nn.Module, save_path: Path, simplify: bool = True):
     """Converts nn.Module to onnx and saves it to save_path.
     Optionally simplifies it."""
-    x = torch.zeros([1] + net.input_shape)
+    x = torch.zeros([1] + list(net.input_shape))
 
     torch.onnx.export(
-        net,
+        net.cpu(),
         x,
         save_path,
         verbose=False,
@@ -88,13 +88,18 @@ def convert_to_tflite(
     if not isinstance(save_path, Path):
         save_path = Path(save_path)
 
+    if not (batch_quant is None):
+        batch_quant = batch_quant.cpu()
+
     vino_sub = save_path.joinpath("vino")
     os.makedirs(vino_sub, exist_ok=True)
     vino_path = convert_to_openvino(net, vino_sub)
     if os.name == "nt":
         openvino2tensorflow_exe_cmd = [
             sys.executable,
-            os.path.join(os.path.dirname(sys.executable), "openvino2tensorflow"),
+            os.path.join(
+                os.path.dirname(sys.executable), "Scripts", "openvino2tensorflow"
+            ),
         ]
     else:
         openvino2tensorflow_exe_cmd = ["openvino2tensorflow"]

micromind/networks/phinet.py

@@ -829,10 +829,10 @@ def __init__(
 
         if include_top:
             # Includes classification head if required
-            self.glob_pooling = lambda x: nn.functional.avg_pool2d(x, x.size()[2:])
-
-            self.new_convolution = nn.Conv2d(
-                int(block_filters * alpha), num_classes, kernel_size=1, bias=True
+            self.classifier = nn.Sequential(
+                nn.AdaptiveAvgPool2d((1, 1)),
+                nn.Flatten(),
+                nn.Linear(int(block_filters * alpha), num_classes, bias=True),
             )
 
     def forward(self, x):
@@ -851,8 +851,6 @@ def forward(self, x):
             x = layers(x)
 
         if self.classify:
-            x = self.glob_pooling(x)
-            x = self.new_convolution(x)
-            x = x.view(-1, x.shape[1])
+            x = self.classifier(x)
 
         return x

pyproject.toml

@@ -36,7 +36,7 @@ dependencies = [
 requires-python = ">=3.9"
 
 [project.optional-dependencies]
-dev = ["black", "bumpver", "flake8", "isort", "pip-tools", "pytest"]
+dev = ["black", "bumpver", "flake8", "isort", "pip-tools", "pytest", "pre-commit"]
 
 [project.urls]
 Homepage = "https://github.com/fpaissan/micromind"

tests/test_networks.py

@@ -13,8 +13,8 @@ def test_onnx():
 
     save_path = "temp.onnx"
 
-    in_shape = list((3, 224, 224))
-    net = PhiNet(in_shape, compatibility=False)
+    in_shape = (3, 224, 224)
+    net = PhiNet(in_shape, compatibility=False, include_top=True)
 
     convert_to_onnx(net, save_path, simplify=True)
     import os
@@ -34,8 +34,8 @@ def test_openvino():
 
     save_dir = "vino"
 
-    in_shape = list((3, 224, 224))
-    net = PhiNet(in_shape, compatibility=False)
+    in_shape = (3, 224, 224)
+    net = PhiNet(in_shape, compatibility=False, include_top=True)
 
     convert_to_openvino(net, save_dir)
 
@@ -50,8 +50,8 @@ def test_tflite():
 
     save_path = "tflite"
 
-    in_shape = list((3, 224, 224))
-    net = PhiNet(in_shape, compatibility=False)
+    in_shape = (3, 224, 224)
+    net = PhiNet(in_shape, compatibility=False, include_top=True)
 
     convert_to_tflite(net, save_path)