Small update (#3)

.github/workflows/python.yml

@@ -21,7 +21,7 @@ jobs:
     - name: Install Poetry
       uses: snok/install-poetry@v1
       with:
-        virtualenv-create: true
+        virtualenvs-create: true
         virtualenvs-in-project: true
     - name: Poetry cache
       id: poetry_cache

facenet_pytorch/models/inception_resnet_v1.py

@@ -1,14 +1,23 @@
+from __future__ import annotations
+
 import os
+from pathlib import Path
+from typing import TYPE_CHECKING
 
 import torch
 from torch import nn
-from torch.nn import functional as F
+from torch.nn import functional as tv_functional
 
 from .utils.download import download_url_to_file
 
+if TYPE_CHECKING:
+    from torch.nn.common_types import _size_2_t
+
 
 class BasicConv2d(nn.Module):
-    def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
+    def __init__(  # noqa: PLR0913
+        self, in_planes: int, out_planes: int, kernel_size: _size_2_t, stride: _size_2_t, padding: _size_2_t = 0
+    ) -> None:
         super().__init__()
         self.conv = nn.Conv2d(
             in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, bias=False
@@ -21,15 +30,15 @@ def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
         )
         self.relu = nn.ReLU(inplace=False)
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         x = self.conv(x)
         x = self.bn(x)
         x = self.relu(x)
-        return x
+        return x  # noqa: RET504
 
 
 class Block35(nn.Module):
-    def __init__(self, scale=1.0):
+    def __init__(self, scale: float = 1.0) -> None:
         super().__init__()
 
         self.scale = scale
@@ -49,19 +58,18 @@ def __init__(self, scale=1.0):
         self.conv2d = nn.Conv2d(96, 256, kernel_size=1, stride=1)
         self.relu = nn.ReLU(inplace=False)
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         x0 = self.branch0(x)
         x1 = self.branch1(x)
         x2 = self.branch2(x)
         out = torch.cat((x0, x1, x2), 1)
         out = self.conv2d(out)
         out = out * self.scale + x
-        out = self.relu(out)
-        return out
+        return self.relu(out)
 
 
 class Block17(nn.Module):
-    def __init__(self, scale=1.0):
+    def __init__(self, scale: float = 1.0) -> None:
         super().__init__()
 
         self.scale = scale
@@ -77,18 +85,17 @@ def __init__(self, scale=1.0):
         self.conv2d = nn.Conv2d(256, 896, kernel_size=1, stride=1)
         self.relu = nn.ReLU(inplace=False)
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         x0 = self.branch0(x)
         x1 = self.branch1(x)
         out = torch.cat((x0, x1), 1)
         out = self.conv2d(out)
         out = out * self.scale + x
-        out = self.relu(out)
-        return out
+        return self.relu(out)
 
 
 class Block8(nn.Module):
-    def __init__(self, scale=1.0, noReLU=False):
+    def __init__(self, scale: float = 1.0, *, noReLU: bool = False) -> None:  # noqa: N803
         super().__init__()
 
         self.scale = scale
@@ -106,7 +113,7 @@ def __init__(self, scale=1.0, noReLU=False):
         if not self.noReLU:
             self.relu = nn.ReLU(inplace=False)
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         x0 = self.branch0(x)
         x1 = self.branch1(x)
         out = torch.cat((x0, x1), 1)
@@ -117,8 +124,8 @@ def forward(self, x):
         return out
 
 
-class Mixed_6a(nn.Module):
-    def __init__(self):
+class Mixed6a(nn.Module):
+    def __init__(self) -> None:
         super().__init__()
 
         self.branch0 = BasicConv2d(256, 384, kernel_size=3, stride=2)
@@ -131,16 +138,15 @@ def __init__(self):
 
         self.branch2 = nn.MaxPool2d(3, stride=2)
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         x0 = self.branch0(x)
         x1 = self.branch1(x)
         x2 = self.branch2(x)
-        out = torch.cat((x0, x1, x2), 1)
-        return out
+        return torch.cat((x0, x1, x2), 1)
 
 
-class Mixed_7a(nn.Module):
-    def __init__(self):
+class Mixed7a(nn.Module):
+    def __init__(self) -> None:
         super().__init__()
 
         self.branch0 = nn.Sequential(
@@ -159,13 +165,12 @@ def __init__(self):
 
         self.branch3 = nn.MaxPool2d(3, stride=2)
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         x0 = self.branch0(x)
         x1 = self.branch1(x)
         x2 = self.branch2(x)
         x3 = self.branch3(x)
-        out = torch.cat((x0, x1, x2, x3), 1)
-        return out
+        return torch.cat((x0, x1, x2, x3), 1)
 
 
 class InceptionResnetV1(nn.Module):
@@ -187,7 +192,15 @@ class InceptionResnetV1(nn.Module):
         dropout_prob {float} -- Dropout probability. (default: {0.6})
     """
 
-    def __init__(self, pretrained=None, classify=False, num_classes=None, dropout_prob=0.6, device=None):
+    def __init__(  # noqa: PLR0913
+        self,
+        pretrained: str | None = None,
+        *,
+        classify: bool = False,
+        num_classes: int | None = None,
+        dropout_prob: float = 0.6,
+        device: torch.device | str | None = None,
+    ) -> None:
         super().__init__()
 
         # Set simple attributes
@@ -200,7 +213,8 @@ def __init__(self, pretrained=None, classify=False, num_classes=None, dropout_pr
         elif pretrained == "casia-webface":
             tmp_classes = 10575
         elif pretrained is None and self.classify and self.num_classes is None:
-            raise Exception('If "pretrained" is not specified and "classify" is True, "num_classes" must be specified')
+            msg = "If 'pretrained' is not specified and 'classify' is True, 'num_classes' must be specified"
+            raise ValueError(msg)
 
         # Define layers
         self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2)
@@ -213,7 +227,7 @@ def __init__(self, pretrained=None, classify=False, num_classes=None, dropout_pr
         self.repeat_1 = nn.Sequential(
             Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17)
         )
-        self.mixed_6a = Mixed_6a()
+        self.mixed_6a = Mixed6a()
         self.repeat_2 = nn.Sequential(
             Block17(scale=0.10),
             Block17(scale=0.10),
@@ -226,7 +240,7 @@ def __init__(self, pretrained=None, classify=False, num_classes=None, dropout_pr
             Block17(scale=0.10),
             Block17(scale=0.10),
         )
-        self.mixed_7a = Mixed_7a()
+        self.mixed_7a = Mixed7a()
         self.repeat_3 = nn.Sequential(
             Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20)
         )
@@ -248,7 +262,7 @@ def __init__(self, pretrained=None, classify=False, num_classes=None, dropout_pr
             self.device = device
             self.to(device)
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         """Calculate embeddings or logits given a batch of input image tensors.
 
         Arguments:
@@ -274,14 +288,11 @@ def forward(self, x):
         x = self.dropout(x)
         x = self.last_linear(x.view(x.shape[0], -1))
         x = self.last_bn(x)
-        if self.classify:
-            x = self.logits(x)
-        else:
-            x = F.normalize(x, p=2, dim=1)
+        x = self.logits(x) if self.classify else tv_functional.normalize(x, p=2, dim=1)
         return x
 
 
-def load_weights(mdl, name):
+def load_weights(mdl: torch.nn.Module, name: str) -> None:
     """Download pretrained state_dict and load into model.
 
     Arguments:
@@ -293,24 +304,25 @@ def load_weights(mdl, name):
     """
     if name == "vggface2":
         path = "https://github.com/timesler/facenet-pytorch/releases/download/v2.2.9/20180402-114759-vggface2.pt"
+        hash_prefix = "281cebca8662831adb987a874bdcb36e73f5b1c6dc5ee5878f305e985625d99b"
     elif name == "casia-webface":
         path = "https://github.com/timesler/facenet-pytorch/releases/download/v2.2.9/20180408-102900-casia-webface.pt"
+        hash_prefix = "7a67afdbbc995fce5e10128675e318799a70698c2f433ba75dd7eb9a2f096e7d"
     else:
-        raise ValueError('Pretrained models only exist for "vggface2" and "casia-webface"')
+        msg = "Pretrained models only exist for 'vggface2' and 'casia-webface'"
+        raise ValueError(msg)
 
-    model_dir = os.path.join(get_torch_home(), "checkpoints")
-    os.makedirs(model_dir, exist_ok=True)
+    model_dir = get_torch_home() / "checkpoints"
+    model_dir.mkdir(parents=True, exist_ok=True)
 
-    cached_file = os.path.join(model_dir, os.path.basename(path))
-    if not os.path.exists(cached_file):
-        download_url_to_file(path, cached_file)
+    cached_file = model_dir / Path(path).name
+    if not cached_file.exists():
+        download_url_to_file(path, cached_file, hash_prefix)
 
     state_dict = torch.load(cached_file)
     mdl.load_state_dict(state_dict)
 
 
-def get_torch_home():
-    torch_home = os.path.expanduser(
-        os.getenv("TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch"))
-    )
-    return torch_home
+def get_torch_home() -> Path:
+    """Get the torch cache directory."""
+    return Path(os.getenv("TORCH_HOME", Path(os.getenv("XDG_CACHE_HOME", "~/.cache")) / "torch")).expanduser()

facenet_pytorch/models/mtcnn.py

@@ -2,15 +2,18 @@
 
 import importlib.resources
 import os
+from typing import TYPE_CHECKING
 
 import numpy as np
 import torch
-from PIL.Image import Image
 from torch import nn
 
 import facenet_pytorch.data
 from facenet_pytorch.models.utils.detect_face import detect_face, extract_face, get_size
 
+if TYPE_CHECKING:
+    from PIL.Image import Image
+
 
 class PNet(nn.Module):
     """MTCNN PNet.