From 723c2408654032babd078de10f81abf28ebb4222 Mon Sep 17 00:00:00 2001
From: Vasilis Vryniotis <vvryniotis@fb.com>
Date: Tue, 24 Nov 2020 16:40:11 +0000
Subject: [PATCH] Adding Python type hints, correcting incorrect types,
 removing unnecessary vars and simplifying code.

---
 torchvision/models/detection/anchor_utils.py | 24 ++++++++------------
 1 file changed, 9 insertions(+), 15 deletions(-)

diff --git a/torchvision/models/detection/anchor_utils.py b/torchvision/models/detection/anchor_utils.py
index 9cf27834f75..defee6e5ae2 100644
--- a/torchvision/models/detection/anchor_utils.py
+++ b/torchvision/models/detection/anchor_utils.py
@@ -1,6 +1,6 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
 import torch
-from torch import nn
+from torch import nn, Tensor
 
 from torch.jit.annotations import List, Optional, Dict
 from .image_list import ImageList
@@ -56,8 +56,8 @@ def __init__(
     # For every (aspect_ratios, scales) combination, output a zero-centered anchor with those values.
     # (scales, aspect_ratios) are usually an element of zip(self.scales, self.aspect_ratios)
     # This method assumes aspect ratio = height / width for an anchor.
-    def generate_anchors(self, scales, aspect_ratios, dtype=torch.float32, device="cpu"):
-        # type: (List[int], List[float], int, Device) -> Tensor  # noqa: F821
+    def generate_anchors(self, scales: List[int], aspect_ratios: List[float], dtype: torch.dtype = torch.float32,
+                         device: torch.device = torch.device("cpu")):
         scales = torch.as_tensor(scales, dtype=dtype, device=device)
         aspect_ratios = torch.as_tensor(aspect_ratios, dtype=dtype, device=device)
         h_ratios = torch.sqrt(aspect_ratios)
@@ -69,8 +69,7 @@ def generate_anchors(self, scales, aspect_ratios, dtype=torch.float32, device="c
         base_anchors = torch.stack([-ws, -hs, ws, hs], dim=1) / 2
         return base_anchors.round()
 
-    def set_cell_anchors(self, dtype, device):
-        # type: (int, Device) -> None  # noqa: F821
+    def set_cell_anchors(self, dtype: torch.dtype, device: torch.device):
         if self.cell_anchors is not None:
             cell_anchors = self.cell_anchors
             assert cell_anchors is not None
@@ -95,8 +94,7 @@ def num_anchors_per_location(self):
 
     # For every combination of (a, (g, s), i) in (self.cell_anchors, zip(grid_sizes, strides), 0:2),
     # output g[i] anchors that are s[i] distance apart in direction i, with the same dimensions as a.
-    def grid_anchors(self, grid_sizes, strides):
-        # type: (List[List[int]], List[List[Tensor]]) -> List[Tensor]
+    def grid_anchors(self, grid_sizes: List[List[int]], strides: List[List[Tensor]]) -> List[Tensor]:
         anchors = []
         cell_anchors = self.cell_anchors
         assert cell_anchors is not None
@@ -134,8 +132,7 @@ def grid_anchors(self, grid_sizes, strides):
 
         return anchors
 
-    def cached_grid_anchors(self, grid_sizes, strides):
-        # type: (List[List[int]], List[List[Tensor]]) -> List[Tensor]
+    def cached_grid_anchors(self, grid_sizes: List[List[int]], strides: List[List[Tensor]]) -> List[Tensor]:
         key = str(grid_sizes) + str(strides)
         if key in self._cache:
             return self._cache[key]
@@ -143,8 +140,7 @@ def cached_grid_anchors(self, grid_sizes, strides):
         self._cache[key] = anchors
         return anchors
 
-    def forward(self, image_list, feature_maps):
-        # type: (ImageList, List[Tensor]) -> List[Tensor]
+    def forward(self, image_list: ImageList, feature_maps: List[Tensor]) -> List[Tensor]:
         grid_sizes = list([feature_map.shape[-2:] for feature_map in feature_maps])
         image_size = image_list.tensors.shape[-2:]
         dtype, device = feature_maps[0].dtype, feature_maps[0].device
@@ -153,10 +149,8 @@ def forward(self, image_list, feature_maps):
         self.set_cell_anchors(dtype, device)
         anchors_over_all_feature_maps = self.cached_grid_anchors(grid_sizes, strides)
         anchors = torch.jit.annotate(List[List[torch.Tensor]], [])
-        for i, (image_height, image_width) in enumerate(image_list.image_sizes):
-            anchors_in_image = []
-            for anchors_per_feature_map in anchors_over_all_feature_maps:
-                anchors_in_image.append(anchors_per_feature_map)
+        for i in range(len(image_list.image_sizes)):
+            anchors_in_image = [anchors_per_feature_map for anchors_per_feature_map in anchors_over_all_feature_maps]
             anchors.append(anchors_in_image)
         anchors = [torch.cat(anchors_per_image) for anchors_per_image in anchors]
         # Clear the cache in case that memory leaks.