Replacing all torch.jit.annotations with typing (#3174)

Summary:
* Replacing all torch.jit.annotations with typing

* Replacing remaining typing

Reviewed By: fmassa

Differential Revision: D25679213

fbshipit-source-id: 297d52d7ed1322d350619e298a9c2bbaa771d2a2

test/test_ops.py

@@ -7,9 +7,9 @@
 import torch
 from torch import Tensor
 from torch.autograd import gradcheck
-from torch.jit.annotations import Tuple
 from torch.nn.modules.utils import _pair
 from torchvision import ops
+from typing import Tuple
 
 
 class OpTester(object):

torchvision/models/detection/_utils.py

@@ -1,8 +1,8 @@
 import math
 
 import torch
-from torch.jit.annotations import List, Tuple
 from torch import Tensor
+from typing import List, Tuple
 
 from torchvision.ops.misc import FrozenBatchNorm2d
 

torchvision/models/detection/anchor_utils.py

@@ -2,7 +2,7 @@
 import torch
 from torch import nn, Tensor
 
-from torch.jit.annotations import List, Optional, Dict
+from typing import List, Optional, Dict
 from .image_list import ImageList
 
 
@@ -148,7 +148,7 @@ def forward(self, image_list: ImageList, feature_maps: List[Tensor]) -> List[Ten
                     torch.tensor(image_size[1] // g[1], dtype=torch.int64, device=device)] for g in grid_sizes]
         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]], [])
+        anchors: List[List[torch.Tensor]] = []
         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)

torchvision/models/detection/generalized_rcnn.py

@@ -4,12 +4,10 @@
 """
 
 from collections import OrderedDict
-from typing import Union
 import torch
-from torch import nn
+from torch import nn, Tensor
 import warnings
-from torch.jit.annotations import Tuple, List, Dict, Optional
-from torch import Tensor
+from typing import Tuple, List, Dict, Optional, Union
 
 
 class GeneralizedRCNN(nn.Module):
@@ -71,7 +69,7 @@ def forward(self, images, targets=None):
                     raise ValueError("Expected target boxes to be of type "
                                      "Tensor, got {:}.".format(type(boxes)))
 
-        original_image_sizes = torch.jit.annotate(List[Tuple[int, int]], [])
+        original_image_sizes: List[Tuple[int, int]] = []
         for img in images:
             val = img.shape[-2:]
             assert len(val) == 2

torchvision/models/detection/image_list.py

@@ -1,7 +1,7 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
 import torch
-from torch.jit.annotations import List, Tuple
 from torch import Tensor
+from typing import List, Tuple
 
 
 class ImageList(object):

torchvision/models/detection/retinanet.py

@@ -3,9 +3,8 @@
 import warnings
 
 import torch
-import torch.nn as nn
-from torch import Tensor
-from torch.jit.annotations import Dict, List, Tuple, Optional
+from torch import nn, Tensor
+from typing import Dict, List, Tuple, Optional
 
 from ._utils import overwrite_eps
 from ..utils import load_state_dict_from_url
@@ -402,7 +401,7 @@ def postprocess_detections(self, head_outputs, anchors, image_shapes):
 
         num_images = len(image_shapes)
 
-        detections = torch.jit.annotate(List[Dict[str, Tensor]], [])
+        detections: List[Dict[str, Tensor]] = []
 
         for index in range(num_images):
             box_regression_per_image = [br[index] for br in box_regression]
@@ -486,7 +485,7 @@ def forward(self, images, targets=None):
                                      "Tensor, got {:}.".format(type(boxes)))
 
         # get the original image sizes
-        original_image_sizes = torch.jit.annotate(List[Tuple[int, int]], [])
+        original_image_sizes: List[Tuple[int, int]] = []
         for img in images:
             val = img.shape[-2:]
             assert len(val) == 2
@@ -524,7 +523,7 @@ def forward(self, images, targets=None):
         anchors = self.anchor_generator(images, features)
 
         losses = {}
-        detections = torch.jit.annotate(List[Dict[str, Tensor]], [])
+        detections: List[Dict[str, Tensor]] = []
         if self.training:
             assert targets is not None
 

torchvision/models/detection/roi_heads.py

@@ -10,7 +10,7 @@
 
 from . import _utils as det_utils
 
-from torch.jit.annotations import Optional, List, Dict, Tuple
+from typing import Optional, List, Dict, Tuple
 
 
 def fastrcnn_loss(class_logits, box_regression, labels, regression_targets):
@@ -379,7 +379,7 @@ def expand_masks(mask, padding):
         scale = expand_masks_tracing_scale(M, padding)
     else:
         scale = float(M + 2 * padding) / M
-    padded_mask = torch.nn.functional.pad(mask, (padding,) * 4)
+    padded_mask = F.pad(mask, (padding,) * 4)
     return padded_mask, scale
 
 
@@ -482,7 +482,7 @@ def paste_masks_in_image(masks, boxes, img_shape, padding=1):
     return ret
 
 
-class RoIHeads(torch.nn.Module):
+class RoIHeads(nn.Module):
     __annotations__ = {
         'box_coder': det_utils.BoxCoder,
         'proposal_matcher': det_utils.Matcher,
@@ -753,7 +753,7 @@ def forward(self,
         box_features = self.box_head(box_features)
         class_logits, box_regression = self.box_predictor(box_features)
 
-        result = torch.jit.annotate(List[Dict[str, torch.Tensor]], [])
+        result: List[Dict[str, torch.Tensor]] = []
         losses = {}
         if self.training:
             assert labels is not None and regression_targets is not None

torchvision/models/detection/rpn.py

@@ -9,7 +9,7 @@
 from . import _utils as det_utils
 from .image_list import ImageList
 
-from torch.jit.annotations import List, Optional, Dict, Tuple
+from typing import List, Optional, Dict, Tuple
 
 # Import AnchorGenerator to keep compatibility.
 from .anchor_utils import AnchorGenerator

torchvision/models/detection/transform.py

@@ -4,7 +4,7 @@
 from torch import nn, Tensor
 from torch.nn import functional as F
 import torchvision
-from torch.jit.annotations import List, Tuple, Dict, Optional
+from typing import List, Tuple, Dict, Optional
 
 from .image_list import ImageList
 from .roi_heads import paste_masks_in_image
@@ -109,7 +109,7 @@ def forward(self,
 
         image_sizes = [img.shape[-2:] for img in images]
         images = self.batch_images(images)
-        image_sizes_list = torch.jit.annotate(List[Tuple[int, int]], [])
+        image_sizes_list: List[Tuple[int, int]] = []
         for image_size in image_sizes:
             assert len(image_size) == 2
             image_sizes_list.append((image_size[0], image_size[1]))

torchvision/models/googlenet.py

@@ -162,7 +162,7 @@ def _forward(self, x: Tensor) -> Tuple[Tensor, Optional[Tensor], Optional[Tensor
         # N x 480 x 14 x 14
         x = self.inception4a(x)
         # N x 512 x 14 x 14
-        aux1 = torch.jit.annotate(Optional[Tensor], None)
+        aux1: Optional[Tensor] = None
         if self.aux1 is not None:
             if self.training:
                 aux1 = self.aux1(x)
@@ -173,7 +173,7 @@ def _forward(self, x: Tensor) -> Tuple[Tensor, Optional[Tensor], Optional[Tensor
         # N x 512 x 14 x 14
         x = self.inception4d(x)
         # N x 528 x 14 x 14
-        aux2 = torch.jit.annotate(Optional[Tensor], None)
+        aux2: Optional[Tensor] = None
         if self.aux2 is not None:
             if self.training:
                 aux2 = self.aux2(x)

torchvision/models/inception.py

@@ -1,9 +1,8 @@
 from collections import namedtuple
 import warnings
 import torch
-import torch.nn as nn
+from torch import nn, Tensor
 import torch.nn.functional as F
-from torch import Tensor
 from .utils import load_state_dict_from_url
 from typing import Callable, Any, Optional, Tuple, List
 
@@ -17,7 +16,7 @@
 }
 
 InceptionOutputs = namedtuple('InceptionOutputs', ['logits', 'aux_logits'])
-InceptionOutputs.__annotations__ = {'logits': torch.Tensor, 'aux_logits': Optional[torch.Tensor]}
+InceptionOutputs.__annotations__ = {'logits': Tensor, 'aux_logits': Optional[Tensor]}
 
 # Script annotations failed with _GoogleNetOutputs = namedtuple ...
 # _InceptionOutputs set here for backwards compat
@@ -171,7 +170,7 @@ def _forward(self, x: Tensor) -> Tuple[Tensor, Optional[Tensor]]:
         # N x 768 x 17 x 17
         x = self.Mixed_6e(x)
         # N x 768 x 17 x 17
-        aux = torch.jit.annotate(Optional[Tensor], None)
+        aux: Optional[Tensor] = None
         if self.AuxLogits is not None:
             if self.training:
                 aux = self.AuxLogits(x)

torchvision/models/quantization/googlenet.py

@@ -2,7 +2,6 @@
 import torch
 import torch.nn as nn
 from torch.nn import functional as F
-from torch.jit.annotations import Optional
 
 from torchvision.models.utils import load_state_dict_from_url
 from torchvision.models.googlenet import (

torchvision/models/quantization/inception.py

@@ -6,7 +6,6 @@
 import torch.nn.functional as F
 from torchvision.models import inception as inception_module
 from torchvision.models.inception import InceptionOutputs
-from torch.jit.annotations import Optional
 from torchvision.models.utils import load_state_dict_from_url
 from .utils import _replace_relu, quantize_model
 

torchvision/ops/_box_convert.py

@@ -1,7 +1,5 @@
 import torch
-from torch.jit.annotations import Tuple
 from torch import Tensor
-import torchvision
 
 
 def _box_cxcywh_to_xyxy(boxes: Tensor) -> Tensor:

torchvision/ops/_utils.py

@@ -1,6 +1,6 @@
 import torch
 from torch import Tensor
-from torch.jit.annotations import List
+from typing import List
 
 
 def _cat(tensors: List[Tensor], dim: int = 0) -> Tensor:

torchvision/ops/boxes.py

@@ -1,6 +1,6 @@
 import torch
-from torch.jit.annotations import Tuple
 from torch import Tensor
+from typing import Tuple
 from ._box_convert import _box_cxcywh_to_xyxy, _box_xyxy_to_cxcywh, _box_xywh_to_xyxy, _box_xyxy_to_xywh
 import torchvision
 from torchvision.extension import _assert_has_ops

torchvision/ops/deform_conv.py

@@ -5,7 +5,7 @@
 from torch.nn import init
 from torch.nn.parameter import Parameter
 from torch.nn.modules.utils import _pair
-from torch.jit.annotations import Optional, Tuple
+from typing import Optional, Tuple
 from torchvision.extension import _assert_has_ops
 
 

torchvision/ops/feature_pyramid_network.py

@@ -1,10 +1,9 @@
 from collections import OrderedDict
 
-import torch
 import torch.nn.functional as F
 from torch import nn, Tensor
 
-from torch.jit.annotations import Tuple, List, Dict, Optional
+from typing import Tuple, List, Dict, Optional
 
 
 class ExtraFPNBlock(nn.Module):

torchvision/ops/misc.py

@@ -10,8 +10,8 @@
 
 import warnings
 import torch
-from torch import Tensor, Size
-from torch.jit.annotations import List, Optional, Tuple
+from torch import Tensor
+from typing import List, Optional
 
 
 class Conv2d(torch.nn.Conv2d):

torchvision/ops/poolers.py

@@ -1,15 +1,12 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-from typing import Union
-
 import torch
-import torch.nn.functional as F
 from torch import nn, Tensor
 
+import torchvision
 from torchvision.ops import roi_align
 from torchvision.ops.boxes import box_area
 
-from torch.jit.annotations import Optional, List, Dict, Tuple
-import torchvision
+from typing import Optional, List, Dict, Tuple, Union
 
 
 # copying result_idx_in_level to a specific index in result[]
@@ -149,7 +146,7 @@ def convert_to_roi_format(self, boxes: List[Tensor]) -> Tensor:
     def infer_scale(self, feature: Tensor, original_size: List[int]) -> float:
         # assumption: the scale is of the form 2 ** (-k), with k integer
         size = feature.shape[-2:]
-        possible_scales = torch.jit.annotate(List[float], [])
+        possible_scales: List[float] = []
         for s1, s2 in zip(size, original_size):
             approx_scale = float(s1) / float(s2)
             scale = 2 ** float(torch.tensor(approx_scale).log2().round())

torchvision/ops/ps_roi_align.py

@@ -2,7 +2,6 @@
 from torch import nn, Tensor
 
 from torch.nn.modules.utils import _pair
-from torch.jit.annotations import List, Tuple
 
 from torchvision.extension import _assert_has_ops
 from ._utils import convert_boxes_to_roi_format, check_roi_boxes_shape

torchvision/ops/ps_roi_pool.py

@@ -2,7 +2,6 @@
 from torch import nn, Tensor
 
 from torch.nn.modules.utils import _pair
-from torch.jit.annotations import List, Tuple
 
 from torchvision.extension import _assert_has_ops
 from ._utils import convert_boxes_to_roi_format, check_roi_boxes_shape

torchvision/ops/roi_align.py

@@ -2,7 +2,7 @@
 from torch import nn, Tensor
 
 from torch.nn.modules.utils import _pair
-from torch.jit.annotations import List, BroadcastingList2
+from torch.jit.annotations import BroadcastingList2
 
 from torchvision.extension import _assert_has_ops
 from ._utils import convert_boxes_to_roi_format, check_roi_boxes_shape

torchvision/ops/roi_pool.py

@@ -2,7 +2,7 @@
 from torch import nn, Tensor
 
 from torch.nn.modules.utils import _pair
-from torch.jit.annotations import List, BroadcastingList2
+from torch.jit.annotations import BroadcastingList2
 
 from torchvision.extension import _assert_has_ops
 from ._utils import convert_boxes_to_roi_format, check_roi_boxes_shape

torchvision/transforms/autoaugment.py

@@ -3,8 +3,7 @@
 
 from enum import Enum
 from torch import Tensor
-from torch.jit.annotations import List, Tuple
-from typing import Optional
+from typing import List, Tuple, Optional
 
 from . import functional as F, InterpolationMode
 

torchvision/transforms/functional.py

@@ -2,14 +2,13 @@
 import numbers
 import warnings
 from enum import Enum
-from typing import Any, Optional
 
 import numpy as np
 from PIL import Image
 
 import torch
 from torch import Tensor
-from torch.jit.annotations import List, Tuple
+from typing import List, Tuple, Any, Optional
 
 try:
     import accimage

torchvision/transforms/functional_tensor.py

@@ -1,10 +1,10 @@
 import warnings
-from typing import Optional, Tuple
 
 import torch
 from torch import Tensor
 from torch.nn.functional import grid_sample, conv2d, interpolate, pad as torch_pad
-from torch.jit.annotations import List, BroadcastingList2
+from torch.jit.annotations import BroadcastingList2
+from typing import Optional, Tuple, List
 
 
 def _is_tensor_a_torch_image(x: Tensor) -> bool: