drop support for pytorch 1.7

Summary: changes in preparation for pytorch 1.10

Reviewed By: zhanghang1989

Differential Revision: D31575602

fbshipit-source-id: 95fa947aafa201386901ad5a6ff2726b0a43dcff

Author: ppwwyyxx

.circleci/config.yml

@@ -229,12 +229,6 @@ workflows:
           name: linux_cpu_tests_pytorch1.9
           pytorch_version: '1.9.0+cpu'
           torchvision_version: '0.10.0+cpu'
-      - linux_gpu_tests:
-          name: linux_gpu_tests_pytorch1.7
-          pytorch_version: '1.7.0'
-          torchvision_version: '0.8.1'
-          # 1.7 wheels don't have +cu102 suffix, so use a cu102 specific index
-          pytorch_index: 'https://download.pytorch.org/whl/cu102/torch_stable.html'
       - linux_gpu_tests:
           name: linux_gpu_tests_pytorch1.8
           pytorch_version: '1.8.1+cu102'

.github/workflows/workflow.yml

@@ -36,10 +36,8 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        torch: [1.7, 1.8, 1.9]
+        torch: [1.8, 1.9]
         include:
-          - torch: 1.7
-            torchvision: 0.8
           - torch: 1.8
             torchvision: 0.9
           - torch: 1.9

INSTALL.md

@@ -2,7 +2,7 @@
 
 ### Requirements
 - Linux or macOS with Python ≥ 3.6
-- PyTorch ≥ 1.7 and [torchvision](https://github.com/pytorch/vision/) that matches the PyTorch installation.
+- PyTorch ≥ 1.8 and [torchvision](https://github.com/pytorch/vision/) that matches the PyTorch installation.
   Install them together at [pytorch.org](https://pytorch.org) to make sure of this
 - OpenCV is optional but needed by demo and visualization
 

detectron2/checkpoint/detection_checkpoint.py

@@ -7,7 +7,6 @@
 from torch.nn.parallel import DistributedDataParallel
 
 import detectron2.utils.comm as comm
-from detectron2.utils.env import TORCH_VERSION
 from detectron2.utils.file_io import PathManager
 
 from .c2_model_loading import align_and_update_state_dicts
@@ -54,8 +53,7 @@ def load(self, path, *args, **kwargs):
 
         if need_sync:
             logger.info("Broadcasting model states from main worker ...")
-            if TORCH_VERSION >= (1, 7):
-                self.model._sync_params_and_buffers()
+            self.model._sync_params_and_buffers()
         return ret
 
     def _load_file(self, filename):

detectron2/export/torchscript.py

@@ -3,7 +3,6 @@
 import os
 import torch
 
-from detectron2.utils.env import TORCH_VERSION
 from detectron2.utils.file_io import PathManager
 
 from .torchscript_patch import freeze_training_mode, patch_instances
@@ -48,7 +47,6 @@ def scripting_with_instances(model, fields):
     Returns:
         torch.jit.ScriptModule: the model in torchscript format
     """
-    assert TORCH_VERSION >= (1, 8), "This feature is not available in PyTorch < 1.8"
     assert (
         not model.training
     ), "Currently we only support exporting models in evaluation mode to torchscript"

detectron2/modeling/proposal_generator/rrpn.py

@@ -11,6 +11,7 @@
 
 from ..box_regression import Box2BoxTransformRotated
 from .build import PROPOSAL_GENERATOR_REGISTRY
+from .proposal_utils import _is_tracing
 from .rpn import RPN
 
 logger = logging.getLogger(__name__)
@@ -67,7 +68,10 @@ def find_top_rrpn_proposals(
         itertools.count(), proposals, pred_objectness_logits
     ):
         Hi_Wi_A = logits_i.shape[1]
-        num_proposals_i = min(pre_nms_topk, Hi_Wi_A)
+        if isinstance(Hi_Wi_A, torch.Tensor):  # it's a tensor in tracing
+            num_proposals_i = torch.clamp(Hi_Wi_A, max=pre_nms_topk)
+        else:
+            num_proposals_i = min(Hi_Wi_A, pre_nms_topk)
 
         # sort is faster than topk (https://github.com/pytorch/pytorch/issues/22812)
         # topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
@@ -101,7 +105,7 @@ def find_top_rrpn_proposals(
         # filter empty boxes
         keep = boxes.nonempty(threshold=min_box_size)
         lvl = level_ids
-        if keep.sum().item() != len(boxes):
+        if _is_tracing() or keep.sum().item() != len(boxes):
             boxes, scores_per_img, lvl = (boxes[keep], scores_per_img[keep], level_ids[keep])
 
         keep = batched_nms_rotated(boxes.tensor, scores_per_img, lvl, nms_thresh)

detectron2/structures/boxes.py

@@ -6,19 +6,9 @@
 import torch
 from torch import device
 
-from detectron2.utils.env import TORCH_VERSION
-
 _RawBoxType = Union[List[float], Tuple[float, ...], torch.Tensor, np.ndarray]
 
 
-if TORCH_VERSION < (1, 8):
-    _maybe_jit_unused = torch.jit.unused
-else:
-
-    def _maybe_jit_unused(x):
-        return x
-
-
 @unique
 class BoxMode(IntEnum):
     """
@@ -173,7 +163,6 @@ def clone(self) -> "Boxes":
         """
         return Boxes(self.tensor.clone())
 
-    @_maybe_jit_unused
     def to(self, device: torch.device):
         # Boxes are assumed float32 and does not support to(dtype)
         return Boxes(self.tensor.to(device=device))
@@ -285,7 +274,6 @@ def scale(self, scale_x: float, scale_y: float) -> None:
         self.tensor[:, 1::2] *= scale_y
 
     @classmethod
-    @_maybe_jit_unused
     def cat(cls, boxes_list: List["Boxes"]) -> "Boxes":
         """
         Concatenates a list of Boxes into a single Boxes

detectron2/structures/keypoints.py

@@ -4,17 +4,6 @@
 import torch
 from torch.nn import functional as F
 
-from detectron2.utils.env import TORCH_VERSION
-
-if TORCH_VERSION < (1, 8):
-
-    def script_if_tracing(fn):
-        return fn
-
-
-else:
-    script_if_tracing = torch.jit.script_if_tracing
-
 
 class Keypoints:
     """
@@ -172,7 +161,7 @@ def _keypoints_to_heatmap(
     return heatmaps, valid
 
 
-@script_if_tracing
+@torch.jit.script_if_tracing
 def heatmaps_to_keypoints(maps: torch.Tensor, rois: torch.Tensor) -> torch.Tensor:
     """
     Extract predicted keypoint locations from heatmaps.

detectron2/structures/rotated_boxes.py

@@ -5,7 +5,7 @@
 
 from detectron2.layers.rotated_boxes import pairwise_iou_rotated
 
-from .boxes import Boxes, _maybe_jit_unused
+from .boxes import Boxes
 
 
 class RotatedBoxes(Boxes):
@@ -229,7 +229,6 @@ def clone(self) -> "RotatedBoxes":
         """
         return RotatedBoxes(self.tensor.clone())
 
-    @_maybe_jit_unused
     def to(self, device: torch.device):
         # Boxes are assumed float32 and does not support to(dtype)
         return RotatedBoxes(self.tensor.to(device=device))
@@ -455,7 +454,6 @@ def scale(self, scale_x: float, scale_y: float) -> None:
         self.tensor[:, 4] = torch.atan2(scale_x * s, scale_y * c) * 180 / math.pi
 
     @classmethod
-    @_maybe_jit_unused
     def cat(cls, boxes_list: List["RotatedBoxes"]) -> "RotatedBoxes":
         """
         Concatenates a list of RotatedBoxes into a single RotatedBoxes

detectron2/utils/comm.py

@@ -11,8 +11,6 @@
 import torch
 import torch.distributed as dist
 
-from .env import TORCH_VERSION
-
 _LOCAL_PROCESS_GROUP = None
 """
 A torch process group which only includes processes that on the same machine as the current process.
@@ -80,7 +78,7 @@ def synchronize():
     world_size = dist.get_world_size()
     if world_size == 1:
         return
-    if dist.get_backend() == dist.Backend.NCCL and TORCH_VERSION >= (1, 8):
+    if dist.get_backend() == dist.Backend.NCCL:
         # This argument is needed to avoid warnings.
         # It's valid only for NCCL backend.
         dist.barrier(device_ids=[torch.cuda.current_device()])