fix link in docs; upgrade container in circleci for gcc5

Summary: Pull Request resolved: fairinternal/detectron2#372 Differential Revision: D19482654 Pulled By: ppwwyyxx fbshipit-source-id: 0772df003713991085ca5f98cfa77a56d3c1f85e
decombas · Jan 21, 2020 · 116e31e · 116e31e
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Showing 6 changed files with 11 additions and 8 deletions.
diff --git a/.circleci/config.yml b/.circleci/config.yml
@@ -9,7 +9,7 @@ version: 2
 # -------------------------------------------------------------------------------------
 cpu: &cpu
   docker:
-    - image: circleci/python:3.6.2
+    - image: circleci/python:3.6.8-stretch
   resource_class: medium
 
 gpu: &gpu

diff --git a/detectron2/engine/defaults.py b/detectron2/engine/defaults.py
@@ -131,7 +131,8 @@ def default_setup(cfg, args):
 
 class DefaultPredictor:
     """
-    Create a simple end-to-end predictor with the given config.
+    Create a simple end-to-end predictor with the given config that runs on
+    single device for a single input image.
     The predictor takes an BGR image, resizes it to the specified resolution,
     runs the model and produces a dict of predictions.
 
@@ -174,7 +175,9 @@ def __call__(self, original_image):
             original_image (np.ndarray): an image of shape (H, W, C) (in BGR order).
 
         Returns:
-            predictions (dict): the output of the model
+            predictions (dict):
+                the output of the model for one image only.
+                See :doc:`/tutorials/models` for details about the format.
         """
         with torch.no_grad():  # https://github.com/sphinx-doc/sphinx/issues/4258
             # Apply pre-processing to image.

diff --git a/docs/tutorials/data_loading.md b/docs/tutorials/data_loading.md
@@ -71,7 +71,7 @@ You can implement it using any tools you like.
 
 If you use [DefaultTrainer](../modules/engine.html#detectron2.engine.defaults.DefaultTrainer),
 you can overwrite its `build_{train,test}__loader` method to use your own dataloader.
-See the [densepose dataloader](/projects/DensePose/train_net.py)
+See the [densepose dataloader](../../projects/DensePose/train_net.py)
 for an example.
 
 If you write your own training loop, you can plug in your data loader easily.
diff --git a/docs/tutorials/deployment.md b/docs/tutorials/deployment.md
@@ -23,7 +23,7 @@ We provide a tool, `tools/caffe2_converter.py` as an example that uses
 these APIs to convert a standard model.
 
 To convert an official Mask R-CNN trained on COCO, first
-[prepare the COCO dataset](datasets), then pick the model from [Model Zoo](MODEL_ZOO.md), and run:
+[prepare the COCO dataset](../../datasets/), then pick the model from [Model Zoo](../../MODEL_ZOO.md), and run:
 ```
 python tools/caffe2_converter.py --config-file configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \
 	--output ./caffe2_model --run-eval \

diff --git a/docs/tutorials/training.md b/docs/tutorials/training.md
@@ -5,7 +5,7 @@ From the previous tutorials, you may now have a custom model and data loader.
 You are free to create your own optimizer, and write the training logic: it's
 usually easy with PyTorch, and allow researchers to see the entire training
 logic more clearly.
-One such example is provided in [tools/plain_train_net.py](https://github.com/facebookresearch/detectron2/blob/master/tools/plain_train_net.py).
+One such example is provided in [tools/plain_train_net.py](../../tools/plain_train_net.py).
 
 We also provide a standarized "trainer" abstraction with a
 [minimal hook system](../modules/engine.html#detectron2.engine.HookBase)

diff --git a/docs/tutorials/write-models.md b/docs/tutorials/write-models.md
@@ -28,9 +28,9 @@ Then, you can use `cfg.MODEL.BACKBONE.NAME = 'ToyBackBone'` in your config objec
 As another example, to add new abilities to the ROI heads in the Generalized R-CNN meta-architecture,
 you can implement a new
 [ROIHeads](../modules/modeling.html#detectron2.modeling.ROIHeads) subclass and put it in the `ROI_HEADS_REGISTRY`.
-See [densepose in detectron2](https://github.com/facebookresearch/detectron2/tree/master/projects/DensePose)
+See [densepose in detectron2](../../projects/DensePose)
 for an example that implements new ROIHeads.
-And [projects/](https://github.com/facebookresearch/detectron2/tree/master/projects/)
+And [projects/](../../projects/)
 contains examples that implement a few different architectures.
 
 A complete list of registries can be found in [API documentation](../modules/modeling.html#model-registries).