specify language in markdown code blocks

Summary: Pull Request resolved: #3872

Differential Revision: D33502656

Pulled By: zhanghang1989

fbshipit-source-id: 53ee62f48ecf99fc2663ea96325f32cdea2f7dba

docs/tutorials/configs.md

@@ -35,7 +35,7 @@ We provided several base configs for standard model architectures.
 Many builtin tools in detectron2 accept command line config overwrite:
 Key-value pairs provided in the command line will overwrite the existing values in the config file.
 For example, [demo.py](../../demo/demo.py) can be used with
-```
+```sh
 ./demo.py --config-file config.yaml [--other-options] \
   --opts MODEL.WEIGHTS /path/to/weights INPUT.MIN_SIZE_TEST 1000
 ```

docs/tutorials/evaluation.md

@@ -13,7 +13,7 @@ You can also implement your own `DatasetEvaluator` that performs some other jobs
 using the inputs/outputs pairs.
 For example, to count how many instances are detected on the validation set:
 
-```
+```python
 class Counter(DatasetEvaluator):
   def reset(self):
     self.count = 0
@@ -28,7 +28,7 @@ class Counter(DatasetEvaluator):
 ## Use evaluators
 
 To evaluate using the methods of evaluators manually:
-```
+```python
 def get_all_inputs_outputs():
   for data in data_loader:
     yield data, model(data)

docs/tutorials/lazyconfigs.md

@@ -66,7 +66,7 @@ cfg = L(Trainer)(
 )
 ```
 creates a dictionary like this:
-```
+```python
 cfg = {
   "_target_": "my_app.Trainer",
   "optimizer": {
@@ -122,7 +122,7 @@ Despite of this, our model zoo configs still follow some simple conventions for
 `cfg.model` defines a model object, `cfg.dataloader.{train,test}` defines dataloader objects,
 and `cfg.train` contains training options in key-value form.
 In addition to `print()`, a better way to view the structure of a config is like this:
-```
+```python
 from detectron2.model_zoo import get_config
 from detectron2.config import LazyConfig
 print(LazyConfig.to_py(get_config("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.py")))

docs/tutorials/models.md

@@ -53,7 +53,7 @@ It includes default behavior including model loading, preprocessing,
 and operates on single image rather than batches. See its documentation for usage.
 
 You can also run inference directly like this:
-```
+```python
 model.eval()
 with torch.no_grad():
   outputs = model(inputs)

docs/tutorials/training.md

@@ -50,7 +50,7 @@ To customize a `DefaultTrainer`:
 
 During training, detectron2 models and trainer put metrics to a centralized [EventStorage](../modules/utils.html#detectron2.utils.events.EventStorage).
 You can use the following code to access it and log metrics to it:
-```
+```python
 from detectron2.utils.events import get_event_storage
 
 # inside the model: