Merge pull request #189 from princeton-vl/bugfix_07-09

Move populate & fix render typo bug

docs/ConfiguringInfinigen.md

@@ -45,7 +45,7 @@ Our `infinigen_examples/generate_nature.py` driver always loads [`infinigen_exam
 
 Now that you understand the two major python programs and how to configure them, you may notice and wonder about the many configs/overrides provided in our original one-command "Hello World" example:
 
-```
+```bash
 # Original hello world command
 python -m infinigen.datagen.manage_jobs --output_folder outputs/hello_world --num_scenes 1 --specific_seed 0 \
 --configs desert.gin simple.gin --pipeline_configs local_16GB.gin monocular.gin blender_gt.gin \ 
@@ -169,23 +169,23 @@ All commands below are shown with using `local_256GB` config, but you can attemp
 
 We recommend this command as a starting point for generating high quality videos. Generating multi-view consistent terrain is not computationally tractible without CUDA accelleration, so make sure to follow the CUDA Terrain instructions in Installation.md, and we recommend not to remove the `cuda_terrain` flag below.
 
-````
+```bash
 python -m infinigen.datagen.manage_jobs --output_folder outputs/my_videos --num_scenes 500 \
     --pipeline_config slurm monocular_video cuda_terrain opengl_gt \
     --cleanup big_files --warmup_sec 60000 --config trailer_video high_quality_terrain
-````
+```
 
 #### Creating large-scale stereo datasets
 
-````
+```bash
 python -m infinigen.datagen.manage_jobs --output_folder outputs/stereo_data --num_scenes 10000 \
     --pipeline_config slurm stereo cuda_terrain opengl_gt \
     --cleanup big_files --warmup_sec 60000 --config high_quality_terrain
-````
+```
 
 #### Creating a few low-resolution images to your test changes
 
-```
+```bash
 screen python -m infinigen.datagen.manage_jobs --output_folder outputs/dev --num_scenes 50 \
     --pipeline_config slurm monocular cuda_terrain \
     --cleanup big_files --warmup_sec 1200 --configs dev
@@ -196,7 +196,7 @@ screen python -m infinigen.datagen.manage_jobs --output_folder outputs/dev --num
 These commands are intended as inspiration - please read docs above for more advice on customizing all aspects of Infinigen.
 
 <b> Create images that always have rain: </b>
-```
+```bash
 python -m infinigen.datagen.manage_jobs --output_folder outputs/my_videos --num_scenes 500 \
     --pipeline_config slurm monocular cuda_terrain opengl_gt \
     --cleanup big_files --warmup_sec 30000  \
@@ -206,7 +206,7 @@ python -m infinigen.datagen.manage_jobs --output_folder outputs/my_videos --num_
 :bulb: You can substitute the `rain_particles` in `rain_particles_chance` for any `run_stage` name argument string in `infinigen_examples/generate_nature.py`, such as `trees` or `ground_creatures`.
 
 <b> Create images that only have terrain: </b>
-```
+```bash
 python -m infinigen.datagen.manage_jobs --output_folder outputs/my_videos --num_scenes 500 \
     --pipeline_config slurm monocular cuda_terrain opengl_gt \
     --cleanup big_files --warmup_sec 30000 --config no_assets
@@ -215,7 +215,7 @@ python -m infinigen.datagen.manage_jobs --output_folder outputs/my_videos --num_
 
 <b> Create videos at birds-eye-view camera altitudes: </b>
 
-```
+```bash
 python -m infinigen.datagen.manage_jobs --output_folder outputs/my_videos --num_scenes 500 \
     --pipeline_config slurm monocular_video cuda_terrain opengl_gt \
     --cleanup big_files --warmup_sec 30000 --config trailer_video high_quality_terrain \
@@ -225,7 +225,7 @@ python -m infinigen.datagen.manage_jobs --output_folder outputs/my_videos --num_
 :bulb: The command shown is overriding `infinigen_examples/configs_nature/base.gin`'s default setting of `camera.camera_pose_proposal.altitude`. You can use a similar syntax to override any number of .gin config entries. Separate multiple entries with spaces. 
 
 <b> Create 1 second video clips: </b>
-```
+```bash
 python -m infinigen.datagen.manage_jobs --output_folder outputs/my_videos --num_scenes 500 \
     --pipeline_config slurm monocular_video cuda_terrain opengl_gt \
     --cleanup big_files --warmup_sec 30000 --config trailer_video high_quality_terrain \

docs/GeneratingFluidSimulations.md

@@ -9,29 +9,29 @@ Before you can generate fluids, you must run an additional installation step: `b
 ## Example Commands
 
 #### Generate a video of a single scene with simulated fire generated on the fly
-```
+```bash
 python -m infinigen.datagen.manage_jobs --specific_seed 3930249d --output_folder outputs/fire --num_scenes 1 --pipeline_config local_256GB.gin monocular_video.gin --cleanup none --config plain.gin fast_terrain_assets.gin use_on_the_fly_fire.gin
 ```
 Because fluid simulation takes a long time, the fire resolution can be reduced in use_on_the_fly_fire.gin, by setting `set_obj_on_fire.resolution = {resolution}`. This will reduce the fire quality but speed up the simulation.
 
 #### Generate a video of a single valley scene with simulated river
-```
+```bash
 python -m infinigen.datagen.manage_jobs --specific_seed 61fc881a --output_folder outputs/river --num_scenes 1 --pipeline_config local_256GB.gin monocular_video.gin opengl_gt.gin cuda_terrain.gin --pipeline_overrides iterate_scene_tasks.frame_range=[100,244] --config river.gin simulated_river.gin no_assets.gin no_creatures.gin fast_terrain_assets.gin --cleanup none 
 ```
 Similar to fire, the simulation can be sped up by reducing the resolution. In simulated_river.gin, the resolution can be modified by setting `make_river.resolution = {resolution}`. The simulation can also be sped up by reducing the simulation duration in simulated_river.gin by setting `make_river.simulation_duration = {duration}`. For instance, before running the command above, the duration can be reduced to a number greater than 200 since that is the last frame of the video.
 
 Also, note that this command will produce a scene without assets to speed up the process. However, the liquids generally interact with the objects by splashing on them, and a scene like this can be produced by removing the `no_assets.gin` option in the above command. 
 
 #### Generate videos of random scene types, with simulated fire generated on the fly when needed
-```
+```bash
 python -m infinigen.datagen.manage_jobs --output_folder outputs/onthefly  --num_scenes 10 \
     --pipeline_config slurm_high_memory.gin monocular_video.gin \
     --config fast_terrain_assets.gin use_on_the_fly_fire.gin \
     --cleanup none --warmup_sec 12000 
 ```
 
 #### Generate videos of valley scenes with simulated rivers
-```
+```bash
 python -m infinigen.datagen.manage_jobs --output_folder /n/fs/pvl-renders/kkayan/river --num_scenes 10 \
     --pipeline_config slurm_high_memory.gin monocular_video.gin opengl_gt.gin cuda_terrain.gin \
     --pipeline_overrides iterate_scene_tasks.frame_range=[100,244] \ 
@@ -44,20 +44,21 @@ python -m infinigen.datagen.manage_jobs --output_folder /n/fs/pvl-renders/kkayan
 ## Using Pre-Generated Fire
 
 High-resolution fluid simulations take a long time, so assets on fire can pre-generated and imported in the scene instead of being baked on-the-fly. This allows for fire to be simulated once, instead of every time a scene is generated. To pre-generate fire assets of all types (bush, tree, creature, cactus, boulder), run:
-```
+```bash
 python -m infinigen.tools.submit_asset_cache -f {fire_asset_folder} -n 1 -s -40 -d 184
 ```
 where `fire_asset_folder` is where you want to save the fire. The number of assets per type, start frame and duration can be adjusted.  
 
 If you only want to pre-generate one type of asset once, run:
-```
+```bash
 python -m infinigen.assets.fluid.run_asset_cache -f {fire_asset_folder} -a {asset} -s {start_frame} -d {simulation_duration}
 ```
 where `fire_asset_folder` is where you want to save the fire. `asset` can be one of `CachedBushFactory`, `CachedTreeFactory`, `CachedCactusFactory`, `CachedCreatureFactory`, `CachedBoulderFactory`. 
 
 ### Import pre-generated fire when generating a scene
 After fire is pre-generated with one of the previous commands, edit config/use_cached_fire.gin and set the `FireCachingSystem.asset_folder` variable to `fire_asset_folder` you used when pre-generating fire. After this `use_cached_fire.gin` can be used instead of `use_on_the_fly_fire.gin` when generating a scene. This will import the fire from the folder it is saved instead of simulating it on-the-fly. 
+
 #### Example Command
-```
+```bash
 python -m infinigen.datagen.manage_jobs --specific_seed 3930249d --output_folder outputs/fire --num_scenes 1 --pipeline_config local_256GB.gin monocular_video.gin --cleanup none --config plain.gin fast_terrain_assets.gin use_cached_fire.gin
 ```

docs/HelloWorld.md

@@ -16,7 +16,7 @@ Infinigen generates scenes by running multiple tasks (usually executed automatic
 
 :exclamation: If you encounter any missing .so files, missing dependencies (such as `gin`), or similar crashes, please check again that all steps of installation ran successfully. If you cannot resolve any issues with installation, please see our README and 'Bug Report' Git Issue template for advice on posting Git Issues to get help quickly - you must include the full installation logs in your issue so that we can help debug.
 
-```
+```bash
 mkdir outputs
 
 # Generate a scene layout
@@ -40,7 +40,7 @@ Output logs should indicate what the code is working on. Use `--debug` for even
 
 We provide `infinigen/datagen/manage_jobs.py`, a utility which runs similar steps automatically.
 
-```
+```bash
 python -m infinigen.datagen.manage_jobs --output_folder outputs/hello_world --num_scenes 1 --specific_seed 0 \
 --configs desert.gin simple.gin --pipeline_configs local_16GB.gin monocular.gin blender_gt.gin --pipeline_overrides LocalScheduleHandler.use_gpu=False
 ```