Flake8 + Bugfixes + Linter

.circleci/config.yml

@@ -15,7 +15,7 @@ jobs:
       - run:
           name: setup
           command: |
-              sudo pip install black "isort[pyproject]" numpy --progress-bar off
+              sudo pip install black flake8 "isort[pyproject]" numpy --progress-bar off
               sudo pip install -r requirements.txt --progress-bar off
       - run:
           name: run black
@@ -28,7 +28,11 @@ jobs:
               isort --version
               isort -rc habitat/. habitat_baselines/. examples/. test/. setup.py --diff
               isort -rc habitat/. habitat_baselines/. examples/. test/. setup.py --check-only
-
+      - run:
+          name: run flake8
+          command: |
+              flake8 --version
+              flake8 habitat/. habitat_baselines/. examples/. tests/. setup.py
   install_and_test_ubuntu:
     <<: *gpu
     steps:

.pre-commit-config.yaml

@@ -20,14 +20,8 @@ repos:
     -   id: mixed-line-ending
         args: ['--fix=lf']
 
--   repo: https://github.com/asottile/seed-isort-config
-    rev: v2.2.0
-    hooks:
-    -   id: seed-isort-config
-        language_version: python3
-
--   repo: https://github.com/pre-commit/mirrors-isort
-    rev: v5.0.7
+-   repo: https://github.com/timothycrosley/isort
+    rev: 5.4.2
     hooks:
     -   id: isort
         exclude: docs/
@@ -43,9 +37,14 @@ repos:
     rev: master
     hooks:
     -   id: autoflake
-        args: ['--expand-star-imports', '--ignore-init-module-imports', '--in-place']
+        args: ['--expand-star-imports', '--ignore-init-module-imports', '--in-place', '-c']
         exclude: docs/
 
+-   repo: https://gitlab.com/pycqa/flake8
+    rev: 3.8.3
+    hooks:
+    -   id: flake8
+
 -   repo: https://github.com/kynan/nbstripout
     rev: master
     hooks:

examples/example.py

@@ -14,12 +14,12 @@ def example():
         config=habitat.get_config("configs/tasks/pointnav.yaml")
     ) as env:
         print("Environment creation successful")
-        observations = env.reset()
+        observations = env.reset()  # noqa: F841
 
         print("Agent stepping around inside environment.")
         count_steps = 0
         while not env.episode_over:
-            observations = env.step(env.action_space.sample())
+            observations = env.step(env.action_space.sample())  # noqa: F841
             count_steps += 1
         print("Episode finished after {} steps.".format(count_steps))
 

examples/tutorials/colabs/Habitat_Interactive_Tasks.ipynb

@@ -87,7 +87,6 @@
     "import habitat_sim\n",
     "from habitat.config import Config\n",
     "from habitat.core.registry import registry\n",
-    "from habitat_sim.utils import common as ut\n",
     "from habitat_sim.utils import viz_utils as vut\n",
     "\n",
     "if \"google.colab\" in sys.modules:\n",
@@ -145,10 +144,6 @@
     "    video_file = output_path + prefix + \".mp4\"\n",
     "    print(\"Encoding the video: %s \" % video_file)\n",
     "    writer = vut.get_fast_video_writer(video_file, fps=fps)\n",
-    "    thumb_size = (int(videodims[0] / 5), int(videodims[1] / 5))\n",
-    "    outline_frame = (\n",
-    "        np.ones((thumb_size[1] + 2, thumb_size[0] + 2, 3), np.uint8) * 150\n",
-    "    )\n",
     "    for ob in observations:\n",
     "        # If in RGB/RGBA format, remove the alpha channel\n",
     "        rgb_im_1st_person = cv2.cvtColor(ob[\"rgb\"], cv2.COLOR_RGBA2RGB)\n",
@@ -815,7 +810,7 @@
     "        sim, \"rgb\", crosshair_pos=[128, 190], max_distance=1.0\n",
     "    )\n",
     "    print(f\"Closest Object ID: {closest_object} using 1.0 threshold\")\n",
-    "    assert closest_object == -1, f\"Agent shoud not be able to pick any object\""
+    "    assert closest_object == -1, \"Agent shoud not be able to pick any object\""
    ]
   },
   {
@@ -1129,7 +1124,6 @@
     "from habitat.core.embodied_task import Measure\n",
     "from habitat.core.simulator import Observations, Sensor, SensorTypes, Simulator\n",
     "from habitat.tasks.nav.nav import PointGoalSensor\n",
-    "from habitat_sim.utils.common import quat_from_magnum\n",
     "\n",
     "\n",
     "@registry.register_sensor\n",
@@ -1555,7 +1549,7 @@
     "            action_name == \"GRAB_RELEASE\"\n",
     "            and observations[\"gripped_object_id\"] >= 0\n",
     "        ):\n",
-    "            obj_id = observations[\"gripped_object_id\"]\n",
+    "            obj_id = observations[\"gripped_object_id\"]  # noqa: 841\n",
     "            self._prev_measure[\"gripped_object_count\"] += 1\n",
     "\n",
     "            gripped_success_reward = (\n",
@@ -1674,19 +1668,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
-    "import time\n",
-    "from collections import defaultdict, deque\n",
-    "from typing import Any, Dict, List, Optional\n",
+    "import os  # noqa: 841\n",
+    "import time  # noqa: 841\n",
+    "from collections import defaultdict, deque  # noqa: 841\n",
+    "from typing import Any, Dict, List, Optional  # noqa :841\n",
     "\n",
-    "import numpy as np\n",
-    "import torch\n",
+    "import numpy as np  # noqa: 841\n",
+    "import torch  # noqa: 841\n",
     "from torch.optim.lr_scheduler import LambdaLR\n",
     "\n",
     "from habitat import Config, logger\n",
-    "from habitat.core.vector_env import ThreadedVectorEnv\n",
     "from habitat.utils.visualizations.utils import observations_to_image\n",
-    "from habitat_baselines.common.base_trainer import BaseRLTrainer\n",
     "from habitat_baselines.common.baseline_registry import baseline_registry\n",
     "from habitat_baselines.common.env_utils import construct_envs, make_env_fn\n",
     "from habitat_baselines.common.environments import get_env_class\n",
@@ -1698,12 +1690,12 @@
     "    linear_decay,\n",
     ")\n",
     "from habitat_baselines.rl.models.rnn_state_encoder import RNNStateEncoder\n",
-    "from habitat_baselines.rl.ppo import PPO, PointNavBaselinePolicy\n",
+    "from habitat_baselines.rl.ppo import PPO\n",
     "from habitat_baselines.rl.ppo.policy import Net, Policy\n",
     "from habitat_baselines.rl.ppo.ppo_trainer import PPOTrainer\n",
     "\n",
     "\n",
-    "def construct_envs(\n",
+    "def construct_envs(  # noqa: 841\n",
     "    config, env_class, workers_ignore_signals=False,\n",
     "):\n",
     "    r\"\"\"Create VectorEnv object with specified config and env class type.\n",
@@ -1721,7 +1713,7 @@
     "    num_processes = config.NUM_PROCESSES\n",
     "    configs = []\n",
     "    env_classes = [env_class for _ in range(num_processes)]\n",
-    "    dataset = make_dataset(config.TASK_CONFIG.DATASET.TYPE)\n",
+    "    dataset = habitat.datasets.make_dataset(config.TASK_CONFIG.DATASET.TYPE)\n",
     "    scenes = config.TASK_CONFIG.DATASET.CONTENT_SCENES\n",
     "    if \"*\" in config.TASK_CONFIG.DATASET.CONTENT_SCENES:\n",
     "        scenes = dataset.get_scenes_to_load(config.TASK_CONFIG.DATASET)\n",
@@ -2154,15 +2146,16 @@
     "# @title Train an RL agent on a single episode\n",
     "!if [ -d \"data/tb\" ]; then rm -r data/tb; fi\n",
     "\n",
-    "import random\n",
+    "import random  # noqa: 841\n",
     "\n",
-    "import numpy as np\n",
-    "import torch\n",
+    "import numpy as np  # noqa: 841\n",
+    "import torch  # noqa: 841\n",
     "\n",
-    "import habitat\n",
-    "from habitat import Config, Env, RLEnv, VectorEnv, make_dataset\n",
-    "from habitat.config import get_config\n",
-    "from habitat_baselines.config.default import get_config as get_baseline_config\n",
+    "import habitat  # noqa: 841\n",
+    "from habitat import Config, make_dataset  # noqa: 841\n",
+    "from habitat_baselines.config.default import (\n",
+    "    get_config as get_baseline_config,  # noqa: 841\n",
+    ")\n",
     "\n",
     "baseline_config = get_baseline_config(\n",
     "    \"habitat_baselines/config/pointnav/ppo_pointnav.yaml\"\n",

examples/tutorials/nb_python/Habitat_Interactive_Tasks.py

@@ -90,7 +90,6 @@
 import habitat_sim
 from habitat.config import Config
 from habitat.core.registry import registry
-from habitat_sim.utils import common as ut
 from habitat_sim.utils import viz_utils as vut
 
 if "google.colab" in sys.modules:
@@ -141,10 +140,6 @@ def make_video_cv2(
     video_file = output_path + prefix + ".mp4"
     print("Encoding the video: %s " % video_file)
     writer = vut.get_fast_video_writer(video_file, fps=fps)
-    thumb_size = (int(videodims[0] / 5), int(videodims[1] / 5))
-    outline_frame = (
-        np.ones((thumb_size[1] + 2, thumb_size[0] + 2, 3), np.uint8) * 150
-    )
     for ob in observations:
         # If in RGB/RGBA format, remove the alpha channel
         rgb_im_1st_person = cv2.cvtColor(ob["rgb"], cv2.COLOR_RGBA2RGB)
@@ -749,7 +744,7 @@ def raycast(sim, sensor_name, crosshair_pos=[128, 128], max_distance=2.0):
         sim, "rgb", crosshair_pos=[128, 190], max_distance=1.0
     )
     print(f"Closest Object ID: {closest_object} using 1.0 threshold")
-    assert closest_object == -1, f"Agent shoud not be able to pick any object"
+    assert closest_object == -1, "Agent shoud not be able to pick any object"
 
 
 # %%
@@ -1039,7 +1034,6 @@ def step(self, action: int):
 from habitat.core.embodied_task import Measure
 from habitat.core.simulator import Observations, Sensor, SensorTypes, Simulator
 from habitat.tasks.nav.nav import PointGoalSensor
-from habitat_sim.utils.common import quat_from_magnum
 
 
 @registry.register_sensor
@@ -1445,7 +1439,7 @@ def get_reward(self, observations):
             action_name == "GRAB_RELEASE"
             and observations["gripped_object_id"] >= 0
         ):
-            obj_id = observations["gripped_object_id"]
+            obj_id = observations["gripped_object_id"]  # noqa: 841
             self._prev_measure["gripped_object_count"] += 1
 
             gripped_success_reward = (
@@ -1559,19 +1553,17 @@ def get_info(self, observations):
 
 
 # %%
-import os
-import time
-from collections import defaultdict, deque
-from typing import Any, Dict, List, Optional
+import os  # noqa: 841
+import time  # noqa: 841
+from collections import defaultdict, deque  # noqa: 841
+from typing import Any, Dict, List, Optional  # noqa :841
 
-import numpy as np
-import torch
+import numpy as np  # noqa: 841
+import torch  # noqa: 841
 from torch.optim.lr_scheduler import LambdaLR
 
 from habitat import Config, logger
-from habitat.core.vector_env import ThreadedVectorEnv
 from habitat.utils.visualizations.utils import observations_to_image
-from habitat_baselines.common.base_trainer import BaseRLTrainer
 from habitat_baselines.common.baseline_registry import baseline_registry
 from habitat_baselines.common.env_utils import construct_envs, make_env_fn
 from habitat_baselines.common.environments import get_env_class
@@ -1583,12 +1575,12 @@ def get_info(self, observations):
     linear_decay,
 )
 from habitat_baselines.rl.models.rnn_state_encoder import RNNStateEncoder
-from habitat_baselines.rl.ppo import PPO, PointNavBaselinePolicy
+from habitat_baselines.rl.ppo import PPO
 from habitat_baselines.rl.ppo.policy import Net, Policy
 from habitat_baselines.rl.ppo.ppo_trainer import PPOTrainer
 
 
-def construct_envs(
+def construct_envs(  # noqa: 841
     config, env_class, workers_ignore_signals=False,
 ):
     r"""Create VectorEnv object with specified config and env class type.
@@ -1606,7 +1598,7 @@ def construct_envs(
     num_processes = config.NUM_PROCESSES
     configs = []
     env_classes = [env_class for _ in range(num_processes)]
-    dataset = make_dataset(config.TASK_CONFIG.DATASET.TYPE)
+    dataset = habitat.datasets.make_dataset(config.TASK_CONFIG.DATASET.TYPE)
     scenes = config.TASK_CONFIG.DATASET.CONTENT_SCENES
     if "*" in config.TASK_CONFIG.DATASET.CONTENT_SCENES:
         scenes = dataset.get_scenes_to_load(config.TASK_CONFIG.DATASET)
@@ -2028,15 +2020,16 @@ def eval(self) -> None:
 # @title Train an RL agent on a single episode
 # !if [ -d "data/tb" ]; then rm -r data/tb; fi
 
-import random
+import random  # noqa: 841
 
-import numpy as np
-import torch
+import numpy as np  # noqa: 841
+import torch  # noqa: 841
 
-import habitat
-from habitat import Config, Env, RLEnv, VectorEnv, make_dataset
-from habitat.config import get_config
-from habitat_baselines.config.default import get_config as get_baseline_config
+import habitat  # noqa: 841
+from habitat import Config, make_dataset  # noqa: 841
+from habitat_baselines.config.default import (
+    get_config as get_baseline_config,  # noqa: 841
+)
 
 baseline_config = get_baseline_config(
     "habitat_baselines/config/pointnav/ppo_pointnav.yaml"

examples/vln_benchmark.py

@@ -6,6 +6,7 @@
 
 import argparse
 from collections import defaultdict
+from typing import Dict
 
 import habitat
 from habitat.config.default import get_config

habitat/__init__.py

@@ -12,7 +12,7 @@
 from habitat.core.embodied_task import EmbodiedTask, Measure, Measurements
 from habitat.core.env import Env, RLEnv
 from habitat.core.logging import logger
-from habitat.core.registry import registry
+from habitat.core.registry import registry  # noqa : F401
 from habitat.core.simulator import Sensor, SensorSuite, SensorTypes, Simulator
 from habitat.core.vector_env import ThreadedVectorEnv, VectorEnv
 from habitat.datasets import make_dataset

habitat/core/benchmark.py

@@ -46,7 +46,7 @@ def remote_evaluate(
         import pickle
         import time
 
-        import evalai_environment_habitat
+        import evalai_environment_habitat  # noqa: F401
         import evaluation_pb2
         import evaluation_pb2_grpc
         import grpc

habitat/core/embodied_task.py

@@ -320,7 +320,7 @@ def step(self, action: Union[int, Dict[str, Any]], episode: Type[Episode]):
 
     def get_action_name(self, action_index: int):
         if action_index >= len(self.actions):
-            raise ValueError(f"Action index '{action}' is out of range.")
+            raise ValueError(f"Action index '{action_index}' is out of range.")
         return self._action_keys[action_index]
 
     @property

habitat/core/utils.py

@@ -8,7 +8,7 @@
 from typing import List
 
 import numpy as np
-import quaternion
+import quaternion  # noqa: F401
 
 from habitat.utils.geometry_utils import quaternion_to_list
 

habitat/datasets/__init__.py

@@ -4,4 +4,4 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-from habitat.datasets.registration import make_dataset
+from habitat.datasets.registration import make_dataset  # noqa: F401 .

habitat/datasets/eqa/__init__.py

@@ -10,17 +10,12 @@
 
 def _try_register_mp3d_eqa_dataset():
     try:
-        from habitat.datasets.eqa.mp3d_eqa_dataset import Matterport3dDatasetV1
-
-        has_mp3deqa = True
+        from habitat.datasets.eqa.mp3d_eqa_dataset import (  # noqa: F401 isort:skip
+            Matterport3dDatasetV1,
+        )
     except ImportError as e:
-        has_mp3deqa = False
         mp3deqa_import_error = e
 
-    if has_mp3deqa:
-        from habitat.datasets.eqa.mp3d_eqa_dataset import Matterport3dDatasetV1
-    else:
-
         @registry.register_dataset(name="MP3DEQA-v1")
         class Matterport3dDatasetImportError(Dataset):
             def __init__(self, *args, **kwargs):

habitat/datasets/eqa/mp3d_eqa_dataset.py

@@ -13,7 +13,7 @@
 from habitat.core.dataset import Dataset
 from habitat.core.registry import registry
 from habitat.core.simulator import AgentState
-from habitat.datasets.utils import VocabDict, VocabFromText
+from habitat.datasets.utils import VocabDict
 from habitat.tasks.eqa.eqa import EQAEpisode, QuestionData
 from habitat.tasks.nav.nav import ShortestPathPoint
 from habitat.tasks.nav.object_nav_task import ObjectGoal