Remove torch/motmetrics/pandas requirements and skip related tests (n…

…utonomy#510)

* Skip tests with non-standard requirements

* Silence test tqdm

Co-authored-by: Holger Caesar <holger.caesar@motional.com>

docs/installation.md

@@ -16,6 +16,9 @@ Download the devkit to your home directory using:
 ```
 cd && git clone https://github.com/nutonomy/nuscenes-devkit.git
 ```
+*Note:* The pip package does not support the prediction or tracking code.
+For these, you will need to download the source code and [manually install the requirements](#install-required-packages).
+
 ## Install Python
 
 The devkit is tested for Python 3.6 onwards, but we recommend to use Python 3.7.
@@ -104,7 +107,12 @@ To install the required packages, run the following command in your favourite vi
 ```
 pip install -r setup/requirements.txt
 ```
-**Note:** The requirements file is internally divided into base requirements (`base`) and requirements specific to certain products or challenges (`nuimages`, `prediction` and `tracking`). If you only plan to use a subset of the codebase, feel free to comment out the lines that you do not need.
+**Note:** The requirements file is internally divided into base requirements (`base`) and requirements specific to certain products or challenges (`nuimages`, `prediction` and `tracking`).
+From December 2020 onwards we are excluding the `prediction` and `tracking` requirements by default to decrease the number of packages that are installed and avoid incompatibility with other pip packages.
+If you want to install these requirements nevertheless, please run the following for `prediction` (and analog for `tracking`):
+```
+pip install -r setup/requirements/requirements_prediction.txt
+``` 
 
 ## Setup environment variable
 Finally, if you want to run the unit tests you need to point the devkit to the `nuscenes` folder on your disk.

python-sdk/nuscenes/eval/tracking/algo.py

@@ -12,12 +12,17 @@
 """
 import os
 from typing import List, Dict, Callable, Tuple
+import unittest
 
 import numpy as np
-import pandas
 import sklearn
 import tqdm
 
+try:
+    import pandas
+except ModuleNotFoundError:
+    raise unittest.SkipTest('Skipping test as pandas was not found!')
+
 from nuscenes.eval.tracking.constants import MOT_METRIC_MAP, TRACKING_METRICS
 from nuscenes.eval.tracking.data_classes import TrackingBox, TrackingMetricData
 from nuscenes.eval.tracking.mot import MOTAccumulatorCustom

python-sdk/nuscenes/eval/tracking/utils.py

@@ -1,12 +1,17 @@
 # nuScenes dev-kit.
 # Code written by Holger Caesar, 2019.
 
+import unittest
 import warnings
 from typing import Optional, Dict
 
-import motmetrics
 import numpy as np
-from motmetrics.metrics import MetricsHost
+
+try:
+    import motmetrics
+    from motmetrics.metrics import MetricsHost
+except ModuleNotFoundError:
+    raise unittest.SkipTest('Skipping test as motmetrics was not found!')
 
 from nuscenes.eval.tracking.data_classes import TrackingMetrics
 from nuscenes.eval.tracking.metrics import motar, mota_custom, motp_custom, faf, track_initialization_duration, \

python-sdk/nuscenes/map_expansion/tests/test_all_maps.py

@@ -68,7 +68,7 @@ def test_egoposes_on_map(self):
         whitelist = ['scene-0499', 'scene-0501', 'scene-0502', 'scene-0515', 'scene-0517']
 
         invalid_scenes = []
-        for scene in tqdm.tqdm(nusc.scene):
+        for scene in tqdm.tqdm(nusc.scene, leave=False):
             if scene['name'] in whitelist:
                 continue
 

python-sdk/nuscenes/prediction/tests/test_backbone.py

@@ -1,7 +1,10 @@
 import unittest
 
-import torch
-from torchvision.models.resnet import BasicBlock, Bottleneck
+try:
+    import torch
+    from torchvision.models.resnet import BasicBlock, Bottleneck
+except ModuleNotFoundError:
+    raise unittest.SkipTest('Skipping test as torch was not found!')
 
 from nuscenes.prediction.models.backbone import ResNetBackbone, MobileNetBackbone
 

python-sdk/nuscenes/prediction/tests/test_covernet.py

@@ -4,8 +4,11 @@
 import math
 import unittest
 
-import torch
-from torch.nn.functional import cross_entropy
+try:
+    import torch
+    from torch.nn.functional import cross_entropy
+except ModuleNotFoundError:
+    raise unittest.SkipTest('Skipping test as torch was not found!')
 
 from nuscenes.prediction.models.backbone import ResNetBackbone
 from nuscenes.prediction.models.covernet import mean_pointwise_l2_distance, ConstantLatticeLoss, CoverNet

python-sdk/nuscenes/prediction/tests/test_mtp.py

@@ -1,6 +1,9 @@
 import unittest
 
-import torch
+try:
+    import torch
+except ModuleNotFoundError:
+    raise unittest.SkipTest('Skipping test as torch was not found!')
 
 from nuscenes.prediction.models import backbone
 from nuscenes.prediction.models import mtp

python-sdk/nuscenes/prediction/tests/test_mtp_loss.py

@@ -2,7 +2,10 @@
 import math
 import unittest
 
-import torch
+try:
+    import torch
+except ModuleNotFoundError:
+    raise unittest.SkipTest('Skipping test as torch was not found!')
 
 from nuscenes.prediction.models import mtp
 

setup/requirements.txt

@@ -1,4 +1,4 @@
 -r requirements/requirements_base.txt
--r requirements/requirements_prediction.txt
--r requirements/requirements_tracking.txt
 -r requirements/requirements_nuimages.txt
+# -r requirements/requirements_prediction.txt  # Uncomment this for the prediction code
+# -r requirements/requirements_tracking.txt  # Uncomment this for the tracking code