[detection] Use ml_hub for building gitub repo urls and github release url

[tests] make deploy test non essential

Author: deepsworld

ml/vision/models/detection/detector.py

@@ -1,11 +1,13 @@
 import os
+import random
 from abc import abstractmethod
 from pathlib import Path
 
 import torch as th
+from torch import nn
 import torchvision.transforms as T
 
-from ml import nn, random, logging
+from ml import logging
 from ...datasets import coco
 
 COLORS91 = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(coco.COCO91_CLASSES))]

ml/vision/models/detection/detr/model.py

@@ -2,8 +2,10 @@
 from pathlib import Path
 
 import torch
-from ml import io, nn, hub, logging
-from ml.nn import functional as F
+from torch import hub
+
+import ml.hub as ml_hub
+from ml import io, logging
 
 GITHUB_DETR = dict(
     owner='facebookresearch',
@@ -29,10 +31,10 @@
 def github(tag='main', deformable=False):
     if deformable:
         tag = TAGS_DEFORMABLE_DETR[tag]
-        return hub.github(owner=GITHUB_DEFORMABLE_DETR['owner'], project=GITHUB_DEFORMABLE_DETR['project'], tag=tag)
+        return ml_hub.github(owner=GITHUB_DEFORMABLE_DETR['owner'], project=GITHUB_DEFORMABLE_DETR['project'], tag=tag)
     else:
         tag = TAGS_DETR[tag]
-        return hub.github(owner=GITHUB_DETR['owner'], project=GITHUB_DETR['project'], tag=tag)
+        return ml_hub.github(owner=GITHUB_DETR['owner'], project=GITHUB_DETR['project'], tag=tag)
 
 def from_pretrained(chkpt, model_dir=None, force_reload=False, **kwargs):
     # TODO naming for custom checkpoints
@@ -65,7 +67,7 @@ def from_pretrained(chkpt, model_dir=None, force_reload=False, **kwargs):
         # GitHub Release
         owner = kwargs.get('owner', GITHUB_DETR['owner'])
         proj = kwargs.get('project', GITHUB_DETR['project'])
-        url = hub.github_release_url(owner, proj, tag, chkpt)
+        url = ml_hub.github_release_url(owner, proj, tag, chkpt)
         chkpt = hub.load_state_dict_from_url(url, 
                                              model_dir=model_dir, 
                                              map_location=torch.device('cpu'),

ml/vision/models/detection/yolox/model.py

@@ -3,8 +3,11 @@
 from pathlib import Path
 
 import torch
+from torch import hub
+
+from ml import logging
+import ml.hub as ml_hub
 
-from ml import hub, logging
 
 GITHUB_YOLOX = dict(
     owner='Megvii-BaseDetection',
@@ -18,7 +21,7 @@
 
 def github(tag='main', deformable=False):
     tag = TAGS_YOLOX[tag]
-    return hub.github(owner=GITHUB_YOLOX['owner'], project=GITHUB_YOLOX['project'], tag=tag)
+    return ml_hub.github(owner=GITHUB_YOLOX['owner'], project=GITHUB_YOLOX['project'], tag=tag)
 
 def custom_forward(self, x, targets=None):
     # fpn output content features of [dark3, dark4, dark5]

setup.py

@@ -189,7 +189,7 @@ def readme():
         license='BSD-3',
         classifiers=[
             'License :: OSI Approved :: BSD License',
-            'Operating System :: macOS/Ubuntu 16.04+',
+            'Operating System :: macOS/Ubuntu 18.04+',
             'Development Status :: 1 - Alpha',
             'Intended Audience :: Developers',
             'Intended Audience :: Education',

tests/test_yolox_deploy.py

@@ -62,7 +62,7 @@ def detector(tag, dev):
     detector.eval()
     return detector.to(dev)
 
-@pytest.mark.essential
+# @pytest.mark.essential
 @pytest.mark.parametrize("B", [1])
 @pytest.mark.parametrize("shape", [(640, 640)])
 def test_deploy_onnx(benchmark, name, batch, detector, dev, B, shape):
@@ -161,7 +161,7 @@ def preprocess(image_path, *shape):
         for torch_feats, feats in zip(torch_features, features):
             th.testing.assert_close(torch_feats.float(), feats.float(), rtol=1e-03, atol=4e-04)
 
-@pytest.mark.essential
+# @pytest.mark.essential
 @pytest.mark.parametrize("B", [8])
 @pytest.mark.parametrize("batch_preprocess", [True, False])
 @pytest.mark.parametrize('fp16', [True, False])