[Fix]: fix numba numpy version not compatiable in circileci

.circleci/config.yml

@@ -22,16 +22,17 @@ workflows:
           mapping: |
             .circle/.* lint_only false
             cmake/.* lint_only false
-            configs/.* lint_only false
+            configs/.* lint_only true
             csrc/.* lint_only false
             demo/csrc/.* lint_only false
-            docker/.* lint_only false
+            docker/.* lint_only true
             docs/.* lint_only true
             mmdeploy/.* lint_only false
             requirements/.* lint_only false
             tests/.* lint_only false
             third_party/.* lint_only false
             tools/.* lint_only false
+            setup.py lint_only false
           base-revision: dev-1.x
           # this is the path of the configuration we should trigger once
           # path filtering and pipeline parameter value updates are

.circleci/test.yml

@@ -86,7 +86,7 @@ commands:
             python -m mim install -r requirements/codebases.txt
             python -m pip install -r requirements/tests.txt
             python -m pip install -r requirements/runtime.txt
-            python -m pip install -U numpy clip
+            python -m pip install -U numpy clip numba
             cd ..
   perform_model_converter_ut:
     steps:
@@ -147,7 +147,7 @@ jobs:
             docker exec mmdeploy mim install -r mmdeploy/requirements/codebases.txt
             docker exec mmdeploy pip install -r mmdeploy/requirements/tests.txt
             docker exec mmdeploy pip install -r mmdeploy/requirements/runtime.txt
-            docker exec mmdeploy pip install -U numpy clip
+            docker exec mmdeploy pip install -U numpy clip numba
       - run:
           name: Perform Model Converter unittests
           command: |

tests/test_codebase/test_mmcls/data/model.py

@@ -1,62 +1,25 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-# model settings
 model = dict(
     type='ImageClassifier',
     backbone=dict(
         type='ResNet',
-        depth=50,
+        depth=18,
         num_stages=4,
         out_indices=(3, ),
         style='pytorch'),
     neck=dict(type='GlobalAveragePooling'),
     head=dict(
         type='LinearClsHead',
         num_classes=1000,
-        in_channels=2048,
-        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
-        topk=(1, 5),
-    ))
-
-# dataset settings
-dataset_type = 'ImageNet'
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='Resize', size=(256, -1)),
-    dict(type='CenterCrop', crop_size=224),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='ImageToTensor', keys=['img']),
-    dict(type='Collect', keys=['img'])
-]
-data = dict(
-    samples_per_gpu=32,
-    workers_per_gpu=2,
-    test=dict(
-        type=dataset_type,
-        data_prefix='tests/test_codebase/test_mmcls/data/imgs',
-        ann_file='ann.txt',
-        pipeline=test_pipeline))
-evaluation = dict(interval=1, metric='accuracy')
-
-model = dict(
-    type='ImageClassifier',
-    backbone=dict(
-        type='ResNet',
-        depth=50,
-        num_stages=4,
-        out_indices=(3, ),
-        style='pytorch'),
-    neck=dict(type='GlobalAveragePooling'),
-    head=dict(
-        type='LinearClsHead',
-        num_classes=1000,
-        in_channels=2048,
+        in_channels=512,
         loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
         topk=(1, 5)))
 dataset_type = 'ImageNet'
 data_preprocessor = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+    num_classes=1000,
+    mean=[123.675, 116.28, 103.53],
+    std=[58.395, 57.12, 57.375],
+    to_rgb=True)
 train_pipeline = [
     dict(type='LoadImageFromFile'),
     dict(type='RandomResizedCrop', scale=224),
@@ -70,8 +33,8 @@
     dict(type='PackClsInputs')
 ]
 train_dataloader = dict(
-    batch_size=32,
-    num_workers=5,
+    batch_size=2,
+    num_workers=1,
     dataset=dict(
         type='ImageNet',
         data_root='tests/test_codebase/test_mmcls/data/imgs',
@@ -83,11 +46,10 @@
             dict(type='RandomFlip', prob=0.5, direction='horizontal'),
             dict(type='PackClsInputs')
         ]),
-    sampler=dict(type='DefaultSampler', shuffle=True),
-    persistent_workers=True)
+    sampler=dict(type='DefaultSampler', shuffle=True))
 val_dataloader = dict(
-    batch_size=32,
-    num_workers=5,
+    batch_size=2,
+    num_workers=1,
     dataset=dict(
         type='ImageNet',
         data_root='tests/test_codebase/test_mmcls/data/imgs',
@@ -99,12 +61,11 @@
             dict(type='CenterCrop', crop_size=224),
             dict(type='PackClsInputs')
         ]),
-    sampler=dict(type='DefaultSampler', shuffle=False),
-    persistent_workers=True)
+    sampler=dict(type='DefaultSampler', shuffle=False))
 val_evaluator = dict(type='Accuracy', topk=(1, 5))
 test_dataloader = dict(
-    batch_size=32,
-    num_workers=5,
+    batch_size=2,
+    num_workers=1,
     dataset=dict(
         type='ImageNet',
         data_root='tests/test_codebase/test_mmcls/data/imgs',
@@ -116,16 +77,16 @@
             dict(type='CenterCrop', crop_size=224),
             dict(type='PackClsInputs')
         ]),
-    sampler=dict(type='DefaultSampler', shuffle=False),
-    persistent_workers=True)
+    sampler=dict(type='DefaultSampler', shuffle=False))
 test_evaluator = dict(type='Accuracy', topk=(1, 5))
 optim_wrapper = dict(
     optimizer=dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001))
 param_scheduler = dict(
     type='MultiStepLR', by_epoch=True, milestones=[30, 60, 90], gamma=0.1)
-train_cfg = dict(by_epoch=True, max_epochs=100)
-val_cfg = dict(interval=1)
+train_cfg = dict(by_epoch=True, max_epochs=100, val_interval=1)
+val_cfg = dict()
 test_cfg = dict()
+auto_scale_lr = dict(base_batch_size=256)
 default_scope = 'mmcls'
 default_hooks = dict(
     timer=dict(type='IterTimerHook'),
@@ -140,9 +101,8 @@
     dist_cfg=dict(backend='nccl'))
 vis_backends = [dict(type='LocalVisBackend')]
 visualizer = dict(
-    type='ClsVisualizer',
-    vis_backends=[dict(type='LocalVisBackend')],
-    name='visualizer')
+    type='ClsVisualizer', vis_backends=[dict(type='LocalVisBackend')])
 log_level = 'INFO'
 load_from = None
 resume = False
+randomness = dict(seed=None, deterministic=False)

tests/test_codebase/test_mmcls/test_classification_model.py

@@ -12,6 +12,7 @@
 
 IMAGE_SIZE = 64
 NUM_CLASS = 1000
+MODEL_CFG_PATH = 'tests/test_codebase/test_mmcls/data/model.py'
 
 try:
     import_codebase(Codebase.MMCLS)
@@ -31,7 +32,7 @@ def setup_class(cls):
         # simplify backend inference
         cls.wrapper = SwitchBackendWrapper(ORTWrapper)
         cls.outputs = {
-            'outputs': torch.rand(1, 100),
+            'outputs': torch.rand(1, NUM_CLASS),
         }
         cls.wrapper.set(outputs=cls.outputs)
         deploy_cfg = Config({'onnx_config': {'output_names': ['outputs']}})
@@ -99,7 +100,7 @@ def test_forward_test(self):
 
 @backend_checker(Backend.ONNXRUNTIME)
 def test_build_classification_model():
-    model_cfg = Config(dict(data=dict(test={'type': 'ImageNet'})))
+    model_cfg = Config.fromfile(MODEL_CFG_PATH)
     deploy_cfg = Config(
         dict(
             backend_config=dict(type='onnxruntime'),