Merge branch 'add_Recommendation' into 'main'

添加openmmlab测试用例

See merge request dcutoolkit/deeplearing/dlexamples_new!32

openmmlab_test/mmclassification-speed-benchmark/configs/shufflenet_v2/metafile.yml

+Collections:
+  - Name: Shufflenet V2
+    Metadata:
+      Training Data: ImageNet
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+        - No BN decay
+      Training Resources: 8x 1080 GPUs
+      Epochs: 300
+      Batch Size: 1024
+      Architecture:
+        - Shufflenet V2
+    Paper: https://openaccess.thecvf.com/content_ECCV_2018/papers/Ningning_Light-weight_CNN_Architecture_ECCV_2018_paper.pdf
+    README: configs/shufflenet_v2/README.md
+
+Models:
+- Config: configs/shufflenet_v2/shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet.py
+  In Collection: Shufflenet V2
+  Metadata:
+    FLOPs: 149000000
+    Parameters: 2280000
+  Name: shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet
+  Results:
+  - Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 69.55
+      Top 5 Accuracy: 88.92
+    Task: Image Classification
+  Weights: https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200812-5bf4721e.pth

openmmlab_test/mmclassification-speed-benchmark/configs/shufflenet_v2/shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet.py

+_base_ = [
+    '../_base_/models/shufflenet_v2_1x.py',
+    '../_base_/datasets/imagenet_bs64_pil_resize.py',
+    '../_base_/schedules/imagenet_bs1024_linearlr_bn_nowd.py',
+    '../_base_/default_runtime.py'
+]

openmmlab_test/mmclassification-speed-benchmark/configs/speed_test/AlexNet.py

+_base_ = [
+    '../_base_/models/mobilenet_v2_1x.py', './datasets/imagenet_bs32.py',
+    '../_base_/schedules/imagenet_bs256_epochstep.py',
+    '../_base_/default_runtime.py'
+]

openmmlab_test/mmclassification-speed-benchmark/configs/speed_test/datasets/imagenet_bs32.py

+# dataset settings
+dataset_type = 'DummyImageNet'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+test_pipeline = [
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    samples_per_gpu=128,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/train',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline),
+    test=dict(
+        # replace `data/val` with `data/test` for standard test
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='accuracy')

openmmlab_test/mmclassification-speed-benchmark/configs/speed_test/datasets/imagenet_bs64.py

+# dataset settings
+dataset_type = 'DummyImageNet'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+test_pipeline = [
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    samples_per_gpu=128,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/train',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline),
+    test=dict(
+        # replace `data/val` with `data/test` for standard test
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='accuracy')

openmmlab_test/mmclassification-speed-benchmark/configs/speed_test/mobilenet_v2_b32x8_fp16_imagenet.py

+_base_ = [
+    '../_base_/models/mobilenet_v2_1x.py', './datasets/imagenet_bs32.py',
+    '../_base_/schedules/imagenet_bs256_epochstep.py',
+    '../_base_/default_runtime.py'
+]
+# fp16 settings
+fp16 = dict(loss_scale=512.)
\ No newline at end of file

openmmlab_test/mmclassification-speed-benchmark/configs/speed_test/mobilenet_v2_b32x8_imagenet.py

+_base_ = [
+    '../_base_/models/mobilenet_v2_1x.py', './datasets/imagenet_bs32.py',
+    '../_base_/schedules/imagenet_bs256_epochstep.py',
+    '../_base_/default_runtime.py'
+]

openmmlab_test/mmclassification-speed-benchmark/configs/speed_test/resnet152_b32x8_fp16_imagenet.py

+_base_ = [
+    '../_base_/models/resnet152.py', './datasets/imagenet_bs32.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]
+# fp16 settings
+fp16 = dict(loss_scale=512.)
\ No newline at end of file

openmmlab_test/mmclassification-speed-benchmark/configs/speed_test/resnet152_b32x8_imagenet.py

+_base_ = [
+    '../_base_/models/resnet152.py', './datasets/imagenet_bs32.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

openmmlab_test/mmclassification-speed-benchmark/configs/speed_test/resnet18_b32x8_fp16_imagenet.py

+_base_ = [
+    '../_base_/models/resnet18.py', './datasets/imagenet_bs32.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]
+# fp16 settings
+fp16 = dict(loss_scale=512.)
\ No newline at end of file

openmmlab_test/mmclassification-speed-benchmark/configs/speed_test/resnet18_b32x8_imagenet.py

+_base_ = [
+    '../_base_/models/resnet18.py', './datasets/imagenet_bs32.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

openmmlab_test/mmclassification-speed-benchmark/configs/speed_test/resnet34_b32x8_fp16_imagenet.py

+_base_ = [
+    '../_base_/models/resnet34.py', './datasets/imagenet_bs32.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]
+# fp16 settings
+fp16 = dict(loss_scale=512.)
\ No newline at end of file

openmmlab_test/mmclassification-speed-benchmark/configs/speed_test/resnet34_b32x8_imagenet.py

+_base_ = [
+    '../_base_/models/resnet34.py', './datasets/imagenet_bs32.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

openmmlab_test/mmclassification-speed-benchmark/configs/speed_test/resnet50_b32x8_fp16_imagenet.py

+#_base_ = ['../resnet/resnet50_b32x8_imagenet.py']
+
+_base_ = [
+    '../_base_/models/resnet50.py', './datasets/imagenet_bs32.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+
+]
+# fp16 settings
+fp16 = dict(loss_scale=512.)
+#fp16 = dict(loss_scale=dynamic)

openmmlab_test/mmclassification-speed-benchmark/configs/speed_test/resnet50_b32x8_imagenet.py

+_base_ = [
+    '../_base_/models/resnet50.py', './datasets/imagenet_bs32.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

openmmlab_test/mmclassification-speed-benchmark/configs/speed_test/resnext50_32x4d_b32x8_fp16_imagenet.py

+_base_ = [
+    '../_base_/models/resnext50_32x4d.py', './datasets/imagenet_bs32.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]
+# fp16 settings
+fp16 = dict(loss_scale=512.)
\ No newline at end of file

openmmlab_test/mmclassification-speed-benchmark/configs/speed_test/resnext50_32x4d_b32x8_imagenet.py

+_base_ = [
+    '../_base_/models/resnext50_32x4d.py', './datasets/imagenet_bs32.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

openmmlab_test/mmclassification-speed-benchmark/configs/speed_test/seresnet50_b32x8_fp16_imagenet.py

+_base_ = [
+    '../_base_/models/seresnet50.py', './datasets/imagenet_bs32.py',
+    '../_base_/schedules/imagenet_bs256_140e.py',
+    '../_base_/default_runtime.py'
+]
+# fp16 settings
+fp16 = dict(loss_scale=512.)
\ No newline at end of file

openmmlab_test/mmclassification-speed-benchmark/configs/speed_test/seresnet50_b32x8_imagenet.py

+_base_ = [
+    '../_base_/models/seresnet50.py', './datasets/imagenet_bs32.py',
+    '../_base_/schedules/imagenet_bs256_140e.py',
+    '../_base_/default_runtime.py'
+]

openmmlab_test/mmclassification-speed-benchmark/configs/speed_test/shufflenet_v1_1x_b64x16_linearlr_bn_nowd_fp16_imagenet.py

+_base_ = [
+    '../_base_/models/shufflenet_v1_1x.py', './datasets/imagenet_bs64.py',
+    '../_base_/schedules/imagenet_bs1024_linearlr_bn_nowd.py',
+    '../_base_/default_runtime.py'
+]
+# fp16 settings
+fp16 = dict(loss_scale=512.)
\ No newline at end of file