mirror of
https://github.com/alibaba/EasyCV.git
synced 2025-06-03 14:49:00 +08:00
9aaa600f79
* add attention layer and more loss function * add attention layer and various loss functions * add siou loss * add tah,various attention layers, and different loss functions * add asff sim, gsconv * blade utils fit faster * blade optimize for yolox static & fp16 * decode output for yolox control by cfg * add reparameterize_models for export * e2e trt_nms plugin export support and numeric test * split preprocess from end2end+blade, speedup from 17ms->7.2ms Co-authored-by: zouxinyi0625 <zouxinyi.zxy@alibaba-inc.com>
123 lines
5.9 KiB
Python
123 lines
5.9 KiB
Python
import os
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IMG_NORM_CFG_255 = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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IMG_NORM_CFG = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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COCO_CLASSES = [
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'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
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'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
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'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
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'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag',
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'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite',
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'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
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'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon',
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'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
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'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant',
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'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
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'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
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'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
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'hair drier', 'toothbrush'
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]
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VOC_CLASSES = [
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'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat',
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'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person',
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'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'
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]
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BASE_OSS_PATH = 'oss://pai-vision-data-hz/unittest/'
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BASE_LOCAL_PATH = os.path.expanduser('~/easycv_nfs/')
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TMP_DIR_OSS = os.path.join(BASE_OSS_PATH, 'tmp')
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TMP_DIR_LOCAL = os.path.join(BASE_LOCAL_PATH, 'tmp')
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CLS_DATA_NPY_LOCAL = os.path.join(BASE_LOCAL_PATH, 'data/classification/npy/')
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SMALL_IMAGENET_RAW_LOCAL = os.path.join(
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BASE_LOCAL_PATH, 'data/classification/small_imagenet_raw')
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CIFAR10_LOCAL = os.path.join(BASE_LOCAL_PATH, 'data/classification/cifar10')
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CIFAR100_LOCAL = os.path.join(BASE_LOCAL_PATH, 'data/classification/cifar100')
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SMALL_IMAGENET_TFRECORD_LOCAL = os.path.join(
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BASE_LOCAL_PATH, 'data/classification/small_imagenet_tfrecord/')
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IMAGENET_LABEL_TXT = os.path.join(
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BASE_LOCAL_PATH, 'data/classification/imagenet/imagenet_label.txt')
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CLS_DATA_NPY_OSS = os.path.join(BASE_OSS_PATH, 'data/classification/npy/')
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SMALL_IMAGENET_TFRECORD_OSS = os.path.join(
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BASE_OSS_PATH, 'data/classification/small_imagenet_tfrecord/')
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IO_DATA_TXTX_OSS = os.path.join(BASE_OSS_PATH, 'data/io_test_dir/txts/')
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DET_DATA_SMALL_COCO_LOCAL = os.path.join(BASE_LOCAL_PATH,
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'data/detection/small_coco')
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CONFIG_PATH = 'configs/detection/yolox/yolox_s_8xb16_300e_coco.py'
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DET_DATA_RAW_LOCAL = os.path.join(BASE_LOCAL_PATH, 'data/detection/raw_data')
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DET_DATA_SMALL_VOC_LOCAL = os.path.join(BASE_LOCAL_PATH,
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'data/detection/small_voc')
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DET_DATA_MANIFEST_OSS = os.path.join(BASE_OSS_PATH,
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'data/detection/small_coco_itag')
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POSE_DATA_SMALL_COCO_LOCAL = os.path.join(BASE_LOCAL_PATH,
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'data/pose/small_coco')
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SSL_SMALL_IMAGENET_FEATURE = os.path.join(
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BASE_LOCAL_PATH, 'data/selfsup/small_imagenet_feature')
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SSL_SMALL_IMAGENET_RAW = os.path.join(BASE_LOCAL_PATH,
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'data/selfsup/small_imagenet')
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TEST_IMAGES_DIR = os.path.join(BASE_LOCAL_PATH, 'data/test_images')
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COMPRESSION_TEST_DATA = os.path.join(BASE_LOCAL_PATH,
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'data/compression/test_data')
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SEG_DATA_SMALL_RAW_LOCAL = os.path.join(BASE_LOCAL_PATH,
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'data/segmentation/small_voc_200')
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PRETRAINED_MODEL_MOCO = os.path.join(
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BASE_LOCAL_PATH, 'pretrained_models/selfsup/moco/moco_epoch_200.pth')
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PRETRAINED_MODEL_RESNET50 = os.path.join(
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BASE_LOCAL_PATH, 'pretrained_models/classification/resnet/resnet50.pth')
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PRETRAINED_MODEL_RESNET50_WITHOUTHEAD = os.path.join(
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BASE_LOCAL_PATH,
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'pretrained_models/classification/resnet/resnet50_withhead.pth')
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PRETRAINED_MODEL_FACEID = os.path.join(BASE_LOCAL_PATH,
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'pretrained_models/faceid')
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PRETRAINED_MODEL_YOLOXS_EXPORT = os.path.join(
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BASE_LOCAL_PATH, 'pretrained_models/detection/infer_yolox/epoch_300.pt')
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PRETRAINED_MODEL_YOLOXS_NOPRE_NOTRT_JIT = os.path.join(
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BASE_LOCAL_PATH,
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'pretrained_models/detection/infer_yolox/epoch_300_nopre_notrt.pt.jit')
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PRETRAINED_MODEL_YOLOXS_PRE_NOTRT_JIT = os.path.join(
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BASE_LOCAL_PATH,
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'pretrained_models/detection/infer_yolox/epoch_300_pre_notrt.pt.jit')
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PRETRAINED_MODEL_YOLOXS_NOPRE_TRT_JIT = os.path.join(
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BASE_LOCAL_PATH,
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'pretrained_models/detection/infer_yolox/epoch_300_nopre_trt.pt.jit')
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PRETRAINED_MODEL_YOLOXS_PRE_TRT_JIT = os.path.join(
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BASE_LOCAL_PATH,
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'pretrained_models/detection/infer_yolox/epoch_300_pre_trt.pt.jit')
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PRETRAINED_MODEL_YOLOXS_NOPRE_NOTRT_BLADE = os.path.join(
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BASE_LOCAL_PATH,
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'pretrained_models/detection/infer_yolox/epoch_300_nopre_notrt.pt.blade')
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PRETRAINED_MODEL_YOLOXS_PRE_NOTRT_BLADE = os.path.join(
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BASE_LOCAL_PATH,
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'pretrained_models/detection/infer_yolox/epoch_300_pre_notrt.pt.blade')
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PRETRAINED_MODEL_YOLOXS_NOPRE_TRT_BLADE = os.path.join(
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BASE_LOCAL_PATH,
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'pretrained_models/detection/infer_yolox/epoch_300_nopre_trt.pt.blade')
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PRETRAINED_MODEL_YOLOXS_PRE_TRT_BLADE = os.path.join(
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BASE_LOCAL_PATH,
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'pretrained_models/detection/infer_yolox/epoch_300_pre_trt.pt.blade')
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PRETRAINED_MODEL_YOLOXS = os.path.join(
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BASE_LOCAL_PATH, 'pretrained_models/detection/infer_yolox/epoch_300.pth')
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PRETRAINED_MODEL_POSE_HRNET_EXPORT = os.path.join(
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BASE_LOCAL_PATH,
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'pretrained_models/pose/hrnet/pose_hrnet_epoch_210_export.pt')
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PRETRAINED_MODEL_YOLOX_COMPRESSION = os.path.join(
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BASE_LOCAL_PATH, 'pretrained_models/compression/yolox_compression.pth')
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PRETRAINED_MODEL_MAE = os.path.join(
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BASE_LOCAL_PATH, 'pretrained_models/classification/vit/mae_vit_b_1600.pth')
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PRETRAINED_MODEL_MASK2FORMER = os.path.join(
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BASE_LOCAL_PATH,
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'pretrained_models/segmentation/mask2former/mask2former_r50_instance.pth')
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