wehub-resource-sync
103 lines
5.2 KiB
Python
103 lines
5.2 KiB
Python
# -*- coding: utf-8 -*-
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def add_layoutlmv2_config(cfg):
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_C = cfg
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# -----------------------------------------------------------------------------
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# Config definition
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# -----------------------------------------------------------------------------
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_C.MODEL.MASK_ON = True
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# When using pre-trained models in Detectron1 or any MSRA models,
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# std has been absorbed into its conv1 weights, so the std needs to be set 1.
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# Otherwise, you can use [57.375, 57.120, 58.395] (ImageNet std)
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_C.MODEL.PIXEL_STD = [57.375, 57.120, 58.395]
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# ---------------------------------------------------------------------------- #
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# Backbone options
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# ---------------------------------------------------------------------------- #
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_C.MODEL.BACKBONE.NAME = "build_resnet_fpn_backbone"
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# ---------------------------------------------------------------------------- #
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# FPN options
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# ---------------------------------------------------------------------------- #
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# Names of the input feature maps to be used by FPN
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# They must have contiguous power of 2 strides
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# e.g., ["res2", "res3", "res4", "res5"]
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_C.MODEL.FPN.IN_FEATURES = ["res2", "res3", "res4", "res5"]
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# ---------------------------------------------------------------------------- #
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# Anchor generator options
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# ---------------------------------------------------------------------------- #
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# Anchor sizes (i.e. sqrt of area) in absolute pixels w.r.t. the network input.
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# Format: list[list[float]]. SIZES[i] specifies the list of sizes
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# to use for IN_FEATURES[i]; len(SIZES) == len(IN_FEATURES) must be true,
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# or len(SIZES) == 1 is true and size list SIZES[0] is used for all
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# IN_FEATURES.
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_C.MODEL.ANCHOR_GENERATOR.SIZES = [[32], [64], [128], [256], [512]]
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# ---------------------------------------------------------------------------- #
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# RPN options
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# ---------------------------------------------------------------------------- #
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# Names of the input feature maps to be used by RPN
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# e.g., ["p2", "p3", "p4", "p5", "p6"] for FPN
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_C.MODEL.RPN.IN_FEATURES = ["p2", "p3", "p4", "p5", "p6"]
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# Number of top scoring RPN proposals to keep before applying NMS
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# When FPN is used, this is *per FPN level* (not total)
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_C.MODEL.RPN.PRE_NMS_TOPK_TRAIN = 2000
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_C.MODEL.RPN.PRE_NMS_TOPK_TEST = 1000
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# Number of top scoring RPN proposals to keep after applying NMS
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# When FPN is used, this limit is applied per level and then again to the union
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# of proposals from all levels
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# NOTE: When FPN is used, the meaning of this config is different from Detectron1.
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# It means per-batch topk in Detectron1, but per-image topk here.
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# See the "find_top_rpn_proposals" function for details.
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_C.MODEL.RPN.POST_NMS_TOPK_TRAIN = 1000
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_C.MODEL.RPN.POST_NMS_TOPK_TEST = 1000
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# ---------------------------------------------------------------------------- #
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# ROI HEADS options
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# ---------------------------------------------------------------------------- #
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_C.MODEL.ROI_HEADS.NAME = "StandardROIHeads"
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# Number of foreground classes
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_C.MODEL.ROI_HEADS.NUM_CLASSES = 5
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# Names of the input feature maps to be used by ROI heads
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# Currently all heads (box, mask, ...) use the same input feature map list
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# e.g., ["p2", "p3", "p4", "p5"] is commonly used for FPN
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_C.MODEL.ROI_HEADS.IN_FEATURES = ["p2", "p3", "p4", "p5"]
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# ---------------------------------------------------------------------------- #
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# Box Head
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# ---------------------------------------------------------------------------- #
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# C4 don't use head name option
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# Options for non-C4 models: FastRCNNConvFCHead,
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_C.MODEL.ROI_BOX_HEAD.NAME = "FastRCNNConvFCHead"
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_C.MODEL.ROI_BOX_HEAD.NUM_FC = 2
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_C.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION = 14
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# ---------------------------------------------------------------------------- #
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# Mask Head
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# ---------------------------------------------------------------------------- #
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_C.MODEL.ROI_MASK_HEAD.NAME = "MaskRCNNConvUpsampleHead"
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_C.MODEL.ROI_MASK_HEAD.NUM_CONV = 4 # The number of convs in the mask head
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_C.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION = 7
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# ---------------------------------------------------------------------------- #
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# ResNe[X]t options (ResNets = {ResNet, ResNeXt}
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# Note that parts of a resnet may be used for both the backbone and the head
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# These options apply to both
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# ---------------------------------------------------------------------------- #
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_C.MODEL.RESNETS.DEPTH = 101
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_C.MODEL.RESNETS.SIZES = [[32], [64], [128], [256], [512]]
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_C.MODEL.RESNETS.ASPECT_RATIOS = [[0.5, 1.0, 2.0]]
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_C.MODEL.RESNETS.OUT_FEATURES = ["res2", "res3", "res4", "res5"] # res4 for C4 backbone, res2..5 for FPN backbone
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# Number of groups to use; 1 ==> ResNet; > 1 ==> ResNeXt
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_C.MODEL.RESNETS.NUM_GROUPS = 32
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# Baseline width of each group.
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# Scaling this parameters will scale the width of all bottleneck layers.
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_C.MODEL.RESNETS.WIDTH_PER_GROUP = 8
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# Place the stride 2 conv on the 1x1 filter
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# Use True only for the original MSRA ResNet; use False for C2 and Torch models
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_C.MODEL.RESNETS.STRIDE_IN_1X1 = False
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