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import torch |
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from torch import nn |
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from opendet.modeling.backbones import build_backbone |
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from opendet.modeling.necks import build_neck |
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from opendet.modeling.heads import build_head |
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__all__ = ['BaseDetector'] |
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class BaseDetector(nn.Module): |
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def __init__(self, config): |
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"""the module for OCR. |
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args: |
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config (dict): the super parameters for module. |
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""" |
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super(BaseDetector, self).__init__() |
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in_channels = config.get('in_channels', 3) |
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self.use_wd = config.get('use_wd', True) |
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if 'Backbone' not in config or config['Backbone'] is None: |
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self.use_backbone = False |
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else: |
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self.use_backbone = True |
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config['Backbone']['in_channels'] = in_channels |
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self.backbone = build_backbone(config['Backbone']) |
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in_channels = self.backbone.out_channels |
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if 'Neck' not in config or config['Neck'] is None: |
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self.use_neck = False |
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else: |
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self.use_neck = True |
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config['Neck']['in_channels'] = in_channels |
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self.neck = build_neck(config['Neck']) |
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in_channels = self.neck.out_channels |
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if 'Head' not in config or config['Head'] is None: |
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self.use_head = False |
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else: |
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self.use_head = True |
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config['Head']['in_channels'] = in_channels |
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self.head = build_head(config['Head']) |
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@torch.jit.ignore |
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def no_weight_decay(self): |
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if self.use_wd: |
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if hasattr(self.backbone, 'no_weight_decay'): |
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no_weight_decay = self.backbone.no_weight_decay() |
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else: |
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no_weight_decay = {} |
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if hasattr(self.head, 'no_weight_decay'): |
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no_weight_decay.update(self.head.no_weight_decay()) |
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return no_weight_decay |
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else: |
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return {} |
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def forward(self, x, data=None): |
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if self.use_backbone: |
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x = self.backbone(x) |
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if self.use_neck: |
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x = self.neck(x) |
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if self.use_head: |
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x = self.head(x, data=data) |
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return x |
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