# encoding: utf-8
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"""
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@author: liaoxingyu
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@contact: sherlockliao01@gmail.com
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"""
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import torch
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from torch import nn
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from modeling.backbones import build_backbone
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from modeling.heads import build_heads
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from modeling.losses import *
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from .build import META_ARCH_REGISTRY
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@META_ARCH_REGISTRY.register()
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class Baseline(nn.Module):
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def __init__(self, cfg):
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super().__init__()
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self._cfg = cfg
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assert len(cfg.MODEL.PIXEL_MEAN) == len(cfg.MODEL.PIXEL_STD)
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self.register_buffer("pixel_mean", torch.tensor(cfg.MODEL.PIXEL_MEAN).view(1, -1, 1, 1))
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self.register_buffer("pixel_std", torch.tensor(cfg.MODEL.PIXEL_STD).view(1, -1, 1, 1))
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# backbone
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self.backbone = build_backbone(cfg)
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# head
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self.heads = build_heads(cfg)
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@property
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def device(self):
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return self.pixel_mean.device
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def forward(self, batched_inputs):
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images = self.preprocess_image(batched_inputs)
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features = self.backbone(images)
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if self.training:
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assert "targets" in batched_inputs, "Person ID annotation are missing in training!"
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targets = batched_inputs["targets"].to(self.device)
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# PreciseBN flag, When do preciseBN on different dataset, the number of classes in new dataset
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# may be larger than that in the original dataset, so the circle/arcface will
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# throw an error. We just set all the targets to 0 to avoid this problem.
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if targets.sum() < 0: targets.zero_()
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outputs = self.heads(features, targets)
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return {
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"outputs": outputs,
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"targets": targets,
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}
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else:
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outputs = self.heads(features)
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return outputs
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def preprocess_image(self, images):
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r"""
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Normalize and batch the input images.
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"""
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# images = images.to(self.device)
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# if isinstance(batched_inputs, dict):
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# images = batched_inputs["images"].to(self.device)
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# elif isinstance(batched_inputs, torch.Tensor):
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# images = batched_inputs.to(self.device)
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# else:
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# raise TypeError("batched_inputs must be dict or torch.Tensor, but get {}".format(type(batched_inputs)))
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# print(images)
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print("-----------------------------------------------------------------------------")
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images.sub_(self.pixel_mean).div_(self.pixel_std)
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return images
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def losses(self, outs):
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r"""
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Compute loss from modeling's outputs, the loss function input arguments
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must be the same as the outputs of the model forwarding.
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"""
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# fmt: off
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outputs = outs["outputs"]
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gt_labels = outs["targets"]
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# model predictions
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pred_class_logits = outputs['pred_class_logits'].detach()
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cls_outputs = outputs['cls_outputs']
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pred_features = outputs['features']
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# fmt: on
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# Log prediction accuracy
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log_accuracy(pred_class_logits, gt_labels)
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loss_dict = {}
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loss_names = self._cfg.MODEL.LOSSES.NAME
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if "CrossEntropyLoss" in loss_names:
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loss_dict['loss_cls'] = cross_entropy_loss(
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cls_outputs,
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gt_labels,
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self._cfg.MODEL.LOSSES.CE.EPSILON,
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self._cfg.MODEL.LOSSES.CE.ALPHA,
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) * self._cfg.MODEL.LOSSES.CE.SCALE
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if "TripletLoss" in loss_names:
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loss_dict['loss_triplet'] = triplet_loss(
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pred_features,
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gt_labels,
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self._cfg.MODEL.LOSSES.TRI.MARGIN,
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self._cfg.MODEL.LOSSES.TRI.NORM_FEAT,
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self._cfg.MODEL.LOSSES.TRI.HARD_MINING,
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) * self._cfg.MODEL.LOSSES.TRI.SCALE
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if "CircleLoss" in loss_names:
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loss_dict['loss_circle'] = circle_loss(
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pred_features,
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gt_labels,
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self._cfg.MODEL.LOSSES.CIRCLE.MARGIN,
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self._cfg.MODEL.LOSSES.CIRCLE.ALPHA,
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) * self._cfg.MODEL.LOSSES.CIRCLE.SCALE
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return loss_dict
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