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import logging
import math
from typing import List
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from torch import nn
from detectron2.layers import ShapeSpec
from detectron2.modeling import META_ARCH_REGISTRY, build_backbone, detector_postprocess
from detectron2.modeling.roi_heads import build_roi_heads
from detectron2.structures import Boxes, ImageList, Instances
from detectron2.utils.logger import log_first_n
from fvcore.nn import giou_loss, smooth_l1_loss
from .loss import SetCriterion, HungarianMatcher
from .head import DynamicHead
from .util.box_ops import box_cxcywh_to_xyxy, box_xyxy_to_cxcywh
from .util.misc import (NestedTensor, nested_tensor_from_tensor_list,
accuracy, get_world_size, interpolate,
is_dist_avail_and_initialized)
from detectron2.layers import Conv2d, get_norm
from .MaskEncoding import PCAMaskEncoding
from detectron2.modeling.backbone import PatchEmbed
__all__ = ["SWINTS"]
class ImgFeatExtractor(nn.Module):
def __init__(self, cfg):
super().__init__()
# self.img_feat_layer = nn.AdaptiveAvgPool2d(1)
self.cfg = cfg
def forward(self, features):
for i, f in enumerate(features):
if i == 0:
x = torch.mean(torch.mean(f, -1), -1) #self.img_feat_layer(f)
else:
x_p = torch.mean(torch.mean(f, -1), -1) #self.img_feat_layer(f)
x = x + x_p
img_feats = x.squeeze(-1).squeeze(-1).unsqueeze(1).repeat(1, self.cfg.MODEL.SWINTS.NUM_PROPOSALS, 1,)
del x_p
del x
return img_feats
@META_ARCH_REGISTRY.register()
class SWINTS(nn.Module):
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
self.device = torch.device(cfg.MODEL.DEVICE)
self.in_features = cfg.MODEL.ROI_HEADS.IN_FEATURES
self.num_classes = cfg.MODEL.SWINTS.NUM_CLASSES
self.num_proposals = cfg.MODEL.SWINTS.NUM_PROPOSALS
self.hidden_dim = cfg.MODEL.SWINTS.HIDDEN_DIM
self.num_heads = cfg.MODEL.SWINTS.NUM_HEADS
# Build Backbone.
self.backbone = build_backbone(cfg)
self.size_divisibility = self.backbone.size_divisibility
# Build Proposals.
self.pos_embeddings = nn.Embedding(self.num_proposals, self.hidden_dim)
self.init_proposal_boxes = nn.Embedding(self.num_proposals, 4)
nn.init.constant_(self.init_proposal_boxes.weight[:, :2], 0.5)
nn.init.constant_(self.init_proposal_boxes.weight[:, 2:], 1.0)
# --------
self.IFE = ImgFeatExtractor(cfg)
self.mask_encoding = PCAMaskEncoding(cfg)
# encoding parameters.
components_path = cfg.MODEL.SWINTS.PATH_COMPONENTS
# update parameters.
parameters = np.load(components_path)
components = nn.Parameter(torch.from_numpy(parameters['components_c'][0]).float().to(self.device),requires_grad=False)
explained_variances = nn.Parameter(torch.from_numpy(parameters['explained_variance_c'][0]).float().to(self.device), requires_grad=False)
means = nn.Parameter(torch.from_numpy(parameters['mean_c'][0]).float().to(self.device),requires_grad=False)
self.mask_encoding.components = components
self.mask_encoding.explained_variances = explained_variances
self.mask_encoding.means = means
# Build Dynamic Head.
self.head = DynamicHead(cfg=cfg, roi_input_shape=self.backbone.output_shape())
# Loss parameters:
class_weight = cfg.MODEL.SWINTS.CLASS_WEIGHT
giou_weight = cfg.MODEL.SWINTS.GIOU_WEIGHT
l1_weight = cfg.MODEL.SWINTS.L1_WEIGHT
rec_weight = cfg.MODEL.SWINTS.REC_WEIGHT
no_object_weight = cfg.MODEL.SWINTS.NO_OBJECT_WEIGHT
mask_weight = cfg.MODEL.SWINTS.MASK_WEIGHT
self.deep_supervision = cfg.MODEL.SWINTS.DEEP_SUPERVISION
# Build Criterion.
matcher = HungarianMatcher(cfg=cfg,
cost_class=class_weight,
cost_bbox=l1_weight,
cost_giou=giou_weight,
cost_mask=mask_weight)
self.matcher = matcher
weight_dict = {"loss_ce": class_weight, "loss_bbox": l1_weight, "loss_giou": giou_weight, "loss_feat": mask_weight, "loss_dice": mask_weight}
if self.deep_supervision:
aux_weight_dict = {}
for i in range(self.num_heads - 1):
aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()})
weight_dict.update(aux_weight_dict)
weight_dict["loss_rec"] = rec_weight
losses = ["labels", "boxes", "masks", "rec"]
self.criterion = SetCriterion(cfg=cfg,
num_classes=self.num_classes,
matcher=matcher,
weight_dict=weight_dict,
eos_coef=no_object_weight,
losses=losses)
pixel_mean = torch.Tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(3, 1, 1)
pixel_std = torch.Tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(3, 1, 1)
self.normalizer = lambda x: (x - pixel_mean) / pixel_std
self.to(self.device)
def forward(self, batched_inputs):
"""
Args:
batched_inputs: a list, batched outputs of :class:`DatasetMapper` .
Each item in the list contains the inputs for one image.
For now, each item in the list is a dict that contains:
* image: Tensor, image in (C, H, W) format.
* instances: Instances
Other information that's included in the original dicts, such as:
* "height", "width" (int): the output resolution of the model, used in inference.
See :meth:`postprocess` for details.
"""
images, images_whwh = self.preprocess_image(batched_inputs)
if isinstance(images, (list, torch.Tensor)):
images = nested_tensor_from_tensor_list(images)
# Feature Extraction.
src = self.backbone(images.tensor)
features = list()
for f in self.in_features:
feature = src[f]
features.append(feature)
# Prepare Proposals.
proposal_boxes = self.init_proposal_boxes.weight.clone()
proposal_boxes = box_cxcywh_to_xyxy(proposal_boxes)
proposal_boxes = proposal_boxes[None] * images_whwh[:, None, :]
img_feats = self.IFE(features)
bs = len(features[0])
pos_embeddings = self.pos_embeddings.weight[None].repeat(bs, 1, 1)
proposal_feats = img_feats + pos_embeddings
del img_feats
if self.training:
gt_instances = [x["instances"].to(self.device) for x in batched_inputs]
targets = self.prepare_targets(gt_instances)
outputs_class, outputs_coord, outputs_mask,out_rec = self.head(features, proposal_boxes, proposal_feats, targets, mask_encoding=self.mask_encoding, matcher=self.matcher)
output = {'pred_logits': outputs_class[-1], 'pred_boxes': outputs_coord[-1], 'pred_masks': outputs_mask[-1], 'pred_rec': out_rec}
if self.deep_supervision:
output['aux_outputs'] = [{'pred_logits': a, 'pred_boxes': b, 'pred_masks': c}
for a, b, c in zip(outputs_class[:-1], outputs_coord[:-1], outputs_mask[:-1])]
loss_dict = self.criterion(output, targets, self.mask_encoding)
weight_dict = self.criterion.weight_dict
for k in loss_dict.keys():
if k in weight_dict:
loss_dict[k] *= weight_dict[k]
return loss_dict
else:
outputs_class, outputs_coord, outputs_mask,out_rec = self.head(features, proposal_boxes, proposal_feats, mask_encoding=self.mask_encoding)
output = {'pred_logits': outputs_class[-1], 'pred_boxes': outputs_coord[-1], 'pred_masks': outputs_mask[-1]}
box_cls = output["pred_logits"]
box_pred = output["pred_boxes"]
mask_pred = output["pred_masks"].unsqueeze(dim=2)
results = Instances(images.image_sizes[0])
results.pred_boxes = Boxes(box_pred)
results.scores = box_cls
results.pred_masks = mask_pred.squeeze(1)
results.pred_rec = out_rec
results = [results]
processed_results = []
for results_per_image, input_per_image, image_size in zip(results, batched_inputs, images.image_sizes):
height = input_per_image.get("height", image_size[0])
width = input_per_image.get("width", image_size[1])
r = detector_postprocess(results_per_image, height, width)
processed_results.append({"instances": r})
return processed_results
@torch.no_grad()
def prepare_targets(self, targets):
new_targets = []
for targets_per_image in targets:
target = {}
h, w = targets_per_image.image_size
image_size_xyxy = torch.as_tensor([w, h, w, h], dtype=torch.float, device=self.device)
gt_classes = targets_per_image.gt_classes
gt_boxes = targets_per_image.gt_boxes.tensor / image_size_xyxy
gt_boxes = box_xyxy_to_cxcywh(gt_boxes)
target["labels"] = gt_classes.to(self.device)
target["boxes"] = gt_boxes.to(self.device)
target["boxes_xyxy"] = targets_per_image.gt_boxes.tensor.to(self.device)
target["image_size_xyxy"] = image_size_xyxy.to(self.device)
image_size_xyxy_tgt = image_size_xyxy.unsqueeze(0).repeat(len(gt_boxes), 1)
target["image_size_xyxy_tgt"] = image_size_xyxy_tgt.to(self.device)
target["area"] = targets_per_image.gt_boxes.area().to(self.device)
target["gt_masks"] = targets_per_image.gt_masks.to(self.device)
masks = target['gt_masks'].crop_and_resize(targets_per_image.gt_boxes, 28)
target["gt_masks"] = masks.float()
target["rec"] = targets_per_image.rec.to(self.device)
new_targets.append(target)
return new_targets
@torch.no_grad()
def inference(self, box_cls, box_pred, mask_pred, image_sizes, recred):
"""
Arguments:
box_cls (Tensor): tensor of shape (batch_size, num_proposals, K).
The tensor predicts the classification probability for each proposal.
box_pred (Tensor): tensors of shape (batch_size, num_proposals, 4).
The tensor predicts 4-vector (x,y,w,h) box
regression values for every proposal
image_sizes (List[torch.Size]): the input image sizes
Returns:
results (List[Instances]): a list of #images elements.
"""
assert len(box_cls) == len(image_sizes)
results = []
#
scores = torch.sigmoid(box_cls)
labels = torch.arange(self.num_classes, device=self.device).\
unsqueeze(0).repeat(self.num_proposals, 1).flatten(0, 1)
for i, (scores_per_image, box_pred_per_image, mask_pred_per_image, image_size, rec_per_image) in enumerate(zip(
scores, box_pred, mask_pred, image_sizes, rec_pred
)):
result = Instances(image_size)
scores_per_image, topk_indices = scores_per_image.flatten(0, 1).topk(self.num_proposals, sorted=False)
labels_per_image = labels[topk_indices]
result.pred_boxes = Boxes(box_pred_per_image)
result.scores = scores_per_image
result.pred_classes = labels_per_image
result.pred_masks = mask_pred_per_image
result.pred_rec = rec_per_image
results.append(result)
return results
def preprocess_image(self, batched_inputs):
"""
Normalize, pad and batch the input images.
"""
images = [self.normalizer(x["image"].to(self.device)) for x in batched_inputs]
images = ImageList.from_tensors(images, self.size_divisibility)
images_whwh = list()
for bi in batched_inputs:
h, w = bi["image"].shape[-2:]
images_whwh.append(torch.tensor([w, h, w, h], dtype=torch.float32, device=self.device))
images_whwh = torch.stack(images_whwh)
return images, images_whwh
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