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# Copyright (c) Facebook, Inc. and its affiliates.
from typing import Tuple
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.data import MetadataCatalog
from detectron2.modeling import META_ARCH_REGISTRY, build_backbone, build_sem_seg_head
from detectron2.modeling.backbone import Backbone
from detectron2.modeling.postprocessing import sem_seg_postprocess
from detectron2.structures import ImageList
from detectron2.utils.memory import _ignore_torch_cuda_oom
from einops import rearrange
@META_ARCH_REGISTRY.register()
class CATSeg(nn.Module):
@configurable
def __init__(
self,
*,
backbone: Backbone,
sem_seg_head: nn.Module,
size_divisibility: int,
pixel_mean: Tuple[float],
pixel_std: Tuple[float],
clip_pixel_mean: Tuple[float],
clip_pixel_std: Tuple[float],
train_class_json: str,
test_class_json: str,
sliding_window: bool,
clip_finetune: str,
backbone_multiplier: float,
clip_pretrained: str,
):
"""
Args:
backbone: a backbone module, must follow detectron2's backbone interface
sem_seg_head: a module that predicts semantic segmentation from backbone features
"""
super().__init__()
self.backbone = backbone
self.sem_seg_head = sem_seg_head
if size_divisibility < 0:
size_divisibility = self.backbone.size_divisibility
self.size_divisibility = size_divisibility
self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False)
self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False)
self.register_buffer("clip_pixel_mean", torch.Tensor(clip_pixel_mean).view(-1, 1, 1), False)
self.register_buffer("clip_pixel_std", torch.Tensor(clip_pixel_std).view(-1, 1, 1), False)
self.train_class_json = train_class_json
self.test_class_json = test_class_json
self.clip_finetune = clip_finetune
for name, params in self.sem_seg_head.predictor.clip_model.named_parameters():
if "visual" in name:
if clip_finetune == "prompt":
params.requires_grad = True if "prompt" in name else False
elif clip_finetune == "attention":
params.requires_grad = True if "attn" in name or "position" in name else False
elif clip_finetune == "full":
params.requires_grad = True
else:
params.requires_grad = False
else:
params.requires_grad = False
finetune_backbone = backbone_multiplier > 0.
for name, params in self.backbone.named_parameters():
if "norm0" in name:
params.requires_grad = False
else:
params.requires_grad = finetune_backbone
self.sliding_window = sliding_window
self.clip_resolution = (384, 384) if clip_pretrained == "ViT-B/16" else (336, 336)
self.sequential = False
@classmethod
def from_config(cls, cfg):
backbone = build_backbone(cfg)
sem_seg_head = build_sem_seg_head(cfg, backbone.output_shape())
return {
"backbone": backbone,
"sem_seg_head": sem_seg_head,
"size_divisibility": cfg.MODEL.MASK_FORMER.SIZE_DIVISIBILITY,
"pixel_mean": cfg.MODEL.PIXEL_MEAN,
"pixel_std": cfg.MODEL.PIXEL_STD,
"clip_pixel_mean": cfg.MODEL.CLIP_PIXEL_MEAN,
"clip_pixel_std": cfg.MODEL.CLIP_PIXEL_STD,
"train_class_json": cfg.MODEL.SEM_SEG_HEAD.TRAIN_CLASS_JSON,
"test_class_json": cfg.MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON,
"sliding_window": cfg.TEST.SLIDING_WINDOW,
"clip_finetune": cfg.MODEL.SEM_SEG_HEAD.CLIP_FINETUNE,
"backbone_multiplier": cfg.SOLVER.BACKBONE_MULTIPLIER,
"clip_pretrained": cfg.MODEL.SEM_SEG_HEAD.CLIP_PRETRAINED,
}
@property
def device(self):
return self.pixel_mean.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": per-region ground truth
* Other information that's included in the original dicts, such as:
"height", "width" (int): the output resolution of the model (may be different
from input resolution), used in inference.
Returns:
list[dict]:
each dict has the results for one image. The dict contains the following keys:
* "sem_seg":
A Tensor that represents the
per-pixel segmentation prediced by the head.
The prediction has shape KxHxW that represents the logits of
each class for each pixel.
"""
images = [x["image"].to(self.device) for x in batched_inputs]
if not self.training and self.sliding_window:
if not self.sequential:
with _ignore_torch_cuda_oom():
return self.inference_sliding_window(batched_inputs)
self.sequential = True
return self.inference_sliding_window(batched_inputs)
clip_images = [(x - self.clip_pixel_mean) / self.clip_pixel_std for x in images]
clip_images = ImageList.from_tensors(clip_images, self.size_divisibility)
images = [(x - self.pixel_mean) / self.pixel_std for x in images]
images = ImageList.from_tensors(images, self.size_divisibility)
clip_images = F.interpolate(clip_images.tensor, size=self.clip_resolution, mode='bilinear', align_corners=False, )
clip_features = self.sem_seg_head.predictor.clip_model.encode_image(clip_images, dense=True)
images_resized = F.interpolate(images.tensor, size=(384, 384), mode='bilinear', align_corners=False,)
features = self.backbone(images_resized)
outputs = self.sem_seg_head(clip_features, features)
if self.training:
targets = torch.stack([x["sem_seg"].to(self.device) for x in batched_inputs], dim=0)
outputs = F.interpolate(outputs, size=(targets.shape[-2], targets.shape[-1]), mode="bilinear", align_corners=False)
num_classes = outputs.shape[1]
mask = targets != self.sem_seg_head.ignore_value
outputs = outputs.permute(0,2,3,1)
_targets = torch.zeros(outputs.shape, device=self.device)
_onehot = F.one_hot(targets[mask], num_classes=num_classes).float()
_targets[mask] = _onehot
loss = F.binary_cross_entropy_with_logits(outputs, _targets)
losses = {"loss_sem_seg" : loss}
return losses
else:
outputs = outputs.sigmoid()
image_size = images.image_sizes[0]
height = batched_inputs[0].get("height", image_size[0])
width = batched_inputs[0].get("width", image_size[1])
output = sem_seg_postprocess(outputs[0], image_size, height, width)
processed_results = [{'sem_seg': output}]
return processed_results
@torch.no_grad()
def inference_sliding_window(self, batched_inputs, kernel=384, overlap=0.333, out_res=[640, 640]):
images = [x["image"].to(self.device, dtype=torch.float32) for x in batched_inputs]
stride = int(kernel * (1 - overlap))
unfold = nn.Unfold(kernel_size=kernel, stride=stride)
fold = nn.Fold(out_res, kernel_size=kernel, stride=stride)
image = F.interpolate(images[0].unsqueeze(0), size=out_res, mode='bilinear', align_corners=False).squeeze()
image = rearrange(unfold(image), "(C H W) L-> L C H W", C=3, H=kernel)
global_image = F.interpolate(images[0].unsqueeze(0), size=(kernel, kernel), mode='bilinear', align_corners=False)
image = torch.cat((image, global_image), dim=0)
images = (image - self.pixel_mean) / self.pixel_std
clip_images = (image - self.clip_pixel_mean) / self.clip_pixel_std
clip_images = F.interpolate(clip_images, size=self.clip_resolution, mode='bilinear', align_corners=False, )
clip_features = self.sem_seg_head.predictor.clip_model.encode_image(clip_images, dense=True)
if self.sequential:
outputs = []
for clip_feat, image in zip(clip_features, images):
feature = self.backbone(image.unsqueeze(0))
output = self.sem_seg_head(clip_feat.unsqueeze(0), feature)
outputs.append(output[0])
outputs = torch.stack(outputs, dim=0)
else:
features = self.backbone(images)
outputs = self.sem_seg_head(clip_features, features)
outputs = F.interpolate(outputs, size=kernel, mode="bilinear", align_corners=False)
outputs = outputs.sigmoid()
global_output = outputs[-1:]
global_output = F.interpolate(global_output, size=out_res, mode='bilinear', align_corners=False,)
outputs = outputs[:-1]
outputs = fold(outputs.flatten(1).T) / fold(unfold(torch.ones([1] + out_res, device=self.device)))
outputs = (outputs + global_output) / 2.
height = batched_inputs[0].get("height", out_res[0])
width = batched_inputs[0].get("width", out_res[1])
output = sem_seg_postprocess(outputs, out_res, height, width)
return [{'sem_seg': output}] |