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# Copyright (c) Facebook, Inc. and its affiliates.
import logging
import numpy as np
from typing import Dict, List, Optional, Tuple
from numpy.lib import pad
import torch
from torch import nn
from torch.nn import functional as F
from random import randint
from detectron2.config import configurable
from detectron2.data.detection_utils import convert_image_to_rgb
from detectron2.structures import ImageList, Instances, Boxes
from detectron2.utils.events import get_event_storage
from detectron2.utils.logger import log_first_n
from ..backbone import Backbone, build_backbone, build_text_backbone
from ..postprocessing import detector_postprocess
from ..proposal_generator import build_proposal_generator
from ..roi_heads import build_roi_heads
from .build import META_ARCH_REGISTRY
from PIL import Image
import torchvision
from torchvision.transforms import Resize, CenterCrop
from detectron2.data.datasets.clip_prompt_utils import get_cls_names, pre_tokenize
import copy
from ..backbone.fpn import build_resnet_fpn_backbone
from ..roi_heads.fast_rcnn import fast_rcnn_inference
from detectron2.layers import ShapeSpec
from ..backbone.clip_backbone import build_clip_language_encoder
from detectron2.utils.comm import gather_tensors, MILCrossEntropy, SoftTargetCrossEntropy
__all__ = ["CLIPRCNN", "CLIPFastRCNN", "PretrainFastRCNN"]
@META_ARCH_REGISTRY.register()
class CLIPRCNN(nn.Module):
"""
CLIP in R-CNN format.
It takes the image regions as inputs and classifies each image.
It contains the following two components:
1. Per-image feature extraction (visual encoder)
2. Per-image prediction (text-based classifier)
"""
@configurable
def __init__(
self,
*,
clip: Backbone,
offline_backbone: Backbone,
offline_proposal_generator: nn.Module,
roi_heads: nn.Module,
pixel_mean: Tuple[float],
pixel_std: Tuple[float],
input_format: Optional[str] = None,
vis_period: int = 0,
clip_crop_region_type: str = 'GT',
test_score_thresh: float = 0.0001,
test_nms_thresh: float = 0.5,
test_topk_per_image: float = 300,
):
"""
Args:
backbone: a backbone module, must follow detectron2's backbone interface
proposal_generator: a module that generates proposals using backbone features
roi_heads: a ROI head that performs per-region computation
pixel_mean, pixel_std: list or tuple with #channels element, representing
the per-channel mean and std to be used to normalize the input image
input_format: describe the meaning of channels of input. Needed by visualization
vis_period: the period to run visualization. Set to 0 to disable.
"""
super().__init__()
self.clip_backbone = clip
self.offline_backbone = offline_backbone
self.offline_proposal_generator = offline_proposal_generator
self.roi_heads = roi_heads
self.input_format = input_format
self.vis_period = vis_period
if vis_period > 0:
assert input_format is not None, "input_format is required for visualization!"
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)
assert (
self.pixel_mean.shape == self.pixel_std.shape
), f"{self.pixel_mean} and {self.pixel_std} have different shapes!"
# Detectron2 default pixel mean and std
self.register_buffer("detectron_pixel_mean", torch.tensor([103.530, 116.280, 123.675]).view(-1, 1, 1), False)
self.register_buffer("detectron_pixel_std", torch.tensor([1.0, 1.0, 1.0]).view(-1, 1, 1), False)
# CLIP image loading
if np.sum(pixel_mean) < 3.0: # converrt pixel value to range [0.0, 1.0] by dividing 255.0
assert input_format == 'RGB'
self.div_pixel = True
else: # default setting
self.div_pixel = False
n_px = 224
self.clip_resize = Resize(n_px, interpolation=Image.BICUBIC) # shorter side becomes n_px
self.clip_center_crop = CenterCrop(n_px) # crop image into n_px * n_px at the center
self.region_crop_scales = (1.0, 1.5) # (1.0, 2.0) # (1.0, 1.2) # (1.0,) #
# CLIP text prompt loading
print("Working on pre_tokenize...")
cls_names = get_cls_names(filter_novel=False, from_file='/home/v-yiwuzhong/projects/azureblobs/vyiwuzhong_phillytools/trained_models/concept_pool/googlecc_nouns_filtered_100.txt') # filter_novel=True; coco='all', coco='base', coco='target'; from_file: a file path for concept pool
# from_file='/home/v-yiwuzhong/projects/azureblobs/vyiwuzhong_phillytools/trained_models/concept_pool/googlecc_nouns_triplet_parser_filtered_100.txt'
print("Got {} class names: {}\n {} class names in total.".format(len(cls_names), cls_names, len(cls_names)))
input_ids = pre_tokenize(cls_names)
self.num_cls = input_ids.size(0)
self.num_prompt = input_ids.size(1)
self.input_ids_flat = input_ids.view(-1, input_ids.size(2)) # [#cls*#prompts, #context_length]
self.clss_emb_all = None
# CLIP crop image configs
self.clip_crop_region_type = clip_crop_region_type
self.test_score_thresh = test_score_thresh
self.test_nms_thresh = test_nms_thresh
self.test_topk_per_image = test_topk_per_image
@classmethod
def from_config(cls, cfg):
if cfg.MODEL.CLIP.CROP_REGION_TYPE == "RPN":
offline_backbone = build_resnet_fpn_backbone(cfg, ShapeSpec(channels=len(cfg.MODEL.PIXEL_MEAN))) # build_backbone(cfg)
offline_rpn = build_proposal_generator(cfg, offline_backbone.output_shape())
roi_heads = None # build_roi_heads(cfg, backbone.output_shape()),
elif cfg.MODEL.CLIP.CROP_REGION_TYPE == "GT":
offline_backbone = None
offline_rpn = None
roi_heads = None
clip = build_backbone(cfg)
return {
"clip": clip,
"offline_backbone": offline_backbone,
"offline_proposal_generator": offline_rpn,
"roi_heads": roi_heads,
"input_format": cfg.INPUT.FORMAT,
"vis_period": cfg.VIS_PERIOD,
"pixel_mean": cfg.MODEL.PIXEL_MEAN,
"pixel_std": cfg.MODEL.PIXEL_STD,
"clip_crop_region_type" : cfg.MODEL.CLIP.CROP_REGION_TYPE,
"test_score_thresh" : cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST,
"test_nms_thresh" : cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST,
"test_topk_per_image" : cfg.TEST.DETECTIONS_PER_IMAGE,
}
@property
def device(self):
return self.pixel_mean.device
def forward(self, batched_inputs: List[Dict[str, torch.Tensor]]):
"""
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 (optional): groundtruth :class:`Instances`
* proposals (optional): :class:`Instances`, precomputed proposals.
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.
Returns:
list[dict]:
Each dict is the output for one input image.
The dict contains one key "instances" whose value is a :class:`Instances`.
The :class:`Instances` object has the following keys:
"pred_boxes", "pred_classes", "scores", "pred_masks", "pred_keypoints"
"""
if not self.training:
return self.inference(batched_inputs)
# No training mode for this arch
def inference(
self,
batched_inputs: List[Dict[str, torch.Tensor]],
detected_instances: Optional[List[Instances]] = None,
do_postprocess: bool = True,
):
"""
Run inference on the given inputs.
Args:
batched_inputs (list[dict]): same as in :meth:`forward`
detected_instances (None or list[Instances]): if not None, it
contains an `Instances` object per image. The `Instances`
object contains "pred_boxes" and "pred_classes" which are
known boxes in the image.
The inference will then skip the detection of bounding boxes,
and only predict other per-ROI outputs.
do_postprocess (bool): whether to apply post-processing on the outputs.
Returns:
When do_postprocess=True, same as in :meth:`forward`.
Otherwise, a list[Instances] containing raw network outputs.
"""
assert not self.training
# get the label prompt, and use CLIP.encode_text() to compute text emb only once
if self.clss_emb_all is None: # compute only once
num_instances = self.input_ids_flat.size(0)
per_split = 1000
num_splits = num_instances // per_split
input_ids_flat = self.input_ids_flat.to(self.device)
#self.clss_emb_all = torch.ones((1203, 512)).to(self.device)
clss_emb_all = []
for i in range(num_splits+1):
if i < num_splits:
clss_emb_i = self.clip_backbone.encode_text(input_ids_flat[per_split*i:per_split*(i+1)]) # per_split x D
else:
clss_emb_i = self.clip_backbone.encode_text(input_ids_flat[per_split*i:]) # per_split x D
# clss_emb_i = clip_model.encode_label(torch.arange(0, 1000).view(-1, 1).long().to(device)) # per_split x D
clss_emb_all.append(clss_emb_i)
self.clss_emb_all = torch.cat(clss_emb_all, 0).view(self.num_cls, self.num_prompt, -1) # [#cls, #prompts, D]
self.clss_emb_all = self.clss_emb_all.mean(1) # ensemble different prompts for each class
# torch.save(self.clss_emb_all.cpu(), "/home/v-yiwuzhong/projects/azureblobs/vyiwuzhong_phillytools/trained_models/lvis_cls_emb/coco_17_target_cls_emb_notnorm_rn50x4.pth")
self.clss_emb_all = F.normalize(self.clss_emb_all, p=2.0, dim=1) # [#cls, emb_dim]
else:
assert self.clss_emb_all.device == self.device
# get the region proposals, from the backbone & RPN of standard Mask-RCNN, trained on base classes
if self.clip_crop_region_type == "GT":
proposals = None
elif self.clip_crop_region_type == "RPN":
images = self.preprocess_image(batched_inputs)
features = self.offline_backbone(images.tensor)
if detected_instances is None:
if self.offline_proposal_generator is not None:
proposals, _ = self.offline_proposal_generator(images, features, None)
# crop image regions, and use CLIP.encode_image() to get the visual feature
images, bbs, num_bbs = self.preprocess_image_crop(batched_inputs, rpn_proposals=proposals)
img_emb = self.clip_backbone.encode_image(images.tensor)
img_emb = img_emb.view(-1, len(self.region_crop_scales), img_emb.size(1))
img_emb = torch.sum(img_emb, dim=1) # ensemble different scales for each region
img_emb = F.normalize(img_emb, p=2.0, dim=1)
# cosine similarity as logits
all_scores = torch.mm(img_emb, self.clss_emb_all.T)
all_scores = F.softmax(all_scores, dim=-1)
scores, pred_cls = torch.max(all_scores, dim=-1) # Note: [0, #cls-1] representing the categories. The value #cls represents "background".
# convert model outputs into regular output result format
scores_per_img = scores.split(num_bbs)
pred_cls_per_img = pred_cls.split(num_bbs)
all_scores_per_img = all_scores.split(num_bbs)
# per-class NMS
if self.clip_crop_region_type == "GT":
image_shapes = [x['instances']._image_size for x in batched_inputs]
bbs = [bb.to(self.device) for bb in bbs]
pred_instances, _ = fast_rcnn_inference(bbs, all_scores_per_img, image_shapes, \
self.test_score_thresh, self.test_nms_thresh, self.test_topk_per_image)
results = pred_instances
# results = []
# for r_i, (b_input, bb, sc, prd) in enumerate(zip(batched_inputs, bbs, scores_per_img, pred_cls_per_img)):
# this_result = copy.deepcopy(b_input["instances"]) # Instance
# if self.clip_crop_region_type == "GT":
# result_boxes = this_result._fields['gt_boxes'].to(self.device)
# elif self.clip_crop_region_type == "RPN": # directly use RPN boxes without per-class NMS
# result_boxes = bb # result_boxes = Boxes(bb)
# this_result._fields = {'pred_boxes': result_boxes, 'scores': sc, 'pred_classes': prd}
# results.append(this_result)
# sanity check: GT boxes + GT classes
# results = []
# for b_input in batched_inputs:
# this_result = copy.deepcopy(b_input["instances"]) # Instance
# gt_boxes = this_result._fields['gt_boxes'].to(self.device)
# gt_cls = this_result._fields['gt_classes'].to(self.device)
# this_result._fields = {'pred_boxes': gt_boxes, 'scores': torch.ones(gt_cls.size(0)).to(self.device), 'pred_classes': gt_cls}
# #this_result._fields = {'pred_boxes': gt_boxes, 'scores': sc, 'pred_classes': prd}
# results.append(this_result)
elif self.clip_crop_region_type == "RPN":
image_shapes = [x.image_size for x in proposals]
pred_instances, _ = fast_rcnn_inference(bbs, all_scores_per_img, image_shapes, \
self.test_score_thresh, self.test_nms_thresh, self.test_topk_per_image)
results = pred_instances
if do_postprocess:
assert not torch.jit.is_scripting(), "Scripting is not supported for postprocess."
return CLIPRCNN._postprocess(results, batched_inputs)
else:
return results
def preprocess_image(self, batched_inputs: List[Dict[str, torch.Tensor]]):
"""
Normalize, pad and batch the input images. Use detectron2 default processing (pixel mean & std).
Note: Due to FPN size_divisibility, images are padded by right/bottom border. So FPN is consistent with C4 and GT boxes.
"""
images = [x["image"].to(self.device) for x in batched_inputs]
images = [(x - self.detectron_pixel_mean) / self.detectron_pixel_std for x in images]
images = ImageList.from_tensors(images, self.offline_backbone.size_divisibility)
return images
def preprocess_image_crop(self, batched_inputs: List[Dict[str, torch.Tensor]], rpn_proposals=None, max_num_rpn=1000):
"""
Crop image regions based on GT or RPN boxes with different scales.
Then apply CLIP tranformation: resizing / cropping the regions into square shape (224 * 224).
Followed by the default preprocessing in Detectron2 as follows.
Normalize, pad and batch the input images.
"""
def clip_crop_region(image, box, scales=(1.0, 1.5)):
"""Crop image regions based on given boxes. Return different scales of region crops. (3 hrs)"""
img_h, img_w = image.size(1), image.size(2)
x1, y1, x2, y2 = list(box)
assert x1 < x2 and y1 < y2 and x2 < (img_w + 1) and y2 < (img_h + 1)
x_center = (x1 + x2) / 2.0
y_center = (y1 + y2) / 2.0
half_w = x_center - x1
half_h = y_center - y1
regions = []
for scale in scales: # get region coordinates
r_y1 = int(max(0, (y_center - half_h * scale).item()))
r_y2 = int(min(img_h, (y_center + half_h * scale).item()))
r_x1 = int(max(0, (x_center - half_w * scale).item()))
r_x2 = int(min(img_w, (x_center + half_w * scale).item()))
# sanity check
if r_y2 - r_y1 <= 1:
r_y2 = int(min(img_h, r_y2 + 2))
if r_y2 - r_y1 <= 1:
r_y1 = int(max(0, r_y1 - 2))
if r_x2 - r_x1 <= 1:
r_x2 = int(min(img_w, r_x2 + 2))
if r_x2 - r_x1 <= 1:
r_x1 = int(max(0, r_x1 - 2))
regions.append(image[:, r_y1:r_y2, r_x1:r_x2])
return regions
def clip_square_crop(image, box, scales=(1.0,)):
"""Crop image regions based on given boxes. Ensure square region as much as possible. (1.75 hrs)"""
img_h, img_w = image.size(1), image.size(2)
x1, y1, x2, y2 = list(box)
assert x1 < x2 and y1 < y2 and x2 < (img_w + 1) and y2 < (img_h + 1)
x_center = (x1 + x2) / 2.0
y_center = (y1 + y2) / 2.0
half_w = x_center - x1
half_h = y_center - y1
square_side = max(half_w, half_h)
half_w = square_side
half_h = square_side
regions = []
for scale in scales: # get region coordinates
if square_side * square_side < 2500: # crop larger context area for tiny objects
scale = 1.5 if scale == 1.0 else 4.0
# elif square_side * square_side > 90000: # crop exact area for large objects
# scale = 1.0 if scale == 1.0 else 1.1
r_y1 = int(max(0, (y_center - half_h * scale).item()))
r_y2 = int(min(img_h, (y_center + half_h * scale).item()))
r_x1 = int(max(0, (x_center - half_w * scale).item()))
r_x2 = int(min(img_w, (x_center + half_w * scale).item()))
# sanity check
if r_y2 - r_y1 <= 1:
r_y2 = int(min(img_h, r_y2 + 2))
if r_y2 - r_y1 <= 1:
r_y1 = int(max(0, r_y1 - 2))
if r_x2 - r_x1 <= 1:
r_x2 = int(min(img_w, r_x2 + 2))
if r_x2 - r_x1 <= 1:
r_x1 = int(max(0, r_x1 - 2))
#regions.append(image[:, r_y1:r_y2, r_x1:r_x2])
# if the cropped image isn't square (due to image boundaries), pad the cropped region
crop_image = image[:, r_y1:r_y2, r_x1:r_x2]
r_h, r_w = crop_image.size(1), crop_image.size(2)
pad_image = torch.zeros((3, int(2 * half_h.item() * scale) + 4 , int(2 * half_w.item() * scale) + 4)) #.fill_(torch.mean(crop_image.float()))
p_h, p_w = pad_image.size(1), pad_image.size(2)
pad_image[:, int(((p_h - r_h) / 2)):int(((p_h - r_h) / 2 + r_h)), int(((p_w - r_w) / 2)):int(((p_w - r_w) / 2 + r_w))] = crop_image
regions.append(pad_image.type(torch.uint8))
return regions
def vis_crop(f_n, images):
"""visualize the crop regions to diagnose the accuracy."""
if f_n not in ['datasets/coco/train2017/000000008691.jpg']:
for p_i, pad_image in enumerate(images):
to_save = pad_image.permute(1, 2, 0).numpy()
to_save = Image.fromarray(np.array(to_save, np.uint8))
#to_save.save("output/regions/" + f_n.split("/")[-1].split(".")[0] + "-{}.png".format(p_i))
pass
# crop image region
images = []
bbs = []
num_bbs = []
for img_i, b_input in enumerate(batched_inputs):
this_img = b_input["image"]
if self.clip_crop_region_type == "GT":
this_boxes = b_input["instances"]._fields['gt_boxes'].tensor # variant #bbox (eg, max 759), might lead to OOM
elif self.clip_crop_region_type == "RPN":
this_boxes = rpn_proposals[img_i]._fields['proposal_boxes'].tensor[:max_num_rpn]
bbs.append(this_boxes)
num_bbs.append(this_boxes.size(0))
for this_box in this_boxes:
#images.extend(clip_crop_region(this_img, this_box, self.region_crop_scales))
images.extend(clip_square_crop(this_img, this_box, self.region_crop_scales))
#vis_crop(batched_inputs[0]['file_name'], images)
images = [self.clip_resize(x) for x in images]
images = [self.clip_center_crop(x) for x in images]
images = [x.to(self.device) for x in images]
if self.div_pixel:
images = [((x / 255.0) - self.pixel_mean) / self.pixel_std for x in images]
else:
images = [(x - self.pixel_mean) / self.pixel_std for x in images]
images = ImageList.from_tensors(images, self.clip_backbone.size_divisibility) # batch images into single tensor by padding to same size
return images, bbs, num_bbs
@staticmethod
def _postprocess(instances, batched_inputs: List[Dict[str, torch.Tensor]]):
"""
Rescale the output instances to the target size.
"""
# note: private function; subject to changes
processed_results = []
for results_per_image, input_per_image in zip(
instances, batched_inputs):
height = input_per_image["height"] # original image size, before resizing
width = input_per_image["width"] # original image size, before resizing
r = detector_postprocess(results_per_image, height, width)
processed_results.append({"instances": r})
return processed_results
def inference_on_cifar(self, pseudo_input):
""" Evaluate recoginition accuracy on CIFAR-10 for sanity check """
# get the label prompt, and use CLIP.encode_text() to compute text emb only once
cifar_cls_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
input_ids = pre_tokenize(cifar_cls_names)
num_cls = input_ids.size(0)
input_ids_flat = input_ids.view(-1, input_ids.size(2))
input_ids_flat = input_ids_flat.to(self.device)
clss_emb_all = self.clip_backbone.encode_text(input_ids_flat)
clss_emb_all = clss_emb_all.view(num_cls, self.num_prompt, -1)
clss_emb_all = clss_emb_all.mean(1)
clss_emb_all = F.normalize(clss_emb_all, p=2.0, dim=1) # [#cls, emb_dim]
# dataset loads images and labels
testset = torchvision.datasets.CIFAR10(root='./datasets', train=False,
download=False, transform=None)
# testloader = torch.utils.data.DataLoader(testset, batch_size=4,
# shuffle=False, num_workers=0)
# inference on each image and calculate accuracy
correct = 0
wrong = 0
for idx, inputs in enumerate(testset):
if idx % 1000 == 0:
print(idx)
# preprocess images
raw_image, label = inputs
image = np.array(raw_image) # [h, w, 3]
image = torch.from_numpy(image)
image = image.permute(2, 0, 1) # [3, h, w]
images = [image]
images = [self.clip_resize(x) for x in images]
images = [self.clip_center_crop(x) for x in images]
images = [x.to(self.device) for x in images]
if self.div_pixel:
images = [((x / 255.0) - self.pixel_mean) / self.pixel_std for x in images]
else:
images = [(x - self.pixel_mean) / self.pixel_std for x in images]
# get image embedding
img_emb = self.clip_backbone.encode_image(images[0].unsqueeze(0))
img_emb = img_emb.view(-1, 1, img_emb.size(1))
img_emb = torch.sum(img_emb, dim=1) # ensemble different scales for each region
img_emb = F.normalize(img_emb, p=2.0, dim=1)
# cosine similarity as logits
all_scores = torch.mm(img_emb, clss_emb_all.T)
scores, pred_cls = torch.max(all_scores, dim=1) # Note: [0, #cls-1] representing the categories. The value #cls represents "background".
pred_cls = pred_cls.item()
if pred_cls == label:
correct += 1
else:
wrong += 1
print("\n\nGot correct {} and wrong {}. Accuracy is {} / {} = {}\n\n".format(correct,wrong,correct,correct+wrong,correct/(correct+wrong)))
return
@META_ARCH_REGISTRY.register()
class CLIPFastRCNN(nn.Module):
"""
CLIP in Fast R-CNN format, where the cropping is conducted on feature maps instead of raw images.
It contains the following two components:
1. Localization modules: pretrained backbone+RPN or equivalent modules and is able to output object proposals
2. Recognition branch: initialized by CLIP and is able to recognize zero-shot regions
"""
@configurable
def __init__(
self,
*,
offline_backbone: Backbone,
backbone: Backbone,
backbone_type: str = "resnet",
text_backbone: Backbone,
offline_proposal_generator: nn.Module,
roi_heads: nn.Module,
pixel_mean: Tuple[float],
pixel_std: Tuple[float],
input_format: Optional[str] = None,
vis_period: int = 0,
clip_crop_region_type: str = 'GT',
use_clip_c4: False,
use_clip_attpool: False,
offline_input_format: Optional[str] = None,
offline_pixel_mean: Tuple[float],
offline_pixel_std: Tuple[float],
):
"""
Args:
backbone: a backbone module, must follow detectron2's backbone interface
proposal_generator: a module that generates proposals using backbone features
roi_heads: a ROI head that performs per-region computation
pixel_mean, pixel_std: list or tuple with #channels element, representing
the per-channel mean and std to be used to normalize the input image
input_format: describe the meaning of channels of input. Needed by visualization
vis_period: the period to run visualization. Set to 0 to disable.
"""
super().__init__()
self.offline_backbone = offline_backbone
self.backbone = backbone
self.backbone_type = backbone_type
self.offline_proposal_generator = offline_proposal_generator
self.roi_heads = roi_heads
self.lang_encoder = text_backbone
self.input_format = input_format
self.vis_period = vis_period
if vis_period > 0:
assert input_format is not None, "input_format is required for visualization!"
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)
assert (
self.pixel_mean.shape == self.pixel_std.shape
), f"{self.pixel_mean} and {self.pixel_std} have different shapes!"
if np.sum(pixel_mean) < 3.0: # converrt pixel value to range [0.0, 1.0] by dividing 255.0
assert input_format == 'RGB'
self.div_pixel = True
else: # default setting
self.div_pixel = False
# input format, pixel mean and std for offline modules
if offline_input_format and offline_pixel_mean and offline_pixel_std:
self.offline_input_format = offline_input_format
self.register_buffer("offline_pixel_mean", torch.tensor(offline_pixel_mean).view(-1, 1, 1), False)
self.register_buffer("offline_pixel_std", torch.tensor(offline_pixel_std).view(-1, 1, 1), False)
if np.sum(offline_pixel_mean) < 3.0: # converrt pixel value to range [0.0, 1.0] by dividing 255.0
assert offline_input_format == 'RGB'
self.offline_div_pixel = True
else: # default setting
self.offline_div_pixel = False
self.clip_crop_region_type = clip_crop_region_type
self.use_clip_c4 = use_clip_c4 # if True, use C4 mode where roi_head uses the last resnet layer from backbone
self.use_clip_attpool = use_clip_attpool # if True (C4+text_emb_as_classifier), use att_pool to replace default mean pool
@classmethod
def from_config(cls, cfg):
if cfg.MODEL.CLIP.CROP_REGION_TYPE == "RPN": # create isolated backbone & RPN
# create offline cfg for the pretrained backbone & RPN
from detectron2.config import get_cfg
offline_cfg = get_cfg()
offline_cfg.merge_from_file(cfg.MODEL.CLIP.OFFLINE_RPN_CONFIG)
if cfg.MODEL.CLIP.OFFLINE_RPN_LSJ_PRETRAINED: # large-scale jittering (LSJ) pretrained RPN
offline_cfg.MODEL.BACKBONE.FREEZE_AT = 0 # make all fronzon layers to "SyncBN"
offline_cfg.MODEL.RESNETS.NORM = "SyncBN" # 5 resnet layers
offline_cfg.MODEL.FPN.NORM = "SyncBN" # fpn layers
offline_cfg.MODEL.RPN.CONV_DIMS = [-1, -1] # rpn layers
if cfg.MODEL.CLIP.OFFLINE_RPN_NMS_THRESH:
offline_cfg.MODEL.RPN.NMS_THRESH = cfg.MODEL.CLIP.OFFLINE_RPN_NMS_THRESH # 0.9
offline_cfg.MODEL.RPN.POST_NMS_TOPK_TEST = cfg.MODEL.RPN.POST_NMS_TOPK_TEST # 0.9
# create offline backbone and RPN
offline_backbone = build_backbone(offline_cfg) # build_resnet_fpn_backbone(cfg, ShapeSpec(channels=len(cfg.MODEL.PIXEL_MEAN)))
offline_rpn = build_proposal_generator(offline_cfg, offline_backbone.output_shape())
# convert to evaluation mode
for p in offline_backbone.parameters(): p.requires_grad = False
for p in offline_rpn.parameters(): p.requires_grad = False
offline_backbone.eval()
offline_rpn.eval()
elif cfg.MODEL.CLIP.CROP_REGION_TYPE == "GT":
offline_backbone = None
offline_rpn = None
offline_cfg = None
backbone = build_backbone(cfg)
text_backbone = build_clip_language_encoder(cfg)
backbone_type = "swin" if "swin" in cfg.MODEL.BACKBONE.NAME else "resnet"
if backbone_type == "swin":
roi_heads = build_roi_heads(cfg, backbone.image_encoder.output_shape())
else:
roi_heads = build_roi_heads(cfg, backbone.output_shape())
return {
"offline_backbone": offline_backbone,
"offline_proposal_generator": offline_rpn,
"backbone": backbone,
"backbone_type": backbone_type,
"text_backbone": text_backbone,
"roi_heads": roi_heads,
"input_format": cfg.INPUT.FORMAT,
"vis_period": cfg.VIS_PERIOD,
"pixel_mean": cfg.MODEL.PIXEL_MEAN,
"pixel_std": cfg.MODEL.PIXEL_STD,
"clip_crop_region_type" : cfg.MODEL.CLIP.CROP_REGION_TYPE,
"use_clip_c4": 'FPN' not in cfg.MODEL.BACKBONE.NAME,
"use_clip_attpool": cfg.MODEL.ROI_HEADS.NAME in ['CLIPRes5ROIHeads', 'CLIPStandardROIHeads'] and cfg.MODEL.CLIP.USE_TEXT_EMB_CLASSIFIER,
"offline_input_format": offline_cfg.INPUT.FORMAT if offline_cfg else None,
"offline_pixel_mean": offline_cfg.MODEL.PIXEL_MEAN if offline_cfg else None,
"offline_pixel_std": offline_cfg.MODEL.PIXEL_STD if offline_cfg else None,
}
@property
def device(self):
return self.pixel_mean.device
def forward(self, queries, batched_inputs: List[Dict[str, torch.Tensor]]):
"""
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 (optional): groundtruth :class:`Instances`
* proposals (optional): :class:`Instances`, precomputed proposals.
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.
Returns:
list[dict]:
Each dict is the output for one input image.
The dict contains one key "instances" whose value is a :class:`Instances`.
The :class:`Instances` object has the following keys:
"pred_boxes", "pred_classes", "scores", "pred_masks", "pred_keypoints"
"""
if not self.training:
return self.inference(queries, batched_inputs)
if "instances" in batched_inputs[0]:
gt_instances = [x["instances"].to(self.device) for x in batched_inputs]
else:
gt_instances = None
# localization branch: offline modules to get the region proposals
with torch.no_grad():
if self.clip_crop_region_type == "GT": # from ground-truth
proposals = []
for r_i, b_input in enumerate(batched_inputs):
this_gt = copy.deepcopy(b_input["instances"]) # Instance
gt_boxes = this_gt._fields['gt_boxes'].to(self.device)
this_gt._fields = {'proposal_boxes': gt_boxes, 'objectness_logits': torch.ones(gt_boxes.tensor.size(0)).to(self.device)}
proposals.append(this_gt)
elif self.clip_crop_region_type == "RPN": # from the backbone & RPN of standard Mask-RCNN, trained on base classes
if self.offline_backbone.training or self.offline_proposal_generator.training: # was set to True in training script
self.offline_backbone.eval()
self.offline_proposal_generator.eval()
images = self.offline_preprocess_image(batched_inputs)
features = self.offline_backbone(images.tensor)
if self.offline_proposal_generator is not None:
proposals, _ = self.offline_proposal_generator(images, features, None)
# recognition branch: get 2D feature maps using the backbone of recognition branch
images = self.preprocess_image(batched_inputs)
features = self.backbone(images.tensor)
if self.backbone_type == "resnet":
head = self.backbone.layer4
elif self.backbone_type == "swin":
head = self.backbone.layers[-1]
# Given the proposals, crop region features from 2D image features and classify the regions
if self.use_clip_c4: # use C4 + resnet weights from CLIP
if self.use_clip_attpool: # use att_pool from CLIP to match dimension
_, detector_losses = self.roi_heads(images, features, proposals, gt_instances, res5=head, attnpool=self.backbone.attnpool)
else: # use default mean pool
_, detector_losses = self.roi_heads(images, features, proposals, gt_instances, res5=head)
else: # default setting
if self.use_clip_attpool: # use att_pool from CLIP to match dimension
_, detector_losses = self.roi_heads(images, features, proposals, gt_instances, attnpool=self.backbone.bottom_up.attnpool)
else: # use default mean pool
_, detector_losses = self.roi_heads(images, features, proposals, gt_instances)
if self.vis_period > 0:
storage = get_event_storage()
if storage.iter % self.vis_period == 0:
self.visualize_training(batched_inputs, proposals)
#visualize_proposals(batched_inputs, proposals, self.input_format)
losses = {}
losses.update(detector_losses)
return losses
def inference(
self,
queries,
batched_inputs: List[Dict[str, torch.Tensor]],
detected_instances: Optional[List[Instances]] = None,
do_postprocess: bool = True,
):
"""
Run inference on the given inputs.
Args:
batched_inputs (list[dict]): same as in :meth:`forward`
detected_instances (None or list[Instances]): if not None, it
contains an `Instances` object per image. The `Instances`
object contains "pred_boxes" and "pred_classes" which are
known boxes in the image.
The inference will then skip the detection of bounding boxes,
and only predict other per-ROI outputs.
do_postprocess (bool): whether to apply post-processing on the outputs.
Returns:
When do_postprocess=True, same as in :meth:`forward`.
Otherwise, a list[Instances] containing raw network outputs.
"""
assert not self.training
# localization branch: offline modules to get the region proposals
if self.clip_crop_region_type == "GT": # from ground-truth
proposals = []
for r_i, b_input in enumerate(batched_inputs):
this_gt = copy.deepcopy(b_input["instances"]) # Instance
gt_boxes = this_gt._fields['gt_boxes'].to(self.device)
this_gt._fields = {'proposal_boxes': gt_boxes} #, 'objectness_logits': None}
proposals.append(this_gt)
elif self.clip_crop_region_type == "RPN": # from the backbone & RPN of standard Mask-RCNN, trained on base classes
images = self.offline_preprocess_image(batched_inputs)
features = self.offline_backbone(images.tensor)
if detected_instances is None:
if self.offline_proposal_generator is not None:
proposals, _ = self.offline_proposal_generator(images, features, None)
# recognition branch: get 2D feature maps using the backbone of recognition branch
print(batched_inputs[0]['image'][0][:10, :10])
print(batched_inputs[0]['image'].shape)
images = self.preprocess_image(batched_inputs)
if self.backbone_type == "swin":
features = self.backbone.encode_image(images.tensor)
text_features = self.backbone.encode_text(queries)
else:
features = self.backbone(images.tensor)
token_embeddings = pre_tokenize([queries]).to(images.tensor.device)[0]
text_features = self.lang_encoder.encode_text(token_embeddings)
text_features = text_features.mean(0, keepdim=True)
text_features = text_features / text_features.norm(dim=-1, keepdim=True)
if self.backbone_type == "resnet":
head = self.backbone.layer4
downsampler = None
norm = None
vision_projection = None
elif self.backbone_type == "swin":
downsampler = self.backbone.image_encoder.layers[-2].downsample
head = self.backbone.image_encoder.layers[-1]
norm = self.backbone.image_encoder.norm
vision_projection = self.backbone.image_projection
# Given the proposals, crop region features from 2D image features and classify the regions
if self.use_clip_c4: # use C4 + resnet weights from CLIP
if self.use_clip_attpool: # use att_pool from CLIP to match dimension
results, _ = self.roi_heads(images, features, proposals, text_features, None,
res5=head, ds=downsampler, norm=norm, vision_projection=vision_projection, attnpool=self.backbone.attnpool)
else: # use default mean pool
results, _ = self.roi_heads(images, features, proposals, text_features, None,
res5=head, ds=downsampler, norm=norm, vision_projection=vision_projection)
else: # default setting
if self.use_clip_attpool: # use att_pool from CLIP to match dimension
results, _ = self.roi_heads(images, features, proposals, text_features, None,
attnpool=self.backbone.bottom_up.attnpool)
else:
results, _ = self.roi_heads(images, features, proposals, text_features, None)
visualize_proposals(batched_inputs, proposals, self.input_format)
vis = visualize_results(batched_inputs, results, self.input_format)
return vis
def offline_preprocess_image(self, batched_inputs: List[Dict[str, torch.Tensor]]):
"""
Normalize, pad and batch the input images. Use detectron2 default processing (pixel mean & std).
Note: Due to FPN size_divisibility, images are padded by right/bottom border. So FPN is consistent with C4 and GT boxes.
"""
images = [x["image"].to(self.device) for x in batched_inputs]
if (self.input_format == 'RGB' and self.offline_input_format == 'BGR') or \
(self.input_format == 'BGR' and self.offline_input_format == 'RGB'): # the input image follows the main config format ('RGB' or 'BGR')
images = [x[[2,1,0],:,:] for x in images]
if self.offline_div_pixel:
images = [((x / 255.0) - self.offline_pixel_mean) / self.offline_pixel_std for x in images]
else:
images = [(x - self.offline_pixel_mean) / self.offline_pixel_std for x in images]
images = ImageList.from_tensors(images, self.offline_backbone.size_divisibility)
return images
def preprocess_image(self, batched_inputs: List[Dict[str, torch.Tensor]]):
"""
Normalize, pad and batch the input images. Use CLIP default processing (pixel mean & std).
Note: Due to FPN size_divisibility, images are padded by right/bottom border. So FPN is consistent with C4 and GT boxes.
"""
images = [x["image"].to(self.device) for x in batched_inputs]
if self.div_pixel:
images = [((x / 255.0) - self.pixel_mean) / self.pixel_std for x in images]
else:
images = [(x - self.pixel_mean) / self.pixel_std for x in images]
images = ImageList.from_tensors(images, self.backbone.size_divisibility)
return images
@staticmethod
def _postprocess(instances, batched_inputs: List[Dict[str, torch.Tensor]]):
"""
Rescale the output instances to the target size.
"""
# note: private function; subject to changes
processed_results = []
for results_per_image, input_per_image in zip(
instances, batched_inputs):
height = input_per_image["height"] # original image size, before resizing
width = input_per_image["width"] # original image size, before resizing
r = detector_postprocess(results_per_image, height, width)
processed_results.append({"instances": r})
return processed_results
@META_ARCH_REGISTRY.register()
class PretrainFastRCNN(nn.Module):
"""
Open-vocabulary region representation via vision-language pretraining from image-text pairs
1. image-text level matching: weakly supervised grounding task with contrastive learning based on region-token representation
2. region-token level matching: use pseudo text to train model, provided by teacher model
"""
@configurable
def __init__(
self,
*,
offline_backbone: Backbone,
backbone: Backbone,
offline_proposal_generator: nn.Module,
roi_heads: nn.Module,
teacher_backbone: nn.Module,
teacher_roi_heads: nn.Module,
pixel_mean: Tuple[float],
pixel_std: Tuple[float],
input_format: Optional[str] = None,
vis_period: int = 0,
clip_crop_region_type: str = 'GT',
use_clip_c4: False,
use_clip_attpool: False,
offline_input_format: Optional[str] = None,
offline_pixel_mean: Tuple[float],
offline_pixel_std: Tuple[float],
language_encoder: nn.Module,
matching_temp: None,
num_regions_per_img: int = 0,
img_txt_level: None,
gather_gpus: False,
grid_regions: False,
concept_emb: None,
):
"""
Args:
backbone: a backbone module, must follow detectron2's backbone interface
proposal_generator: a module that generates proposals using backbone features
roi_heads: a ROI head that performs per-region computation
pixel_mean, pixel_std: list or tuple with #channels element, representing
the per-channel mean and std to be used to normalize the input image
input_format: describe the meaning of channels of input. Needed by visualization
vis_period: the period to run visualization. Set to 0 to disable.
"""
super().__init__()
self.offline_backbone = offline_backbone
self.backbone = backbone
self.offline_proposal_generator = offline_proposal_generator
self.roi_heads = roi_heads
self.input_format = input_format
self.vis_period = vis_period
if vis_period > 0:
assert input_format is not None, "input_format is required for visualization!"
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)
assert (
self.pixel_mean.shape == self.pixel_std.shape
), f"{self.pixel_mean} and {self.pixel_std} have different shapes!"
if np.sum(pixel_mean) < 3.0: # converrt pixel value to range [0.0, 1.0] by dividing 255.0
assert input_format == 'RGB'
self.div_pixel = True
else: # default setting
self.div_pixel = False
# input format, pixel mean and std for offline modules
if offline_input_format and offline_pixel_mean and offline_pixel_std:
self.offline_input_format = offline_input_format
self.register_buffer("offline_pixel_mean", torch.tensor(offline_pixel_mean).view(-1, 1, 1), False)
self.register_buffer("offline_pixel_std", torch.tensor(offline_pixel_std).view(-1, 1, 1), False)
if np.sum(offline_pixel_mean) < 3.0: # converrt pixel value to range [0.0, 1.0] by dividing 255.0
assert offline_input_format == 'RGB'
self.offline_div_pixel = True
else: # default setting
self.offline_div_pixel = False
self.clip_crop_region_type = clip_crop_region_type
self.use_clip_c4 = use_clip_c4 # if True, use C4 mode where roi_head uses the last resnet layer from backbone
self.use_clip_attpool = use_clip_attpool # if True (C4+text_emb_as_classifier), use att_pool to replace default mean pool
# image-text level pretraining
self.img_txt_level = img_txt_level[0]
self.only_eot = img_txt_level[1]
if self.img_txt_level:
self.lang_encoder = language_encoder
for p in self.lang_encoder.parameters(): # freeze language encoder
p.requires_grad = False
if matching_temp > 0.0: # fixed temp
self.matching_temp = matching_temp
else: # leanable temp
self.matching_temp = nn.Parameter(torch.ones([]) * 4.6052) # nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
self.context_length = 77 # defined in clip_img_txt_pair_tsv class
self.num_regions_per_img = num_regions_per_img
self.gather_gpus = gather_gpus
self.grid_regions = grid_regions
# region-token level pretraining
if concept_emb[0]:
self.register_buffer("concept_emb", torch.load(concept_emb[0]), False) # [#concepts, 1024]
self.concept_thres = concept_emb[1]
self.teacher_backbone = teacher_backbone # None
# when resume, create teacher model in advance to load ckpt
# self.teacher_backbone = copy.deepcopy(self.backbone)
# # # oai_clip = torch.load("/mnt/output_storage/trained_models/oai_clip_weights/RN50_OAI_CLIP.pth") #("/home/v-yiwuzhong/projects/azureblobs/vyiwuzhong_phillytools/trained_models/oai_clip_weights/RN50_OAI_CLIP.pth")
# # # oai_clip_visual = {}
# # # for key in oai_clip['model']:
# # # if 'visual' in key and 'num_batches_tracked' not in key:
# # # oai_clip_visual[key.replace('visual.','')] = oai_clip['model'][key]
# # # self.teacher_backbone.load_state_dict(oai_clip_visual)
for p in self.teacher_backbone.parameters(): # freeze visual encoder of teacher model
p.requires_grad = False
if concept_emb[2] is None: # teacher model uses the same concept embedding as student model
self.register_buffer("teacher_concept_emb", torch.load(concept_emb[0]), False)
else: # teacher model uses a seperate concept embedding
self.register_buffer("teacher_concept_emb", torch.load(concept_emb[2]), False)
self.teacher_roi_heads = teacher_roi_heads
else:
self.concept_emb = None
@classmethod
def from_config(cls, cfg):
if cfg.MODEL.CLIP.CROP_REGION_TYPE == "RPN": # create isolated backbone & RPN
# create offline cfg for the pretrained backbone & RPN
from detectron2.config import get_cfg
offline_cfg = get_cfg()
offline_cfg.merge_from_file(cfg.MODEL.CLIP.OFFLINE_RPN_CONFIG)
if cfg.MODEL.CLIP.OFFLINE_RPN_LSJ_PRETRAINED: # large-scale jittering (LSJ) pretrained RPN
offline_cfg.MODEL.BACKBONE.FREEZE_AT = 0 # make all fronzon layers to "SyncBN"
offline_cfg.MODEL.RESNETS.NORM = "SyncBN" # 5 resnet layers
offline_cfg.MODEL.FPN.NORM = "SyncBN" # fpn layers
offline_cfg.MODEL.RPN.CONV_DIMS = [-1, -1] # rpn layers
if cfg.MODEL.CLIP.PRETRAIN_RPN_REGIONS:
offline_cfg.MODEL.RPN.POST_NMS_TOPK_TEST = cfg.MODEL.CLIP.PRETRAIN_RPN_REGIONS
if cfg.MODEL.CLIP.OFFLINE_RPN_NMS_THRESH:
offline_cfg.MODEL.RPN.NMS_THRESH = cfg.MODEL.CLIP.OFFLINE_RPN_NMS_THRESH # 0.9
# offline_cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST = 0.6
# print("\n\n Set offline RPN.NMS_THRESH to {} and ROI_HEADS.NMS_THRESH_TEST to {}.\n\n".format(offline_cfg.MODEL.RPN.NMS_THRESH, offline_cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST))
# create offline backbone and RPN
offline_backbone = build_backbone(offline_cfg) # build_resnet_fpn_backbone(cfg, ShapeSpec(channels=len(cfg.MODEL.PIXEL_MEAN)))
offline_rpn = build_proposal_generator(offline_cfg, offline_backbone.output_shape())
# convert to evaluation mode
for p in offline_backbone.parameters(): p.requires_grad = False
for p in offline_rpn.parameters(): p.requires_grad = False
offline_backbone.eval()
offline_rpn.eval()
elif cfg.MODEL.CLIP.CROP_REGION_TYPE in ["GLOBAL", "GRID", "RANDOM"]:
offline_backbone = None
offline_rpn = None
offline_cfg = None
# visual encoder and roi_heads of student model
backbone = build_backbone(cfg)
if "swin" in cfg.MODEL.BACKBONE.NAME:
roi_heads = build_roi_heads(cfg, backbone.image_encoder.output_shape())
else:
roi_heads = build_roi_heads(cfg, backbone.output_shape())
# language encoder of student model
language_encoder = build_clip_language_encoder(cfg)
# visual encoder of teacher model
teacher_cfg = copy.deepcopy(cfg)
teacher_cfg.defrost()
teacher_cfg.MODEL.RESNETS.DEPTH = teacher_cfg.MODEL.CLIP.TEACHER_RESNETS_DEPTH
teacher_backbone = build_backbone(teacher_cfg)
teacher_cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION = teacher_cfg.MODEL.CLIP.TEACHER_POOLER_RESOLUTION
teacher_roi_heads = build_roi_heads(teacher_cfg, teacher_backbone.output_shape())
return {
"offline_backbone": offline_backbone,
"offline_proposal_generator": offline_rpn,
"backbone": backbone,
"roi_heads": roi_heads,
"teacher_backbone": teacher_backbone,
"teacher_roi_heads": teacher_roi_heads,
"input_format": cfg.INPUT.FORMAT,
"vis_period": cfg.VIS_PERIOD,
"pixel_mean": cfg.MODEL.PIXEL_MEAN,
"pixel_std": cfg.MODEL.PIXEL_STD,
"clip_crop_region_type" : cfg.MODEL.CLIP.CROP_REGION_TYPE,
"use_clip_c4": 'FPN' not in cfg.MODEL.BACKBONE.NAME,
"use_clip_attpool": cfg.MODEL.ROI_HEADS.NAME == 'PretrainRes5ROIHeads',
"offline_input_format": offline_cfg.INPUT.FORMAT if offline_cfg else None,
"offline_pixel_mean": offline_cfg.MODEL.PIXEL_MEAN if offline_cfg else None,
"offline_pixel_std": offline_cfg.MODEL.PIXEL_STD if offline_cfg else None,
"language_encoder": language_encoder,
"matching_temp": cfg.MODEL.CLIP.CLSS_TEMP,
"num_regions_per_img": cfg.MODEL.CLIP.PRETRAIN_SAMPLE_REGIONS,
"img_txt_level": (cfg.MODEL.CLIP.PRETRAIN_IMG_TXT_LEVEL, cfg.MODEL.CLIP.PRETRAIN_ONLY_EOT),
"gather_gpus": cfg.MODEL.CLIP.GATHER_GPUS,
"grid_regions": cfg.MODEL.CLIP.GRID_REGIONS,
"concept_emb": (cfg.MODEL.CLIP.CONCEPT_POOL_EMB, cfg.MODEL.CLIP.CONCEPT_THRES, cfg.MODEL.CLIP.TEACHER_CONCEPT_POOL_EMB),
}
@property
def device(self):
return self.pixel_mean.device
def forward(self, batched_inputs: List[Dict[str, torch.Tensor]]):
"""
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 (optional): groundtruth :class:`Instances`
* proposals (optional): :class:`Instances`, precomputed proposals.
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.
Returns:
list[dict]:
Each dict is the output for one input image.
The dict contains one key "instances" whose value is a :class:`Instances`.
The :class:`Instances` object has the following keys:
"pred_boxes", "pred_classes", "scores", "pred_masks", "pred_keypoints"
"""
if not self.training:
return self.inference(batched_inputs)
if self.concept_emb is not None and self.teacher_backbone is None: # create a teacher model from an initialized student model; if resume, simply comment out this section
self.teacher_backbone = copy.deepcopy(self.backbone)
for p in self.teacher_backbone.parameters(): # freeze visual encoder of teacher model
p.requires_grad = False
gt_instances = None
losses = {}
# localization branch: offline modules to get the region proposals
proposals = self.get_region_proposals(batched_inputs)
global_proposals = self.create_global_proposals(batched_inputs)
# for prop, g_prop in zip(proposals, global_proposals): # append global proposal into each image
# prop.proposal_boxes.tensor = torch.cat((prop.proposal_boxes.tensor, g_prop.tensor), dim=0)
# recognition branch: get 2D feature maps using the backbone of recognition branch
images = self.preprocess_image(batched_inputs)
features = self.backbone(images.tensor)
region_feats = self.get_region_features(images, features, proposals, gt_instances)
global_feats = self.get_region_features(images, features, global_proposals, gt_instances)
# image-text level matching
if self.img_txt_level:
self.image_text_matching(batched_inputs, proposals, region_feats, losses, global_feats=global_feats, only_global=True)
# region-phrase level matching
if len(batched_inputs[0]) > 6: # controlled by dataset loading
phrase_text_embs = self.encode_phrase_text(batched_inputs)
else:
phrase_text_embs = None
# region-concept level matching
if self.concept_emb is not None:
self.region_concept_matching(images, proposals, gt_instances, region_feats, losses, phrase_embs=phrase_text_embs)
return losses
def encode_phrase_text(self, batched_inputs):
text = [x[6].view(-1,self.context_length).to(self.device) for i, x in enumerate(batched_inputs)]
text = torch.cat(text, dim=0)
text_embs = self.lang_encoder.encode_text(text, only_eot=True) # [#phrases, transformer.width]
return text_embs
def region_concept_matching(self, images, proposals, gt_instances, region_feats, losses, phrase_embs=None):
use_distill = True
use_contrastive = True
# get psuedo concept labels from teacher model
concept_scores, target_inds, keep_regions, target_embs, label_mtx, phrase_label_mtx, phrase_target_regions \
= self.get_psuedo_concept_labels(images, proposals, gt_instances, phrase_embs=phrase_embs)
# prepare region features for the kept regions
keep_region_feats = region_feats[keep_regions]
keep_region_feats = keep_region_feats / keep_region_feats.norm(dim=-1, keepdim=True)
if use_distill:
# distillation learning: learns from the predictions of teacher model
concept_emb = self.concept_emb / self.concept_emb.norm(dim=-1, keepdim=True)
cls_scores = keep_region_feats @ concept_emb.t() # [#kept_regions, #concepts]
if isinstance(self.matching_temp, float): # Typical good values are 100.0 for euclidean, 10.0 for dot, 0.01 for cosine
cls_scores_temp = cls_scores / self.matching_temp
else:
cls_scores_temp = cls_scores * self.matching_temp.exp()
# loss weights
#rpn_weights = torch.cat([torch.sigmoid(p.objectness_logits) for p in proposals])[keep_regions]
#focal_weights = self.focal_scaling(cls_scores_temp, target_inds)
# calculate loss
cls_loss = F.kl_div(F.softmax(cls_scores_temp, dim=1).log(), concept_scores, reduction='batchmean') # input is log-probabilities, target is probabilities
#cls_loss = SoftTargetCrossEntropy()(cls_scores_temp, concept_scores)
#cls_loss = F.cross_entropy(cls_scores_temp, target_inds)
#cls_loss = (F.cross_entropy(cls_scores_temp, target_inds, reduction="none") * focal_weights).mean()
losses.update({"loss_region_distill": cls_loss}) # * 0.8})
if use_contrastive:
# contrastive learning: matching student visual features with target teacher concept embs
target_embs = target_embs / target_embs.norm(dim=-1, keepdim=True)
match_scores = keep_region_feats @ target_embs.t() # [#kept_regions, #kept_regions]
if isinstance(self.matching_temp, float): # Typical good values are 100.0 for euclidean, 10.0 for dot, 0.01 for cosine
match_scores_temp = match_scores / self.matching_temp
else:
match_scores_temp = match_scores * self.matching_temp.exp()
# loss weights
#rpn_weights = torch.cat([torch.sigmoid(p.objectness_logits) for p in proposals])[keep_regions]
#focal_weights = (1 - torch.sigmoid(torch.diag(match_scores_temp))) ** 0.8 # 1.0 # 2.0 #
# calculate loss given matching scores and label matrix
contrastive_loss = MILCrossEntropy()(match_scores_temp, label_mtx, weights=None, avg_positives=False) # SoftTargetCrossEntropy()(match_scores_temp, label_mtx)
#contrastive_loss = (MILCrossEntropy()(match_scores, label_mtx) + MILCrossEntropy()(match_scores.t(), label_mtx)) / 2.0
losses.update({"loss_concept_contrastive": contrastive_loss})
if phrase_embs is not None:
phrase_embs = phrase_embs / phrase_embs.norm(dim=-1, keepdim=True)
phrase_scores = phrase_embs @ phrase_target_regions.t()
if isinstance(self.matching_temp, float): # Typical good values are 100.0 for euclidean, 10.0 for dot, 0.01 for cosine
phrase_scores_temp = phrase_scores / self.matching_temp
else:
phrase_scores_temp = phrase_scores * self.matching_temp.exp()
contrastive_loss = MILCrossEntropy()(phrase_scores_temp, phrase_label_mtx, weights=None, avg_positives=False)
#contrastive_loss = SoftTargetCrossEntropy()(phrase_scores_temp, phrase_label_mtx)
losses.update({"loss_phrase_contrastive": contrastive_loss})
def image_text_matching(self, batched_inputs, proposals, region_feats, losses, global_feats=None, only_global=False):
# encode text
num_cap = int(batched_inputs[0][1].size(0) / self.context_length)
if num_cap == 1: # one caption per image
text = [x[1].view(1,-1).to(self.device) for x in batched_inputs]
else: # multiple caption pers image, then randomly pick one
rand_ind = [randint(0, num_cap-1) for _ in range(len(batched_inputs))]
text = [x[1].view(-1,self.context_length)[rand_ind[i]:rand_ind[i]+1].to(self.device) for i, x in enumerate(batched_inputs)]
text = torch.cat(text, dim=0)
text_embs = self.lang_encoder.encode_text(text, only_eot=self.only_eot) # [img_batch, n_ctx, transformer.width] or [img_batch, transformer.width]
eot_pos = text.argmax(dim=-1)
# prepare region features and text embeddings
if isinstance(proposals[0], Boxes):
num_bbs = [len(prop) for prop in proposals]
else:
num_bbs = [len(prop.proposal_boxes) for prop in proposals]
if global_feats is not None and only_global: # only global feature
assert self.only_eot
region_feats = global_feats
region_feats = region_feats / region_feats.norm(dim=-1, keepdim=True)
text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True)
num_bbs = [1 for _ in num_bbs]
elif global_feats is not None and not only_global: # combine both global and region features
assert self.only_eot
keep_num = 20
region_feats = region_feats.split(num_bbs)
region_feats = [torch.mean(rg_f, dim=0, keepdim=True) for rg_f in region_feats]
region_g_feats = [torch.cat((r_f[:keep_num], global_feats[i:i+1]), dim=0) for i, r_f in enumerate(region_feats)]
region_g_feats = [torch.mean(rg_f, dim=0, keepdim=True) for rg_f in region_g_feats]
region_g_feats = [rg_f / rg_f.norm(dim=-1, keepdim=True) for rg_f in region_g_feats]
region_feats = torch.cat(region_g_feats)
text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True)
num_bbs = [1 for _ in num_bbs]
else: # only region features
num_bbs = torch.tensor(num_bbs).long().to(self.device)
region_feats_full, min_bs = gather_tensors(region_feats) if self.gather_gpus else (region_feats, None) # gather across GPUs
text_embs_full, min_bs = gather_tensors(text_embs) if self.gather_gpus else (text_embs, None) # gather across GPUs
# matching visual features with text embs
match_scores = region_feats_full @ text_embs_full.view(-1, text_embs_full.size(-1)).t() # [#regions, img_batch * n_ctx]
if global_feats is not None: # only global feature or combine both global and region features
img_b = int(region_feats_full.size(0))
pooled_score = match_scores
else: # only region features
eot_pos_full, min_bs = gather_tensors(eot_pos) if self.gather_gpus else (eot_pos, None) # gather across GPUs
num_bbs_full, min_bs = gather_tensors(num_bbs) if self.gather_gpus else (num_bbs, None) # gather across GPUs
pooled_score = []
token_b = self.context_length
# region_b = self.num_regions_per_img if global_feats is None else 1
# img_b = int(region_feats_full.size(0) / region_b)
img_b = num_bbs_full.size(0)
rb_start = 0 # the starting index of regions
for i in range(img_b): # for each image
region_b = num_bbs_full[i].item()
for j in range(img_b): # for each text
if self.only_eot: # sentence level embs
# max pool over regions
this_s = torch.max(match_scores[rb_start:(rb_start+region_b), j:(j+1)], dim=0)[0]
else: # token level embs
# 3. softmax over regions as soft attention, then multiply attention with original logits, finally sum over matrix and divided by #tokens
# this_matrix = match_scores[rb_start:(rb_start+region_b), j*token_b:(j*token_b+eot_pos_full[j]+1)]
# this_att = F.softmax(this_matrix, dim=0)
# this_s = torch.sum(this_matrix * this_att) / (eot_pos_full[j]+1)
# 2. max pool over regions, and then avg over text tokens
# this_s = torch.sum(torch.max(match_scores[rb_start:(rb_start+region_b), j*token_b:(j*token_b+eot_pos_full[j]+1)], dim=0)[0]) / (eot_pos_full[j]+1)
# 1. max pool over regions, and then sum over text tokens
this_s = torch.sum(torch.max(match_scores[rb_start:(rb_start+region_b), j*token_b:(j*token_b+eot_pos_full[j]+1)], dim=0)[0])
pooled_score.append(this_s.view(1,1))
rb_start += region_b
assert rb_start == match_scores.size(0)
pooled_score = torch.cat(pooled_score).view(img_b, img_b) # diagnal elements are positive pairs and the others are negative pairs
if isinstance(self.matching_temp,float): # Typical good values are 100.0 for euclidean, 10.0 for dot, 0.01 for cosine
pooled_score = pooled_score / self.matching_temp
else:
pooled_score = pooled_score * self.matching_temp.exp()
contrast_target = torch.arange(img_b).to(self.device)
row_loss = F.cross_entropy(pooled_score, contrast_target)
col_loss = F.cross_entropy(pooled_score.t(), contrast_target)
losses.update({"loss_img_txt_level": (row_loss + col_loss) / 2.0}) # losses.update({"loss_img_txt_level": (row_loss + col_loss) / 4.0}) #
def focal_scaling(self, logits, targets, gamma=1.0):
p = F.softmax(logits, dim=1)
p_t = p[torch.arange(p.size(0)).to(p.device), targets] # get prob of target class
weights = (1 - p_t) ** gamma
return weights
def get_psuedo_concept_labels(self, images, proposals, gt_instances, s_temp=0.01, norm=True, phrase_embs=None):
""" Input images and region proposals, return matching results from teacher model
"""
with torch.no_grad():
# extract visual features from teacher model
features = self.teacher_backbone(images.tensor)
teacher_region_feats = self.teacher_roi_heads(images, features, proposals, gt_instances, res5=self.teacher_backbone.layer4, attnpool=self.teacher_backbone.attnpool)
# match teacher visual features with teacher concept embs to create pseudo labels
if norm:
teacher_region_feats = teacher_region_feats / teacher_region_feats.norm(dim=-1, keepdim=True)
teacher_concept_emb = self.teacher_concept_emb / self.teacher_concept_emb.norm(dim=-1, keepdim=True)
else:
teacher_concept_emb = self.teacher_concept_emb
concept_scores = teacher_region_feats @ teacher_concept_emb.t() # [#regions, #concepts]
concept_scores = F.softmax(concept_scores / s_temp, dim=1)
max_scores, max_inds = torch.max(concept_scores, dim=1)
keep_regions = max_scores > self.concept_thres # only keep the regions that have high matching score with a concept
if keep_regions.nonzero().size(0) == 0: # if all regions can't match to any concept
print("all regions can't match to any concept!")
keep_regions = max_scores > 0.0
target_inds = max_inds[keep_regions]
target_embs = self.concept_emb[target_inds] # the target embedding of student model
label_mtx = (target_inds.view(-1, 1) == target_inds.view(1, -1)).type_as(teacher_region_feats)
concept_scores = concept_scores[keep_regions]
# matching kept regions with phrase-text to create labels
if phrase_embs is None:
phrase_label_mtx = None
phrase_target_regions = None
else:
if norm:
phrase_embs = phrase_embs / phrase_embs.norm(dim=-1, keepdim=True)
teacher_kept_feats = teacher_region_feats[keep_regions]
phrase_scores = phrase_embs @ teacher_kept_feats.t() # [#phrases, #keep regions]
phrase_scores = F.softmax(phrase_scores / s_temp, dim=1)
_, max_region_inds = torch.max(phrase_scores, dim=1)
phrase_label_mtx = (max_region_inds.view(-1, 1) == max_region_inds.view(1, -1)).type_as(teacher_region_feats)
phrase_target_regions = teacher_kept_feats[max_region_inds]
return concept_scores, target_inds, keep_regions, target_embs, label_mtx, phrase_label_mtx, phrase_target_regions
def get_region_features(self, images, features, proposals, gt_instances):
""" Input images and region proposals, return region features
"""
# Given the proposals, crop region features from 2D image features
if self.use_clip_c4: # use C4 + resnet weights from CLIP
if self.use_clip_attpool: # use att_pool from CLIP to match dimension
region_feats = self.roi_heads(images, features, proposals, gt_instances, res5=self.backbone.layer4, attnpool=self.backbone.attnpool)
else: # use default mean pool
region_feats = self.roi_heads(images, features, proposals, gt_instances, res5=self.backbone.layer4)
else: # default setting
region_feats = self.roi_heads(images, features, proposals, gt_instances)
return region_feats
def get_region_proposals(self, batched_inputs):
""" Given image, return object proposals
"""
if self.grid_regions: # use grid boxes
proposals = self.create_grid_boxes(batched_inputs)
else: # use object proposals
with torch.no_grad():
if self.clip_crop_region_type == "GLOBAL": # from a global box per image
proposals = self.create_global_proposals(batched_inputs)
elif self.clip_crop_region_type == "GRID": # from grid proposals
proposals = self.create_grid_boxes(batched_inputs)
elif self.clip_crop_region_type == "RANDOM": # from random proposals
proposals = self.create_rand_boxes(batched_inputs)
elif self.clip_crop_region_type == "RPN": # from the backbone & RPN of standard Mask-RCNN, trained on base classes
if self.offline_backbone.training or self.offline_proposal_generator.training: # was set to True in training script
self.offline_backbone.eval()
self.offline_proposal_generator.eval()
images = self.offline_preprocess_image(batched_inputs)
features = self.offline_backbone(images.tensor)
if self.offline_proposal_generator is not None:
proposals, _ = self.offline_proposal_generator(images, features, None)
#visualize_proposals(batched_inputs, proposals, self.input_format, vis_pretrain=True)
# randomly select proposals to avoid overfitting
if self.training:
#rand_inds = [torch.arange(len(p))[:self.num_regions_per_img].to(self.device) for p in proposals]
rand_inds = [torch.randperm(len(p))[:self.num_regions_per_img].to(self.device) for p in proposals]
proposals = [p[rand_inds[i]] for i, p in enumerate(proposals)]
return proposals
def offline_preprocess_image(self, batched_inputs: List[Dict[str, torch.Tensor]]):
"""
NOTE: the image tsv in pretraining are already normalized pixel values and thus opposite to Detectron2 default input.
Normalize, pad and batch the input images. Use detectron2 default processing (pixel mean & std).
Note: Due to FPN size_divisibility, images are padded by right/bottom border. So FPN is consistent with C4 and GT boxes.
"""
images = [x[0].to(self.device) for x in batched_inputs]
if (self.input_format == 'RGB' and self.offline_input_format == 'BGR') or \
(self.input_format == 'BGR' and self.offline_input_format == 'RGB'): # the input image follows the main config format ('RGB' or 'BGR')
images = [x[[2,1,0],:,:] for x in images]
if self.offline_div_pixel:
images = [(x - self.offline_pixel_mean) / self.offline_pixel_std for x in images]
else:
images = [((x * 255.0) - self.offline_pixel_mean) / self.offline_pixel_std for x in images]
images = ImageList.from_tensors(images, self.offline_backbone.size_divisibility)
return images
def preprocess_image(self, batched_inputs: List[Dict[str, torch.Tensor]]):
"""
NOTE: the image tsv in pretraining are already normalized pixel values and thus opposite to Detectron2 default input.
Normalize, pad and batch the input images. Use CLIP default processing (pixel mean & std).
Note: Due to FPN size_divisibility, images are padded by right/bottom border. So FPN is consistent with C4 and GT boxes.
"""
images = [x[0].to(self.device) for x in batched_inputs]
if self.div_pixel:
images = [(x - self.pixel_mean) / self.pixel_std for x in images]
else:
images = [((x * 255.0) - self.pixel_mean) / self.pixel_std for x in images]
images = ImageList.from_tensors(images, self.backbone.size_divisibility)
return images
def create_rand_boxes(self, batched_inputs, grid_length=8):
""" create random boxes within an image, output random self.num_regions_per_img boxes
return a list of Boxes
"""
images = self.preprocess_image(batched_inputs)
image_height = images.tensor.size(2)
image_width = images.tensor.size(3)
left_top_x = torch.tensor([i*(grid_length) for i in range(image_width // grid_length)])
left_top_y = torch.tensor([i*(grid_length) for i in range(image_height // grid_length)])
right_bot_x = torch.tensor([(i+1)*(grid_length) for i in range(image_width // grid_length)])
right_bot_y = torch.tensor([(i+1)*(grid_length) for i in range(image_height // grid_length)])
x_inds = torch.randint(0, left_top_x.size(0), (self.num_regions_per_img,))
y_inds = torch.randint(0, left_top_y.size(0), (self.num_regions_per_img,))
proposals = []
for i in range(self.num_regions_per_img):
rb_x_candidates = right_bot_x[x_inds[i]:]
rb_x = rb_x_candidates[torch.randperm(rb_x_candidates.size(0))[0]]
rb_y_candidates = right_bot_y[y_inds[i]:]
rb_y = rb_y_candidates[torch.randperm(rb_y_candidates.size(0))[0]]
this_box = torch.cat((left_top_x[x_inds[i]].view(1,1), left_top_y[y_inds[i]].view(1,1), rb_x.view(1,1), rb_y.view(1,1)),dim=-1)
proposals.append(this_box)
proposals = torch.cat(proposals).float().to(self.device)
proposals = [Boxes(proposals) for i in range(len(batched_inputs))] # a list of Boxes
return proposals
def create_grid_boxes(self, batched_inputs, grid_length=32):
""" create (image_height/32) * (image_width/32) pseudo grid boxes, and randomly sample self.num_regions_per_img boxes
return a list of Boxes
"""
images = self.preprocess_image(batched_inputs)
image_height = images.tensor.size(2)
image_width = images.tensor.size(3)
left_top_x = torch.tensor([i*(grid_length) for i in range(image_width // grid_length)])
left_top_y = torch.tensor([i*(grid_length) for i in range(image_height // grid_length)])
right_bot_x = torch.tensor([(i+1)*(grid_length) for i in range(image_width // grid_length)])
right_bot_y = torch.tensor([(i+1)*(grid_length) for i in range(image_height // grid_length)])
left_top_x, left_top_y = torch.meshgrid(left_top_x, left_top_y)
right_bot_x, right_bot_y = torch.meshgrid(right_bot_x, right_bot_y)
grid_boxes = torch.cat((left_top_x.flatten().view(-1,1), left_top_y.flatten().view(-1,1),\
right_bot_x.flatten().view(-1,1), right_bot_y.flatten().view(-1,1),), dim=1)
sample_ind = torch.randperm(grid_boxes.size(0))[:self.num_regions_per_img]
grid_boxes = grid_boxes[sample_ind]
grid_boxes = grid_boxes.float().to(self.device)
proposals = [Boxes(grid_boxes) for i in range(len(batched_inputs))] # a list of Boxes
return proposals
def create_global_proposals(self, batched_inputs):
""" create a single global box for an image, so as to extract global image features with RoIAlign on high-resolution images.
"""
images = self.preprocess_image(batched_inputs)
image_height = images.tensor.size(2)
image_width = images.tensor.size(3)
global_box = torch.tensor([0, 0, image_width, image_height]).view(1,4).float().to(self.device)
proposals = [Boxes(global_box) for i in range(len(batched_inputs))] # a list of Boxes
return proposals
def inference(self, batched_inputs, detected_instances=None, do_postprocess=True):
"""
Grounding inference: map region features with sentence tokens
return: matching scores between region features and tokenized texts, region boxes in raw image resolution, image id & raw string texts & tokenized texts
"""
assert len(batched_inputs) == 1 # only one instance per image during inference
gt_instances = None
losses = {}
# localization branch: offline modules to get the region proposals
proposals = self.get_region_proposals(batched_inputs)
# recognition branch: get 2D feature maps using the backbone of recognition branch
images = self.preprocess_image(batched_inputs)
features = self.backbone(images.tensor)
region_feats = self.get_region_features(images, features, proposals, gt_instances)
# encode text
num_cap = int(batched_inputs[0][1].size(0) / self.context_length)
text = batched_inputs[0][1].view(num_cap, -1).to(self.device) # [num_cap, context_length]
text_embs = self.lang_encoder.encode_text(text, only_eot=False) # [img_batch, n_ctx, transformer.width] or [img_batch, transformer.width]
# matching visual features with text embs
region_feats = region_feats / region_feats.norm(dim=-1, keepdim=True)
text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True)
match_scores = region_feats @ text_embs.view(-1, text_embs.size(-1)).t() # [#regions, img_batch * n_ctx]
# visualize_proposals(batched_inputs, proposals, self.input_format, vis_pretrain=True)
# multiply RPN logits
rpn_scores = [p.get('objectness_logits') for p in proposals][0]
match_scores = (match_scores * rpn_scores[:, None]) ** 0.5
# scale the object proposals back to raw image resolution
if do_postprocess:
assert not torch.jit.is_scripting(), "Scripting is not supported for postprocess."
processed_results = PretrainFastRCNN._postprocess(proposals, batched_inputs)
return match_scores, processed_results
@staticmethod
def _postprocess(instances, batched_inputs: List[Dict[str, torch.Tensor]]):
"""
Rescale the output instances to the target size.
"""
# note: private function; subject to changes
processed_results = []
for results_per_image, input_per_image in zip(instances, batched_inputs):
height, width = input_per_image[-1][2] # original image size, before resizing
r = detector_postprocess(results_per_image, height, width)
processed_results.append({"instances": r})
return processed_results
def visualize_proposals(batched_inputs, proposals, input_format, vis_pretrain=False):
"""
A function used to visualize images and proposals. It shows ground truth
bounding boxes on the original image and up to 20 top-scoring predicted
object proposals on the original image. Users can implement different
visualization functions for different models.
Args:
batched_inputs (list): a list that contains input to the model.
proposals (list): a list that contains predicted proposals. Both
batched_inputs and proposals should have the same length.
"""
from detectron2.utils.visualizer import Visualizer
max_vis_prop = 50
if vis_pretrain:
for i, (input, prop) in enumerate(zip(batched_inputs, proposals)):
img = input[0] * 255.0
img = convert_image_to_rgb(img.permute(1, 2, 0), input_format)
box_size = min(len(prop.proposal_boxes), max_vis_prop)
v_pred = Visualizer(img, None)
v_pred = v_pred.overlay_instances(
boxes=prop.proposal_boxes[0:box_size].tensor.cpu().numpy()
)
prop_img = v_pred.get_image()
vis_img = prop_img
to_save = Image.fromarray(np.array(vis_img, np.uint8))
#to_save.save("output/regions/" + str(i) + ".png")
#break # only visualize one image in a batch
else:
for input, prop in zip(batched_inputs, proposals):
img = input["image"]
img = convert_image_to_rgb(img.permute(1, 2, 0), input_format)
box_size = min(len(prop.proposal_boxes), max_vis_prop)
v_pred = Visualizer(img, None)
v_pred = v_pred.overlay_instances(
boxes=prop.proposal_boxes[0:box_size].tensor.cpu().numpy()
)
prop_img = v_pred.get_image()
vis_img = prop_img
# f_n = input['file_name']
to_save = Image.fromarray(np.array(vis_img, np.uint8))
#to_save.save("output/regions/" + "proposals.png")
#break # only visualize one image in a batch
def visualize_results(batched_inputs, results, input_format, vis_pretrain=False):
"""
A function used to visualize images and results. It shows ground truth
bounding boxes on the original image and up to 20 top-scoring predicted
object results on the original image. Users can implement different
visualization functions for different models.
Args:
batched_inputs (list): a list that contains input to the model.
results (list): a list that contains predicted results. Both
batched_inputs and results should have the same length.
"""
from detectron2.utils.visualizer import Visualizer
max_vis_prop = 1
if vis_pretrain:
for i, (input, prop) in enumerate(zip(batched_inputs, results)):
img = input[0] * 255.0
img = convert_image_to_rgb(img.permute(1, 2, 0), input_format)
box_size = min(len(prop.proposal_boxes), max_vis_prop)
v_pred = Visualizer(img, None)
v_pred = v_pred.overlay_instances(
boxes=prop.proposal_boxes[0:box_size].tensor.cpu().numpy()
)
prop_img = v_pred.get_image()
vis_img = prop_img
to_save = Image.fromarray(np.array(vis_img, np.uint8))
#to_save.save("output/regions/" + str(i) + ".png")
#break # only visualize one image in a batch
else:
for input, prop in zip(batched_inputs, results):
img = input["image"]
img = convert_image_to_rgb(img.permute(1, 2, 0), input_format)
box_size = min(len(prop.pred_boxes), max_vis_prop)
v_pred = Visualizer(img, None)
v_pred = v_pred.overlay_instances(
boxes=prop.pred_boxes[0:box_size].tensor.cpu().numpy()
)
prop_img = v_pred.get_image()
vis_img = prop_img
# f_n = input['file_name']
to_save = Image.fromarray(np.array(vis_img, np.uint8))
#to_save.save("output/regions/" + "results.png")
#break # only visualize one image in a batch
return to_save |