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try_dataset.py
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try_dataset.py
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import os
import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from detectron2.config import get_cfg
from detectron2.modeling import build_backbone
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.structures import ImageList, Instances, BitMasks
from detectron2.engine import default_argument_parser, default_setup
from detectron2.data import build_detection_test_loader, build_detection_train_loader
from detectron2.evaluation import COCOEvaluator, print_csv_format
from detectron2.data import DatasetMapper
from sparseinst import build_sparse_inst_encoder, build_sparse_inst_decoder, add_sparse_inst_config
from sparseinst.gt_generate import GenerateGT
import cv2
device = torch.device('cuda:0')
dtype = torch.float32
__all__ = ["SparseInst"]
pixel_mean = torch.Tensor([123.675, 116.280, 103.530]).to(device).view(3, 1, 1)
pixel_std = torch.Tensor([58.395, 57.120, 57.375]).to(device).view(3, 1, 1)
@torch.jit.script
def normalizer(x, mean, std): return (x - mean) / std
def synchronize():
torch.cuda.synchronize()
def process_batched_inputs(batched_inputs):
images = [x["image"].to(device) for x in batched_inputs]
images = [normalizer(x, pixel_mean, pixel_std) for x in images]
images = ImageList.from_tensors(images, 32)
ori_size = (batched_inputs[0]["height"], batched_inputs[0]["width"])
return images.tensor, images.image_sizes[0], ori_size
@torch.jit.script
def rescoring_mask(scores, mask_pred, masks):
mask_pred_ = mask_pred.float()
return scores * ((masks * mask_pred_).sum([1, 2]) / (mask_pred_.sum([1, 2]) + 1e-6))
def cal_similarity(base_w, anchor_w, sim_type="cosine"):
"""
base_w shape: b, n, dim
anchor_w shape: b, dim, h ,w
return: b, n, m
"""
b, dim, h, w = anchor_w.shape
_, n, _ = base_w.shape
anchor_w = anchor_w.reshape(b, dim, -1)
anchor_w = anchor_w.transpose(-2, -1) # b, h*w, dim
if sim_type == "cosine":
a_n, b_n = base_w.norm(dim=-1).unsqueeze(-1), anchor_w.norm(dim=-1).unsqueeze(-1)
a_norm = base_w / a_n.clamp(min=1e-8)
b_norm = anchor_w / b_n.clamp(min=1e-8)
similarity = torch.matmul(a_norm, b_norm.transpose(-2, -1))
elif sim_type == "L2":
similarity = 1. - (base_w - anchor_w).abs().clamp(min=1e-6).norm(dim=1)
else:
raise NotImplementedError
similarity = similarity.reshape(b, n, h, w)
return similarity
class SparseInst(nn.Module):
def __init__(self, cfg):
super().__init__()
self.device = torch.device(cfg.MODEL.DEVICE)
# backbone
self.backbone = build_backbone(cfg)
self.size_divisibility = self.backbone.size_divisibility
output_shape = self.backbone.output_shape()
self.encoder = build_sparse_inst_encoder(cfg, output_shape)
self.decoder = build_sparse_inst_decoder(cfg)
self.to(self.device)
# inference
self.cls_threshold = cfg.MODEL.SPARSE_INST.CLS_THRESHOLD
self.mask_threshold = cfg.MODEL.SPARSE_INST.MASK_THRESHOLD
self.max_detections = cfg.MODEL.SPARSE_INST.MAX_DETECTIONS
self.mask_format = cfg.INPUT.MASK_FORMAT
self.num_classes = cfg.MODEL.SPARSE_INST.DECODER.NUM_CLASSES
def forward(self, image, resized_size, ori_size):
max_size = image.shape[2:]
features = self.backbone(image)
features = self.encoder(features)
output = self.decoder(features)
#result = self.inference_single(output, resized_size, max_size, ori_size)
return output
def inference_single(self, outputs, img_shape, pad_shape, ori_shape):
"""
inference for only one sample
Args:
scores (tensor): [NxC]
masks (tensor): [NxHxW]
img_shape (list): (h1, w1), image after resized
pad_shape (list): (h2, w2), padded resized image
ori_shape (list): (h3, w3), original shape h3*w3 < h1*w1 < h2*w2
"""
result = Instances(ori_shape)
# scoring
pred_logits = outputs["pred_logits"][0].sigmoid().squeeze()
pred_scores = outputs["pred_masks"][0].sigmoid()
return result
def preprocess_inputs(batched_inputs):
images = [x["image"] for x in batched_inputs]
images = ImageList.from_tensors(images, 32).tensor
return images
def draw_instance_test(img, instances, instance_indexs):
for index in range(len(instance_indexs)):
if instance_indexs[index] == 0:
break
mask = instances[index]
r, g, b = cv2.split(img)
r[mask > 0.5] = 255
new_img = cv2.merge([b, g, r])
cv2.imshow("img", new_img)
cv2.waitKey(1000)
def draw_guass_map_test(img, guass_map):
"""
img shape: h, w, 3
guass_map shape: 1, 1, h, w
"""
for index in range(len(guass_map)):
print("img shape", img.shape)
mask = guass_map[index]
mask = mask*255
mask = mask.clip(0, 255)
mask = mask.astype(np.uint8)
print(type(mask[0,0]))
print("mask shape", mask.shape)
r, g, b = cv2.split(img)
print(type(b[0, 0]))
r = mask
new_img = cv2.merge([b, g, r])
cv2.imshow("img", new_img)
cv2.waitKey(1000)
def test_sparseinst_speed(cfg):
device = torch.device('cuda:0')
mapper = DatasetMapper(cfg, True)
data_loader = build_detection_train_loader(cfg, mapper=mapper)
get_ground_truth = GenerateGT(cfg)
for idx, inputs in enumerate(data_loader):
image_tensor = preprocess_inputs(inputs)
_, _, img_h, img_w = image_tensor.shape
feat = torch.randn(1, 1, int(img_h/4), int(img_w/4))
mask_feat = torch.randn(1, 1, int(img_h), int(img_w))
new_targets = get_ground_truth.generate(inputs, feat, mask_feat)
gt_scoremaps = new_targets["gt_scoremaps"] #b, class_num, h, w
print("gt_scoremaps",gt_scoremaps.shape)
print("image_tensor",image_tensor.shape)
gt_instances = new_targets["gt_instances"] #b, 100, h, w
gt_inst_nums = new_targets["gt_inst_nums"].cpu().numpy() #b, 100
gt_classes = new_targets["gt_classes"].cpu().numpy() #b, 100
gt_scoremaps = F.interpolate(gt_scoremaps, scale_factor=4, mode='bilinear', align_corners=True)
gt_instances = F.interpolate(gt_instances, scale_factor=4, mode='bilinear', align_corners=True)
gt_scoremaps = gt_scoremaps.cpu().numpy() #b, class_num, h, w
gt_instances = gt_instances.cpu().numpy() # b, inst_num, h, w
imgs = []
for input in inputs:
image_tensor = input["image"]
img = image_tensor.numpy().astype(np.uint8)
pad_img = np.zeros(shape=(img_h, img_w, 3))
#print("orgil img shape",img.shape)
img = img.transpose(1, 2, 0)
pad_img[:img.shape[0], :img.shape[1], :] = img
pad_img = pad_img.astype(np.uint8)
imgs.append(pad_img)
for index in range(len(imgs)):
img = imgs[index]
gt_scoremap = gt_scoremaps[index]
print("img shape: ", img.shape)
print("gt_scoremap shape: ", gt_scoremap.shape)
print("index:", index)
draw_guass_map_test(img, gt_scoremap)
gt_instance = gt_instances[index]
gt_inst_num = gt_inst_nums[index]
#draw_instance_test(img, gt_instance, gt_inst_num)
"""
r, g, b = cv2.split(img)
r[gt_scoremap > 0.5] = 255
new_img = cv2.merge([b, g, r])
cv2.imshow("img", new_img)
cv2.waitKey(1000)
"""
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
add_sparse_inst_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(cfg, args)
return cfg
if __name__ == '__main__':
args = default_argument_parser()
args = args.parse_args()
print("Command Line Args:", args)
cfg = setup(args)
test_sparseinst_speed(cfg)