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c81bba7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | import argparse
import json
import math
import os
import numpy as np
import skimage
import torch
from torch.nn import DataParallel
from torch.utils.data import DataLoader
from torchvision import ops
from torchvision.transforms import Resize
from tqdm import tqdm
from models.counter_infer import build_model
from models.matcher import build_matcher
from utils.arg_parser import get_argparser
from utils.box_ops import BoxList
from utils.data import FSC147DATASET, pad_collate_test
from utils.losses import SetCriterion
@torch.no_grad()
def evaluate(args):
gpu=0
torch.cuda.set_device(gpu)
device = torch.device(gpu)
model = DataParallel(
build_model(args).to(device),
device_ids=[gpu],
output_device=gpu
)
state_dict = torch.load(os.path.join(args.model_path, f'{args.model_name}.pth'))['model']
state_dict = {k if 'module.' in k else 'module.' + k: v for k, v in state_dict.items()}
model.load_state_dict(state_dict, strict=False)
for split in ['val', 'test']:
test = FSC147DATASET(
args.data_path,
args.image_size,
split=split,
num_objects=args.num_objects,
tiling_p=args.tiling_p,
return_ids=True,
training=False
)
test_loader = DataLoader(
test,
batch_size=args.batch_size,
drop_last=False,
num_workers=args.num_workers,
collate_fn=pad_collate_test,
)
ae = torch.tensor(0.0).to(device)
se = torch.tensor(0.0).to(device)
model.eval()
matcher = build_matcher(args)
criterion = SetCriterion(0, matcher, {"loss_giou":args.giou_loss_coef}, ["bboxes", "ce"], focal_alpha=args.focal_alpha)
criterion.to(device)
predictions = dict()
predictions["categories"] = [{"name": "fg", "id": 1}]
predictions["images"] = list()
predictions["annotations"] = list()
anno_id = 1
for img, bboxes, density_map, ids, gt_bboxes, scaling_factor, padwh in test_loader:
img = img.to(device)
bboxes = bboxes.to(device)
gt_bboxes = gt_bboxes.to(device)
outputs, ref_points, _, _, masks = model(img, bboxes)
w, h = img.shape[-1], img.shape[-2]
losses = []
num_objects_gt = []
num_objects_pred = []
nms_bboxes = []
nms_scores = []
nms_masks = []
for idx in range(img.shape[0]):
thr = 1/0.11
if len(outputs[idx]['pred_boxes'][-1]) == 0:
nms_bboxes.append(torch.zeros((0, 4)))
nms_scores.append(torch.zeros((0)))
num_objects_pred.append(0)
else:
# threshold and NMS
v = outputs[idx]["box_v"]
v_thr = v.max() / thr
mask = v > v_thr
keep = ops.nms(
outputs[idx]["pred_boxes"][mask],
v[mask],
0.5,
)
boxes = outputs[idx]["pred_boxes"][mask][keep]
boxes = torch.clamp(boxes, 0, 1)
scores = outputs[idx]["scores"][mask][keep]
# remove bboxes in padded area
maxw = (img.shape[-1] - padwh[idx][0]).to(device)
maxh = (img.shape[-2] - padwh[idx][1]).to(device)
center = (boxes[:, :2] + boxes[:, 2:]) / 2
valid = (center[:, 0] * h < maxw) & (center[:, 1] * w < maxh)
scores = scores[valid]
boxes = boxes[valid]
nms_bboxes.append(boxes)
nms_scores.append(scores)
num_objects_pred.append(len(boxes))
if False:
from matplotlib import pyplot as plt
fig1 = plt.figure(figsize=(8, 8))
((ax1_11, ax1_12), (ax1_21, ax1_22)) = fig1.subplots(2, 2)
fig1.tight_layout(pad=2.5)
img_ = np.array((img).cpu()[idx].permute(1, 2, 0))
img_ = img_ - np.min(img_)
img_ = img_ / np.max(img_)
ax1_11.imshow(img_)
ax1_11.set_title("Input", fontsize=8)
bboxes_ = np.array(bboxes.cpu())[idx]
for i in range(3):
ax1_11.plot([bboxes_[i][0], bboxes_[i][0], bboxes_[i][2], bboxes_[i][2], bboxes_[i][0]],
[bboxes_[i][1], bboxes_[i][3], bboxes_[i][3], bboxes_[i][1], bboxes_[i][1]], c='r')
ax1_12.imshow(img_)
ax1_12.set_title("gt bboxes", fontsize=8)
target_bboxes = gt_bboxes[idx][torch.logical_not((gt_bboxes[idx] == 0).all(dim=1))]
bboxes_ = ((target_bboxes)).detach().cpu()
for i in range(len(bboxes_)):
ax1_12.plot([bboxes_[i][0], bboxes_[i][0], bboxes_[i][2], bboxes_[i][2], bboxes_[i][0]],
[bboxes_[i][1], bboxes_[i][3], bboxes_[i][3], bboxes_[i][1], bboxes_[i][1]], c='g')
ax1_21.imshow(img_)
bboxes_pred = nms_bboxes[idx]
bboxes_ = ((bboxes_pred * img_.shape[0])).detach().cpu()
for i in range(len(bboxes_)):
ax1_21.plot([bboxes_[i][0], bboxes_[i][0], bboxes_[i][2], bboxes_[i][2], bboxes_[i][0]],
[bboxes_[i][1], bboxes_[i][3], bboxes_[i][3], bboxes_[i][1], bboxes_[i][1]],
c='orange', linewidth=0.5)
ax1_21.set_title("#GT-#PRED=" + str(len(target_bboxes) - len(bboxes_pred)))
from torchvision import transforms as T
res = T.Resize((1024, 1024))
ax1_21.imshow(res(centerness).detach().cpu()[idx][0], alpha=0.6)
plt.savefig(test.image_names[ids[idx].item()], dpi=200)
plt.close()
for idx in range(img.shape[0]):
img_info = {
"id": test.map_img_name_to_ori_id()[test.image_names[ids[idx].item()]],
"file_name": "None",
}
bboxes = ops.box_convert(nms_bboxes[idx], 'xyxy', 'xywh')
bboxes = bboxes * img.shape[-1] / scaling_factor[idx]
for idxi in range(len(nms_bboxes[idx])):
box = bboxes[idxi].detach().cpu()
anno = {
"id": anno_id,
"image_id": test.map_img_name_to_ori_id()[test.image_names[ids[idx].item()]],
"area": int((box[2] * box[3]).item()),
"bbox": [int(box[0].item()), int(box[1].item()), int(box[2].item()), int(box[3].item())],
"category_id": 1,
"score": float(nms_scores[idx][idxi].item()),
}
anno_id += 1
predictions["annotations"].append(anno)
predictions["images"].append(img_info)
num_objects_gt = density_map.flatten(1).sum(dim=1)
num_objects_pred = torch.tensor(num_objects_pred)
ae += torch.abs(
num_objects_gt - num_objects_pred
).sum()
se += torch.pow(
num_objects_gt - num_objects_pred, 2
).sum()
print(
f"{split.capitalize()} set",
f"MAE: {ae.item() / len(test):.2f}",
f"RMSE: {torch.sqrt(se / len(test)).item():.2f}",
)
with open("geco2_" + split + ".json", "w") as handle:
json.dump(predictions, handle)
if __name__ == '__main__':
parser = argparse.ArgumentParser('GECO2', parents=[get_argparser()])
args = parser.parse_args()
print(args)
print("model_name: ", args.model_name)
evaluate(args)
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