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eval_all_dataset.py
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eval_all_dataset.py
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from retinanet.dataset import Ring_Cell_all_dataset
import torch
from torch.utils.data import Dataset, DataLoader
import model_all_dataset as model
import os
from tensorboardX import SummaryWriter
import numpy as np
from tqdm import tqdm
import cv2
import shutil
# from lib.nms.pth_nms import pth_nms
from lib_new.nms.gpu_nms import gpu_nms
from metric import detection_metric, calculate_metric_final
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
retinanet = model.resnet101(num_classes=2, pretrained=True)
retinanet = torch.nn.DataParallel(retinanet).cuda()
def nms(dets, thresh):
"Dispatch to either CPU or GPU NMS implementations.\
Accept dets as tensor"""
dets = dets.cpu().detach().numpy()
return gpu_nms(dets, thresh)
# return whole image once
test_dataset = Ring_Cell_all_dataset('/data/sqy/code/miccai2019/train_test_4/test_0.txt')
# description = 'resnet152_no_neg'
model_path = 'ckpt/latest_resnet101_test_fold_0_all_dataset_round_1;nms_0.4;scores_threshold_0.2.pth'
retinanet.module.load_state_dict(torch.load(model_path))
retinanet.eval()
image_size = 1024
stride_num = 3
score_threshold = 0.2
vis_dir = './vis_1024_new'
#
# if os.path.isdir(vis_dir):
# shutil.rmtree(vis_dir)
# os.mkdir(vis_dir)
pred_boxes_total = []
pred_scores_total = []
gt_boxes_total = []
font = cv2.FONT_HERSHEY_SIMPLEX
for i, (image, bbox, image_, image_name) in enumerate(tqdm(test_dataset)):
h, w = image.size()[1:]
stride_h = (h - image_size) / (stride_num - 1)
stride_w = (w - image_size) / (stride_num - 1)
pred_boxes = []
pred_scores = []
for h_index in range(stride_num):
for w_index in range(stride_num):
image_patch = image[:, int(h_index * stride_h) : int(h_index * stride_h) + image_size,
int(w_index * stride_w): int(w_index * stride_w) + image_size]
# predict
scores_patch, labels_patch, boxes_patch = retinanet(image_patch.unsqueeze(0).cuda().float())
scores_patch = scores_patch.cpu().detach().numpy() # size -> [num_box]
# labels_patch = la bels_patch.cpu().detach().numpy() # size -> [num_box]
boxes_patch = boxes_patch.cpu().detach().numpy() # size -> [num_box, 4]
# change bbox coordinates
if boxes_patch.shape[0] != 0:
start_x = int(w_index * stride_w)
start_y = int(h_index * stride_h)
box_index = (boxes_patch[:, 0] > 5) & (boxes_patch[:, 1] > 5) & (boxes_patch[:, 2] < image_size - 6)\
& (boxes_patch[:, 3] < image_size - 6) & (scores_patch > score_threshold)
boxes_patch = boxes_patch[box_index]
scores_patch = scores_patch[box_index]
boxes_patch[:, 0] = boxes_patch[:, 0] + start_x
boxes_patch[:, 1] = boxes_patch[:, 1] + start_y
boxes_patch[:, 2] = boxes_patch[:, 2] + start_x
boxes_patch[:, 3] = boxes_patch[:, 3] + start_y
boxes_patch = boxes_patch.tolist()
scores_patch = scores_patch.tolist()
pred_boxes.extend(boxes_patch)
pred_scores.extend(scores_patch)
image = image_.permute(1, 2, 0).numpy()
# for box in pred_boxes:
# image = cv2.rectangle(image, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), (0, 0, 255), 2)
# nms
if len(pred_boxes) != 0:
pred_boxes = torch.Tensor(pred_boxes).unsqueeze(0) # size -> [1, num_box, 4]
pred_scores = torch.Tensor(pred_scores).unsqueeze(0).unsqueeze(-1) # size -> [1, num_box, 1]
pred_boxes_w = pred_boxes[0, :, 2] - pred_boxes[0, :, 0]
pred_boxes_h = pred_boxes[0, :, 3] - pred_boxes[0, :, 1]
# wh_idx = (pred_boxes_w > 10) & (pred_boxes_h > 10)
# pred_boxes = pred_boxes[:, wh_idx, :]
# pred_scores = pred_scores[:, wh_idx, :]
anchors_nms_idx = nms(torch.cat([pred_boxes, pred_scores], dim=2)[0, :, :], 0.4)
pred_boxes = pred_boxes[0, anchors_nms_idx, :]
pred_scores = pred_scores[0, anchors_nms_idx, 0]
pred_boxes = pred_boxes.numpy().tolist()
pred_scores = pred_scores.numpy().tolist()
pred_boxes_total.append(pred_boxes)
pred_scores_total.append(pred_scores)
gt_boxes_total.append(bbox)
else:
pred_boxes_total.append([])
pred_scores_total.append([])
gt_boxes_total.append(bbox)
for j, box in enumerate(pred_boxes):
if float(pred_scores[j]) >=score_threshold:
image = cv2.rectangle(image, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), (0, 0, 255), 2)
image = cv2.putText(image, str(float(pred_scores[j]))[:3], (int(box[0]) + 10, int(box[1]) + 20), font, 0.8, (0, 0, 0),
2)
for box in bbox:
image = cv2.rectangle(image, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), (0, 255, 0), 2)
cv2.imwrite(os.path.join(vis_dir, 'test_{}_101_all_dataset_{}_latest_round1.jpg'.format(i, score_threshold)), image)
recall, precision, froc, FPs = calculate_metric_final(pred_boxes_total, gt_boxes_total, pred_scores_total, score_threshold=score_threshold)
print('froc: {}, recall: {}, precision: {}, FPs: {}'.format(froc, recall[-1], precision[-1], FPs))