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eval_ssd.py
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eval_ssd.py
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import sys
import logging
import argparse
import pathlib
import torch
import numpy as np
from vision.datasets.voc_dataset import VOCDataset
from vision.datasets.open_images import OpenImagesDataset
from vision.datasets.voc_format_dataset import VOCFormatDataset
from vision.utils import box_utils, measurements
from vision.utils.misc import str2bool, Timer
from vision.ssd.mobilenet_v2_ssd_lite import create_mobilenetv2_ssd_lite, create_mobilenetv2_ssd_lite_predictor
parser = argparse.ArgumentParser(description="SSD Evaluation on VOC Dataset.")
parser.add_argument('--net', default="vgg16-ssd",
help="The network architecture, it should be of mb1-ssd, mb1-ssd-lite, mb2-ssd-lite or vgg16-ssd.")
parser.add_argument("--trained_model", type=str)
parser.add_argument("--dataset_type", default="voc", type=str,
help='Specify dataset type. Currently support voc and open_images.')
parser.add_argument("--dataset", type=str, help="The root directory of the VOC dataset or Open Images dataset.")
parser.add_argument("--label_file", type=str, help="The label file path.")
parser.add_argument("--use_cuda", type=str2bool, default=True)
parser.add_argument("--use_2007_metric", type=str2bool, default=True)
parser.add_argument("--nms_method", type=str, default="hard")
parser.add_argument("--iou_threshold", type=float, default=0.75, help="The threshold of Intersection over Union.")
parser.add_argument("--eval_dir", default="eval_results", type=str, help="The directory to store evaluation results.")
parser.add_argument('--mb2_width_mult', default=1.0, type=float,
help='Width Multiplifier for MobilenetV2')
args = parser.parse_args()
DEVICE = torch.device("cuda:0" if torch.cuda.is_available() and args.use_cuda else "cpu")
def group_annotation_by_class(dataset):
true_case_stat = {}
all_gt_boxes = {}
all_difficult_cases = {}
for i in range(len(dataset)):
image_id, annotation = dataset.get_annotation(i)
gt_boxes, classes, is_difficult = annotation
gt_boxes = torch.from_numpy(gt_boxes)
for i, difficult in enumerate(is_difficult):
class_index = int(classes[i])
gt_box = gt_boxes[i]
if not difficult:
true_case_stat[class_index] = true_case_stat.get(class_index, 0) + 1
if class_index not in all_gt_boxes:
all_gt_boxes[class_index] = {}
if image_id not in all_gt_boxes[class_index]:
all_gt_boxes[class_index][image_id] = []
all_gt_boxes[class_index][image_id].append(gt_box)
if class_index not in all_difficult_cases:
all_difficult_cases[class_index]={}
if image_id not in all_difficult_cases[class_index]:
all_difficult_cases[class_index][image_id] = []
all_difficult_cases[class_index][image_id].append(difficult)
for class_index in all_gt_boxes:
for image_id in all_gt_boxes[class_index]:
all_gt_boxes[class_index][image_id] = torch.stack(all_gt_boxes[class_index][image_id])
for class_index in all_difficult_cases:
for image_id in all_difficult_cases[class_index]:
all_gt_boxes[class_index][image_id] = torch.tensor(all_gt_boxes[class_index][image_id])
return true_case_stat, all_gt_boxes, all_difficult_cases
def compute_average_precision_per_class(num_true_cases, gt_boxes, difficult_cases,
prediction_file, iou_threshold, use_2007_metric):
with open(prediction_file) as f:
image_ids = []
boxes = []
scores = []
for line in f:
t = line.rstrip().split(" ")
image_ids.append(t[0])
scores.append(float(t[1]))
box = torch.tensor([float(v) for v in t[2:]]).unsqueeze(0)
box -= 1.0 # convert to python format where indexes start from 0
boxes.append(box)
scores = np.array(scores)
sorted_indexes = np.argsort(-scores)
boxes = [boxes[i] for i in sorted_indexes]
image_ids = [image_ids[i] for i in sorted_indexes]
true_positive = np.zeros(len(image_ids))
false_positive = np.zeros(len(image_ids))
matched = set()
for i, image_id in enumerate(image_ids):
box = boxes[i]
if image_id not in gt_boxes:
false_positive[i] = 1
continue
gt_box = gt_boxes[image_id]
ious = box_utils.iou_of(box, gt_box)
max_iou = torch.max(ious).item()
max_arg = torch.argmax(ious).item()
if max_iou > iou_threshold:
if difficult_cases[image_id][max_arg] == 0:
if (image_id, max_arg) not in matched:
true_positive[i] = 1
matched.add((image_id, max_arg))
else:
false_positive[i] = 1
else:
false_positive[i] = 1
true_positive = true_positive.cumsum()
false_positive = false_positive.cumsum()
precision = true_positive / (true_positive + false_positive)
recall = true_positive / num_true_cases
if use_2007_metric:
return measurements.compute_voc2007_average_precision(precision, recall)
else:
return measurements.compute_average_precision(precision, recall)
if __name__ == '__main__':
eval_path = pathlib.Path(args.eval_dir)
eval_path.mkdir(exist_ok=True)
timer = Timer()
class_names = [name.strip() for name in open(args.label_file).readlines()]
if args.dataset_type == "voc":
dataset = VOCDataset(args.dataset, is_test=True)
elif args.dataset_type == 'open_images':
dataset = OpenImagesDataset(args.dataset, dataset_type="test")
elif args.dataset_type == "voc_format":
dataset = VOCFormatDataset(args.dataset, transform=None,
target_transform=None)
true_case_stat, all_gb_boxes, all_difficult_cases = group_annotation_by_class(dataset)
if args.net == 'mb2-ssd-lite':
net = create_mobilenetv2_ssd_lite(len(class_names), width_mult=args.mb2_width_mult, is_test=True)
else:
logging.fatal(f"The net type is not supported : {args.net}.")
parser.print_help(sys.stderr)
sys.exit(1)
timer.start("Load Model")
net.load(args.trained_model)
net = net.to(DEVICE)
print(f'It took {timer.end("Load Model")} seconds to load the model.')
if args.net == 'mb2-ssd-lite':
predictor = create_mobilenetv2_ssd_lite_predictor(net, nms_method=args.nms_method, device=DEVICE)
results = []
for i in range(len(dataset)):
print("process image", i)
timer.start("Load Image")
image = dataset.get_image(i)
print("Load Image: {:4f} seconds.".format(timer.end("Load Image")))
timer.start("Predict")
boxes, labels, probs = predictor.predict(image)
print("Prediction: {:4f} seconds.".format(timer.end("Predict")))
indexes = torch.ones(labels.size(0), 1, dtype=torch.float32) * i
results.append(torch.cat([
indexes.reshape(-1, 1),
labels.reshape(-1, 1).float(),
probs.reshape(-1, 1),
boxes + 1.0 # matlab's indexes start from 1
], dim=1))
results = torch.cat(results)
for class_index, class_name in enumerate(class_names):
if class_index == 0: continue # ignore background
prediction_path = eval_path / f"det_test_{class_name}.txt"
with open(prediction_path, "w") as f:
sub = results[results[:, 1] == class_index, :]
for i in range(sub.size(0)):
prob_box = sub[i, 2:].numpy()
image_id = dataset.ids[int(sub[i, 0])]
print(
image_id + " " + " ".join([str(v) for v in prob_box]),
file=f
)
aps = []
print("\n\nAverage Precision Per-class:")
for class_index, class_name in enumerate(class_names):
if class_index == 0:
continue
prediction_path = eval_path / f"det_test_{class_name}.txt"
ap = compute_average_precision_per_class(
true_case_stat[class_index],
all_gb_boxes[class_index],
all_difficult_cases[class_index],
prediction_path,
args.iou_threshold,
args.use_2007_metric
)
aps.append(ap)
print(f"{class_name}: {ap}")
print(f"\nAverage Precision Across All Classes:{sum(aps)/len(aps)}")