Commit e7a61063 authored by tjsongzw's avatar tjsongzw
Browse files

minor fix

parent 554e20a2
import mmcv
import numpy as np import numpy as np
from terminaltables import AsciiTable from terminaltables import AsciiTable
...@@ -234,8 +235,9 @@ def eval_map(det_results, ...@@ -234,8 +235,9 @@ def eval_map(det_results,
gt_ignore (list): gt ignore indicators of each image, a list of K array gt_ignore (list): gt ignore indicators of each image, a list of K array
scale_ranges (list, optional): [(min1, max1), (min2, max2), ...] scale_ranges (list, optional): [(min1, max1), (min2, max2), ...]
iou_thr (float): IoU threshold iou_thr (float): IoU threshold
dataset (None or str): dataset name, there are minor differences in dataset (None or str or list): dataset name or dataset classes, there
metrics for different datsets, e.g. "voc07", "imagenet_det", etc. are minor differences in metrics for different datsets, e.g.
"voc07", "imagenet_det", etc.
print_summary (bool): whether to print the mAP summary print_summary (bool): whether to print the mAP summary
Returns: Returns:
...@@ -333,7 +335,7 @@ def print_map_summary(mean_ap, results, dataset=None): ...@@ -333,7 +335,7 @@ def print_map_summary(mean_ap, results, dataset=None):
Args: Args:
mean_ap(float): calculated from `eval_map` mean_ap(float): calculated from `eval_map`
results(list): calculated from `eval_map` results(list): calculated from `eval_map`
dataset(None or str or list): dataset name. dataset(None or str or list): dataset name or dataset classes.
""" """
num_scales = len(results[0]['ap']) if isinstance(results[0]['ap'], num_scales = len(results[0]['ap']) if isinstance(results[0]['ap'],
np.ndarray) else 1 np.ndarray) else 1
...@@ -353,8 +355,10 @@ def print_map_summary(mean_ap, results, dataset=None): ...@@ -353,8 +355,10 @@ def print_map_summary(mean_ap, results, dataset=None):
if dataset is None: if dataset is None:
label_names = [str(i) for i in range(1, num_classes + 1)] label_names = [str(i) for i in range(1, num_classes + 1)]
else: elif mmcv.is_str(dataset):
label_names = get_classes(dataset) label_names = get_classes(dataset)
else:
label_names = dataset
if not isinstance(mean_ap, list): if not isinstance(mean_ap, list):
mean_ap = [mean_ap] mean_ap = [mean_ap]
......
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