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import torch
import numpy as np
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
def compute_overlap(a, b):
if type(a) == torch.Tensor:
if len(a.shape) == 2:
area = (b[:, 2] - b[:, 0] + 1) * (b[:, 3] - b[:, 1] + 1)
iw = torch.min(a[:, 2].unsqueeze(dim=1), b[:, 2]) - torch.max(a[:, 0].unsqueeze(dim=1), b[:, 0])
ih = torch.min(a[:, 3].unsqueeze(dim=1), b[:, 3]) - torch.max(a[:, 1].unsqueeze(dim=1), b[:, 1])
iw[iw<0] = 0
ih[ih<0] = 0
ua = torch.unsqueeze((a[:, 2] - a[:, 0] + 1) * (a[:, 3] - a[:, 1] + 1), dim=1) + area - iw * ih
ua[ua < 1e-8] = 1e-8
intersection = iw * ih
return intersection / ua
elif type(a) == np.ndarray:
if len(a.shape) == 2:
area = np.expand_dims((b[:, 2] - b[:, 0] + 1) * (b[:, 3] - b[:, 1] + 1), axis=0) #(1, K)
iw = np.minimum(np.expand_dims(a[:, 2], axis=1), np.expand_dims(b[:, 2], axis=0)) \
- np.maximum(np.expand_dims(a[:, 0], axis=1), np.expand_dims(b[:, 0], axis=0)) \
+ 1
ih = np.minimum(np.expand_dims(a[:, 3], axis=1), np.expand_dims(b[:, 3], axis=0)) \
- np.maximum(np.expand_dims(a[:, 1], axis=1), np.expand_dims(b[:, 1], axis=0)) \
+ 1
iw[iw<0] = 0 # (N, K)
ih[ih<0] = 0 # (N, K)
intersection = iw * ih
ua = np.expand_dims((a[:, 2] - a[:, 0] + 1) * (a[:, 3] - a[:, 1] + 1), axis=1) + area - intersection
ua[ua < 1e-8] = 1e-8
return intersection / ua
elif len(a.shape) == 1:
area = np.expand_dims((b[:, 2] - b[:, 0] + 1) * (b[:, 3] - b[:, 1] + 1), axis=0) #(1, K)
iw = np.minimum(np.expand_dims([a[2]], axis=1), np.expand_dims(b[:, 2], axis=0)) \
- np.maximum(np.expand_dims([a[0]], axis=1), np.expand_dims(b[:, 0], axis=0))
ih = np.minimum(np.expand_dims([a[3]], axis=1), np.expand_dims(b[:, 3], axis=0)) \
- np.maximum(np.expand_dims([a[1]], axis=1), np.expand_dims(b[:, 1], axis=0))
iw[iw<0] = 0 # (N, K)
ih[ih<0] = 0 # (N, K)
ua = np.expand_dims([(a[2] - a[0] + 1) * (a[3] - a[1] + 1)], axis=1) + area - iw * ih
ua[ua < 1e-8] = 1e-8
intersection = iw * ih
return intersection / ua
def _compute_ap(recall, precision):
""" Compute the average precision, given the recall and precision curves.
Code originally from https://github.com/rbgirshick/py-faster-rcnn.
# Arguments
recall: The recall curve (list).
precision: The precision curve (list).
# Returns
The average precision as computed in py-faster-rcnn.
"""
# correct AP calculation
# first append sentinel values at the end
mrec = np.concatenate(([0.], recall, [1.]))
mpre = np.concatenate(([0.], precision, [0.]))
# compute the precision envelope
for i in range(mpre.size - 1, 0, -1):
mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
# to calculate area under PR curve, look for points
# where X axis (recall) changes value
i = np.where(mrec[1:] != mrec[:-1])[0]
# and sum (\Delta recall) * prec
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
return ap |