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csv_eval_ensemble.py
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csv_eval_ensemble.py
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from __future__ import print_function
import numpy as np
import json
import os
import csv
import torch
def GeneralEnsemble(dets, iou_thresh = 0.5, weights=None):
assert(type(iou_thresh) == float)
ndets = len(dets)
if weights is None:
w = 1/float(ndets)
weights = [w]*ndets
else:
print('len_weights',len(weights))
print('ndets',ndets)
assert(len(weights) == ndets)
s = sum(weights)
for i in range(0, len(weights)):
weights[i] /= s
out = list()
used = list()
for idet in range(0,ndets):
det = dets[idet]
for box in det:
if box in used:
continue
used.append(box)
# Search the other detectors for overlapping box of same class
found = []
for iodet in range(0, ndets):
odet = dets[iodet]
if odet == det:
continue
bestbox = None
bestiou = iou_thresh
for obox in odet:
if not obox in used:
# Not already used
if box[4] == obox[4]:
# Same class
iou = computeIOU(box, obox)
if iou > bestiou:
bestiou = iou
bestbox = obox
if not bestbox is None:
w = weights[iodet]
found.append((bestbox,w))
used.append(bestbox)
# Now we've gone through all other detectors
if len(found) == 0:
new_box = list(box)
new_box[5] /= ndets
out.append(new_box)
else:
allboxes = [(box, weights[idet])]
allboxes.extend(found)
xc = 0.0
yc = 0.0
bw = 0.0
bh = 0.0
conf = 0.0
wsum = 0.0
for bb in allboxes:
w = bb[1]
wsum += w
b = bb[0]
xc += w*b[0]
yc += w*b[1]
bw += w*b[2]
bh += w*b[3]
conf += w*b[5]
xc /= wsum
yc /= wsum
bw /= wsum
bh /= wsum
new_box = [xc, yc, bw, bh, box[4], conf]
out.append(new_box)
return out
# In[3]:
def getCoords(box):
x1 = float(box[0]) - float(box[2])/2
x2 = float(box[0]) + float(box[2])/2
y1 = float(box[1]) - float(box[3])/2
y2 = float(box[1]) + float(box[3])/2
return x1, x2, y1, y2
# In[4]:
def computeIOU(box1, box2):
x11, x12, y11, y12 = getCoords(box1)
x21, x22, y21, y22 = getCoords(box2)
x_left = max(x11, x21)
y_top = max(y11, y21)
x_right = min(x12, x22)
y_bottom = min(y12, y22)
if x_right < x_left or y_bottom < y_top:
return 0.0
intersect_area = (x_right - x_left) * (y_bottom - y_top)
box1_area = (x12 - x11) * (y12 - y11)
box2_area = (x22 - x21) * (y22 - y21)
iou = intersect_area / (box1_area + box2_area - intersect_area)
return iou
def compute_overlap(a, b):
"""
Parameters
----------
a: (N, 4) ndarray of float
b: (K, 4) ndarray of float
Returns
-------
overlaps: (N, K) ndarray of overlap between boxes and query_boxes
"""
area = (b[:, 2] - b[:, 0]) * (b[:, 3] - b[:, 1])
iw = np.minimum(np.expand_dims(a[:, 2], axis=1), b[:, 2]) - np.maximum(np.expand_dims(a[:, 0], 1), b[:, 0])
ih = np.minimum(np.expand_dims(a[:, 3], axis=1), b[:, 3]) - np.maximum(np.expand_dims(a[:, 1], 1), b[:, 1])
iw = np.maximum(iw, 0)
ih = np.maximum(ih, 0)
ua = np.expand_dims((a[:, 2] - a[:, 0]) * (a[:, 3] - a[:, 1]), axis=1) + area - iw * ih
ua = np.maximum(ua, np.finfo(float).eps)
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
def _get_detections(dataset, retinanet, score_threshold=0.05, max_detections=100, save_path=None):
""" Get the detections from the retinanet using the generator.
The result is a list of lists such that the size is:
all_detections[num_images][num_classes] = detections[num_detections, 4 + num_classes]
# Arguments
dataset : The generator used to run images through the retinanet.
retinanet : The retinanet to run on the images.
score_threshold : The score confidence threshold to use.
max_detections : The maximum number of detections to use per image.
save_path : The path to save the images with visualized detections to.
# Returns
A list of lists containing the detections for each image in the generator.
"""
all_detections = [[None for i in range(dataset.num_classes())] for j in range(len(dataset))]
retinanet.eval()
with torch.no_grad():
for index in range(len(dataset)):
data = dataset[index]
scale = data['scale']
# run network
scores, labels, boxes = retinanet(data['img'].permute(2, 0, 1).cuda().float().unsqueeze(dim=0))
scores = scores.cpu().numpy()
labels = labels.cpu().numpy()
boxes = boxes.cpu().numpy()
# correct boxes for image scale
boxes /= scale
# select indices which have a score above the threshold
indices = np.where(scores > score_threshold)[0]
if indices.shape[0] > 0:
# select those scores
scores = scores[indices]
# find the order with which to sort the scores
scores_sort = np.argsort(-scores)[:max_detections]
# select detections
image_boxes = boxes[indices[scores_sort], :]
image_scores = scores[scores_sort]
image_labels = labels[indices[scores_sort]]
image_detections = np.concatenate([image_boxes, np.expand_dims(image_scores, axis=1), np.expand_dims(image_labels, axis=1)], axis=1)
# copy detections to all_detections
for label in range(dataset.num_classes()):
all_detections[index][label] = image_detections[image_detections[:, -1] == label, :-1]
else:
# copy detections to all_detections
for label in range(dataset.num_classes()):
all_detections[index][label] = np.zeros((0, 5))
print('{}/{}'.format(index + 1, len(dataset)), end='\r')
return all_detections
def _get_annotations(generator):
""" Get the ground truth annotations from the generator.
The result is a list of lists such that the size is:
all_detections[num_images][num_classes] = annotations[num_detections, 5]
# Arguments
generator : The generator used to retrieve ground truth annotations.
# Returns
A list of lists containing the annotations for each image in the generator.
"""
all_annotations = [[None for i in range(generator.num_classes())] for j in range(len(generator))]
for i in range(len(generator)):
# load the annotations
annotations = generator.load_annotations(i)
# copy detections to all_annotations
for label in range(generator.num_classes()):
all_annotations[i][label] = annotations[annotations[:, 4] == label, :4].copy()
print('{}/{}'.format(i + 1, len(generator)), end='\r')
return all_annotations
def evaluate(
generator,
retinanets,
epoch,
iou_threshold=0.5,
score_threshold=0.05,
max_detections=100,
save_path=None,
):
""" Evaluate a given dataset using a given retinanet.
# Arguments
generator : The generator that represents the dataset to evaluate.
retinanet : The retinanet to evaluate.
iou_threshold : The threshold used to consider when a detection is positive or negative.
score_threshold : The score confidence threshold to use for detections.
max_detections : The maximum number of detections to use per image.
save_path : The path to save images with visualized detections to.
# Returns
A dict mapping class names to mAP scores.
"""
# gather all detections and annotations
all_detections_ensemble = []
for k in range(len(retinanets)):
all_detections = _get_detections(generator, retinanets[k], score_threshold=score_threshold, max_detections=max_detections, save_path=save_path)
all_detections_ensemble.append(all_detections)
# print('all_detections',len(all_detections))
# print(all_detections[0])
# print(all_detections[1])
all_annotations = _get_annotations(generator)
average_precisions = {}
for label in range(generator.num_classes()):
false_positives = np.zeros((0,))
true_positives = np.zeros((0,))
scores = np.zeros((0,))
num_annotations = 0.0
for i in range(len(generator)):
######## make changes to accomodate ensemble here #########
dets = {}
for kk in range(len(all_detections_ensemble)):
detections = all_detections_ensemble[kk][i][label]
dets[kk] = detections
ensembled_detection = None
max_len_val = 0 # check initial value
f = 1
for key,val in dets.items():
if len(val)!=1:
f=0 # some model predicts more than 1 bbox
if f==0:
for key,val in dets.items(): # if more than 1 bbox , take model which predicts max bboxs
max_len_val = len(val)
ensembled_detection = val
else:
bboxs_for_ensemble = []
for kk in range(len(all_detections_ensemble)):
# print('dets',kk,dets[kk])
x1 = dets[kk][0][0]
y1 = dets[kk][0][1]
x2 = dets[kk][0][2]
y2 = dets[kk][0][3]
score = dets[kk][0][4]
width = x2-x1
height = y2-y1
x_c = x1+width/2
y_c = y1+height/2
bbox_for_ensemble = [x_c,y_c,width,height,int(label),score]
bboxs_for_ensemble.append([bbox_for_ensemble])
# print('bboxs_for_ensemble',bboxs_for_ensemble)
ens = GeneralEnsemble(bboxs_for_ensemble, weights = [1]*len(all_detections_ensemble))
x1_ens,x2_ens,y1_ens,y2_ens = getCoords(ens[0])
ensembled_detection = [[x1_ens,y1_ens,x2_ens,y2_ens,ens[0][5]]]
# detections = all_detections[i][label]
detections = ensembled_detection
# print(i,label,detections)
annotations = all_annotations[i][label]
# print(annotations)
num_annotations += annotations.shape[0]
detected_annotations = []
for d in detections:
# print('d',d)
scores = np.append(scores, d[4])
if annotations.shape[0] == 0:
false_positives = np.append(false_positives, 1)
true_positives = np.append(true_positives, 0)
continue
overlaps = compute_overlap(np.expand_dims(d, axis=0), annotations)
assigned_annotation = np.argmax(overlaps, axis=1)
max_overlap = overlaps[0, assigned_annotation]
if max_overlap >= iou_threshold and assigned_annotation not in detected_annotations:
false_positives = np.append(false_positives, 0)
true_positives = np.append(true_positives, 1)
detected_annotations.append(assigned_annotation)
else:
false_positives = np.append(false_positives, 1)
true_positives = np.append(true_positives, 0)
# no annotations -> AP for this class is 0 (is this correct?)
if num_annotations == 0:
average_precisions[label] = 0, 0
continue
# sort by score
indices = np.argsort(-scores)
false_positives = false_positives[indices]
true_positives = true_positives[indices]
# compute false positives and true positives
false_positives = np.cumsum(false_positives)
true_positives = np.cumsum(true_positives)
# compute recall and precision
recall = true_positives / num_annotations
precision = true_positives / np.maximum(true_positives + false_positives, np.finfo(np.float64).eps)
# compute average precision
average_precision = _compute_ap(recall, precision)
average_precisions[label] = average_precision, num_annotations
print('\nmAP:')
all_maps = {}
for label in range(generator.num_classes()):
label_name = generator.label_to_name(label)
print('{}: {}'.format(label_name, average_precisions[label][0]))
all_maps[label_name] = str(average_precisions[label][0])
#all_maps.append(str(average_precisions[label][0]))
store_map_dir = './map_eval_testing/'
if not os.path.exists(store_map_dir):
os.makedirs(store_map_dir)
save_name = str(epoch)+str('_eval_map.csv')
with open(os.path.join(store_map_dir,save_name), 'w+') as writefile:
writer = csv.writer(writefile)
for key, value in all_maps.items():
writer.writerow([key, value])
print (all_maps)
writefile.close()
return average_precisions