mAP Update (#176)
* updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates
This commit is contained in:
Glenn Jocher
GitHub
+15
-9
@@ -10,8 +10,8 @@ sudo reboot now
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# Re-clone
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sudo rm -rf yolov3
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git clone https://github.com/ultralytics/yolov3 # master
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# git clone -b multi_gpu --depth 1 https://github.com/ultralytics/yolov3 # branch
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# git clone https://github.com/ultralytics/yolov3 # master
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git clone -b map_update --depth 1 https://github.com/ultralytics/yolov3 yolov3 # branch
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cp -r weights yolov3
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cp -r cocoapi/PythonAPI/pycocotools yolov3
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cd yolov3
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@@ -26,11 +26,11 @@ python3 train.py --resume
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python3 detect.py
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# Test
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python3 detect.py --save-json --conf-thres 0.001 --img-size 416
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python3 test.py --save-json
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# Git pull
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git pull https://github.com/ultralytics/yolov3 # master
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git pull https://github.com/ultralytics/yolov3 multi_gpu # branch
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git pull https://github.com/ultralytics/yolov3 map_update # branch
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# Test Darknet training
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python3 test.py --weights ../darknet/backup/yolov3.backup
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@@ -40,10 +40,16 @@ gsutil cp yolov3/weights/latest1gpu.pt gs://ultralytics
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# Copy latest.pt FROM bucket
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gsutil cp gs://ultralytics/latest.pt yolov3/weights/latest.pt
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wget https://storage.googleapis.com/ultralytics/latest.pt -O weights/latest.pt
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wget https://storage.googleapis.com/ultralytics/yolov3/latest_v1_0.pt -O weights/latest_v1_0.pt
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wget https://storage.googleapis.com/ultralytics/yolov3/best_v1_0.pt -O weights/best_v1_0.pt
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# Trade Studies
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sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3
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# Debug/Development
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sudo rm -rf yolov3
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# git clone https://github.com/ultralytics/yolov3 # master
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git clone -b map_update --depth 1 https://github.com/ultralytics/yolov3 yolov3 # branch
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cp -r weights yolov3
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cd yolov3 && python3 train.py --batch-size 16 --epochs 1
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sudo shutdown
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cp -r cocoapi/PythonAPI/pycocotools yolov3
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cd yolov3
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#git pull https://github.com/ultralytics/yolov3 map_update # branch
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python3 test.py --img-size 320
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+84
-69
@@ -7,7 +7,6 @@ import matplotlib.pyplot as plt
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from utils import torch_utils
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@@ -106,10 +105,10 @@ def xyxy2xywh(x):
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def xywh2xyxy(x):
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# Convert bounding box format from [x, y, w, h] to [x1, y1, x2, y2]
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y = torch.zeros_like(x) if isinstance(x, torch.Tensor) else np.zeros_like(x)
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y[:, 0] = (x[:, 0] - x[:, 2] / 2)
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y[:, 1] = (x[:, 1] - x[:, 3] / 2)
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y[:, 2] = (x[:, 0] + x[:, 2] / 2)
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y[:, 3] = (x[:, 1] + x[:, 3] / 2)
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y[:, 0] = x[:, 0] - x[:, 2] / 2
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y[:, 1] = x[:, 1] - x[:, 3] / 2
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y[:, 2] = x[:, 0] + x[:, 2] / 2
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y[:, 3] = x[:, 1] + x[:, 3] / 2
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return y
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@@ -142,25 +141,25 @@ def ap_per_class(tp, conf, pred_cls, target_cls):
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tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
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# Find unique classes
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unique_classes = np.unique(np.concatenate((pred_cls, target_cls), 0))
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unique_classes = np.unique(target_cls)
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# Create Precision-Recall curve and compute AP for each class
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ap, p, r = [], [], []
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for c in unique_classes:
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i = pred_cls == c
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n_gt = sum(target_cls == c) # Number of ground truth objects
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n_p = sum(i) # Number of predicted objects
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n_gt = (target_cls == c).sum() # Number of ground truth objects
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n_p = i.sum() # Number of predicted objects
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if (n_p == 0) and (n_gt == 0):
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if n_p == 0 and n_gt == 0:
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continue
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elif (n_p == 0) or (n_gt == 0):
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elif n_p == 0 or n_gt == 0:
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ap.append(0)
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r.append(0)
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p.append(0)
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else:
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# Accumulate FPs and TPs
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fpc = np.cumsum(1 - tp[i])
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tpc = np.cumsum(tp[i])
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fpc = (1 - tp[i]).cumsum()
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tpc = (tp[i]).cumsum()
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# Recall
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recall_curve = tpc / (n_gt + 1e-16)
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@@ -328,15 +327,18 @@ def build_targets(model, targets):
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return txy, twh, tcls, indices
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# @profile
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def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
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"""
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Removes detections with lower object confidence score than 'conf_thres'
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Non-Maximum Suppression to further filter detections.
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Returns detections with shape:
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(x1, y1, x2, y2, object_conf, class_score, class_pred)
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(x1, y1, x2, y2, object_conf, class_conf, class)
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"""
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output = [None for _ in range(len(prediction))]
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min_wh = 2 # (pixels) minimum box width and height
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output = [None] * len(prediction)
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for image_i, pred in enumerate(prediction):
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# Experiment: Prior class size rejection
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# x, y, w, h = pred[:, 0], pred[:, 1], pred[:, 2], pred[:, 3]
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@@ -352,56 +354,53 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
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# multivariate_normal.pdf(x, mean=mat['class_mu'][c, :2], cov=mat['class_cov'][c, :2, :2])
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# Filter out confidence scores below threshold
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class_prob, class_pred = torch.max(F.softmax(pred[:, 5:], 1), 1)
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v = pred[:, 4] > conf_thres
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v = v.nonzero().squeeze()
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if len(v.shape) == 0:
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v = v.unsqueeze(0)
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class_conf, class_pred = pred[:, 5:].max(1)
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# pred[:, 4] *= class_conf
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pred = pred[v]
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class_prob = class_prob[v]
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class_pred = class_pred[v]
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i = (pred[:, 4] > conf_thres) & (pred[:, 2] > min_wh) & (pred[:, 3] > min_wh)
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pred = pred[i]
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# If none are remaining => process next image
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nP = pred.shape[0]
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if not nP:
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if len(pred) == 0:
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continue
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# From (center x, center y, width, height) to (x1, y1, x2, y2)
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# Select predicted classes
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class_conf = class_conf[i]
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class_pred = class_pred[i].unsqueeze(1).float()
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# Box (center x, center y, width, height) to (x1, y1, x2, y2)
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pred[:, :4] = xywh2xyxy(pred[:, :4])
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pred[:, 4] *= class_conf # improves mAP from 0.549 to 0.551
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# Detections ordered as (x1, y1, x2, y2, obj_conf, class_prob, class_pred)
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detections = torch.cat((pred[:, :5], class_prob.float().unsqueeze(1), class_pred.float().unsqueeze(1)), 1)
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# Iterate through all predicted classes
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unique_labels = detections[:, -1].cpu().unique().to(prediction.device)
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# Detections ordered as (x1y1x2y2, obj_conf, class_conf, class_pred)
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pred = torch.cat((pred[:, :5], class_conf.unsqueeze(1), class_pred), 1)
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nms_style = 'OR' # 'OR' (default), 'AND', 'MERGE' (experimental)
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for c in unique_labels:
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# Get the detections with class c
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dc = detections[detections[:, -1] == c]
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# Sort the detections by maximum object confidence
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_, conf_sort_index = torch.sort(dc[:, 4] * dc[:, 5], descending=True)
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dc = dc[conf_sort_index]
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# Get detections sorted by decreasing confidence scores
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pred = pred[(-pred[:, 4]).argsort()]
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det_max = []
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nms_style = 'MERGE' # 'OR' (default), 'AND', 'MERGE' (experimental)
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for c in pred[:, -1].unique():
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dc = pred[pred[:, -1] == c] # select class c
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dc = dc[:min(len(dc), 100)] # limit to first 100 boxes: https://github.com/ultralytics/yolov3/issues/117
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# Non-maximum suppression
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det_max = []
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ind = list(range(len(dc)))
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if nms_style == 'OR': # default
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while len(ind):
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j = ind[0]
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det_max.append(dc[j:j + 1]) # save highest conf detection
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reject = bbox_iou(dc[j], dc[ind]) > nms_thres
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[ind.pop(i) for i in reversed(reject.nonzero())]
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# while dc.shape[0]: # SLOWER METHOD
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# det_max.append(dc[:1]) # save highest conf detection
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# if len(dc) == 1: # Stop if we're at the last detection
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# break
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# iou = bbox_iou(dc[0], dc[1:]) # iou with other boxes
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# dc = dc[1:][iou < nms_thres] # remove ious > threshold
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# METHOD1
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# ind = list(range(len(dc)))
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# while len(ind):
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# j = ind[0]
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# det_max.append(dc[j:j + 1]) # save highest conf detection
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# reject = (bbox_iou(dc[j], dc[ind]) > nms_thres).nonzero()
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# [ind.pop(i) for i in reversed(reject)]
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# Image Total P R mAP
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# 4964 5000 0.629 0.594 0.586
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# METHOD2
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while dc.shape[0]:
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det_max.append(dc[:1]) # save highest conf detection
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if len(dc) == 1: # Stop if we're at the last detection
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break
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iou = bbox_iou(dc[0], dc[1:]) # iou with other boxes
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dc = dc[1:][iou < nms_thres] # remove ious > threshold
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elif nms_style == 'AND': # requires overlap, single boxes erased
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while len(dc) > 1:
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@@ -411,22 +410,16 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
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dc = dc[1:][iou < nms_thres] # remove ious > threshold
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elif nms_style == 'MERGE': # weighted mixture box
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while len(dc) > 0:
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iou = bbox_iou(dc[0], dc[0:]) # iou with other boxes
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i = iou > nms_thres
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weights = dc[i, 4:5] * dc[i, 5:6]
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while len(dc):
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i = bbox_iou(dc[0], dc) > nms_thres # iou with other boxes
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weights = dc[i, 4:5]
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dc[0, :4] = (weights * dc[i, :4]).sum(0) / weights.sum()
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det_max.append(dc[:1])
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dc = dc[iou < nms_thres]
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dc = dc[i == 0]
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# Image Total P R mAP
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# 4964 5000 0.633 0.598 0.589 # normal
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if len(det_max) > 0:
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det_max = torch.cat(det_max)
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# Add max detections to outputs
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output[image_i] = det_max if output[image_i] is None else torch.cat((output[image_i], det_max))
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if len(det_max):
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det_max = torch.cat(det_max) # concatenate
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output[image_i] = det_max[(-det_max[:, 4]).argsort()] # sort
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return output
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@@ -463,20 +456,42 @@ def coco_only_people(path='../coco/labels/val2014/'):
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print(labels.shape[0], file)
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def plot_results(start=0):
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def plot_wh_methods(): # from utils.utils import *; plot_wh_methods()
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# Compares the two methods for width-height anchor multiplication
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# https://github.com/ultralytics/yolov3/issues/168
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x = np.arange(-4.0, 4.0, .1)
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ya = np.exp(x)
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yb = (torch.sigmoid(torch.from_numpy(x)).numpy() * 2)
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fig = plt.figure(figsize=(6, 3), dpi=150)
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plt.plot(x, ya, '.-', label='yolo method')
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plt.plot(x, yb ** 2, '.-', label='^2 power method')
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plt.plot(x, yb ** 2.5, '.-', label='^2.5 power method')
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plt.xlim(left=-4, right=4)
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plt.ylim(bottom=0, top=6)
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plt.xlabel('input')
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plt.ylabel('output')
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plt.legend()
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fig.tight_layout()
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fig.savefig('comparison.jpg', dpi=fig.dpi)
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def plot_results(start=0): # from utils.utils import *; plot_results()
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# Plot YOLO training results file 'results.txt'
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# import os; os.system('wget https://storage.googleapis.com/ultralytics/yolov3/results_v3.txt')
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# from utils.utils import *; plot_results()
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fig = plt.figure(figsize=(14, 7))
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s = ['X + Y', 'Width + Height', 'Confidence', 'Classification', 'Total Loss', 'Precision', 'Recall', 'mAP']
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for f in sorted(glob.glob('results*.txt')):
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results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 9, 10, 11]).T # column 11 is mAP
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x = range(1, results.shape[1])
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results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 9, 10, 11, 12]).T # column 11 is mAP
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x = range(start, results.shape[1])
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for i in range(8):
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plt.subplot(2, 4, i + 1)
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plt.plot(results[i, x[start:]], marker='.', label=f)
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plt.plot(x, results[i, x], marker='.', label=f)
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plt.title(s[i])
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if i == 0:
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plt.legend()
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if i == 7:
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plt.plot(x, results[i + 1, x], marker='.', label=f)
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fig.tight_layout()
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fig.savefig('results.jpg', dpi=fig.dpi)
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