updates
This commit is contained in:
Glenn Jocher
parent
a46e500f9e
commit
809667404f
27
test.py
27
test.py
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@ -16,7 +16,7 @@ parser.add_argument('-nms_thres', type=float, default=0.45, help='iou threshold
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parser.add_argument('-n_cpu', type=int, default=0, help='number of cpu threads to use during batch generation')
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parser.add_argument('-img_size', type=int, default=416, help='size of each image dimension')
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opt = parser.parse_args()
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print(opt)
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print(opt, end='\n\n')
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cuda = torch.cuda.is_available()
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device = torch.device('cuda:0' if cuda else 'cpu')
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@ -49,10 +49,8 @@ def main(opt):
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# dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batch_size, shuffle=False, num_workers=opt.n_cpu)
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dataloader = load_images_and_labels(test_path, batch_size=opt.batch_size, img_size=opt.img_size)
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print('Compute mAP...')
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mAP = 0
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outputs, mAPs, TP, confidence, pred_class, target_class = [], [], [], [], [], []
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print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP'))
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outputs, mAPs, mR, mP, TP, confidence, pred_class, target_class = [], [], [], [], [], [], [], []
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AP_accum, AP_accum_count = np.zeros(nC), np.zeros(nC)
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for batch_i, (imgs, targets) in enumerate(dataloader):
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imgs = imgs.to(device)
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@ -107,22 +105,25 @@ def main(opt):
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correct.append(0)
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# Compute Average Precision (AP) per class
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AP, AP_class = ap_per_class(tp=correct, conf=detections[:, 4], pred_cls=detections[:, 6],
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AP, AP_class, R, P = ap_per_class(tp=correct, conf=detections[:, 4], pred_cls=detections[:, 6],
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target_cls=target_cls)
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# Accumulate AP per class
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AP_accum_count += np.bincount(AP_class, minlength=nC)
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AP_accum += np.bincount(AP_class, minlength=nC, weights=AP)
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# Compute mean AP for this image
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mAP = AP.mean()
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# Compute mean AP across all classes in this image, and append to image list
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mAPs.append(AP.mean())
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mR.append(R.mean())
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mP.append(P.mean())
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# Append image mAP to list
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mAPs.append(mAP)
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# Means of all images
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mean_mAP = np.mean(mAPs)
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mean_R = np.mean(mR)
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mean_P = np.mean(mP)
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# Print image mAP and running mean mAP
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print('Image %d/%d AP: %.4f (%.4f)' % (len(mAPs), len(dataloader) * opt.batch_size, mAP, mean_mAP))
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print(('%11s%11s' + '%11.3g' * 3) % (len(mAPs), len(dataloader) * opt.batch_size, mean_P, mean_R, mean_mAP))
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# Print mAP per class
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classes = load_classes(opt.class_path) # Extracts class labels from file
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@ -130,8 +131,8 @@ def main(opt):
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print('%15s: %-.4f' % (c, AP_accum[i] / AP_accum_count[i]))
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# Print mAP
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print('Mean Average Precision: %.4f' % mean_mAP)
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return mean_mAP
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print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP'))
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return mean_mAP, mean_R, mean_P
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if __name__ == '__main__':
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6
train.py
6
train.py
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@ -125,7 +125,7 @@ def main(opt):
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g['lr'] = lr
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# Compute loss, compute gradient, update parameters
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loss = model(imgs.to(device), targets, requestPrecision=True)
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loss = model(imgs.to(device), targets, requestPrecision=False)
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loss.backward()
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# accumulated_batches = 1 # accumulate gradient for 4 batches before stepping optimizer
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@ -183,11 +183,11 @@ def main(opt):
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# Calculate mAP
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import test
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test.opt.weights_path = 'weights/latest.pt'
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mAP = test.main(test.opt)
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mAP, R, P = test.main(test.opt)
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# Write epoch results
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with open('results.txt', 'a') as file:
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file.write(s + '%11.3g' % mAP + '\n')
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file.write(s + '%11.3g' * 3 % (mAP, P, R) + '\n')
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# Save final model
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dt = time.time() - t0
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@ -14,20 +14,20 @@ def load_classes(path):
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"""
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Loads class labels at 'path'
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"""
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fp = open(path, "r")
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names = fp.read().split("\n")[:-1]
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fp = open(path, 'r')
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names = fp.read().split('\n')[:-1]
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return names
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def model_info(model): # Plots a line-by-line description of a PyTorch model
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nP = sum(x.numel() for x in model.parameters()) # number parameters
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nG = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients
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n_p = sum(x.numel() for x in model.parameters()) # number parameters
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n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients
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print('\n%4s %70s %9s %12s %20s %12s %12s' % ('', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
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for i, (name, p) in enumerate(model.named_parameters()):
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name = name.replace('module_list.', '')
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print('%4g %70s %9s %12g %20s %12g %12g' % (
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i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
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print('\n%g layers, %g parameters, %g gradients' % (i + 1, nP, nG))
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print('\nModel Summary: %g layers, %g parameters, %g gradients\n' % (i + 1, n_p, n_g))
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def class_weights(): # frequency of each class in coco train2014
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@ -104,7 +104,7 @@ def ap_per_class(tp, conf, pred_cls, target_cls):
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unique_classes = np.unique(np.concatenate((pred_cls, target_cls), 0))
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# Create Precision-Recall curve and compute AP for each class
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ap = []
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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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@ -112,25 +112,27 @@ def ap_per_class(tp, conf, pred_cls, target_cls):
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if (n_p == 0) and (n_gt == 0):
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continue
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elif (np == 0) and (n_gt > 0):
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ap.append(0)
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elif (n_p > 0) and (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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fpa = np.cumsum(1 - tp[i])
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tpa = np.cumsum(tp[i])
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fpc = np.cumsum(1 - tp[i])
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tpc = np.cumsum(tp[i])
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# Recall
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recall = tpa / (n_gt + 1e-16)
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recall_curve = tpc / (n_gt + 1e-16)
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r.append(tpc[-1] / (n_gt + 1e-16))
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# Precision
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precision = tpa / (tpa + fpa)
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precision_curve = tpc / (tpc + fpc)
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p.append(tpc[-1] / (tpc[-1] + fpc[-1]))
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# AP from recall-precision curve
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ap.append(compute_ap(recall, precision))
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ap.append(compute_ap(recall_curve, precision_curve))
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return np.array(ap), unique_classes.astype('int32')
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return np.array(ap), unique_classes.astype('int32'), np.array(r), np.array(p)
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def compute_ap(recall, precision):
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@ -431,7 +433,7 @@ def coco_class_count(path='/Users/glennjocher/downloads/DATA/coco/labels/train20
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def plot_results():
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# Plot YOLO training results file "results.txt"
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# Plot YOLO training results file 'results.txt'
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import numpy as np
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import matplotlib.pyplot as plt
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plt.figure(figsize=(16, 8))
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@ -445,4 +447,3 @@ def plot_results():
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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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