452 lines
17 KiB
Python
Executable File
452 lines
17 KiB
Python
Executable File
import random
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import cv2
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import numpy as np
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import torch
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import torch.nn.functional as F
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from utils import torch_utils
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# Set printoptions
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torch.set_printoptions(linewidth=1320, precision=5, profile='long')
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np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5
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def init_seeds(seed=0):
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random.seed(seed)
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np.random.seed(seed)
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torch_utils.init_seeds(seed=seed)
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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')
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return list(filter(None, names)) # filter removes empty strings (such as last line)
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def model_info(model): # Plots a line-by-line description of a PyTorch model
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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%5s %50s %9s %12s %20s %12s %12s' % ('layer', '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('%5g %50s %9s %12g %20s %12.3g %12.3g' % (
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i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
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print('Model Summary: %g layers, %g parameters, %g gradients\n' % (i + 1, n_p, n_g))
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def coco_class_weights(): # frequency of each class in coco train2014
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weights = 1 / torch.FloatTensor(
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[187437, 4955, 30920, 6033, 3838, 4332, 3160, 7051, 7677, 9167, 1316, 1372, 833, 6757, 7355, 3302, 3776, 4671,
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6769, 5706, 3908, 903, 3686, 3596, 6200, 7920, 8779, 4505, 4272, 1862, 4698, 1962, 4403, 6659, 2402, 2689,
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4012, 4175, 3411, 17048, 5637, 14553, 3923, 5539, 4289, 10084, 7018, 4314, 3099, 4638, 4939, 5543, 2038, 4004,
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5053, 4578, 27292, 4113, 5931, 2905, 11174, 2873, 4036, 3415, 1517, 4122, 1980, 4464, 1190, 2302, 156, 3933,
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1877, 17630, 4337, 4624, 1075, 3468, 135, 1380])
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weights /= weights.sum()
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return weights
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def plot_one_box(x, img, color=None, label=None, line_thickness=None): # Plots one bounding box on image img
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tl = line_thickness or round(0.002 * max(img.shape[0:2])) + 1 # line thickness
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color = color or [random.randint(0, 255) for _ in range(3)]
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c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
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cv2.rectangle(img, c1, c2, color, thickness=tl)
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if label:
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tf = max(tl - 1, 1) # font thickness
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t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
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c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
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cv2.rectangle(img, c1, c2, color, -1) # filled
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cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
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def weights_init_normal(m):
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classname = m.__class__.__name__
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if classname.find('Conv') != -1:
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torch.nn.init.normal_(m.weight.data, 0.0, 0.03)
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elif classname.find('BatchNorm2d') != -1:
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torch.nn.init.normal_(m.weight.data, 1.0, 0.03)
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torch.nn.init.constant_(m.bias.data, 0.0)
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def xyxy2xywh(x):
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# Convert bounding box format from [x1, y1, x2, y2] to [x, y, w, h]
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y = torch.zeros(x.shape) if x.dtype is torch.float32 else np.zeros(x.shape)
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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[:, 2] - x[:, 0]
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y[:, 3] = x[:, 3] - x[:, 1]
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return y
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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(x.shape) if x.dtype is torch.float32 else np.zeros(x.shape)
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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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def scale_coords(img_size, coords, img0_shape):
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# Rescale x1, y1, x2, y2 from 416 to image size
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gain = float(img_size) / max(img0_shape) # gain = old / new
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pad_x = (img_size - img0_shape[1] * gain) / 2 # width padding
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pad_y = (img_size - img0_shape[0] * gain) / 2 # height padding
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coords[:, [0, 2]] -= pad_x
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coords[:, [1, 3]] -= pad_y
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coords[:, :4] /= gain
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coords[:, :4] = torch.round(torch.clamp(coords[:, :4], min=0))
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return coords
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def ap_per_class(tp, conf, pred_cls, target_cls):
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""" Compute the average precision, given the recall and precision curves.
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Method originally from https://github.com/rafaelpadilla/Object-Detection-Metrics.
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# Arguments
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tp: True positives (list).
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conf: Objectness value from 0-1 (list).
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pred_cls: Predicted object classes (list).
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target_cls: True object classes (list).
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# Returns
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The average precision as computed in py-faster-rcnn.
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"""
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# lists/pytorch to numpy
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tp, conf, pred_cls, target_cls = np.array(tp), np.array(conf), np.array(pred_cls), np.array(target_cls)
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# Sort by objectness
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i = np.argsort(-conf)
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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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# 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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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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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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# Recall
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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_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_curve, precision_curve))
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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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""" Compute the average precision, given the recall and precision curves.
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Code originally from https://github.com/rbgirshick/py-faster-rcnn.
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# Arguments
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recall: The recall curve (list).
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precision: The precision curve (list).
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# Returns
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The average precision as computed in py-faster-rcnn.
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"""
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# correct AP calculation
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# first append sentinel values at the end
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mrec = np.concatenate(([0.], recall, [1.]))
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mpre = np.concatenate(([0.], precision, [0.]))
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# compute the precision envelope
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for i in range(mpre.size - 1, 0, -1):
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mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
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# to calculate area under PR curve, look for points
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# where X axis (recall) changes value
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i = np.where(mrec[1:] != mrec[:-1])[0]
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# and sum (\Delta recall) * prec
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ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
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return ap
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def bbox_iou(box1, box2, x1y1x2y2=True):
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"""
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Returns the IoU of two bounding boxes
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"""
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if x1y1x2y2:
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# Get the coordinates of bounding boxes
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b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3]
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b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]
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else:
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# Transform from center and width to exact coordinates
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b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2
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b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2
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b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2
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b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2
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# get the coordinates of the intersection rectangle
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inter_rect_x1 = torch.max(b1_x1, b2_x1)
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inter_rect_y1 = torch.max(b1_y1, b2_y1)
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inter_rect_x2 = torch.min(b1_x2, b2_x2)
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inter_rect_y2 = torch.min(b1_y2, b2_y2)
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# Intersection area
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inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1, 0) * torch.clamp(inter_rect_y2 - inter_rect_y1, 0)
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# Union Area
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b1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1)
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b2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1)
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return inter_area / (b1_area + b2_area - inter_area + 1e-16)
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def build_targets(target, anchor_wh, nA, nC, nG):
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"""
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returns nT, nCorrect, tx, ty, tw, th, tconf, tcls
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"""
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nB = len(target) # number of images in batch
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nT = [len(x) for x in target]
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txy = torch.zeros(nB, nA, nG, nG, 2) # batch size, anchors, grid size
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twh = torch.zeros(nB, nA, nG, nG, 2)
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tconf = torch.ByteTensor(nB, nA, nG, nG).fill_(0)
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tcls = torch.ByteTensor(nB, nA, nG, nG, nC).fill_(0) # nC = number of classes
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for b in range(nB):
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nTb = nT[b] # number of targets
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if nTb == 0:
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continue
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t = target[b]
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gxy, gwh = t[:, 1:3] * nG, t[:, 3:5] * nG
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# Get grid box indices and prevent overflows (i.e. 13.01 on 13 anchors)
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gi, gj = torch.clamp(gxy.long(), min=0, max=nG - 1).t()
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# iou of targets-anchors (using wh only)
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box1 = gwh
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box2 = anchor_wh.unsqueeze(1)
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inter_area = torch.min(box1, box2).prod(2)
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iou = inter_area / (box1.prod(1) + box2.prod(2) - inter_area + 1e-16)
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# Select best iou_pred and anchor
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iou_best, a = iou.max(0) # best anchor [0-2] for each target
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# Select best unique target-anchor combinations
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if nTb > 1:
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iou_order = torch.argsort(-iou_best) # best to worst
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# Unique anchor selection
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u = torch.cat((gi, gj, a), 0).view((3, -1))
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_, first_unique = np.unique(u[:, iou_order], axis=1, return_index=True) # first unique indices
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# _, first_unique = torch.unique(u[:, iou_order], dim=1, return_inverse=True) # different than numpy?
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i = iou_order[first_unique]
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# best anchor must share significant commonality (iou) with target
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i = i[iou_best[i] > 0.10]
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if len(i) == 0:
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continue
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a, gj, gi, t = a[i], gj[i], gi[i], t[i]
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if len(t.shape) == 1:
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t = t.view(1, 5)
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else:
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if iou_best < 0.10:
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continue
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tc, gxy, gwh = t[:, 0].long(), t[:, 1:3] * nG, t[:, 3:5] * nG
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# XY coordinates
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txy[b, a, gj, gi] = gxy - gxy.floor()
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# Width and height
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twh[b, a, gj, gi] = torch.log(gwh / anchor_wh[a]) # yolo method
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# twh[b, a, gj, gi] = torch.sqrt(gwh / anchor_wh[a]) / 2 # power method
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# One-hot encoding of label
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tcls[b, a, gj, gi, tc] = 1
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tconf[b, a, gj, gi] = 1
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return txy, twh, tconf, tcls
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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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"""
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output = [None for _ in range(len(prediction))]
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for image_i, pred in enumerate(prediction):
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# Filter out confidence scores below threshold
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# Get score and class with highest confidence
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# cross-class NMS (experimental)
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cross_class_nms = False
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if cross_class_nms:
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a = pred.clone()
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_, indices = torch.sort(-a[:, 4], 0) # sort best to worst
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a = a[indices]
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radius = 30 # area to search for cross-class ious
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for i in range(len(a)):
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if i >= len(a) - 1:
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break
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close = (torch.abs(a[i, 0] - a[i + 1:, 0]) < radius) & (torch.abs(a[i, 1] - a[i + 1:, 1]) < radius)
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close = close.nonzero()
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if len(close) > 0:
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close = close + i + 1
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iou = bbox_iou(a[i:i + 1, :4], a[close.squeeze(), :4].reshape(-1, 4), x1y1x2y2=False)
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bad = close[iou > nms_thres]
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if len(bad) > 0:
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mask = torch.ones(len(a)).type(torch.ByteTensor)
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mask[bad] = 0
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a = a[mask]
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pred = a
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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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# a = w * h # area
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# ar = w / (h + 1e-16) # aspect ratio
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# n = len(w)
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# log_w, log_h, log_a, log_ar = torch.log(w), torch.log(h), torch.log(a), torch.log(ar)
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# shape_likelihood = np.zeros((n, 60), dtype=np.float32)
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# x = np.concatenate((log_w.reshape(-1, 1), log_h.reshape(-1, 1)), 1)
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# from scipy.stats import multivariate_normal
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# for c in range(60):
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# shape_likelihood[:, c] =
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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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class_prob, class_pred = torch.max(F.softmax(pred[:, 5:], 1), 1)
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v = ((pred[:, 4] > conf_thres) & (class_prob > .4)) # TODO examine arbitrary 0.4 thres here
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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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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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# 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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continue
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# From (center x, center y, width, height) to (x1, y1, x2, y2)
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pred[:, :4] = xywh2xyxy(pred[:, :4])
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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()
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if prediction.is_cuda:
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unique_labels = unique_labels.cuda(prediction.device)
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nms_style = 'MERGE' # '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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# Non-maximum suppression
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det_max = []
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if nms_style == 'OR': # default
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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(det_max[-1], dc[1:]) # iou with other boxes
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dc = dc[1:][iou < nms_thres] # remove ious > threshold
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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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elif nms_style == 'AND': # requires overlap, single boxes erased
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while len(dc) > 1:
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iou = bbox_iou(dc[:1], dc[1:]) # iou with other boxes
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if iou.max() > 0.5:
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det_max.append(dc[:1])
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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[:1], 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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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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# 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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return output
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def strip_optimizer_from_checkpoint(filename='weights/best.pt'):
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# Strip optimizer from *.pt files for lighter files (reduced by 2/3 size)
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import torch
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a = torch.load(filename, map_location='cpu')
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a['optimizer'] = []
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torch.save(a, filename.replace('.pt', '_lite.pt'))
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def coco_class_count(path='../coco/labels/train2014/'):
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# histogram of occurrences per class
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import glob
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|
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nC = 80 # number classes
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x = np.zeros(nC, dtype='int32')
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files = sorted(glob.glob('%s/*.*' % path))
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for i, file in enumerate(files):
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labels = np.loadtxt(file, dtype=np.float32).reshape(-1, 5)
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x += np.bincount(labels[:, 0].astype('int32'), minlength=nC)
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print(i, len(files))
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|
|
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def plot_results():
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# Plot YOLO training results file 'results.txt'
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|
import glob
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|
import matplotlib.pyplot as plt
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import numpy as np
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# import os; os.system('rm -rf results.txt && wget https://storage.googleapis.com/ultralytics/results_v1_0.txt')
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|
|
|
plt.figure(figsize=(16, 8))
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|
s = ['XY', 'Width-Height', 'Confidence', 'Classification', 'Total Loss', 'mAP', 'Recall', 'Precision']
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|
files = sorted(glob.glob('results*.txt'))
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|
for f in files:
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|
results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 11, 12, 13]).T # column 11 is mAP
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|
n = 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(range(1, n), results[i, 1:], marker='.', label=f)
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|
plt.title(s[i])
|
|
if i == 0:
|
|
plt.legend()
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