933 lines
37 KiB
Python
Executable File
933 lines
37 KiB
Python
Executable File
import glob
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import os
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import random
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import shutil
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from pathlib import Path
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import cv2
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import matplotlib
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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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from tqdm import tqdm
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from . import torch_utils # , google_utils
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matplotlib.rc('font', **{'size': 11})
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# Set printoptions
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torch.set_printoptions(linewidth=320, 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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# Prevent OpenCV from multithreading (to use PyTorch DataLoader)
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cv2.setNumThreads(0)
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def floatn(x, n=3): # format floats to n decimals
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return float(format(x, '.%gf' % n))
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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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# Loads *.names file at 'path'
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with open(path, 'r') as f:
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names = f.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 labels_to_class_weights(labels, nc=80):
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# Get class weights (inverse frequency) from training labels
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if labels[0] is None: # no labels loaded
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return torch.Tensor()
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labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO
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classes = labels[:, 0].astype(np.int) # labels = [class xywh]
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weights = np.bincount(classes, minlength=nc) # occurences per class
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# Prepend gridpoint count (for uCE trianing)
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# gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image
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# weights = np.hstack([gpi * len(labels) - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start
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weights[weights == 0] = 1 # replace empty bins with 1
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weights = 1 / weights # number of targets per class
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weights /= weights.sum() # normalize
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return torch.from_numpy(weights)
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def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
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# Produces image weights based on class mAPs
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n = len(labels)
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class_counts = np.array([np.bincount(labels[i][:, 0].astype(np.int), minlength=nc) for i in range(n)])
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image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1)
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# index = random.choices(range(n), weights=image_weights, k=1) # weight image sample
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return image_weights
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def coco_class_weights(): # frequency of each class in coco train2014
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n = [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 = 1 / torch.Tensor(n)
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weights /= weights.sum()
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# with open('data/coco.names', 'r') as f:
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# for k, v in zip(f.read().splitlines(), n):
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# print('%20s: %g' % (k, v))
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return weights
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def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper)
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# https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
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# a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
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# b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
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# x = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco
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x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
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35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
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64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
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return x
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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_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[:, 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_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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return y
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def scale_coords(img1_shape, coords, img0_shape):
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# Rescale coords (xyxy) from img1_shape to img0_shape
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gain = max(img1_shape) / max(img0_shape) # gain = old / new
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coords[:, [0, 2]] -= (img1_shape[1] - img0_shape[1] * gain) / 2 # x padding
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coords[:, [1, 3]] -= (img1_shape[0] - img0_shape[0] * gain) / 2 # y padding
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coords[:, :4] /= gain
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clip_coords(coords, img0_shape)
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return coords
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def clip_coords(boxes, img_shape):
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# Clip bounding xyxy bounding boxes to image shape (height, width)
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boxes[:, [0, 2]] = boxes[:, [0, 2]].clamp(min=0, max=img_shape[1]) # clip x
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boxes[:, [1, 3]] = boxes[:, [1, 3]].clamp(min=0, max=img_shape[0]) # clip y
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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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Source: 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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# 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(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 = (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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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 = (1 - tp[i]).cumsum()
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tpc = (tp[i]).cumsum()
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# Recall
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recall = tpc / (n_gt + 1e-16) # recall curve
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r.append(recall[-1])
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# Precision
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precision = tpc / (tpc + fpc) # precision curve
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p.append(precision[-1])
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# AP from recall-precision curve
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ap.append(compute_ap(recall, precision))
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# Plot
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# fig, ax = plt.subplots(1, 1, figsize=(4, 4))
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# ax.plot(np.concatenate(([0.], recall)), np.concatenate(([0.], precision)))
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# ax.set_xlabel('YOLOv3-SPP')
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# ax.set_xlabel('Recall')
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# ax.set_ylabel('Precision')
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# ax.set_xlim(0, 1)
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# fig.tight_layout()
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# fig.savefig('PR_curve.png', dpi=300)
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# Compute F1 score (harmonic mean of precision and recall)
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p, r, ap = np.array(p), np.array(r), np.array(ap)
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f1 = 2 * p * r / (p + r + 1e-16)
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return p, r, ap, f1, unique_classes.astype('int32')
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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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Source: 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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# Append sentinel values to beginning and end
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mrec = np.concatenate(([0.], recall, [min(recall[-1] + 1E-3, 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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# Integrate area under curve
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method = 'interp' # methods: 'continuous', 'interp'
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if method == 'interp':
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x = np.linspace(0, 1, 101) # 101-point interp (COCO)
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ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate
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else: # 'continuous'
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i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes
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ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve
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return ap
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def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False):
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# Returns the IoU of box1 to box2. box1 is 4, box2 is nx4
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box2 = box2.t()
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# Get the coordinates of bounding boxes
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if x1y1x2y2:
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# x1, y1, x2, y2 = box1
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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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# x, y, w, h = box1
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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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# Intersection area
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inter_area = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \
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(torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)
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# Union Area
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union_area = ((b1_x2 - b1_x1) * (b1_y2 - b1_y1) + 1e-16) + \
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(b2_x2 - b2_x1) * (b2_y2 - b2_y1) - inter_area
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iou = inter_area / union_area # iou
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if GIoU: # Generalized IoU https://arxiv.org/pdf/1902.09630.pdf
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c_x1, c_x2 = torch.min(b1_x1, b2_x1), torch.max(b1_x2, b2_x2)
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c_y1, c_y2 = torch.min(b1_y1, b2_y1), torch.max(b1_y2, b2_y2)
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c_area = (c_x2 - c_x1) * (c_y2 - c_y1) + 1e-16 # convex area
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return iou - (c_area - union_area) / c_area # GIoU
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return iou
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def wh_iou(box1, box2):
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# Returns the IoU of wh1 to wh2. wh1 is 2, wh2 is nx2
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box2 = box2.t()
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# w, h = box1
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w1, h1 = box1[0], box1[1]
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w2, h2 = box2[0], box2[1]
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# Intersection area
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inter_area = torch.min(w1, w2) * torch.min(h1, h2)
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# Union Area
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union_area = (w1 * h1 + 1e-16) + w2 * h2 - inter_area
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return inter_area / union_area # iou
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class FocalLoss(nn.Module):
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# Wraps focal loss around existing loss_fcn() https://arxiv.org/pdf/1708.02002.pdf
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# i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=2.5)
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def __init__(self, loss_fcn, gamma=0.5, alpha=1, reduction='mean'):
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super(FocalLoss, self).__init__()
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loss_fcn.reduction = 'none' # required to apply FL to each element
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self.loss_fcn = loss_fcn
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self.gamma = gamma
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self.alpha = alpha
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self.reduction = reduction
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def forward(self, input, target):
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loss = self.loss_fcn(input, target)
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loss *= self.alpha * (1.000001 - torch.exp(-loss)) ** self.gamma # non-zero power for gradient stability
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if self.reduction == 'mean':
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return loss.mean()
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elif self.reduction == 'sum':
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return loss.sum()
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else: # 'none'
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return loss
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def compute_loss(p, targets, model): # predictions, targets, model
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ft = torch.cuda.FloatTensor if p[0].is_cuda else torch.Tensor
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lcls, lbox, lobj = ft([0]), ft([0]), ft([0])
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tcls, tbox, indices, anchor_vec = build_targets(model, targets)
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h = model.hyp # hyperparameters
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arc = model.arc # # (default, uCE, uBCE) detection architectures
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# Define criteria
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BCEcls = nn.BCEWithLogitsLoss(pos_weight=ft([h['cls_pw']]))
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BCEobj = nn.BCEWithLogitsLoss(pos_weight=ft([h['obj_pw']]))
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BCE = nn.BCEWithLogitsLoss()
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CE = nn.CrossEntropyLoss() # weight=model.class_weights
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if 'F' in arc: # add focal loss
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g = h['fl_gamma']
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BCEcls, BCEobj, BCE, CE = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g), FocalLoss(BCE, g), FocalLoss(CE, g)
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# Compute losses
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for i, pi in enumerate(p): # layer index, layer predictions
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b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
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tobj = torch.zeros_like(pi[..., 0]) # target obj
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# Compute losses
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nb = len(b)
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if nb: # number of targets
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ps = pi[b, a, gj, gi] # prediction subset corresponding to targets
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tobj[b, a, gj, gi] = 1.0 # obj
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# ps[:, 2:4] = torch.sigmoid(ps[:, 2:4]) # wh power loss (uncomment)
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# GIoU
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pxy = torch.sigmoid(ps[:, 0:2]) # pxy = pxy * s - (s - 1) / 2, s = 1.5 (scale_xy)
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pbox = torch.cat((pxy, torch.exp(ps[:, 2:4]) * anchor_vec[i]), 1) # predicted box
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giou = bbox_iou(pbox.t(), tbox[i], x1y1x2y2=False, GIoU=True) # giou computation
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lbox += (1.0 - giou).mean() # giou loss
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if 'default' in arc and model.nc > 1: # cls loss (only if multiple classes)
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t = torch.zeros_like(ps[:, 5:]) # targets
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t[range(nb), tcls[i]] = 1.0
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lcls += BCEcls(ps[:, 5:], t) # BCE
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# lcls += CE(ps[:, 5:], tcls[i]) # CE
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# Instance-class weighting (use with reduction='none')
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# nt = t.sum(0) + 1 # number of targets per class
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# lcls += (BCEcls(ps[:, 5:], t) / nt).mean() * nt.mean() # v1
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# lcls += (BCEcls(ps[:, 5:], t) / nt[tcls[i]].view(-1,1)).mean() * nt.mean() # v2
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# Append targets to text file
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# with open('targets.txt', 'a') as file:
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# [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
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if 'default' in arc: # separate obj and cls
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lobj += BCEobj(pi[..., 4], tobj) # obj loss
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elif 'BCE' in arc: # unified BCE (80 classes)
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t = torch.zeros_like(pi[..., 5:]) # targets
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if nb:
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t[b, a, gj, gi, tcls[i]] = 1.0
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lobj += BCE(pi[..., 5:], t)
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elif 'CE' in arc: # unified CE (1 background + 80 classes)
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t = torch.zeros_like(pi[..., 0], dtype=torch.long) # targets
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if nb:
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t[b, a, gj, gi] = tcls[i] + 1
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lcls += CE(pi[..., 4:].view(-1, model.nc + 1), t.view(-1))
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lbox *= h['giou']
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lobj *= h['obj']
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lcls *= h['cls']
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loss = lbox + lobj + lcls
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return loss, torch.cat((lbox, lobj, lcls, loss)).detach()
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def build_targets(model, targets):
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# targets = [image, class, x, y, w, h]
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nt = len(targets)
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tcls, tbox, indices, av = [], [], [], []
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multi_gpu = type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
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for i in model.yolo_layers:
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# get number of grid points and anchor vec for this yolo layer
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if multi_gpu:
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ng, anchor_vec = model.module.module_list[i].ng, model.module.module_list[i].anchor_vec
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else:
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ng, anchor_vec = model.module_list[i].ng, model.module_list[i].anchor_vec
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|
# iou of targets-anchors
|
|
t, a = targets, []
|
|
gwh = t[:, 4:6] * ng
|
|
if nt:
|
|
iou = torch.stack([wh_iou(x, gwh) for x in anchor_vec], 0)
|
|
|
|
use_best_anchor = False
|
|
if use_best_anchor:
|
|
iou, a = iou.max(0) # best iou and anchor
|
|
else: # use all anchors
|
|
na = len(anchor_vec) # number of anchors
|
|
a = torch.arange(na).view((-1, 1)).repeat([1, nt]).view(-1)
|
|
t = targets.repeat([na, 1])
|
|
gwh = gwh.repeat([na, 1])
|
|
iou = iou.view(-1) # use all ious
|
|
|
|
# reject anchors below iou_thres (OPTIONAL, increases P, lowers R)
|
|
reject = True
|
|
if reject:
|
|
j = iou > model.hyp['iou_t'] # iou threshold hyperparameter
|
|
t, a, gwh = t[j], a[j], gwh[j]
|
|
|
|
# Indices
|
|
b, c = t[:, :2].long().t() # target image, class
|
|
gxy = t[:, 2:4] * ng # grid x, y
|
|
gi, gj = gxy.long().t() # grid x, y indices
|
|
indices.append((b, a, gj, gi))
|
|
|
|
# GIoU
|
|
gxy -= gxy.floor() # xy
|
|
tbox.append(torch.cat((gxy, gwh), 1)) # xywh (grids)
|
|
av.append(anchor_vec[a]) # anchor vec
|
|
|
|
# Class
|
|
tcls.append(c)
|
|
if c.shape[0]: # if any targets
|
|
assert c.max() <= model.nc, 'Target classes exceed model classes'
|
|
|
|
return tcls, tbox, indices, av
|
|
|
|
|
|
def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.5):
|
|
"""
|
|
Removes detections with lower object confidence score than 'conf_thres'
|
|
Non-Maximum Suppression to further filter detections.
|
|
Returns detections with shape:
|
|
(x1, y1, x2, y2, object_conf, class_conf, class)
|
|
"""
|
|
|
|
min_wh = 2 # (pixels) minimum box width and height
|
|
|
|
output = [None] * len(prediction)
|
|
for image_i, pred in enumerate(prediction):
|
|
# Experiment: Prior class size rejection
|
|
# x, y, w, h = pred[:, 0], pred[:, 1], pred[:, 2], pred[:, 3]
|
|
# a = w * h # area
|
|
# ar = w / (h + 1e-16) # aspect ratio
|
|
# n = len(w)
|
|
# log_w, log_h, log_a, log_ar = torch.log(w), torch.log(h), torch.log(a), torch.log(ar)
|
|
# shape_likelihood = np.zeros((n, 60), dtype=np.float32)
|
|
# x = np.concatenate((log_w.reshape(-1, 1), log_h.reshape(-1, 1)), 1)
|
|
# from scipy.stats import multivariate_normal
|
|
# for c in range(60):
|
|
# shape_likelihood[:, c] =
|
|
# multivariate_normal.pdf(x, mean=mat['class_mu'][c, :2], cov=mat['class_cov'][c, :2, :2])
|
|
|
|
# Multiply conf by class conf to get combined confidence
|
|
class_conf, class_pred = pred[:, 5:].max(1)
|
|
pred[:, 4] *= class_conf
|
|
|
|
# # Merge classes (optional)
|
|
# class_pred[(class_pred.view(-1,1) == torch.LongTensor([2, 3, 5, 6, 7]).view(1,-1)).any(1)] = 2
|
|
#
|
|
# # Remove classes (optional)
|
|
# pred[class_pred != 2, 4] = 0.0
|
|
|
|
# Select only suitable predictions
|
|
i = (pred[:, 4] > conf_thres) & (pred[:, 2:4] > min_wh).all(1) & torch.isfinite(pred).all(1)
|
|
pred = pred[i]
|
|
|
|
# If none are remaining => process next image
|
|
if len(pred) == 0:
|
|
continue
|
|
|
|
# Select predicted classes
|
|
class_conf = class_conf[i]
|
|
class_pred = class_pred[i].unsqueeze(1).float()
|
|
|
|
# Box (center x, center y, width, height) to (x1, y1, x2, y2)
|
|
pred[:, :4] = xywh2xyxy(pred[:, :4])
|
|
# pred[:, 4] *= class_conf # improves mAP from 0.549 to 0.551
|
|
|
|
# Detections ordered as (x1y1x2y2, obj_conf, class_conf, class_pred)
|
|
pred = torch.cat((pred[:, :5], class_conf.unsqueeze(1), class_pred), 1)
|
|
|
|
# Get detections sorted by decreasing confidence scores
|
|
pred = pred[(-pred[:, 4]).argsort()]
|
|
|
|
det_max = []
|
|
nms_style = 'MERGE' # 'OR' (default), 'AND', 'MERGE' (experimental)
|
|
for c in pred[:, -1].unique():
|
|
dc = pred[pred[:, -1] == c] # select class c
|
|
n = len(dc)
|
|
if n == 1:
|
|
det_max.append(dc) # No NMS required if only 1 prediction
|
|
continue
|
|
elif n > 100:
|
|
dc = dc[:100] # limit to first 100 boxes: https://github.com/ultralytics/yolov3/issues/117
|
|
|
|
# Non-maximum suppression
|
|
if nms_style == 'OR': # default
|
|
# METHOD1
|
|
# ind = list(range(len(dc)))
|
|
# while len(ind):
|
|
# j = ind[0]
|
|
# det_max.append(dc[j:j + 1]) # save highest conf detection
|
|
# reject = (bbox_iou(dc[j], dc[ind]) > nms_thres).nonzero()
|
|
# [ind.pop(i) for i in reversed(reject)]
|
|
|
|
# METHOD2
|
|
while dc.shape[0]:
|
|
det_max.append(dc[:1]) # save highest conf detection
|
|
if len(dc) == 1: # Stop if we're at the last detection
|
|
break
|
|
iou = bbox_iou(dc[0], dc[1:]) # iou with other boxes
|
|
dc = dc[1:][iou < nms_thres] # remove ious > threshold
|
|
|
|
elif nms_style == 'AND': # requires overlap, single boxes erased
|
|
while len(dc) > 1:
|
|
iou = bbox_iou(dc[0], dc[1:]) # iou with other boxes
|
|
if iou.max() > 0.5:
|
|
det_max.append(dc[:1])
|
|
dc = dc[1:][iou < nms_thres] # remove ious > threshold
|
|
|
|
elif nms_style == 'MERGE': # weighted mixture box
|
|
while len(dc):
|
|
if len(dc) == 1:
|
|
det_max.append(dc)
|
|
break
|
|
i = bbox_iou(dc[0], dc) > nms_thres # iou with other boxes
|
|
weights = dc[i, 4:5]
|
|
dc[0, :4] = (weights * dc[i, :4]).sum(0) / weights.sum()
|
|
det_max.append(dc[:1])
|
|
dc = dc[i == 0]
|
|
|
|
elif nms_style == 'SOFT': # soft-NMS https://arxiv.org/abs/1704.04503
|
|
sigma = 0.5 # soft-nms sigma parameter
|
|
while len(dc):
|
|
if len(dc) == 1:
|
|
det_max.append(dc)
|
|
break
|
|
det_max.append(dc[:1])
|
|
iou = bbox_iou(dc[0], dc[1:]) # iou with other boxes
|
|
dc = dc[1:]
|
|
dc[:, 4] *= torch.exp(-iou ** 2 / sigma) # decay confidences
|
|
# dc = dc[dc[:, 4] > nms_thres] # new line per https://github.com/ultralytics/yolov3/issues/362
|
|
|
|
if len(det_max):
|
|
det_max = torch.cat(det_max) # concatenate
|
|
output[image_i] = det_max[(-det_max[:, 4]).argsort()] # sort
|
|
|
|
return output
|
|
|
|
|
|
def get_yolo_layers(model):
|
|
bool_vec = [x['type'] == 'yolo' for x in model.module_defs]
|
|
return [i for i, x in enumerate(bool_vec) if x] # [82, 94, 106] for yolov3
|
|
|
|
|
|
def print_model_biases(model):
|
|
# prints the bias neurons preceding each yolo layer
|
|
print('\nModel Bias Summary (per output layer):')
|
|
multi_gpu = type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
|
|
for l in model.yolo_layers: # print pretrained biases
|
|
if multi_gpu:
|
|
na = model.module.module_list[l].na # number of anchors
|
|
b = model.module.module_list[l - 1][0].bias.view(na, -1) # bias 3x85
|
|
else:
|
|
na = model.module_list[l].na
|
|
b = model.module_list[l - 1][0].bias.view(na, -1) # bias 3x85
|
|
print('regression: %5.2f+/-%-5.2f ' % (b[:, :4].mean(), b[:, :4].std()),
|
|
'objectness: %5.2f+/-%-5.2f ' % (b[:, 4].mean(), b[:, 4].std()),
|
|
'classification: %5.2f+/-%-5.2f' % (b[:, 5:].mean(), b[:, 5:].std()))
|
|
|
|
|
|
def strip_optimizer(f='weights/last.pt'): # from utils.utils import *; strip_optimizer()
|
|
# Strip optimizer from *.pt files for lighter files (reduced by 2/3 size)
|
|
x = torch.load(f, map_location=torch.device('cpu'))
|
|
x['optimizer'] = None
|
|
# x['training_results'] = None # uncomment to create a backbone
|
|
# x['epoch'] = -1 # uncomment to create a backbone
|
|
torch.save(x, f)
|
|
|
|
|
|
def create_backbone(f='weights/last.pt'): # from utils.utils import *; create_backbone()
|
|
# create a backbone from a *.pt file
|
|
x = torch.load(f, map_location=torch.device('cpu'))
|
|
x['optimizer'] = None
|
|
x['training_results'] = None
|
|
x['epoch'] = -1
|
|
for p in x['model'].values():
|
|
try:
|
|
p.requires_grad = True
|
|
except:
|
|
pass
|
|
torch.save(x, 'weights/backbone.pt')
|
|
|
|
|
|
def coco_class_count(path='../coco/labels/train2014/'):
|
|
# Histogram of occurrences per class
|
|
nc = 80 # number classes
|
|
x = np.zeros(nc, dtype='int32')
|
|
files = sorted(glob.glob('%s/*.*' % path))
|
|
for i, file in enumerate(files):
|
|
labels = np.loadtxt(file, dtype=np.float32).reshape(-1, 5)
|
|
x += np.bincount(labels[:, 0].astype('int32'), minlength=nc)
|
|
print(i, len(files))
|
|
|
|
|
|
def coco_only_people(path='../coco/labels/val2014/'):
|
|
# Find images with only people
|
|
files = sorted(glob.glob('%s/*.*' % path))
|
|
for i, file in enumerate(files):
|
|
labels = np.loadtxt(file, dtype=np.float32).reshape(-1, 5)
|
|
if all(labels[:, 0] == 0):
|
|
print(labels.shape[0], file)
|
|
|
|
|
|
def select_best_evolve(path='evolve*.txt'): # from utils.utils import *; select_best_evolve()
|
|
# Find best evolved mutation
|
|
for file in sorted(glob.glob(path)):
|
|
x = np.loadtxt(file, dtype=np.float32, ndmin=2)
|
|
print(file, x[fitness(x).argmax()])
|
|
|
|
|
|
def crop_images_random(path='../images/', scale=0.50): # from utils.utils import *; crop_images_random()
|
|
# crops images into random squares up to scale fraction
|
|
# WARNING: overwrites images!
|
|
for file in tqdm(sorted(glob.glob('%s/*.*' % path))):
|
|
img = cv2.imread(file) # BGR
|
|
if img is not None:
|
|
h, w = img.shape[:2]
|
|
|
|
# create random mask
|
|
a = 30 # minimum size (pixels)
|
|
mask_h = random.randint(a, int(max(a, h * scale))) # mask height
|
|
mask_w = mask_h # mask width
|
|
|
|
# box
|
|
xmin = max(0, random.randint(0, w) - mask_w // 2)
|
|
ymin = max(0, random.randint(0, h) - mask_h // 2)
|
|
xmax = min(w, xmin + mask_w)
|
|
ymax = min(h, ymin + mask_h)
|
|
|
|
# apply random color mask
|
|
cv2.imwrite(file, img[ymin:ymax, xmin:xmax])
|
|
|
|
|
|
def coco_single_class_labels(path='../coco/labels/train2014/', label_class=43):
|
|
# Makes single-class coco datasets. from utils.utils import *; coco_single_class_labels()
|
|
if os.path.exists('new/'):
|
|
shutil.rmtree('new/') # delete output folder
|
|
os.makedirs('new/') # make new output folder
|
|
os.makedirs('new/labels/')
|
|
os.makedirs('new/images/')
|
|
for file in tqdm(sorted(glob.glob('%s/*.*' % path))):
|
|
with open(file, 'r') as f:
|
|
labels = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)
|
|
i = labels[:, 0] == label_class
|
|
if any(i):
|
|
img_file = file.replace('labels', 'images').replace('txt', 'jpg')
|
|
labels[:, 0] = 0 # reset class to 0
|
|
with open('new/images.txt', 'a') as f: # add image to dataset list
|
|
f.write(img_file + '\n')
|
|
with open('new/labels/' + Path(file).name, 'a') as f: # write label
|
|
for l in labels[i]:
|
|
f.write('%g %.6f %.6f %.6f %.6f\n' % tuple(l))
|
|
shutil.copyfile(src=img_file, dst='new/images/' + Path(file).name.replace('txt', 'jpg')) # copy images
|
|
|
|
|
|
def kmeans_targets(path='../coco/trainvalno5k.txt', n=9, img_size=416): # from utils.utils import *; kmeans_targets()
|
|
# Produces a list of target kmeans suitable for use in *.cfg files
|
|
from utils.datasets import LoadImagesAndLabels
|
|
from scipy import cluster
|
|
|
|
# Get label wh
|
|
dataset = LoadImagesAndLabels(path, augment=True, rect=True, cache_labels=True)
|
|
for s, l in zip(dataset.shapes, dataset.labels):
|
|
l[:, [1, 3]] *= s[0] # normalized to pixels
|
|
l[:, [2, 4]] *= s[1]
|
|
l[:, 1:] *= img_size / max(s) * random.uniform(0.5, 1.5) # nominal img_size for training
|
|
wh = np.concatenate(dataset.labels, 0)[:, 3:5] # wh from cxywh
|
|
|
|
# Kmeans calculation
|
|
k = cluster.vq.kmeans(wh, n)[0]
|
|
k = k[np.argsort(k.prod(1))] # sort small to large
|
|
|
|
# Measure IoUs
|
|
iou = torch.stack([wh_iou(torch.Tensor(wh).T, torch.Tensor(x).T) for x in k], 0)
|
|
biou = iou.max(0)[0] # closest anchor IoU
|
|
print('Best possible recall: %.3f' % (biou > 0.2635).float().mean()) # BPR (best possible recall)
|
|
|
|
# Print
|
|
print('kmeans anchors (n=%g, img_size=%g, IoU=%.2f/%.2f/%.2f-min/mean/best): ' %
|
|
(n, img_size, biou.min(), iou.mean(), biou.mean()), end='')
|
|
for i, x in enumerate(k):
|
|
print('%i,%i' % (round(x[0]), round(x[1])), end=', ' if i < len(k) - 1 else '\n') # use in *.cfg
|
|
|
|
# Plot
|
|
# plt.hist(biou.numpy().ravel(), 100)
|
|
|
|
|
|
def print_mutation(hyp, results, bucket=''):
|
|
# Print mutation results to evolve.txt (for use with train.py --evolve)
|
|
a = '%10s' * len(hyp) % tuple(hyp.keys()) # hyperparam keys
|
|
b = '%10.3g' * len(hyp) % tuple(hyp.values()) # hyperparam values
|
|
c = '%10.3g' * len(results) % results # results (P, R, mAP, F1, test_loss)
|
|
print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c))
|
|
|
|
if bucket:
|
|
os.system('gsutil cp gs://%s/evolve.txt .' % bucket) # download evolve.txt
|
|
|
|
with open('evolve.txt', 'a') as f: # append result
|
|
f.write(c + b + '\n')
|
|
x = np.unique(np.loadtxt('evolve.txt', ndmin=2), axis=0) # load unique rows
|
|
np.savetxt('evolve.txt', x[np.argsort(-fitness(x))], '%10.3g') # save sort by fitness
|
|
|
|
if bucket:
|
|
os.system('gsutil cp evolve.txt gs://%s' % bucket) # upload evolve.txt
|
|
|
|
|
|
def apply_classifier(x, model, img, im0):
|
|
# applies a second stage classifier to yolo outputs
|
|
|
|
for i, d in enumerate(x): # per image
|
|
if d is not None and len(d):
|
|
d = d.clone()
|
|
|
|
# Reshape and pad cutouts
|
|
b = xyxy2xywh(d[:, :4]) # boxes
|
|
b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # rectangle to square
|
|
b[:, 2:] = b[:, 2:] * 1.3 + 30 # pad
|
|
d[:, :4] = xywh2xyxy(b).long()
|
|
|
|
# Rescale boxes from img_size to im0 size
|
|
scale_coords(img.shape[2:], d[:, :4], im0.shape)
|
|
|
|
# Classes
|
|
pred_cls1 = d[:, 6].long()
|
|
ims = []
|
|
for j, a in enumerate(d): # per item
|
|
cutout = im0[int(a[1]):int(a[3]), int(a[0]):int(a[2])]
|
|
im = cv2.resize(cutout, (224, 224)) # BGR
|
|
# cv2.imwrite('test%i.jpg' % j, cutout)
|
|
|
|
im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
|
|
im = np.ascontiguousarray(im, dtype=np.float32) # uint8 to float32
|
|
im /= 255.0 # 0 - 255 to 0.0 - 1.0
|
|
ims.append(im)
|
|
|
|
pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1) # classifier prediction
|
|
x[i] = x[i][pred_cls1 == pred_cls2] # retain matching class detections
|
|
|
|
return x
|
|
|
|
|
|
def fitness(x):
|
|
# Returns fitness (for use with results.txt or evolve.txt)
|
|
return x[:, 2] * 0.8 + x[:, 3] * 0.2 # weighted mAP and F1 combination
|
|
|
|
|
|
# Plotting functions ---------------------------------------------------------------------------------------------------
|
|
def plot_one_box(x, img, color=None, label=None, line_thickness=None):
|
|
# Plots one bounding box on image img
|
|
tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line thickness
|
|
color = color or [random.randint(0, 255) for _ in range(3)]
|
|
c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
|
|
cv2.rectangle(img, c1, c2, color, thickness=tl)
|
|
if label:
|
|
tf = max(tl - 1, 1) # font thickness
|
|
t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
|
|
c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
|
|
cv2.rectangle(img, c1, c2, color, -1) # filled
|
|
cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
|
|
|
|
|
|
def plot_wh_methods(): # from utils.utils import *; plot_wh_methods()
|
|
# Compares the two methods for width-height anchor multiplication
|
|
# https://github.com/ultralytics/yolov3/issues/168
|
|
x = np.arange(-4.0, 4.0, .1)
|
|
ya = np.exp(x)
|
|
yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2
|
|
|
|
fig = plt.figure(figsize=(6, 3), dpi=150)
|
|
plt.plot(x, ya, '.-', label='yolo method')
|
|
plt.plot(x, yb ** 2, '.-', label='^2 power method')
|
|
plt.plot(x, yb ** 2.5, '.-', label='^2.5 power method')
|
|
plt.xlim(left=-4, right=4)
|
|
plt.ylim(bottom=0, top=6)
|
|
plt.xlabel('input')
|
|
plt.ylabel('output')
|
|
plt.legend()
|
|
fig.tight_layout()
|
|
fig.savefig('comparison.png', dpi=200)
|
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def plot_images(imgs, targets, paths=None, fname='images.jpg'):
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# Plots training images overlaid with targets
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imgs = imgs.cpu().numpy()
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targets = targets.cpu().numpy()
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# targets = targets[targets[:, 1] == 21] # plot only one class
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fig = plt.figure(figsize=(10, 10))
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bs, _, h, w = imgs.shape # batch size, _, height, width
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bs = min(bs, 16) # limit plot to 16 images
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ns = np.ceil(bs ** 0.5) # number of subplots
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for i in range(bs):
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boxes = xywh2xyxy(targets[targets[:, 0] == i, 2:6]).T
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boxes[[0, 2]] *= w
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boxes[[1, 3]] *= h
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plt.subplot(ns, ns, i + 1).imshow(imgs[i].transpose(1, 2, 0))
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plt.plot(boxes[[0, 2, 2, 0, 0]], boxes[[1, 1, 3, 3, 1]], '.-')
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plt.axis('off')
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if paths is not None:
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s = Path(paths[i]).name
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plt.title(s[:min(len(s), 40)], fontdict={'size': 8}) # limit to 40 characters
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fig.tight_layout()
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fig.savefig(fname, dpi=200)
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plt.close()
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def plot_test_txt(): # from utils.utils import *; plot_test()
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# Plot test.txt histograms
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x = np.loadtxt('test.txt', dtype=np.float32)
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box = xyxy2xywh(x[:, :4])
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cx, cy = box[:, 0], box[:, 1]
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fig, ax = plt.subplots(1, 1, figsize=(6, 6))
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ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0)
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ax.set_aspect('equal')
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fig.tight_layout()
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plt.savefig('hist2d.jpg', dpi=300)
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fig, ax = plt.subplots(1, 2, figsize=(12, 6))
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ax[0].hist(cx, bins=600)
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ax[1].hist(cy, bins=600)
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fig.tight_layout()
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plt.savefig('hist1d.jpg', dpi=200)
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def plot_targets_txt(): # from utils.utils import *; plot_targets_txt()
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# Plot test.txt histograms
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x = np.loadtxt('targets.txt', dtype=np.float32)
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x = x.T
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s = ['x targets', 'y targets', 'width targets', 'height targets']
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fig, ax = plt.subplots(2, 2, figsize=(8, 8))
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ax = ax.ravel()
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for i in range(4):
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ax[i].hist(x[i], bins=100, label='%.3g +/- %.3g' % (x[i].mean(), x[i].std()))
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ax[i].legend()
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ax[i].set_title(s[i])
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fig.tight_layout()
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plt.savefig('targets.jpg', dpi=200)
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def plot_evolution_results(hyp): # from utils.utils import *; plot_evolution_results(hyp)
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# Plot hyperparameter evolution results in evolve.txt
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x = np.loadtxt('evolve.txt', ndmin=2)
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f = fitness(x)
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weights = (f - f.min()) ** 2 # for weighted results
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fig = plt.figure(figsize=(12, 10))
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matplotlib.rc('font', **{'size': 8})
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for i, (k, v) in enumerate(hyp.items()):
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y = x[:, i + 7]
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# mu = (y * weights).sum() / weights.sum() # best weighted result
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mu = y[f.argmax()] # best single result
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plt.subplot(4, 5, i + 1)
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plt.plot(mu, f.max(), 'o', markersize=10)
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plt.plot(y, f, '.')
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plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9}) # limit to 40 characters
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print('%15s: %.3g' % (k, mu))
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fig.tight_layout()
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plt.savefig('evolve.png', dpi=200)
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def plot_results(start=0, stop=0): # from utils.utils import *; plot_results()
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# Plot training results files 'results*.txt'
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fig, ax = plt.subplots(2, 5, figsize=(14, 7))
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|
ax = ax.ravel()
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|
s = ['GIoU', 'Objectness', 'Classification', 'Precision', 'Recall',
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'val GIoU', 'val Objectness', 'val Classification', 'mAP@0.5', 'F1']
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for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
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results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
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n = results.shape[1] # number of rows
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x = range(start, min(stop, n) if stop else n)
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|
for i in range(10):
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|
y = results[i, x]
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|
if i in [0, 1, 2, 5, 6, 7]:
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|
y[y == 0] = np.nan # dont show zero loss values
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|
ax[i].plot(x, y, marker='.', label=f.replace('.txt', ''))
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|
ax[i].set_title(s[i])
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if i in [5, 6, 7]: # share train and val loss y axes
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|
ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
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|
|
|
fig.tight_layout()
|
|
ax[1].legend()
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|
fig.savefig('results.png', dpi=200)
|
|
|
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def plot_results_overlay(start=0, stop=0): # from utils.utils import *; plot_results_overlay()
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|
# Plot training results files 'results*.txt', overlaying train and val losses
|
|
s = ['train', 'train', 'train', 'Precision', 'mAP@0.5', 'val', 'val', 'val', 'Recall', 'F1'] # legends
|
|
t = ['GIoU', 'Objectness', 'Classification', 'P-R', 'mAP-F1'] # titles
|
|
for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
|
|
results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
|
|
n = results.shape[1] # number of rows
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|
x = range(start, min(stop, n) if stop else n)
|
|
fig, ax = plt.subplots(1, 5, figsize=(14, 3.5))
|
|
ax = ax.ravel()
|
|
for i in range(5):
|
|
for j in [i, i + 5]:
|
|
y = results[j, x]
|
|
if i in [0, 1, 2]:
|
|
y[y == 0] = np.nan # dont show zero loss values
|
|
ax[i].plot(x, y, marker='.', label=s[j])
|
|
ax[i].set_title(t[i])
|
|
ax[i].legend()
|
|
ax[i].set_ylabel(f) if i == 0 else None # add filename
|
|
fig.tight_layout()
|
|
fig.savefig(f.replace('.txt', '.png'), dpi=200)
|
|
|
|
|
|
def version_to_tuple(version):
|
|
# Used to compare versions of library
|
|
return tuple(map(int, (version.split("."))))
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