Commit
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96a8446
1
Parent(s):
97a5227
Update labels_to_image_weights() (#1545)
Browse files- utils/general.py +5 -10
- utils/plots.py +1 -0
utils/general.py
CHANGED
@@ -2,7 +2,6 @@
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import glob
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import logging
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import math
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import os
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import platform
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import random
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@@ -12,7 +11,7 @@ import time
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from pathlib import Path
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import cv2
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import
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import numpy as np
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import torch
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import torchvision
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@@ -22,13 +21,10 @@ from utils.google_utils import gsutil_getsize
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from utils.metrics import fitness
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from utils.torch_utils import init_torch_seeds
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#
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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 set_logging(rank=-1):
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@@ -121,9 +117,8 @@ def labels_to_class_weights(labels, nc=80):
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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
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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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import glob
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import logging
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import os
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import platform
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import random
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from pathlib import Path
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import cv2
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import math
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import numpy as np
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import torch
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import torchvision
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from utils.metrics import fitness
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from utils.torch_utils import init_torch_seeds
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# Settings
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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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cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
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def set_logging(rank=-1):
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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_weights and image contents
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class_counts = np.array([np.bincount(x[:, 0].astype(np.int), minlength=nc) for x in labels])
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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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utils/plots.py
CHANGED
@@ -20,6 +20,7 @@ from utils.general import xywh2xyxy, xyxy2xywh
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from utils.metrics import fitness
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# Settings
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matplotlib.use('Agg') # for writing to files only
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from utils.metrics import fitness
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# Settings
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matplotlib.rc('font', **{'size': 11})
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matplotlib.use('Agg') # for writing to files only
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