updates
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4
test.py
4
test.py
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@ -89,6 +89,10 @@ def test(
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stats.append(([], torch.Tensor(), torch.Tensor(), tcls))
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stats.append(([], torch.Tensor(), torch.Tensor(), tcls))
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continue
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continue
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# Append to text file
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# with open('test.txt', 'a') as file:
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# [file.write('%11.5g' * 7 % tuple(x) + '\n') for x in pred]
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# Append to pycocotools JSON dictionary
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# Append to pycocotools JSON dictionary
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if save_json:
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if save_json:
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# [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
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# [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
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@ -599,6 +599,25 @@ def plot_images(imgs, targets, fname='images.jpg'):
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plt.close()
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plt.close()
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def plot_test_txt(): # from test 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=300)
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def plot_results(start=0, stop=0): # from utils.utils import *; plot_results()
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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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# Plot training results files 'results*.txt'
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# import os; os.system('wget https://storage.googleapis.com/ultralytics/yolov3/results_v3.txt')
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# import os; os.system('wget https://storage.googleapis.com/ultralytics/yolov3/results_v3.txt')
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