updates
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defae83d77
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2
train.py
2
train.py
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@ -65,7 +65,7 @@ def main(opt):
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# p.requires_grad = False
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# p.requires_grad = False
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# Set optimizer
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# Set optimizer
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# optimizer = torch.optim.SGD(model.parameters(), lr=.001, momentum=.9, weight_decay=0.0005 * 0, nesterov=True)
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# optimizer = torch.optim.SGD(model.parameters(), lr=.001, momentum=.9, weight_decay=5e-4, nesterov=True)
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# optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()))
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# optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()))
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optimizer = torch.optim.Adam(model.parameters())
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optimizer = torch.optim.Adam(model.parameters())
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optimizer.load_state_dict(checkpoint['optimizer'])
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optimizer.load_state_dict(checkpoint['optimizer'])
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@ -368,10 +368,10 @@ def plotResults():
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plt.figure(figsize=(16, 8))
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plt.figure(figsize=(16, 8))
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s = ['X', 'Y', 'Width', 'Height', 'Objectness', 'Classification', 'Total Loss', 'Precision', 'Recall']
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s = ['X', 'Y', 'Width', 'Height', 'Objectness', 'Classification', 'Total Loss', 'Precision', 'Recall']
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for f in ('/Users/glennjocher/Downloads/results.txt',
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for f in ('/Users/glennjocher/Downloads/results.txt',
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'/Users/glennjocher/Downloads/resultsBCE2.txt'):
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'/Users/glennjocher/Downloads/resultsBCE.txt'):
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results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 7, 8, 9, 10]).T
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results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 7, 8, 9, 10]).T
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for i in range(9):
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for i in range(9):
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plt.subplot(2, 5, i + 1)
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plt.subplot(2, 5, i + 1)
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plt.plot(results[i, :], marker='.', label=f)
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plt.plot(results[i, :], marker='.', label=f)
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plt.title(s[i])
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plt.title(s[i])
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plt.legend(cocococosadfc)
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plt.legend()
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