Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2018-11-05 23:32:36 +01:00
parent 19ccb41eaf
commit 6e5da1ce27
2 changed files with 7 additions and 7 deletions
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6
train.py
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@ -7,7 +7,7 @@ from utils.utils import *
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
parser.add_argument('-epochs', type=int, default=100, help='number of epochs') parser.add_argument('-epochs', type=int, default=100, help='number of epochs')
parser.add_argument('-batch_size', type=int, default=16, help='size of each image batch') parser.add_argument('-batch_size', type=int, default=4, help='size of each image batch')
parser.add_argument('-data_config_path', type=str, default='cfg/coco.data', help='data config file path') parser.add_argument('-data_config_path', type=str, default='cfg/coco.data', help='data config file path')
parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path') parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
parser.add_argument('-img_size', type=int, default=32 * 19, help='size of each image dimension') parser.add_argument('-img_size', type=int, default=32 * 19, help='size of each image dimension')
@ -128,8 +128,8 @@ def main(opt):
loss = model(imgs.to(device), targets, requestPrecision=True) loss = model(imgs.to(device), targets, requestPrecision=True)
loss.backward() loss.backward()
# accumulated_batches = 4 # accumulate gradient for 4 batches before stepping optimizer accumulated_batches = 4 # accumulate gradient for 4 batches before stepping optimizer
# if ((i+1) % accumulated_batches == 0) or (i == len(dataloader) - 1): if ((i+1) % accumulated_batches == 0) or (i == len(dataloader) - 1):
optimizer.step() optimizer.step()
optimizer.zero_grad() optimizer.zero_grad()

4
utils/utils.py
View File

@ -436,11 +436,11 @@ def plot_results():
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
plt.figure(figsize=(16, 8)) plt.figure(figsize=(16, 8))
s = ['X', 'Y', 'Width', 'Height', 'Objectness', 'Classification', 'Total Loss', 'Precision', 'Recall'] s = ['X', 'Y', 'Width', 'Height', 'Objectness', 'Classification', 'Total Loss', 'Precision', 'Recall']
for f in ('results.txt', for f in ('results_orig.txt','results.txt',
): ):
results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 7, 8, 9, 10]).T results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 7, 8, 9, 10]).T
for i in range(9): for i in range(9):
plt.subplot(2, 5, i + 1) plt.subplot(2, 5, i + 1)
plt.plot(results[i, :], marker='.', label=f) plt.plot(results[i, :250], marker='.', label=f)
plt.title(s[i]) plt.title(s[i])
plt.legend() plt.legend()