apoorva/greenhouse
2
1
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

removed xy/wh loss reporting

Browse Source
This commit is contained in:
Glenn Jocher
2019-08-24 16:43:43 +02:00
parent 852487654f
commit 39dcf0d561
4 changed files with 26 additions and 46 deletions
Download Patch File Download Diff File Expand all files Collapse all files
utils/gcp.sh
+1 -1
View File
@@ -16,7 +16,7 @@ sudo shutdown
# Re-clone
rm -rf yolov3 # Warning: remove existing
git clone https://github.com/ultralytics/yolov3 # master
git clone https://github.com/ultralytics/yolov3 && cd yolov3 # master
# git clone -b test --depth 1 https://github.com/ultralytics/yolov3 test # branch
# Train
utils/utils.py
+11 -11
View File
@@ -374,7 +374,7 @@ def compute_loss(p, targets, model): # predictions, targets, model
lobj *= k * h['obj']
lcls *= k * h['cls']
loss = lbox + lobj + lcls
return loss, torch.cat((lbox, ft([0]), lobj, lcls, loss)).detach()
return loss, torch.cat((lbox, lobj, lcls, loss)).detach()
def build_targets(model, targets):
@@ -661,9 +661,9 @@ def kmeans_targets(path='../coco/trainvalno5k.txt', n=9, img_size=416): # from
def print_mutation(hyp, results, bucket=''):
# Print mutation results to evolve.txt (for use with train.py --evolve)
a = '%11s' * len(hyp) % tuple(hyp.keys()) # hyperparam keys
b = '%11.3g' * len(hyp) % tuple(hyp.values()) # hyperparam values
c = '%11.3g' * len(results) % results # results (P, R, mAP, F1, test_loss)
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:
@@ -672,7 +672,7 @@ def print_mutation(hyp, results, bucket=''):
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))], '%11.3g') # save sort by fitness
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
@@ -680,7 +680,7 @@ def print_mutation(hyp, results, bucket=''):
def fitness(x):
# Returns fitness (for use with results.txt or evolve.txt)
return 0.50 * x[:, 2] + 0.50 * x[:, 3] # fitness = 0.9 * mAP + 0.1 * F1
return 0.50 * x[:, 2] + 0.50 * x[:, 3] # fitness = 0.5 * mAP + 0.5 * F1
# Plotting functions ---------------------------------------------------------------------------------------------------
@@ -803,10 +803,10 @@ def plot_results(start=0, stop=0): # from utils.utils import *; plot_results()
# Plot training results files 'results*.txt'
fig, ax = plt.subplots(2, 5, figsize=(14, 7))
ax = ax.ravel()
s = ['GIoU', 'Confidence', 'Classification', 'Precision', 'Recall',
'val GIoU', 'val Confidence', 'val Classification', 'mAP', 'F1']
s = ['GIoU', 'Objectness', 'Classification', 'Precision', 'Recall',
'val GIoU', 'val Objectness', 'val Classification', 'mAP', 'F1']
for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
results = np.loadtxt(f, usecols=[2, 4, 5, 9, 10, 13, 14, 15, 11, 12], ndmin=2).T
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
x = range(start, min(stop, n) if stop else n)
for i in range(10):
@@ -826,9 +826,9 @@ def plot_results(start=0, stop=0): # from utils.utils import *; plot_results()
def plot_results_overlay(start=0, stop=0): # from utils.utils import *; plot_results_overlay()
# Plot training results files 'results*.txt', overlaying train and val losses
s = ['train', 'train', 'train', 'Precision', 'mAP', 'val', 'val', 'val', 'Recall', 'F1'] # legends
t = ['GIoU', 'Confidence', 'Classification', 'P-R', 'mAP-F1'] # titles
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, 4, 5, 9, 11, 13, 14, 15, 10, 12], ndmin=2).T
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
x = range(start, min(stop, n) if stop else n)
fig, ax = plt.subplots(1, 5, figsize=(14, 3.5))