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This commit is contained in:
Glenn Jocher
2019-05-21 17:37:34 +02:00
parent 520e58aa05
commit 19c7697434
4 changed files with 31 additions and 12 deletions
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utils/datasets.py
+21 -8
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@@ -130,14 +130,19 @@ class LoadWebcam: # for inference
class LoadImagesAndLabels(Dataset): # for training/testing
def __init__(self, path, img_size=416, batch_size=16, augment=False, rect=True, image_weights=False, cache=False):
def __init__(self, path, img_size=416, batch_size=16, augment=False, rect=True, image_weights=False, cache=False,
multi_scale=False):
with open(path, 'r') as f:
img_files = f.read().splitlines()
self.img_files = list(filter(lambda x: len(x) > 0, img_files))
n = len(self.img_files)
self.n = n
bi = np.floor(np.arange(n) / batch_size).astype(np.int) # batch index
nb = bi[-1] + 1 # number of batches
assert n > 0, 'No images found in %s' % path
self.n = n
self.batch = bi # batch index of image
self.img_size = img_size
self.augment = augment
self.image_weights = image_weights
@@ -148,11 +153,13 @@ class LoadImagesAndLabels(Dataset): # for training/testing
replace('.bmp', '.txt').
replace('.png', '.txt') for x in self.img_files]
if multi_scale:
s = img_size / 32
self.multi_scale = ((np.linspace(0.5, 1.5, nb) * s).round().astype(np.int) * 32)
# Rectangular Training https://github.com/ultralytics/yolov3/issues/232
if self.rect:
from PIL import Image
bi = np.floor(np.arange(n) / batch_size).astype(np.int) # batch index
nb = bi[-1] + 1 # number of batches
# Read image shapes
sp = 'data' + os.sep + path.replace('.txt', '.shapes').split(os.sep)[-1] # shapefile path
@@ -182,7 +189,6 @@ class LoadImagesAndLabels(Dataset): # for training/testing
shapes[i] = [1, 1 / mini]
self.batch_shapes = np.ceil(np.array(shapes) * img_size / 32.).astype(np.int) * 32
self.batch = bi # batch index of image
# Preload images
if cache and (n < 1001): # preload all images into memory if possible
@@ -207,6 +213,12 @@ class LoadImagesAndLabels(Dataset): # for training/testing
def __len__(self):
return len(self.img_files)
# def __iter__(self):
# self.count = -1
# print('ran dataset iter')
# #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
# return self
def __getitem__(self, index):
if self.image_weights:
index = self.indices[index]
@@ -242,10 +254,11 @@ class LoadImagesAndLabels(Dataset): # for training/testing
# Letterbox
h, w, _ = img.shape
if self.rect:
new_shape = self.batch_shapes[self.batch[index]]
img, ratio, padw, padh = letterbox(img, new_shape=new_shape, mode='rect')
shape = self.batch_shapes[self.batch[index]]
img, ratio, padw, padh = letterbox(img, new_shape=shape, mode='rect')
else:
img, ratio, padw, padh = letterbox(img, new_shape=self.img_size, mode='square')
shape = int(self.multi_scale[self.batch[index]]) if hasattr(self, 'multi_scale') else self.img_size
img, ratio, padw, padh = letterbox(img, new_shape=shape, mode='square')
# Load labels
labels = []
utils/gcp.sh
+1 -1
View File
@@ -72,7 +72,7 @@ rm -rf darknet && git clone https://github.com/AlexeyAB/darknet && cd darknet &&
./darknet detector train ../supermarket2/supermarket2.data cfg/yolov3-spp-sm2-1cls.cfg darknet53.conv.74 -map -dont_show # train
./darknet detector train ../supermarket2/supermarket2.data cfg/yolov3-spp-sm2-1cls.cfg backup/yolov3-spp-sm2-1cls_last.weights # resume
python3 train.py --data ../supermarket2/supermarket2.data --cfg cfg/yolov3-spp-sm2-1cls.cfg # test
python3 test.py --data ../supermarket2/supermarket2.data --weights ../darknet/backup/yolov3-spp-sm2-1cls_3000.weights # test
python3 test.py --data ../supermarket2/supermarket2.data --weights ../darknet/backup/yolov3-spp-sm2-1cls_5000.weights --cfg cfg/yolov3-spp-sm2-1cls.cfg # test
gsutil cp -r backup/*.weights gs://sm4/weights # weights to bucket
# Debug/Development