Bienqq
/
car-detection-bayes
2
1
Fork 0
Code Issues Pull Requests Releases Wiki Activity

updates

This commit is contained in:
Glenn Jocher 2020-01-10 11:45:51 -08:00
parent 6235d76976
commit 6e52f985fe
1 changed files with 15 additions and 16 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

31
utils/datasets.py
View File

@ -379,12 +379,13 @@ class LoadImagesAndLabels(Dataset): # for training/testing
nf, nm, ne, nd, n) nf, nm, ne, nd, n)
assert nf > 0, 'No labels found. See %s' % help_url assert nf > 0, 'No labels found. See %s' % help_url
# Cache images into memory for faster training (WARNING: Large datasets may exceed system RAM) # Cache images into memory for faster training (WARNING: large datasets may exceed system RAM)
if cache_images: # if training if cache_images: # if training
gb = 0 # Gigabytes of cached images gb = 0 # Gigabytes of cached images
pbar = tqdm(range(len(self.img_files)), desc='Caching images') pbar = tqdm(range(len(self.img_files)), desc='Caching images')
self.img_hw0, self.img_hw = [None] * n, [None] * n
for i in pbar: # max 10k images for i in pbar: # max 10k images
self.imgs[i] = load_image(self, i) self.imgs[i], self.img_hw0[i], self.img_hw[i] = load_image(self, i) # img, hw_original, hw_resized
gb += self.imgs[i].nbytes gb += self.imgs[i].nbytes
pbar.desc = 'Caching images (%.1fGB)' % (gb / 1E9) pbar.desc = 'Caching images (%.1fGB)' % (gb / 1E9)
@ -419,15 +420,13 @@ class LoadImagesAndLabels(Dataset): # for training/testing
if mosaic: if mosaic:
# Load mosaic # Load mosaic
img, labels = load_mosaic(self, index) img, labels = load_mosaic(self, index)
h, w = img.shape[:2] h0, w0, ratio, pad = None, None, None, None
ratio, pad = None, None
else: else:
# Load image # Load image
img = load_image(self, index) img, (h0, w0), (h, w) = load_image(self, index)
# Letterbox # Letterbox
h, w = img.shape[:2]
shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape
img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment) img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment)
@ -495,7 +494,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
img = np.ascontiguousarray(img) img = np.ascontiguousarray(img)
return torch.from_numpy(img), labels_out, img_path, ((h, w), (ratio, pad)) return torch.from_numpy(img), labels_out, img_path, ((h0, w0), (ratio, pad))
@staticmethod @staticmethod
def collate_fn(batch): def collate_fn(batch):
@ -506,17 +505,18 @@ class LoadImagesAndLabels(Dataset): # for training/testing
def load_image(self, index): def load_image(self, index):
# loads 1 image from dataset # loads 1 image from dataset, returns img, original hw, resized hw
img = self.imgs[index] if self.imgs[index] is None: # not cached
if img is None:
img_path = self.img_files[index] img_path = self.img_files[index]
img = cv2.imread(img_path) # BGR img = cv2.imread(img_path) # BGR
assert img is not None, 'Image Not Found ' + img_path assert img is not None, 'Image Not Found ' + img_path
r = self.img_size / max(img.shape) # resize image to img_size h0, w0 = img.shape[:2] # orig hw
r = self.img_size / max(h0, w0) # resize image to img_size
if self.augment and (r != 1): # always resize down, only resize up if training with augmentation if self.augment and (r != 1): # always resize down, only resize up if training with augmentation
h, w = img.shape[:2] h, w = int(h0 * r), int(w0 * r)
return cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_LINEAR) # _LINEAR fastest return cv2.resize(img, (w, h), interpolation=cv2.INTER_LINEAR), (h0, w0), (h, w) # _LINEAR fastest
return img else: # cached
return self.imgs[index], self.img_hw0[index], self.img_hw[index] # img, hw
def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5): def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5):
@ -535,8 +535,7 @@ def load_mosaic(self, index):
indices = [index] + [random.randint(0, len(self.labels) - 1) for _ in range(3)] # 3 additional image indices indices = [index] + [random.randint(0, len(self.labels) - 1) for _ in range(3)] # 3 additional image indices
for i, index in enumerate(indices): for i, index in enumerate(indices):
# Load image # Load image
img = load_image(self, index) img, _, (h, w) = load_image(self, index)
h, w, _ = img.shape
# place img in img4 # place img in img4
if i == 0: # top left if i == 0: # top left