Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-11-30 18:45:43 -08:00
parent 8be4b41b3d
commit 6a05cf56c2
3 changed files with 6 additions and 5 deletions
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2
test.py
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@ -101,7 +101,7 @@ def test(cfg,
# [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ... # [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
image_id = int(Path(paths[si]).stem.split('_')[-1]) image_id = int(Path(paths[si]).stem.split('_')[-1])
box = pred[:, :4].clone() # xyxy box = pred[:, :4].clone() # xyxy
scale_coords(imgs[si].shape[1:], box, shapes[si]) # to original shape scale_coords(imgs[si].shape[1:], box, shapes[si][0], shapes[si][1]) # to original shape
box = xyxy2xywh(box) # xywh box = xyxy2xywh(box) # xywh
box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
for di, d in enumerate(pred): for di, d in enumerate(pred):

7
utils/datasets.py
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@ -416,6 +416,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
# Load mosaic # Load mosaic
img, labels = load_mosaic(self, index) img, labels = load_mosaic(self, index)
h, w = img.shape[:2] h, w = img.shape[:2]
ratio, pad = None, None
else: else:
# Load image # Load image
@ -492,14 +493,14 @@ class LoadImagesAndLabels(Dataset): # for training/testing
img = np.ascontiguousarray(img, dtype=np.float32) # uint8 to float32 img = np.ascontiguousarray(img, dtype=np.float32) # uint8 to float32
img /= 255.0 # 0 - 255 to 0.0 - 1.0 img /= 255.0 # 0 - 255 to 0.0 - 1.0
return torch.from_numpy(img), labels_out, img_path, (h, w) return torch.from_numpy(img), labels_out, img_path, ((h, w), (ratio, pad))
@staticmethod @staticmethod
def collate_fn(batch): def collate_fn(batch):
img, label, path, hw = list(zip(*batch)) # transposed img, label, path, shapes = list(zip(*batch)) # transposed
for i, l in enumerate(label): for i, l in enumerate(label):
l[:, 0] = i # add target image index for build_targets() l[:, 0] = i # add target image index for build_targets()
return torch.stack(img, 0), torch.cat(label, 0), path, hw return torch.stack(img, 0), torch.cat(label, 0), path, shapes
def load_image(self, index): def load_image(self, index):

2
utils/utils.py
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@ -126,7 +126,7 @@ def xywh2xyxy(x):
def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None): def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
# Rescale coords (xyxy) from img1_shape to img0_shape # Rescale coords (xyxy) from img1_shape to img0_shape
if ratio_pad is None: # not supplied, calculate if ratio_pad is None: # calculate from img0_shape
gain = max(img1_shape) / max(img0_shape) # gain = old / new gain = max(img1_shape) / max(img0_shape) # gain = old / new
pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
else: else: