updates
This commit is contained in:
parent
8ed89d8c88
commit
e99bda0c54
11
test.py
11
test.py
|
@ -87,11 +87,12 @@ for batch_i, (imgs, targets) in enumerate(dataloader):
|
||||||
correct.extend([0 for _ in range(len(detections))])
|
correct.extend([0 for _ in range(len(detections))])
|
||||||
else:
|
else:
|
||||||
# Extract target boxes as (x1, y1, x2, y2)
|
# Extract target boxes as (x1, y1, x2, y2)
|
||||||
target_boxes = torch.FloatTensor(annotations[:, 1:].shape)
|
# target_boxes = torch.FloatTensor(annotations[:, 1:].shape)
|
||||||
target_boxes[:, 0] = (annotations[:, 1] - annotations[:, 3] / 2)
|
# target_boxes[:, 0] = (annotations[:, 1] - annotations[:, 3] / 2)
|
||||||
target_boxes[:, 1] = (annotations[:, 2] - annotations[:, 4] / 2)
|
# target_boxes[:, 1] = (annotations[:, 2] - annotations[:, 4] / 2)
|
||||||
target_boxes[:, 2] = (annotations[:, 1] + annotations[:, 3] / 2)
|
# target_boxes[:, 2] = (annotations[:, 1] + annotations[:, 3] / 2)
|
||||||
target_boxes[:, 3] = (annotations[:, 2] + annotations[:, 4] / 2)
|
# target_boxes[:, 3] = (annotations[:, 2] + annotations[:, 4] / 2)
|
||||||
|
target_boxes = xywh2xyxy(annotations[:,1:5])
|
||||||
target_boxes *= opt.img_size
|
target_boxes *= opt.img_size
|
||||||
|
|
||||||
detected = []
|
detected = []
|
||||||
|
|
|
@ -23,6 +23,7 @@ class ImageFolder(): # for eval-only
|
||||||
self.nB = math.ceil(self.nF / batch_size) # number of batches
|
self.nB = math.ceil(self.nF / batch_size) # number of batches
|
||||||
self.batch_size = batch_size
|
self.batch_size = batch_size
|
||||||
self.height = img_size
|
self.height = img_size
|
||||||
|
|
||||||
assert self.nF > 0, 'No images found in path %s' % path
|
assert self.nF > 0, 'No images found in path %s' % path
|
||||||
|
|
||||||
# RGB normalization values
|
# RGB normalization values
|
||||||
|
@ -65,7 +66,7 @@ class ListDataset(): # for training
|
||||||
with open(path, 'r') as file:
|
with open(path, 'r') as file:
|
||||||
self.img_files = file.readlines()
|
self.img_files = file.readlines()
|
||||||
|
|
||||||
if platform == 'darwin': # macos
|
if platform == 'darwin': # MacOS (local)
|
||||||
self.img_files = [path.replace('\n', '').replace('/images', '/Users/glennjocher/Downloads/DATA/coco/images')
|
self.img_files = [path.replace('\n', '').replace('/images', '/Users/glennjocher/Downloads/DATA/coco/images')
|
||||||
for path in self.img_files]
|
for path in self.img_files]
|
||||||
else: # linux (gcp cloud)
|
else: # linux (gcp cloud)
|
||||||
|
@ -77,10 +78,10 @@ class ListDataset(): # for training
|
||||||
self.nF = len(self.img_files) # number of image files
|
self.nF = len(self.img_files) # number of image files
|
||||||
self.nB = math.ceil(self.nF / batch_size) # number of batches
|
self.nB = math.ceil(self.nF / batch_size) # number of batches
|
||||||
self.batch_size = batch_size
|
self.batch_size = batch_size
|
||||||
|
|
||||||
# assert self.nB > 0, 'No images found in path %s' % path
|
|
||||||
self.height = img_size
|
self.height = img_size
|
||||||
|
|
||||||
|
assert self.nB > 0, 'No images found in path %s' % path
|
||||||
|
|
||||||
# RGB normalization values
|
# RGB normalization values
|
||||||
# self.rgb_mean = np.array([60.134, 49.697, 40.746], dtype=np.float32).reshape((1, 3, 1, 1))
|
# self.rgb_mean = np.array([60.134, 49.697, 40.746], dtype=np.float32).reshape((1, 3, 1, 1))
|
||||||
# self.rgb_std = np.array([29.99, 24.498, 22.046], dtype=np.float32).reshape((1, 3, 1, 1))
|
# self.rgb_std = np.array([29.99, 24.498, 22.046], dtype=np.float32).reshape((1, 3, 1, 1))
|
||||||
|
|
|
@ -62,7 +62,7 @@ def weights_init_normal(m):
|
||||||
|
|
||||||
|
|
||||||
def xyxy2xywh(x): # Convert bounding box format from [x1, y1, x2, y2] to [x, y, w, h]
|
def xyxy2xywh(x): # Convert bounding box format from [x1, y1, x2, y2] to [x, y, w, h]
|
||||||
y = np.zeros(x.shape)
|
y = torch.zeros(x.shape) if x.dtype is torch.float32 else np.zeros(x.shape)
|
||||||
y[:, 0] = (x[:, 0] + x[:, 2]) / 2
|
y[:, 0] = (x[:, 0] + x[:, 2]) / 2
|
||||||
y[:, 1] = (x[:, 1] + x[:, 3]) / 2
|
y[:, 1] = (x[:, 1] + x[:, 3]) / 2
|
||||||
y[:, 2] = x[:, 2] - x[:, 0]
|
y[:, 2] = x[:, 2] - x[:, 0]
|
||||||
|
@ -71,11 +71,11 @@ def xyxy2xywh(x): # Convert bounding box format from [x1, y1, x2, y2] to [x, y,
|
||||||
|
|
||||||
|
|
||||||
def xywh2xyxy(x): # Convert bounding box format from [x, y, w, h] to [x1, y1, x2, y2]
|
def xywh2xyxy(x): # Convert bounding box format from [x, y, w, h] to [x1, y1, x2, y2]
|
||||||
y = np.zeros(x.shape)
|
y = torch.zeros(x.shape) if x.dtype is torch.float32 else np.zeros(x.shape)
|
||||||
y[:, 0] = (x[:, 1] - x[:, 3] / 2)
|
y[:, 0] = (x[:, 0] - x[:, 2] / 2)
|
||||||
y[:, 1] = (x[:, 2] - x[:, 4] / 2)
|
y[:, 1] = (x[:, 1] - x[:, 3] / 2)
|
||||||
y[:, 2] = (x[:, 1] + x[:, 3] / 2)
|
y[:, 2] = (x[:, 0] + x[:, 2] / 2)
|
||||||
y[:, 3] = (x[:, 2] + x[:, 4] / 2)
|
y[:, 3] = (x[:, 1] + x[:, 3] / 2)
|
||||||
return y
|
return y
|
||||||
|
|
||||||
|
|
||||||
|
|
Loading…
Reference in New Issue