Set auto=False for int8 calibration

utils/datasets.py

@@ -107,7 +107,7 @@ def __iter__(self):
 
 
 class LoadImages:  # for inference
-    def __init__(self, path, img_size=640):
+    def __init__(self, path, img_size=640, auto=True):
         p = str(Path(path))  # os-agnostic
         p = os.path.abspath(p)  # absolute path
         if '*' in p:
@@ -128,6 +128,7 @@ def __init__(self, path, img_size=640):
         self.nf = ni + nv  # number of files
         self.video_flag = [False] * ni + [True] * nv
         self.mode = 'images'
+        self.auto = auto
         if any(videos):
             self.new_video(videos[0])  # new video
         else:
@@ -169,7 +170,7 @@ def __next__(self):
             print('image %g/%g %s: ' % (self.count, self.nf, path), end='')
 
         # Padded resize
-        img = letterbox(img0, new_shape=self.img_size)[0]
+        img = letterbox(img0, new_shape=self.img_size, auto=self.auto)[0]
 
         # Convert
         img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
@@ -253,7 +254,7 @@ def __len__(self):
 
 
 class LoadStreams:  # multiple IP or RTSP cameras
-    def __init__(self, sources='streams.txt', img_size=640):
+    def __init__(self, sources='streams.txt', img_size=640, auto=True):
         self.mode = 'images'
         self.img_size = img_size
 
@@ -266,6 +267,7 @@ def __init__(self, sources='streams.txt', img_size=640):
         n = len(sources)
         self.imgs = [None] * n
         self.sources = sources
+        self.auto = auto
         for i, s in enumerate(sources):
             # Start the thread to read frames from the video stream
             print('%g/%g: %s... ' % (i + 1, n, s), end='')
@@ -281,7 +283,7 @@ def __init__(self, sources='streams.txt', img_size=640):
         print('')  # newline
 
         # check for common shapes
-        s = np.stack([letterbox(x, new_shape=self.img_size)[0].shape for x in self.imgs], 0)  # inference shapes
+        s = np.stack([letterbox(x, new_shape=self.img_size, auto=self.auto)[0].shape for x in self.imgs], 0)  # inference shapes
         self.rect = np.unique(s, axis=0).shape[0] == 1  # rect inference if all shapes equal
         if not self.rect:
             print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.')
@@ -310,7 +312,7 @@ def __next__(self):
             raise StopIteration
 
         # Letterbox
-        img = [letterbox(x, new_shape=self.img_size, auto=self.rect)[0] for x in img0]
+        img = [letterbox(x, new_shape=self.img_size, auto=self.rect and self.auto)[0] for x in img0]
 
         # Stack
         img = np.stack(img, 0)