Refactor common functions for data processing

benchmark/models/yolo_v5/utils/common.py

@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: © 2023 Tenstorrent Inc.
+# SPDX-FileCopyrightText: © 2024 Tenstorrent AI ULC
 # SPDX-License-Identifier: Apache-2.0
 
 from pathlib import Path
@@ -31,27 +31,36 @@ def data_preprocessing(ims: Image.Image, size: tuple) -> tuple:
         List of images, number of samples, filenames, image size, inference size, preprocessed images
     """
 
-    n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims])  # number, list of images
-    shape0, shape1, files = [], [], []  # image and inference shapes, filenames
-
-    for i, im in enumerate(ims):
-        f = f"image{i}"  # filename
-        im, f = np.asarray(exif_transpose(im)), getattr(im, "filename", f) or f
-        files.append(Path(f).with_suffix(".jpg").name)
-        if im.shape[0] < 5:  # image in CHW
-            im = im.transpose((1, 2, 0))  # reverse dataloader .transpose(2, 0, 1)
-        im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR)  # enforce 3ch input
-        s = im.shape[:2]  # HWC
-        shape0.append(s)  # image shape
-        g = max(size) / max(s)  # gain
-        shape1.append([int(y * g) for y in s])
-        ims[i] = im if im.data.contiguous else np.ascontiguousarray(im)  # update
-    shape1 = [size[0] for _ in np.array(shape1).max(0)]  # inf shape
-    x = [letterbox(im, shape1, auto=False)[0] for im in ims]  # pad
-    x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2)))  # stack and BHWC to BCHW
-    x = torch.from_numpy(x) / 255  # uint8 to fp16/32
-
-    return ims, n, files, shape0, shape1, x
+    if not isinstance(ims, (list, tuple)):
+        ims = [ims]
+    num_images = len(ims)
+    shape_orig, shape_infer, filenames = [], [], []
+
+    for idx, img in enumerate(ims):
+        filename = getattr(img, "filename", f"image{idx}")
+        img = np.asarray(exif_transpose(img))
+        filename = Path(filename).with_suffix(".jpg").name
+        filenames.append(filename)
+
+        if img.shape[0] < 5:
+            img = img.transpose((1, 2, 0))
+
+        if img.ndim == 3:
+            img = img[..., :3]
+        else:
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+
+        shape_orig.append(img.shape[:2])
+        scale = max(size) / max(img.shape[:2])
+        shape_infer.append([int(dim * scale) for dim in img.shape[:2]])
+        ims[idx] = img if img.flags["C_CONTIGUOUS"] else np.ascontiguousarray(img)
+
+    shape_infer = [size[0] for _ in np.array(shape_infer).max(0)]
+    imgs_padded = [letterbox(img, shape_infer, auto=False)[0] for img in ims]
+    imgs_padded = np.ascontiguousarray(np.array(imgs_padded).transpose((0, 3, 1, 2)))
+    tensor_imgs = torch.from_numpy(imgs_padded) / 255
+
+    return ims, num_images, filenames, shape_orig, shape_infer, tensor_imgs
 
 
 def yolov5_preprocessing(dataset, target_height, target_width):