Fix seg inference with torch and numpy

infer-seg-without-torch.py

@@ -4,7 +4,7 @@ from pathlib import Path
 import cv2
 import numpy as np
 
-from config import CLASSES, COLORS
+from config import ALPHA, CLASSES, COLORS, MASK_COLORS
 from models.utils import blob, letterbox, path_to_list, seg_postprocess
 
 
@@ -41,9 +41,12 @@ def main(args: argparse.Namespace) -> None:
         seg_img = seg_img[dh:H - dh, dw:W - dw, [2, 1, 0]]
         bboxes, scores, labels, masks = seg_postprocess(
             data, bgr.shape[:2], args.conf_thres, args.iou_thres)
-        mask, mask_color = [m[:, dh:H - dh, dw:W - dw, :] for m in masks]
-        inv_alph_masks = (1 - mask * 0.5).cumprod(0)
-        mcs = (mask_color * inv_alph_masks).sum(0) * 2
+        masks = masks[:, dh:H - dh, dw:W - dw, :]
+        mask_colors = MASK_COLORS[labels % len(MASK_COLORS)]
+        mask_colors = mask_colors.reshape(-1, 1, 1, 3) * ALPHA
+        mask_colors = masks @ mask_colors
+        inv_alph_masks = (1 - masks * 0.5).cumprod(0)
+        mcs = (mask_colors * inv_alph_masks).sum(0) * 2
         seg_img = (seg_img * inv_alph_masks[-1] + mcs) * 255
         draw = cv2.resize(seg_img.astype(np.uint8), draw.shape[:2][::-1])
 

infer-seg.py

@@ -6,7 +6,7 @@ import cv2
 import numpy as np
 import torch
 
-from config import CLASSES, COLORS
+from config import ALPHA, CLASSES, COLORS, MASK_COLORS
 from models.torch_utils import seg_postprocess
 from models.utils import blob, letterbox, path_to_list
 
@@ -41,9 +41,13 @@ def main(args: argparse.Namespace) -> None:
                                 device=device)
         bboxes, scores, labels, masks = seg_postprocess(
             data, bgr.shape[:2], args.conf_thres, args.iou_thres)
-        mask, mask_color = [m[:, dh:H - dh, dw:W - dw, :] for m in masks]
-        inv_alph_masks = (1 - mask * 0.5).cumprod(0)
-        mcs = (mask_color * inv_alph_masks).sum(0) * 2
+        masks = masks[:, dh:H - dh, dw:W - dw, :]
+        indices = (labels % len(MASK_COLORS)).long()
+        mask_colors = torch.asarray(MASK_COLORS, device=device)[indices]
+        mask_colors = mask_colors.view(-1, 1, 1, 3) * ALPHA
+        mask_colors = masks @ mask_colors
+        inv_alph_masks = (1 - masks * 0.5).cumprod(0)
+        mcs = (mask_colors * inv_alph_masks).sum(0) * 2
         seg_img = (seg_img * inv_alph_masks[-1] + mcs) * 255
         draw = cv2.resize(seg_img.cpu().numpy().astype(np.uint8),
                           draw.shape[:2][::-1])

models/engine.py

@@ -303,7 +303,7 @@ class TRTModule(torch.nn.Module):
 
         for i in range(self.num_outputs):
             j = i + self.num_inputs
-            if self.is_dynamic:
+            if self.odynamic:
                 shape = tuple(self.context.get_binding_shape(j))
                 output = torch.empty(size=shape,
                                      dtype=self.out_info[i].dtype,

models/utils.py

@@ -121,5 +121,5 @@ def seg_postprocess(
     masks = cv2.resize(masks.transpose([1, 2, 0]),
                        shape,
                        interpolation=cv2.INTER_LINEAR).transpose(2, 0, 1)
-    masks = np.ascontiguousarray((masks > 0.5)[..., None])
+    masks = np.ascontiguousarray((masks > 0.5)[..., None], dtype=np.float32)
     return bboxes, scores, labels, masks

requirements.txt

@@ -1,4 +1,4 @@
-numpy
+numpy<=1.23.5
 onnx
 onnxsim
 opencv-python