`ultralytics 8.1.9` replace `.size(0)` with `.shape[0]` (#7957)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

.github/workflows/docker.yaml

@@ -109,10 +109,18 @@ jobs:
 
       - name: Build Image
         if: github.event_name == 'push' || github.event.inputs[matrix.dockerfile] == 'true'
-        run: |
-          docker build --platform ${{ matrix.platforms }} -f docker/${{ matrix.dockerfile }} \
-          -t ultralytics/ultralytics:${{ matrix.tags }} \
-          -t ultralytics/ultralytics:${{ steps.get_version.outputs.version_tag }} .
+        uses: nick-invision/retry@v2
+        with:
+          timeout_minutes: 60
+          retry_wait_seconds: 0
+          max_attempts: 1
+          command: |
+            docker build \
+            --platform ${{ matrix.platforms }} \
+            -f docker/${{ matrix.dockerfile }} \
+            -t ultralytics/ultralytics:${{ matrix.tags }} \
+            -t ultralytics/ultralytics:${{ steps.get_version.outputs.version_tag }} \
+            .
 
       - name: Run Tests
         if: (github.event_name == 'push' || github.event.inputs[matrix.dockerfile] == 'true') && matrix.platforms == 'linux/amd64' && matrix.dockerfile != 'Dockerfile-conda' # arm64 images not supported on GitHub CI runners

docker/Dockerfile

@@ -7,7 +7,9 @@ FROM pytorch/pytorch:2.1.0-cuda12.1-cudnn8-runtime
 RUN pip install --no-cache nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com
 
 # Downloads to user config dir
-ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \
+    /root/.config/Ultralytics/
 
 # Install linux packages
 # g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package

docker/Dockerfile-arm64

@@ -6,7 +6,9 @@
 FROM arm64v8/ubuntu:22.04
 
 # Downloads to user config dir
-ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \
+    /root/.config/Ultralytics/
 
 # Install linux packages
 # g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package

docker/Dockerfile-conda

@@ -6,7 +6,9 @@
 FROM continuumio/miniconda3:latest
 
 # Downloads to user config dir
-ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \
+    /root/.config/Ultralytics/
 
 # Install linux packages
 RUN apt update \

docker/Dockerfile-cpu

@@ -6,7 +6,9 @@
 FROM ubuntu:23.10
 
 # Downloads to user config dir
-ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \
+    /root/.config/Ultralytics/
 
 # Install linux packages
 # g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package

docker/Dockerfile-jetson

@@ -6,7 +6,9 @@
 FROM nvcr.io/nvidia/l4t-pytorch:r35.2.1-pth2.0-py3
 
 # Downloads to user config dir
-ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \
+    /root/.config/Ultralytics/
 
 # Install linux packages
 # g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package

docker/Dockerfile-python

@@ -6,7 +6,9 @@
 FROM python:3.10-slim-bookworm
 
 # Downloads to user config dir
-ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \
+    /root/.config/Ultralytics/
 
 # Install linux packages
 # g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.1.8"
+__version__ = "8.1.9"
 
 from ultralytics.data.explorer.explorer import Explorer
 from ultralytics.models import RTDETR, SAM, YOLO

ultralytics/engine/trainer.py

@@ -400,7 +400,7 @@ class BaseTrainer:
 
                 # Log
                 mem = f"{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G"  # (GB)
-                loss_len = self.tloss.shape[0] if len(self.tloss.size()) else 1
+                loss_len = self.tloss.shape[0] if len(self.tloss.shape) else 1
                 losses = self.tloss if loss_len > 1 else torch.unsqueeze(self.tloss, 0)
                 if RANK in (-1, 0):
                     pbar.set_description(

ultralytics/models/sam/modules/decoders.py

@@ -121,7 +121,7 @@ class MaskDecoder(nn.Module):
         """
         # Concatenate output tokens
         output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
-        output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)
+        output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.shape[0], -1, -1)
         tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
 
         # Expand per-image data in batch direction to be per-mask

ultralytics/models/sam/modules/tiny_encoder.py

@@ -732,7 +732,7 @@ class TinyViT(nn.Module):
         for i in range(start_i, len(self.layers)):
             layer = self.layers[i]
             x = layer(x)
-        B, _, C = x.size()
+        B, _, C = x.shape
         x = x.view(B, 64, 64, C)
         x = x.permute(0, 3, 1, 2)
         return self.neck(x)

ultralytics/trackers/utils/gmc.py

@@ -258,7 +258,7 @@ class GMC:
         #     import matplotlib.pyplot as plt
         #     matches_img = np.hstack((self.prevFrame, frame))
         #     matches_img = cv2.cvtColor(matches_img, cv2.COLOR_GRAY2BGR)
-        #     W = np.size(self.prevFrame, 1)
+        #     W = self.prevFrame.shape[1]
         #     for m in goodMatches:
         #         prev_pt = np.array(self.prevKeyPoints[m.queryIdx].pt, dtype=np.int_)
         #         curr_pt = np.array(keypoints[m.trainIdx].pt, dtype=np.int_)
@@ -275,7 +275,7 @@ class GMC:
         #     plt.show()
 
         # Find rigid matrix
-        if (np.size(prevPoints, 0) > 4) and (np.size(prevPoints, 0) == np.size(prevPoints, 0)):
+        if (prevPoints.shape[0] > 4) and (prevPoints.shape[0] == prevPoints.shape[0]):
             H, inliers = cv2.estimateAffinePartial2D(prevPoints, currPoints, cv2.RANSAC)
 
             # Handle downscale
@@ -343,7 +343,7 @@ class GMC:
         currPoints = np.array(currPoints)
 
         # Find rigid matrix
-        if np.size(prevPoints, 0) > 4 and np.size(prevPoints, 0) == np.size(prevPoints, 0):
+        if (prevPoints.shape[0] > 4) and (prevPoints.shape[0] == prevPoints.shape[0]):
             H, inliers = cv2.estimateAffinePartial2D(prevPoints, currPoints, cv2.RANSAC)
 
             if self.downscale > 1.0:

ultralytics/utils/metrics.py

@@ -331,7 +331,7 @@ class ConfusionMatrix:
             gt_bboxes (Array[M, 4]): Ground truth bounding boxes with xyxy format.
             gt_cls (Array[M]): The class labels.
         """
-        if gt_cls.size(0) == 0:  # Check if labels is empty
+        if gt_cls.shape[0] == 0:  # Check if labels is empty
             if detections is not None:
                 detections = detections[detections[:, 4] > self.conf]
                 detection_classes = detections[:, 5].int()

ultralytics/utils/tal.py

@@ -56,8 +56,8 @@ class TaskAlignedAssigner(nn.Module):
             fg_mask (Tensor): shape(bs, num_total_anchors)
             target_gt_idx (Tensor): shape(bs, num_total_anchors)
         """
-        self.bs = pd_scores.size(0)
-        self.n_max_boxes = gt_bboxes.size(1)
+        self.bs = pd_scores.shape[0]
+        self.n_max_boxes = gt_bboxes.shape[1]
 
         if self.n_max_boxes == 0:
             device = gt_bboxes.device

ultralytics/utils/torch_utils.py

@@ -190,7 +190,7 @@ def fuse_conv_and_bn(conv, bn):
     fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))
 
     # Prepare spatial bias
-    b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
+    b_conv = torch.zeros(conv.weight.shape[0], device=conv.weight.device) if conv.bias is None else conv.bias
     b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
     fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
 
@@ -221,7 +221,7 @@ def fuse_deconv_and_bn(deconv, bn):
     fuseddconv.weight.copy_(torch.mm(w_bn, w_deconv).view(fuseddconv.weight.shape))
 
     # Prepare spatial bias
-    b_conv = torch.zeros(deconv.weight.size(1), device=deconv.weight.device) if deconv.bias is None else deconv.bias
+    b_conv = torch.zeros(deconv.weight.shape[1], device=deconv.weight.device) if deconv.bias is None else deconv.bias
     b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
     fuseddconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)