feat: 完善淘宝小程序拍照、报告与登录流程

接入本地实时识别和自动三连拍，调整报告裁切、商品跳转、分享标题及居中页眉；完善游客会话、后台渠道隔离、部署脚本与回归测试。
2026-11-04 15:07:51 +08:00
parent 381da60ae4
commit 774376fa16
147 changed files with 17420 additions and 597 deletions
@@ -0,0 +1,35 @@
+// Exact CPU pad specialization: copy contiguous blocks instead of converting
+// every scalar index to/from coordinates. Complex tensors use the original.
+module.exports = function install(face) {
+  var backend = face.tf.backend(), original = backend.pad, enabled = false;
+  backend.pad = function (input, paddings, constantValue) {
+    if (!enabled || input.dtype === 'complex64' || !input.shape.length) return original.call(this, input, paddings, constantValue);
+    var rank = input.shape.length, outShape = [], lastPadded = -1;
+    for (var axis = 0; axis < rank; axis++) {
+      if (!paddings[axis] || paddings[axis].length !== 2) return original.call(this, input, paddings, constantValue);
+      var before = paddings[axis][0], after = paddings[axis][1];
+      if (before < 0 || after < 0 || Math.floor(before) !== before || Math.floor(after) !== after) return original.call(this, input, paddings, constantValue);
+      if (before || after) lastPadded = axis;
+      outShape.push(input.shape[axis] + before + after);
+    }
+    var output = face.tf.buffer(outShape, input.dtype), source = this.readSync(input.dataId), target = output.values;
+    if (constantValue !== 0) target.fill(constantValue);
+    var firstBlockAxis = lastPadded < 0 ? 0 : lastPadded;
+    var blockSize = 1, padOffset = 0;
+    for (var dimension = 0; dimension < rank; dimension++) {
+      padOffset += paddings[dimension][0] * (dimension === rank - 1 ? 1 : output.strides[dimension]);
+      if (dimension >= firstBlockAxis) blockSize *= input.shape[dimension];
+    }
+    var blocks = input.size / blockSize;
+    for (var block = 0; block < blocks; block++) {
+      var remainder = block, destination = padOffset;
+      for (var outer = firstBlockAxis - 1; outer >= 0; outer--) {
+        var coordinate = remainder % input.shape[outer]; remainder = Math.floor(remainder / input.shape[outer]);
+        destination += coordinate * output.strides[outer];
+      }
+      target.set(source.subarray(block * blockSize, (block + 1) * blockSize), destination);
+    }
+    return output.toTensor();
+  };
+  return { setEnabled: function (value) { enabled = value === true; } };
+};