text: import { __awaiter, __extends, __generator } from "tslib";
  
  import { depthwiseSeparableConv } from '../common';
  import { toNetInput } from '../dom';
  import { NeuralNetwork } from '../NeuralNetwork';
  import { normalize } from '../ops';
  import { range } from '../utils';
  import { extractParams } from './extractParams';
  import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
  function conv(x, params, stride) {
      return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);
  }
  function reductionBlock(x, params, isActivateInput) {
      if (isActivateInput === void 0) { isActivateInput = true; }
      var out = isActivateInput ? tf.relu(x) : x;
      out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);
      out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);
      out = tf.maxPool(out, [3, 3], [2, 2], 'same');
      out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));
      return out;
  }
  function mainBlock(x, params) {
      var out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);
      out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);
      out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);
      out = tf.add(out, x);
      return out;
  }
  var TinyXception = /** @class */ (function (_super) {
      __extends(TinyXception, _super);
      function TinyXception(numMainBlocks) {
          var _this = _super.call(this, 'TinyXception') || this;
          _this._numMainBlocks = numMainBlocks;
          return _this;
      }
      TinyXception.prototype.forwardInput = function (input) {
          var _this = this;
          var params = this.params;
          if (!params) {
              throw new Error('TinyXception - load model before inference');
          }
          return tf.tidy(function () {
              var batchTensor = input.toBatchTensor(112, true);
              var meanRgb = [122.782, 117.001, 104.298];
              var normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256));
              var out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));
              out = reductionBlock(out, params.entry_flow.reduction_block_0, false);
              out = reductionBlock(out, params.entry_flow.reduction_block_1);
              range(_this._numMainBlocks, 0, 1).forEach(function (idx) {
                  out = mainBlock(out, params.middle_flow["main_block_" + idx]);
              });
              out = reductionBlock(out, params.exit_flow.reduction_block);
              out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));
              return out;
          });
      };
      TinyXception.prototype.forward = function (input) {
          return __awaiter(this, void 0, void 0, function () {
              var _a;
              return __generator(this, function (_b) {
                  switch (_b.label) {
                      case 0:
                          _a = this.forwardInput;
                          return [4 /*yield*/, toNetInput(input)];
                      case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
                  }
              });
          });
      };
      TinyXception.prototype.getDefaultModelName = function () {
          return 'tiny_xception_model';
      };
      TinyXception.prototype.extractParamsFromWeigthMap = function (weightMap) {
          return extractParamsFromWeigthMap(weightMap, this._numMainBlocks);
      };
      TinyXception.prototype.extractParams = function (weights) {
          return extractParams(weights, this._numMainBlocks);
      };
      return TinyXception;
  }(NeuralNetwork));
  export { TinyXception };
  //# sourceMappingURL=TinyXception.js.map