@tensorflow/tfjs-converter - npm Package Compare versions

Comparing version 0.5.1 to 0.5.2

dist/data/types.d.ts

 import { DataType, Tensor } from '@tensorflow/tfjs-core';
+import { TensorArray } from '../executor/tensor_array';
 export declare type NamedTensorMap = {
@@ -8,2 +9,5 @@ [key: string]: Tensor;
 };
+export declare type TensorArrayMap = {
+    [key: number]: TensorArray;
+};
 export interface TensorInfo {
@@ -10,0 +14,0 @@ name: string;

dist/executor/execution_context.d.ts

 import { Tensor } from '@tensorflow/tfjs-core';
-import { NamedTensorsMap } from '../data/types';
+import { NamedTensorsMap, TensorArrayMap } from '../data/types';
+import { TensorArray } from './tensor_array';
 export interface ExecutionContextInfo {
@@ -9,3 +10,4 @@ id: number;
 export declare class ExecutionContext {
-    weightMap: NamedTensorsMap;
+    readonly weightMap: NamedTensorsMap;
+    readonly tensorArrayMap: TensorArrayMap;
     private rootContext;
@@ -15,3 +17,3 @@ private contexts;
     private _currentContextIds;
-    constructor(weightMap: NamedTensorsMap);
+    constructor(weightMap: NamedTensorsMap, tensorArrayMap: TensorArrayMap);
     private newFrame(id, frameName);
@@ -27,2 +29,4 @@ currentContext: ExecutionContextInfo[];
     getWeight(name: string): Tensor[];
+    addTensorArray(tensorArray: TensorArray): void;
+    getTensorArray(id: number): TensorArray;
 }

dist/executor/execution_context.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
 var ExecutionContext = (function () {
-    function ExecutionContext(weightMap) {
+    function ExecutionContext(weightMap, tensorArrayMap) {
         this.weightMap = weightMap;
+        this.tensorArrayMap = tensorArrayMap;
         this.rootContext = { id: 0, frameName: '', iterationId: 0 };
@@ -94,2 +95,8 @@ this.contexts = [this.rootContext];
     };
+    ExecutionContext.prototype.addTensorArray = function (tensorArray) {
+        this.tensorArrayMap[tensorArray.id] = tensorArray;
+    };
+    ExecutionContext.prototype.getTensorArray = function (id) {
+        return this.tensorArrayMap[id];
+    };
     return ExecutionContext;
@@ -96,0 +103,0 @@ }());

dist/executor/frozen_model.d.ts

 import * as tfc from '@tensorflow/tfjs-core';
-import { TensorInfo } from '../data/types';
+import { NamedTensorsMap, TensorInfo } from '../data/types';
 export declare class FrozenModel implements tfc.InferenceModel {
@@ -16,2 +16,3 @@ private modelUrl;
     readonly outputs: TensorInfo[];
+    readonly weights: NamedTensorsMap;
     constructor(modelUrl: string, weightManifestUrl: string, requestOption?: RequestInit);
@@ -18,0 +19,0 @@ getPathPrefix(): string;

dist/executor/frozen_model.js

@@ -86,2 +86,9 @@ "use strict";
     });
+    Object.defineProperty(FrozenModel.prototype, "weights", {
+        get: function () {
+            return this.executor.weightMap;
+        },
+        enumerable: true,
+        configurable: true
+    });
     FrozenModel.prototype.getPathPrefix = function () {
@@ -88,0 +95,0 @@ var url = Url.parse(this.weightManifestUrl);

dist/executor/graph_executor.js

@@ -146,4 +146,5 @@ "use strict";
         this.checkInputShapeAndType(inputs);
+        var tensorArrayMap = {};
         var result = tfjs_core_1.tidy(function () {
-            var context = new execution_context_1.ExecutionContext(_this._weightMap);
+            var context = new execution_context_1.ExecutionContext(_this._weightMap, tensorArrayMap);
             var tensors = _this.compiledOrder.reduce(function (map, node) {
@@ -160,3 +161,3 @@ map[node.name] = operation_executor_1.executeOp(node, map, context);
             var _this = this;
-            var context, tensors, results, outputIds, inputIdArray, inputIds;
+            var tensorArrayMap, context, tensors, results, outputIds, inputIdArray, inputIds;
             return __generator(this, function (_a) {
@@ -167,3 +168,4 @@ switch (_a.label) {
                         this.checkInputShapeAndType(inputs);
-                        context = new execution_context_1.ExecutionContext(this._weightMap);
+                        tensorArrayMap = {};
+                        context = new execution_context_1.ExecutionContext(this._weightMap, tensorArrayMap);
                         return [4, this.executeWithControlFlow(inputs, context)];
@@ -193,3 +195,3 @@ case 1:
         return __awaiter(this, void 0, void 0, function () {
-            var stack, tensorMap, added, item, tensors, nodeName, _a, _b;
+            var stack, tensorMap, added, item, nodeName, tensors, _a, _b;
             return __generator(this, function (_c) {
@@ -208,4 +210,11 @@ switch (_c.label) {
                         context.currentContext = item.contexts;
+                        nodeName = '';
+                        if (item.node.op === 'enter' &&
+                            utils_1.getParamValue('isConstant', item.node, tensorMap, context)) {
+                            nodeName = utils_1.getNodeNameAndIndex(item.node.name, context)[0];
+                        }
                         tensors = operation_executor_1.executeOp(item.node, tensorMap, context);
-                        nodeName = utils_1.getNodeNameAndIndex(item.node.name, context)[0];
+                        if (!nodeName) {
+                            nodeName = utils_1.getNodeNameAndIndex(item.node.name, context)[0];
+                        }
                         _a = tensorMap;
@@ -212,0 +221,0 @@ _b = nodeName;

dist/executor/tensor_array.d.ts

@@ -16,4 +16,6 @@ import { DataType, Tensor } from '@tensorflow/tfjs-core';
     readonly clearAfterRead: boolean;
+    private static nextId;
     private tensors;
     private closed_;
+    readonly id: number;
     constructor(name: string, dtype: DataType, maxSize: number, elementShape: number[], identicalElementShapes: boolean, dynamicSize: boolean, clearAfterRead: boolean);
@@ -20,0 +22,0 @@ readonly closed: boolean;

dist/executor/tensor_array.js

@@ -15,2 +15,3 @@ "use strict";
         this.closed_ = false;
+        this.id = TensorArray.nextId++;
     }
@@ -65,2 +66,5 @@ Object.defineProperty(TensorArray.prototype, "closed", {
         }
+        if (this.size() === 0 && this.elementShape.length === 0) {
+            this.elementShape = tensor.shape;
+        }
         tfjs_core_1.util.assertShapesMatch(this.elementShape, tensor.shape, "TensorArray " + this.name + ": Could not write to TensorArray index " + index + ".");
@@ -165,2 +169,3 @@ if (t && t.read) {
     };
+    TensorArray.nextId = 0;
     return TensorArray;
@@ -167,0 +172,0 @@ }());

dist/operations/executors/control_executor.js

@@ -38,6 +38,8 @@ "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
+var tfjs_core_1 = require("@tensorflow/tfjs-core");
+var tensor_array_1 = require("../../executor/tensor_array");
 var utils_1 = require("./utils");
 function executeOp(node, tensorMap, context) {
     return __awaiter(this, void 0, void 0, function () {
-        var _a, pred, data_1, inputName, frameId, data, tensor, input;
+        var _a, pred, data_1, inputName, frameId, data, tensor, input, size, dtype, elementShape, dynamicSize, clearAfterRead, identicalElementShapes, name_1, tensorArray, id, index, writeTensor, writeTensorArray, readId, readIndex, readTensorArray, gatherId, gatherIndices, gatherDtype, gatherTensorArray, scatterId, scatterIndices, scatterTensor, scatterTensorArray, concatId, concatTensorArray, concatDtype, splitId, splitTensor, lengths, splitTensorArray, sizeId, sizeTensorArray, closeId, closeTensorArray;
         return __generator(this, function (_b) {
@@ -54,4 +56,13 @@ switch (_b.label) {
                         case 'nextIteration': return [3, 7];
+                        case 'tensorArray': return [3, 8];
+                        case 'tensorArrayWrite': return [3, 9];
+                        case 'tensorArrayRead': return [3, 10];
+                        case 'tensorArrayGather': return [3, 11];
+                        case 'tensorArrayScatter': return [3, 12];
+                        case 'tensorArrayConcat': return [3, 13];
+                        case 'tensorArraySplit': return [3, 14];
+                        case 'tensorArraySize': return [3, 15];
+                        case 'tensorArrayClose': return [3, 16];
                     }
-                    return [3, 8];
+                    return [3, 17];
                 case 1: return [2, [utils_1.getParamValue('pred', node, tensorMap, context)]];
@@ -79,3 +90,60 @@ case 2:
                     return [2, [input]];
-                case 8: throw TypeError("Node type " + node.op + " is not implemented");
+                case 8:
+                    size = utils_1.getParamValue('size', node, tensorMap, context);
+                    dtype = utils_1.getParamValue('dtype', node, tensorMap, context);
+                    elementShape = utils_1.getParamValue('elementShape', node, tensorMap, context);
+                    dynamicSize = utils_1.getParamValue('dynamicSize', node, tensorMap, context);
+                    clearAfterRead = utils_1.getParamValue('clearAfterRead', node, tensorMap, context);
+                    identicalElementShapes = utils_1.getParamValue('identicalElementShapes', node, tensorMap, context);
+                    name_1 = utils_1.getParamValue('name', node, tensorMap, context);
+                    tensorArray = new tensor_array_1.TensorArray(name_1, dtype, size, elementShape, identicalElementShapes, dynamicSize, clearAfterRead);
+                    context.addTensorArray(tensorArray);
+                    return [2, [tfjs_core_1.scalar(tensorArray.id), tfjs_core_1.scalar(1.0)]];
+                case 9:
+                    id = utils_1.getParamValue('tensorArrayId', node, tensorMap, context);
+                    index = utils_1.getParamValue('index', node, tensorMap, context);
+                    writeTensor = utils_1.getParamValue('tensor', node, tensorMap, context);
+                    writeTensorArray = context.getTensorArray(id);
+                    writeTensorArray.write(index, writeTensor);
+                    return [2, [tfjs_core_1.scalar(1.0)]];
+                case 10:
+                    readId = utils_1.getParamValue('tensorArrayId', node, tensorMap, context);
+                    readIndex = utils_1.getParamValue('index', node, tensorMap, context);
+                    readTensorArray = context.getTensorArray(readId);
+                    return [2, [readTensorArray.read(readIndex)]];
+                case 11:
+                    gatherId = utils_1.getParamValue('tensorArrayId', node, tensorMap, context);
+                    gatherIndices = utils_1.getParamValue('indices', node, tensorMap, context);
+                    gatherDtype = utils_1.getParamValue('dtype', node, tensorMap, context);
+                    gatherTensorArray = context.getTensorArray(gatherId);
+                    return [2, [gatherTensorArray.gather(gatherIndices, gatherDtype)]];
+                case 12:
+                    scatterId = utils_1.getParamValue('tensorArrayId', node, tensorMap, context);
+                    scatterIndices = utils_1.getParamValue('indices', node, tensorMap, context);
+                    scatterTensor = utils_1.getParamValue('tensor', node, tensorMap, context);
+                    scatterTensorArray = context.getTensorArray(scatterId);
+                    scatterTensorArray.scatter(scatterIndices, scatterTensor);
+                    return [2, [tfjs_core_1.scalar(1.0)]];
+                case 13:
+                    concatId = utils_1.getParamValue('tensorArrayId', node, tensorMap, context);
+                    concatTensorArray = context.getTensorArray(concatId);
+                    concatDtype = utils_1.getParamValue('dtype', node, tensorMap, context);
+                    return [2, [concatTensorArray.concat(concatDtype)]];
+                case 14:
+                    splitId = utils_1.getParamValue('tensorArrayId', node, tensorMap, context);
+                    splitTensor = utils_1.getParamValue('tensor', node, tensorMap, context);
+                    lengths = utils_1.getParamValue('lengths', node, tensorMap, context);
+                    splitTensorArray = context.getTensorArray(splitId);
+                    splitTensorArray.split(lengths, splitTensor);
+                    return [2, [tfjs_core_1.scalar(1.0)]];
+                case 15:
+                    sizeId = utils_1.getParamValue('tensorArrayId', node, tensorMap, context);
+                    sizeTensorArray = context.getTensorArray(sizeId);
+                    return [2, [tfjs_core_1.scalar(sizeTensorArray.size(), 'int32')]];
+                case 16:
+                    closeId = utils_1.getParamValue('tensorArrayId', node, tensorMap, context);
+                    closeTensorArray = context.getTensorArray(closeId);
+                    closeTensorArray.clearAndClose();
+                    return [2, []];
+                case 17: throw TypeError("Node type " + node.op + " is not implemented");
             }
@@ -82,0 +150,0 @@ });

dist/operations/executors/slice_join_executor.js

@@ -34,3 +34,11 @@ "use strict";
             var endMask = utils_1.getParamValue('endMask', node, tensorMap, context);
-            return [tfc.stridedSlice(utils_1.getParamValue('x', node, tensorMap, context), begin, end, strides, beginMask, endMask)];
+            var tensor = utils_1.getParamValue('x', node, tensorMap, context);
+            if (begin.length === 1 && tensor.shape.length > 1) {
+                for (var i = 1; i < tensor.shape.length; i++) {
+                    begin.push(0);
+                    end.push(tensor.shape[i]);
+                    strides.push(strides[0]);
+                }
+            }
+            return [tfc.stridedSlice(tensor, begin, end, strides, beginMask, endMask)];
         }
@@ -37,0 +45,0 @@ case 'stack': {

dist/operations/op_list/control.json

@@ -107,3 +107,259 @@ [
     ]
+  },
+  {
+    "tfOpName": "TensorArrayV3",
+    "dlOpName": "tensorArray",
+    "category": "control",
+    "params": [
+      {
+        "tfInputIndex": 0,
+        "dlParamName": "size",
+        "type": "number"
+      },
+      {
+        "tfParamName": "dtype",
+        "dlParamName": "dtype",
+        "type": "dtype"
+      },
+      {
+        "tfParamName": "element_shape",
+        "dlParamName": "elementShape",
+        "type": "shape"
+      },
+      {
+        "tfParamName": "dynamic_size",
+        "dlParamName": "dynamicSize",
+        "type": "bool"
+      },
+      {
+        "tfParamName": "clear_after_read",
+        "dlParamName": "clearAfterRead",
+        "type": "bool"
+      },
+      {
+        "tfParamName": "identical_element_shapes",
+        "dlParamName": "identicalElementShapes",
+        "type": "bool"
+      },
+      {
+        "tfParamName": "tensor_array_name",
+        "dlParamName": "name",
+        "type": "string"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorArrayWriteV3",
+    "dlOpName": "tensorArrayWrite",
+    "category": "control",
+    "params": [
+      {
+        "tfInputIndex": 0,
+        "dlParamName": "tensorArrayId",
+        "type": "number"
+      },
+      {
+        "tfInputIndex": 1,
+        "dlParamName": "index",
+        "type": "number"
+      },
+      {
+        "tfInputIndex": 2,
+        "dlParamName": "tensor",
+        "type": "tensor"
+      },
+      {
+        "tfInputIndex": 3,
+        "dlParamName": "flowIn",
+        "type": "number"
+      },
+      {
+        "tfParamName": "T",
+        "dlParamName": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorArrayReadV3",
+    "dlOpName": "tensorArrayRead",
+    "category": "control",
+    "params": [
+      {
+        "tfInputIndex": 0,
+        "dlParamName": "tensorArrayId",
+        "type": "number"
+      },
+      {
+        "tfInputIndex": 1,
+        "dlParamName": "index",
+        "type": "number"
+      },
+      {
+        "tfInputIndex": 2,
+        "dlParamName": "flowIn",
+        "type": "number"
+      },
+      {
+        "tfParamName": "dtype",
+        "dlParamName": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorArrayGatherV3",
+    "dlOpName": "tensorArrayGather",
+    "category": "control",
+    "params": [
+      {
+        "tfInputIndex": 0,
+        "dlParamName": "tensorArrayId",
+        "type": "number"
+      },
+      {
+        "tfInputIndex": 1,
+        "dlParamName": "indices",
+        "type": "number[]"
+      },
+      {
+        "tfInputIndex": 2,
+        "dlParamName": "flowIn",
+        "type": "number"
+      },
+      {
+        "tfParamName": "dtype",
+        "dlParamName": "dtype",
+        "type": "dtype"
+      },
+      {
+        "tfParamName": "element_shape",
+        "dlParamName": "elementShape",
+        "type": "shape"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorArrayScatterV3",
+    "dlOpName": "tensorArrayScatter",
+    "category": "control",
+    "params": [
+      {
+        "tfInputIndex": 0,
+        "dlParamName": "tensorArrayId",
+        "type": "number"
+      },
+      {
+        "tfInputIndex": 1,
+        "dlParamName": "indices",
+        "type": "number[]"
+      },
+      {
+        "tfInputIndex": 2,
+        "dlParamName": "tensor",
+        "type": "number[]"
+      },
+      {
+        "tfInputIndex": 3,
+        "dlParamName": "flowIn",
+        "type": "number"
+      },
+      {
+        "tfParamName": "T",
+        "dlParamName": "dtype",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorArrayConcatV3",
+    "dlOpName": "tensorArrayConcat",
+    "category": "control",
+    "params": [
+      {
+        "tfInputIndex": 0,
+        "dlParamName": "tensorArrayId",
+        "type": "number"
+      },
+      {
+        "tfInputIndex": 1,
+        "dlParamName": "flowIn",
+        "type": "number"
+      },
+      {
+        "tfParamName": "dtype",
+        "dlParamName": "dtype",
+        "type": "dtype"
+      },
+      {
+        "tfParamName": "element_shape_except0",
+        "dlParamName": "elementShapeExcept0",
+        "type": "shape",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorArraySplitV3",
+    "dlOpName": "tensorArraySplit",
+    "category": "control",
+    "params": [
+      {
+        "tfInputIndex": 0,
+        "dlParamName": "tensorArrayId",
+        "type": "number"
+      },
+      {
+        "tfInputIndex": 1,
+        "dlParamName": "tensor",
+        "type": "tensor"
+      },
+      {
+        "tfInputIndex": 2,
+        "dlParamName": "lengths",
+        "type": "number[]"
+      },
+      {
+        "tfInputIndex": 3,
+        "dlParamName": "flowIn",
+        "type": "number"
+      },
+      {
+        "tfParamName": "T",
+        "dlParamName": "dtype",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorArraySizeV3",
+    "dlOpName": "tensorArraySize",
+    "category": "control",
+    "params": [
+      {
+        "tfInputIndex": 0,
+        "dlParamName": "tensorArrayId",
+        "type": "number"
+      },
+      {
+        "tfInputIndex": 1,
+        "dlParamName": "flowIn",
+        "type": "number"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorArrayCloseV3",
+    "dlOpName": "tensorArrayClose",
+    "category": "control",
+    "params": [
+      {
+        "tfInputIndex": 0,
+        "dlParamName": "tensorArrayId",
+        "type": "number"
+      }
+    ]
   }
 ]

dist/version.d.ts

@@ -1,2 +0,2 @@
-declare const version = "0.5.1";
+declare const version = "0.5.2";
 export { version };

dist/version.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
-var version = '0.5.1';
+var version = '0.5.2';
 exports.version = version;
 //# sourceMappingURL=version.js.map

package.json

 {
   "name": "@tensorflow/tfjs-converter",
-  "version": "0.5.1",
+  "version": "0.5.2",
   "description": "Tensorflow model converter for javascript",
@@ -17,6 +17,6 @@ "main": "dist/index.js",
   "peerDependencies": {
-    "@tensorflow/tfjs-core": "~0.12.0"
+    "@tensorflow/tfjs-core": "~0.12.4"
   },
   "devDependencies": {
-    "@tensorflow/tfjs-core": "~0.12.0",
+    "@tensorflow/tfjs-core": "~0.12.4",
     "@types/jasmine": "~2.8.6",
@@ -23,0 +23,0 @@ "@types/node-fetch": "1.6.9",

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Improved metrics

Total package byte prevSize

939254

increased by6.19%

Lines of code

18975

increased by5.38%

No dependency changes