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Add Darknet tensor shape error handler
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@lutzroeder
lutzroeder committed Mar 21, 2020
1 parent b21cf82 commit a8acfa3
Showing 1 changed file with 32 additions and 25 deletions.
57 changes: 32 additions & 25 deletions src/darknet.js
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Expand Up @@ -114,9 +114,16 @@ darknet.Graph = class {
return value !== undefined ? value : defaultValue;
};

const make_shape = (dimensions, source) => {
if (dimensions.some((dimension) => dimension === 0 || dimension === undefined || isNaN(dimension))) {
throw new darknet.Error("Invalid tensor shape '" + JSON.stringify(dimensions) + "' in '" + source + "'.");
}
return new darknet.TensorShape(dimensions);
}

const load_weights = (name, shape, visible) => {
let data = weights ? weights.bytes(4 * shape.reduce((a, b) => a * b)) : null;
const type = new darknet.TensorType('float32', new darknet.TensorShape(shape));
const type = new darknet.TensorType('float32', make_shape(shape, 'load_weights'));
const initializer = new darknet.Tensor(type, data);
const argument = new darknet.Argument('', null, initializer);
return new darknet.Parameter(name, visible === false ? false : true, [ argument ]);
Expand All @@ -138,7 +145,7 @@ darknet.Graph = class {
load_batch_normalize_weights(layer, prefix, n);
}
layer.weights.push(load_weights(prefix + 'weights', [ Math.floor(c / groups), n, size, size ], prefix === ''));
layer.outputs[0].type = new darknet.TensorType('float32', new darknet.TensorShape([ layer.out_w, layer.out_h, layer.out_c ]));
layer.outputs[0].type = new darknet.TensorType('float32', make_shape([ layer.out_w, layer.out_h, layer.out_c ], 'make_convolutional_layer'));
}

const make_connected_layer = (layer, prefix, inputs, outputs, batch_normalize) => {
Expand All @@ -151,7 +158,7 @@ darknet.Graph = class {
load_batch_normalize_weights(layer, prefix, outputs);
}
layer.weights.push(load_weights(prefix + 'weights', [ inputs, outputs ], prefix === ''));
layer.outputs[0].type = new darknet.TensorType('float32', new darknet.TensorShape([ outputs ]));
layer.outputs[0].type = new darknet.TensorType('float32', make_shape([ outputs ], 'make_connected_layer'));
}

let params = {};
Expand All @@ -168,8 +175,8 @@ darknet.Graph = class {
}

const inputType = params.w && params.h && params.c ?
new darknet.TensorType('float32', new darknet.TensorShape([ params.w, params.h, params.c ])) :
new darknet.TensorType('float32', new darknet.TensorShape([ params.inputs ]));
new darknet.TensorType('float32', make_shape([ params.w, params.h, params.c ], 'params-if')) :
new darknet.TensorType('float32', make_shape([ params.inputs ], 'params-else'));
const inputName = 'input';
params.arguments = [ new darknet.Argument(inputName, inputType, null) ];
this._inputs.push(new darknet.Parameter(inputName, true, params.arguments));
Expand Down Expand Up @@ -284,7 +291,7 @@ darknet.Graph = class {
layer.out = layer.out_w * layer.out_h * layer.out_c;
layer.weights.push(load_weights('weights', [ params.c, n, size, size, layer.out_h * layer.out_w ]));
layer.weights.push(load_weights('biases',[ layer.out_w * layer.out_h * layer.out_c ]));
layer.outputs[0].type = new darknet.TensorType('float32', new darknet.TensorShape([ layer.out_w, layer.out_h, layer.out_c ]));
layer.outputs[0].type = new darknet.TensorType('float32', make_shape([ layer.out_w, layer.out_h, layer.out_c ], 'local'));
if (activation !== 'logistic') {
section.chain.push({ type: activation });
}
Expand All @@ -296,15 +303,15 @@ darknet.Graph = class {
layer.out_c = params.c;
layer.out = layer.in;
load_batch_normalize_weights(weights, section, '', layer.out);
layer.outputs[0].type = new darknet.TensorType('float32', new darknet.TensorShape([ layer.ouputs ]));
layer.outputs[0].type = new darknet.TensorType('float32', make_shape([ layer.ouputs ], 'batchnorm'));
break;
}
case 'activation': {
layer.out_h = params.h;
layer.out_w = params.w;
layer.out_c = params.c;
layer.out = layer.in;
layer.outputs[0].type = new darknet.TensorType('float32', new darknet.TensorShape([ layer.ouputs ]));
layer.outputs[0].type = new darknet.TensorType('float32', make_shape([ layer.ouputs ], 'activation'));
break;
}
case 'max':
Expand Down Expand Up @@ -343,7 +350,7 @@ darknet.Graph = class {
layer.out_c = layer.input_layer.out_c;
}
else {
layer.outputs[0].type = new darknet.TensorType('float32', new darknet.TensorShape([ layer.out_w, layer.out_h, layer.out_c ]));
layer.outputs[0].type = new darknet.TensorType('float32', make_shape([ layer.out_w, layer.out_h, layer.out_c ], 'maxpool'));
}
layer.out = layer.out_w * layer.out_h * layer.out_c;
break;
Expand All @@ -357,7 +364,7 @@ darknet.Graph = class {
layer.out_h = 1;
layer.out_c = params.c;
layer.out = layer.out_c;
layer.outputs[0].type = new darknet.TensorType('float32', new darknet.TensorShape([ layer.out_w, layer.out_h, layer.out_c ]));
layer.outputs[0].type = new darknet.TensorType('float32', make_shape([ layer.out_w, layer.out_h, layer.out_c ], 'avgpool'));
break;
}
case 'crnn': {
Expand Down Expand Up @@ -427,7 +434,7 @@ darknet.Graph = class {
layer.weights = layer.weights.concat(layer.input_h_layer.weights);
layer.weights = layer.weights.concat(layer.state_h_layer.weights);
layer.out = outputs;
layer.outputs[0].type = new darknet.TensorType('float32', new darknet.TensorShape([ outputs ]));
layer.outputs[0].type = new darknet.TensorType('float32', make_shape([ outputs ], 'gru'));
break;
}
case 'lstm': {
Expand Down Expand Up @@ -462,7 +469,7 @@ darknet.Graph = class {
layer.out_h = 1;
layer.out_c = outputs;
layer.out = outputs;
layer.outputs[0].type = new darknet.TensorType('float32', new darknet.TensorShape([ outputs ]));
layer.outputs[0].type = new darknet.TensorType('float32', make_shape([ outputs ], 'lstm'));
weights = null;
break;
}
Expand All @@ -471,15 +478,15 @@ darknet.Graph = class {
layer.out_h = params.h;
layer.out_c = params.c;
layer.out = params.inputs;
layer.outputs[0].type = new darknet.TensorType('float32', new darknet.TensorShape([ layer.out ]));
layer.outputs[0].type = new darknet.TensorType('float32', make_shape([ layer.out ], 'softmax'));
break;
}
case 'dropout': {
layer.out_w = params.w;
layer.out_h = params.h;
layer.out_c = params.c;
layer.out = params.inputs;
layer.outputs[0].type = new darknet.TensorType('float32', new darknet.TensorShape([ layer.out_w, layer.out_h, layer.out_c ]));
layer.outputs[0].type = new darknet.TensorType('float32', make_shape([ layer.out_w, layer.out_h, layer.out_c ], 'dropout'));
break;
}
case 'upsample': {
Expand All @@ -488,7 +495,7 @@ darknet.Graph = class {
layer.out_h = params.h * stride;
layer.out_c = params.c;
layer.out = layer.out_w * layer.out_h * layer.out_c;
layer.outputs[0].type = new darknet.TensorType('float32', new darknet.TensorShape([ layer.out_w, layer.out_h, layer.out_c ]));
layer.outputs[0].type = new darknet.TensorType('float32', make_shape([ layer.out_w, layer.out_h, layer.out_c ], 'upsample'));
break;
}
case 'crop': {
Expand All @@ -502,7 +509,7 @@ darknet.Graph = class {
layer.out_h = crop_height;
layer.out_c = params.c;
layer.out = layer.out_w * layer.out_h * layer.out_c;
layer.outputs[0].type = new darknet.TensorType('float32', new darknet.TensorShape([ layer.out_w, layer.out_h, layer.out_c ]));
layer.outputs[0].type = new darknet.TensorType('float32', make_shape([ layer.out_w, layer.out_h, layer.out_c ], 'crop'));
break;
}
case 'yolo': {
Expand All @@ -512,7 +519,7 @@ darknet.Graph = class {
layer.out_w = params.w;
layer.out_c = n * (classes + 4 + 1);
layer.out = layer.out_h * layer.out_w * layer.out_c;
layer.outputs[0].type = new darknet.TensorType('float32', new darknet.TensorShape([ layer.out_w, layer.out_h, layer.out_c ]));
layer.outputs[0].type = new darknet.TensorType('float32', make_shape([ layer.out_w, layer.out_h, layer.out_c ], 'yolo'));
break;
}
case 'Gaussian_yolo': {
Expand All @@ -522,20 +529,20 @@ darknet.Graph = class {
layer.out_w = params.w;
layer.out_c = n * (classes + 8 + 1);
layer.out = layer.out_h * layer.out_w * layer.out_c;
layer.outputs[0].type = new darknet.TensorType('float32', new darknet.TensorShape([ layer.out_w, layer.out_h, layer.out_c ]));
layer.outputs[0].type = new darknet.TensorType('float32', make_shape([ layer.out_w, layer.out_h, layer.out_c ], 'Gaussian_yolo'));
break;
}
case 'region': {
const coords = option_find_int(options, 'coords', 4);
const classes = option_find_int(options, 'classes', 20);
const num = option_find_int(options, 'num', 1);
layer.out = params.h * params.w * num * (classes + coords + 1);
layer.outputs[0].type = new darknet.TensorType('float32', new darknet.TensorShape([ params.h, params.w, num, (classes + coords + 1) ]));
layer.outputs[0].type = new darknet.TensorType('float32', make_shape([ params.h, params.w, num, (classes + coords + 1) ], 'region'));
break;
}
case 'cost': {
layer.out = params.inputs;
layer.outputs[0].type = new darknet.TensorType('float32', new darknet.TensorShape([ layer.out ]));
layer.outputs[0].type = new darknet.TensorType('float32', make_shape([ layer.out ], 'cost'));
break;
}
case 'reorg': {
Expand All @@ -559,7 +566,7 @@ darknet.Graph = class {
layer.out_c = 0;
layer.out = (params.h * params.w * params.c) + extra;
}
layer.outputs[0].type = new darknet.TensorType('float32', new darknet.TensorShape([ layer.out ]));
layer.outputs[0].type = new darknet.TensorType('float32', make_shape([ layer.out ], 'reorg'));
break;
}
case 'route': {
Expand All @@ -583,7 +590,7 @@ darknet.Graph = class {
layer.out_c = 0;
}
}
layer.outputs[0].type = new darknet.TensorType('float32', new darknet.TensorShape([ layer.out_w, layer.out_h, layer.out_c ]));
layer.outputs[0].type = new darknet.TensorType('float32', make_shape([ layer.out_w, layer.out_h, layer.out_c ], 'route'));
break;
}
case 'shortcut':
Expand All @@ -594,7 +601,7 @@ darknet.Graph = class {
layer.out_h = params.h;
layer.out_c = params.c;
layer.out = params.w * params.h * params.c;
layer.outputs[0].type = new darknet.TensorType('float32', new darknet.TensorShape([ params.w, params.h, params.c ]));
layer.outputs[0].type = new darknet.TensorType('float32', make_shape([ params.w, params.h, params.c ], 'shortcut|scale_channels|sam'));
if (activation !== 'linear') {
section.chain.push({ type: activation });
}
Expand All @@ -605,7 +612,7 @@ darknet.Graph = class {
layer.out_h = params.h;
layer.out_c = params.c;
layer.out = params.inputs;
layer.outputs[0].type = new darknet.TensorType('float32', new darknet.TensorShape([ layer.out ]));
layer.outputs[0].type = new darknet.TensorType('float32', make_shape([ layer.out ], 'detection'));
break;
}
default: {
Expand Down Expand Up @@ -919,7 +926,7 @@ darknet.TensorShape = class {

constructor(dimensions) {
if (dimensions.some((dimension) => dimension === 0 || dimension === undefined || isNaN(dimension))) {
throw new darknet.Error('Invalid tensor shape.');
throw new darknet.Error("Invalid tensor shape '" + JSON.stringify(dimensions) + "'.");
}
this._dimensions = dimensions;
}
Expand Down

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