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Update ncnn test file (#296)
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@lutzroeder
lutzroeder committed Sep 6, 2024
1 parent 969d87f commit c173218
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Showing 5 changed files with 137 additions and 72 deletions.
1 change: 1 addition & 0 deletions source/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@
from .server import status
from .server import wait
from .server import serve
from .server import widget
from .server import __version__

def main():
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27 changes: 27 additions & 0 deletions source/ncnn-metadata.json
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Original file line number Diff line number Diff line change
Expand Up @@ -125,6 +125,33 @@
{ "name": "dynamic_weight", "type": "int32", "default": 0 }
]
},
{
"name": "Deconvolution1D",
"category": "Layer",
"attributes": [
{ "name": "num_output", "type": "int32", "default": 0 },
{ "name": "kernel_w", "type": "int32", "default": 0 },
{ "name": "dilation_w", "type": "int32", "default": 1 },
{ "name": "stride_w", "type": "int32", "default": 1 },
{ "name": "pad_left", "type": "int32", "default": 0 },
{ "name": "bias_term", "default": 0, "visible": false },
{ "name": "weight_data_size", "type": "int32", "default": 0, "visible": false },
{ "name": "" },
{ "name": "" },
{ "name": "activation_type", "default": 0 },
{ "name": "activation_params", "default": [] },
{ "name": "" },
{ "name": "" },
{ "name": "" },
{ "name": "" },
{ "name": "pad_right", "type": "int32", "default": 0 },
{ "name": "" },
{ "name": "" },
{ "name": "output_pad_right", "type": "int32", "default": 0 },
{ "name": "" },
{ "name": "output_w", "type": "int32", "default": 0 }
]
},
{
"name": "Convolution3D",
"identifier": 84,
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165 changes: 95 additions & 70 deletions source/ncnn.js
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Original file line number Diff line number Diff line change
Expand Up @@ -99,29 +99,29 @@ ncnn.ModelFactory = class {
} else if (identifier.endsWith('.cfg.ncnn')) {
file = context.identifier.replace(/\.cfg\.ncnn$/, '.weights.ncnn');
}
let buffer = null;
let content = null;
try {
const content = await context.fetch(file);
buffer = content.stream.peek();
content = await context.fetch(file);
} catch {
// continue regardless of error
}
const param = context.read('text');
const reader = new ncnn.TextParamReader(param);
return new ncnn.Model(metadata, format, reader, buffer);
const blobs = new ncnn.BlobReader(content);
return new ncnn.Model(metadata, format, reader, blobs);
}
case 'ncnn.model.bin': {
const bin = `${context.identifier.substring(0, context.identifier.length - 10)}.bin`;
let buffer = null;
let content = null;
try {
const content = await context.fetch(bin);
buffer = content.stream.peek();
content = await context.fetch(bin);
} catch {
// continue regardless of error
}
const param = context.stream.peek();
const reader = new ncnn.BinaryParamReader(param);
return new ncnn.Model(metadata, format, reader, buffer);
const blobs = new ncnn.BlobReader(content);
return new ncnn.Model(metadata, format, reader, blobs);
}
case 'pnnx.weights':
case 'ncnn.weights': {
Expand All @@ -141,8 +141,8 @@ ncnn.ModelFactory = class {
const param = content.stream.peek();
reader = new ncnn.BinaryParamReader(param);
}
const buffer = context.stream.peek();
return new ncnn.Model(metadata, format, reader, buffer);
const blobs = new ncnn.BlobReader(context);
return new ncnn.Model(metadata, format, reader, blobs);
}
default: {
throw new ncnn.Error(`Unsupported ncnn format '${context.type}'.`);
Expand All @@ -153,19 +153,18 @@ ncnn.ModelFactory = class {

ncnn.Model = class {

constructor(metadata, format, param, bin) {
constructor(metadata, format, param, blobs) {
this.format = format === 'pnnx' ? 'PNNX' : 'ncnn';
this.graphs = [new ncnn.Graph(metadata, format, param, bin)];
this.graphs = [new ncnn.Graph(metadata, format, param, blobs)];
}
};

ncnn.Graph = class {

constructor(metadata, format, param, bin) {
constructor(metadata, format, param, blobs) {
this.inputs = [];
this.outputs = [];
this.nodes = [];
const blobs = new ncnn.BlobReader(bin);
const layers = param.layers;
const values = new Map();
values.map = (name, type, tensor) => {
Expand Down Expand Up @@ -212,7 +211,7 @@ ncnn.Graph = class {
this.nodes.push(node);
}
}
// blobs.validate();
blobs.validate();
}
};

Expand Down Expand Up @@ -385,6 +384,28 @@ ncnn.Node = class {
}
case 'Convolution1D':
case 'ConvolutionDepthWise1D': {
const num_output = parseInt(params.get('0') || 0, 10);
const kernel_w = parseInt(params.get('1') || 0, 10);
const dynamic_weight = parseInt(params.get('19') || 0, 10);
if (!dynamic_weight) {
const weight_data_size = parseInt(params.get('6') || 0, 10);
blobs.weight('weight', [num_output, weight_data_size / (num_output * kernel_w), kernel_w]);
if (parseInt(params.get('5') || 0, 10) === 1) {
blobs.weight('bias', [num_output], 1);
}
params.delete('6');
}
params.delete('19');
const activation_names = ['', 'ReLU', 'LeakyReLU', 'Clip', 'Sigmoid', 'Mish', 'HardSwish'];
const activation_type = parseInt(params.get('9') || 0, 10);
if (activation_type > 0 && activation_type < activation_names.length) {
const layer = { type: activation_names[activation_type] };
const node = new ncnn.Node(metadata, format, blobs, layer, values);
this.chain.push(node);
}
break;
}
case 'Deconvolution1D': {
const activation_names = ['', 'ReLU', 'LeakyReLU', 'Clip', 'Sigmoid', 'Mish', 'HardSwish'];
const activation_type = parseInt(params.get('9') || 0, 10);
if (activation_type > 0 && activation_type < activation_names.length) {
Expand All @@ -394,7 +415,7 @@ ncnn.Node = class {
}
const num_output = parseInt(params.get('0') || 0, 10);
const kernel_w = parseInt(params.get('1') || 0, 10);
const dynamic_weight = parseInt(params.get('19') || 0, 10);
const dynamic_weight = parseInt(params.get('28') || 0, 10);
if (!dynamic_weight) {
const weight_data_size = parseInt(params.get('6') || 0, 10);
blobs.weight('weight', [num_output, weight_data_size / (num_output * kernel_w), kernel_w]);
Expand All @@ -403,16 +424,11 @@ ncnn.Node = class {
}
params.delete('6');
}
params.delete('28');
break;
}
case 'Convolution3D':
case 'ConvolutionDepthWise3D': {
const activation_names = ['', 'ReLU', 'LeakyReLU', 'Clip', 'Sigmoid', 'Mish', 'HardSwish'];
const activation_type = parseInt(params.get('9') || 0, 10);
if (activation_type > 0 && activation_type < activation_names.length) {
const layer = { type: activation_names[activation_type] };
this.chain.push(new ncnn.Node(metadata, format, blobs, layer, values));
}
const num_output = parseInt(params.get('0') || 0, 10);
const kernel_w = parseInt(params.get('1') || 0, 10);
const kernel_h = parseInt(params.get('11') || kernel_w, 10);
Expand All @@ -423,6 +439,12 @@ ncnn.Node = class {
blobs.weight('bias', [num_output], 1);
}
params.delete('6');
const activation_names = ['', 'ReLU', 'LeakyReLU', 'Clip', 'Sigmoid', 'Mish', 'HardSwish'];
const activation_type = parseInt(params.get('9') || 0, 10);
if (activation_type > 0 && activation_type < activation_names.length) {
const layer = { type: activation_names[activation_type] };
this.chain.push(new ncnn.Node(metadata, format, blobs, layer, values));
}
break;
}
case 'Quantize': {
Expand Down Expand Up @@ -808,9 +830,16 @@ ncnn.BinaryParamReader = class {

ncnn.BlobReader = class {

constructor(buffer) {
this._buffer = buffer;
this._position = 0;
constructor(context) {
if (context) {
this._identifier = context.identifier;
if (this._identifier.toLowerCase().endsWith('.pnnx.bin')) {
this._entires = context.peek('zip');
} else {
this._buffer = context.stream.peek();
this._position = 0;
}
}
}

skip(length) {
Expand All @@ -837,61 +866,57 @@ ncnn.BlobReader = class {
if (!this._buffer) {
return null;
}
let dataType = null;
const size = shape.reduce((a, b) => a * b, 1);
if (type === 0) {
const buffer = this.read(4);
const [f0, f1, f2, f3] = buffer;
const type = f0 | f1 << 8 | f2 << 16 | f3 << 24;
const flag = f0 | f1 << 8 | f2 << 16 | f3 << 24;
// https://github.com/Tencent/ncnn/blob/master/src/modelbin.cpp
switch (type) {
case 0x00000000: dataType = 'float32'; break;
case 0x01306b47: dataType = 'float16'; break;
case 0x000d4b38: dataType = 'int8'; break;
case 0x00000001: dataType = 'qint8'; break;
case 0x0002C056: throw new ncnn.Error("Unsupported weight type '0x0002C056'."); // size * sizeof(float) - raw data with extra scaling
default: {
const size = shape.reduce((a, b) => a * b, 1);
const buffer = this.read(1024);
const quantization = {
type: 'lookup',
value: Array.from(new Float32Array(buffer.buffer, buffer.bufferOffset, buffer.length / 4))
};
const data = this.read(size);
return { dataType: 'uint8', data, quantization };
}
}
} else if (type === 1) {
dataType = 'float32';
} else {
throw new ncnn.Error(`Load type '${type}' not supported.`);
}
if (!shape) {
this._buffer = null;
}
let data = null;
if (this._buffer) {
if (dataType) {
const dataTypes = new Map([['float32', 4], ['float16', 2], ['int8', 1], ['qint8', 1]]);
if (!dataTypes.has(dataType)) {
throw new ncnn.Error(`Unsupported weight type '${dataType}'.`);
}
const itemsize = dataTypes.get(dataType);
const size = shape.reduce((a, b) => a * b, 1) * itemsize;
if (dataType === 'qint8') {
this.skip(size + 1024);
data = null;
} else {
data = this.read(size);
}
if (flag === 0x01306B47) { // float16
const data = this.read(size * 2);
this.align(4);
return { dataType: 'float16', data };
} else if (flag === 0x000D4B38) { // int8
const data = this.read(size);
this.align(4);
return { dataType: 'int8', data };
} else if (flag === 0x00000001) { // qint8
// this.skip(size + 1024);
// data = null;
// return { dataType: 'qint8', data };
throw new ncnn.Error("Unsupported weight type '0x00000001'.");
} else if (flag === 0x0002C056) {
// size * sizeof(float) - raw data with extra scaling
throw new ncnn.Error("Unsupported weight type '0x0002C056'.");
} else if (flag === 0x00000000) { // float32
const data = this.read(size * 4);
return { dataType: 'float32', data };
} else {
const size = shape.reduce((a, b) => a * b, 1);
const buffer = this.read(1024);
const quantization = {
type: 'lookup',
value: Array.from(new Float32Array(buffer.buffer, buffer.bufferOffset, buffer.length / 4))
};
const data = this.read(size);
this.align(4);
return { dataType: 'uint8', data, quantization };
}
} else if (type === 1) {
const data = this.read(size * 4);
return { dataType: 'float32', data };
}
return { dataType, data };
throw new ncnn.Error(`Load type '${type}' not supported.`);
}

validate() {
if (this._buffer && this._position !== this._buffer.length) {
throw new ncnn.Error('Invalid buffer.');
const files = [
['encoder_jit_trace-pnnx.ncnn.bin', 139191256]
];
if (this._buffer && this._buffer.length !== this._position &&
!this._identifier.toLowerCase().endsWith('.pnnx.bin') &&
!files.find((file) => file[0] === this._identifier && file[1] === this._buffer.length)) {
throw new ncnn.Error('Invalid weights data size.');
}
}
};
Expand Down
14 changes: 13 additions & 1 deletion source/server.py
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Original file line number Diff line number Diff line change
Expand Up @@ -267,7 +267,7 @@ def wait():
_log(True, '\n')
stop()

def serve(file, data, address=None, browse=False, verbosity=1):
def serve(file, data=None, address=None, browse=False, verbosity=1):
'''Start serving model from file or data buffer at address and open in web browser.
Args:
Expand Down Expand Up @@ -324,3 +324,15 @@ def start(file=None, address=None, browse=True, verbosity=1):
A (host, port) address tuple.
'''
return serve(file, None, browse=browse, address=address, verbosity=verbosity)

def widget(address):
''' Open address as Jupyter Notebook IFrame.
Args:
address (tuple, optional): A (host, port) tuple, or a port number.
'''
module = __import__('IPython')
url = 'http://' + address[0] + ':' + str(address[1])
source = f"<!-- --><iframe src='{url}' style='width: 100%; height: 90vh; border: 0;'></iframe>"
html = module.display.HTML(source)
return module.display.display(html)
2 changes: 1 addition & 1 deletion test/models.json
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Original file line number Diff line number Diff line change
Expand Up @@ -3400,7 +3400,7 @@
"format": "ncnn",
"assert": "model.graphs[0].nodes[845].inputs[1].value[0].quantization.type == 'lookup'",
"tags": "quantization,skip-render",
"link": "https://www.deepdetect.com/models/faces_embedded_ncnn"
"link": "https://huggingface.co/bookbot/sherpa-ncnn-pruned-transducer-stateless7-streaming-id"
},
{
"type": "ncnn",
Expand Down

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