Delete convert.py

convert.py

@@ -1,344 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-from rknn.api import RKNN
-from math import exp
-from sys import exit
-
-import onnx
-import onnxscript
-
-batch_size = 1
-# embed_seq_len = 590
-
-prompt_tokens_list = [15, 17, 21, 25]
-
-encoder_seq_len_list = [577 + p for p in prompt_tokens_list]
-
-decoder_seq_len = 1
-
-# set current directory to the directory of this file
-import os
-os.chdir(os.path.dirname(os.path.abspath(__file__)))
-
-import subprocess
-import select
-
-def run_python_code(code):
-    # 启动子进程并执行代码
-    process = subprocess.Popen(
-        ['python', '-c', code],
-        stdout=subprocess.PIPE,
-        stderr=subprocess.PIPE,
-        text=True
-    )
-
-    # 实时读取子进程的输出和错误输出
-    while True:
-        reads = [process.stdout.fileno(), process.stderr.fileno()]
-        ret = select.select(reads, [], [])
-        
-        for fd in ret[0]:
-            if fd == process.stdout.fileno():
-                output = process.stdout.readline()
-                if output:
-                    print(output.strip())
-            if fd == process.stderr.fileno():
-                err = process.stderr.readline()
-                if err:
-                    print(f"Error: {err.strip()}")
-        
-        if process.poll() is not None:
-            break
-
-def convert_decoder():
-    rknn = RKNN(verbose=True)
-
-    ONNX_MODEL="decoder_model.onnx"
-    RKNN_MODEL=ONNX_MODEL.replace(".onnx",".rknn")
-    DATASET="dataset.txt"
-    QUANTIZE=False
-
-    # [[batch_size, encoder_seq_len], 
-    # [batch_size, encoder_seq_len, 768], 
-    # [batch_size, decoder_seq_len, 768]]
-    input_shapes =[[[batch_size, encoder_seq_len],
-                            [batch_size, encoder_seq_len, 768],
-                            [batch_size, decoder_seq_len, 768]] for encoder_seq_len in encoder_seq_len_list]
-    # pre-process config
-    print('--> Config model')
-    rknn.config(quantized_algorithm='normal', quantized_method='channel', target_platform='rk3588', optimization_level=3, single_core_mode=True,
-                dynamic_input=input_shapes)
-    print('done')
-
-    # Load ONNX model
-    print('--> Loading model')
-    ret = rknn.load_onnx(model=ONNX_MODEL,
-                         )
-    if ret != 0:
-        print('Load model failed!')
-        exit(ret)
-    print('done')
-
-    # Build model
-    print('--> Building model')
-    ret = rknn.build(do_quantization=QUANTIZE, dataset=DATASET, rknn_batch_size=None)
-    if ret != 0:
-        print('Build model failed!')
-        exit(ret)
-    print('done')
-
-    #export
-    print('--> Export RKNN model')
-    ret = rknn.export_rknn(RKNN_MODEL)
-    if ret != 0:
-        print('Export RKNN model failed!')
-        exit(ret)
-    print('done')
-
-def convert_encoder():
-    rknn = RKNN(verbose=True)
-
-    ONNX_MODEL="encoder_model.onnx"
-    RKNN_MODEL=ONNX_MODEL.replace(".onnx",".rknn")
-    DATASET="dataset.txt"
-    QUANTIZE=False
-
-    #[[batch_size, encoder_seq_len], [batch_size, encoder_seq_len, 768]]
-    input_shapes = [[[batch_size, encoder_seq_len], [batch_size, encoder_seq_len, 768]] for encoder_seq_len in encoder_seq_len_list]
-    # pre-process config
-    print('--> Config model')
-    rknn.config(quantized_algorithm='normal', quantized_method='channel', target_platform='rk3588', optimization_level=3, single_core_mode=True, dynamic_input=input_shapes)
-    print('done')
-
-    # Load ONNX model
-    print('--> Loading model')
-    ret = rknn.load_onnx(model=ONNX_MODEL
-                         )
-    if ret != 0:
-        print('Load model failed!')
-        exit(ret)
-    print('done')
-
-    # Build model
-    print('--> Building model')
-    ret = rknn.build(do_quantization=QUANTIZE, dataset=DATASET, rknn_batch_size=None)
-    if ret != 0:
-        print('Build model failed!')
-        exit(ret)
-    print('done')
-
-    # Export RKNN model
-    print('--> Export RKNN model')
-    ret = rknn.export_rknn(RKNN_MODEL)
-    if ret != 0:
-        print('Export RKNN model failed!')
-        exit(ret)
-    print('done')
-
-def convert_vision():
-    rknn = RKNN(verbose=True)
-
-    ONNX_MODEL="vision_encoder.onnx"
-    DATASET="dataset.txt"
-    QUANTIZE=False
-
-    # split the first Transformers block into a separate model because it's too large to fit in the rknn
-    onnx.utils.extract_model(ONNX_MODEL, "vision_encoder_part1.onnx", ['pixel_values'], ['/blocks.0/blocks.0.0/channel_block/channel_attn/Add_output_0'])
-    
-    ##### Build stage 1, this will crash the python process, so we need to run it in a separate process
-    code = f"""
-from rknn.api import RKNN
-rknn = RKNN(verbose=True)
-ONNX_MODEL="vision_encoder.onnx"
-RKNN_MODEL=ONNX_MODEL.replace(".onnx",".rknn")
-DATASET="dataset.txt"
-QUANTIZE=False
-batch_size = {batch_size}
-# pre-process config
-print('--> Config model')
-rknn.config(quantized_algorithm='normal', quantized_method='channel', target_platform='rk3588', optimization_level=3, single_core_mode=True)
-print('done')
-
-# Load ONNX model
-print('--> Loading model')
-ret = rknn.load_onnx(model=ONNX_MODEL,
-                        inputs=["pixel_values"],
-                        input_size_list=[[batch_size, 3, 768, 768]],
-                        )
-if ret != 0:
-    print('Load model failed!')
-    exit(ret)
-print('done')
-
-print('--> Building model stage 1')
-ret = rknn.build(do_quantization=QUANTIZE, dataset=DATASET, rknn_batch_size=None)
-if ret != 0:
-    print('Build model failed!')
-    exit(ret)
-print('done')
-    """
-    run_python_code(code)
-    print("Build stage 1 done")
-
-    intermidiate_model = onnx.load("check3_fuse_ops.onnx")
-
-    # fuse ops
-    from onnxscript.rewriter import pattern
-    import onnx.numpy_helper as onh
-    import numpy as np
-    def tp_rs_tp_rs_tp_pattern(op, input1, perm1, shape2, perm3, shape4, perm5):
-        i1 = op.Transpose(input1, perm=perm1)
-        i2 = op.Reshape(i1, shape2)
-        i3 = op.Transpose(i2, perm=perm3)
-        i4 = op.Reshape(i3, shape4)
-        i5 = op.Transpose(i4, perm=perm5)
-        return i5
-
-    def fused_pattern(op, input1, perm1, shape2, perm3, shape4, perm5):
-        rs1_shape = op.Constant(value=onh.from_array(np.array([input1.shape[0]* 3, input1.shape[1]//3, input1.shape[2], input1.shape[3]], dtype=np.int64)))
-        fi1 = op.Reshape(input1, rs1_shape)
-        fi2 = op.Transpose(fi1, perm=[0, 2, 1, 3])
-        elems = input1.shape[0] * input1.shape[1] * input1.shape[2] * input1.shape[3]
-        rs4_shape = op.Constant(value=onh.from_array(np.array([elems / 32 / 144, 32, 1, 144], dtype=np.int64)))
-        fi3 = op.Reshape(fi2, rs4_shape)
-        return fi3
-
-    rewrite_rule = pattern.RewriteRule(tp_rs_tp_rs_tp_pattern, fused_pattern)
-    rewrite_rule_set = pattern.RewriteRuleSet([rewrite_rule],commute=True)
-    fused_model = onnxscript.rewriter.rewrite(
-        intermidiate_model,
-        pattern_rewrite_rules=rewrite_rule_set
-    )
-    onnx.save(fused_model, "vision_encoder_part2.onnx")
-    ONNX_MODEL = "vision_encoder_part2.onnx"
-    RKNN_MODEL=ONNX_MODEL.replace(".onnx",".rknn")
-    del intermidiate_model
-    del fused_model
-
-
-    rknn = RKNN(verbose=True)
-
-    # pre-process config
-    print('--> Config model')
-    rknn.config(quantized_algorithm='normal', quantized_method='channel', target_platform='rk3588', optimization_level=3, single_core_mode=True)
-    print('done')
-
-    # Load ONNX model
-    print('--> Loading model')
-    ret = rknn.load_onnx(model="check3_fuse_ops.onnx",
-                         inputs=["/blocks.0/blocks.0.0/channel_block/channel_attn/Add_output_0-rs"],
-                         input_size_list=[[batch_size, 128, 1, 36864]],)
-    if ret != 0:
-        print('Load model failed!')
-        exit(ret)
-    print('done')
-
-    # Build model
-    print('--> Building model stage 2')
-    ret = rknn.build(do_quantization=QUANTIZE, dataset=DATASET, rknn_batch_size=None)
-    if ret != 0:
-        print('Build model failed!')
-        exit(ret)
-    print('done')
-
-    # Export RKNN model
-    print('--> Export RKNN model')
-    ret = rknn.export_rknn(RKNN_MODEL)
-    if ret != 0:
-        print('Export RKNN model failed!')
-        exit(ret)
-    print('done')
-    
-    
-    
-    
-
-
-
-def check_vision_model():
-    rknn = RKNN(verbose=True)
-
-    ONNX_MODEL="vision_encoder.onnx"
-    RKNN_MODEL=ONNX_MODEL.replace(".onnx",".rknn")
-    DATASET="dataset.txt"
-    QUANTIZE=False
-
-    # pre-process config
-    print('--> Config model')
-    rknn.config(quantized_algorithm='normal', quantized_method='channel', target_platform='rk3588', optimization_level=3, single_core_mode=True )
-    print('done')
-
-    # Load ONNX model
-    print('--> Loading model')
-    ret = rknn.load_onnx(model=ONNX_MODEL,
-                         inputs=["pixel_values"],
-                         input_size_list=[[batch_size, 3, vision_size[0], vision_size[1]]],
-                         )
-    if ret != 0:
-        print('Load model failed!')
-        exit(ret)
-    print('done')
-    
-    # Build model
-    print('--> Building model')
-    ret = rknn.build(do_quantization=QUANTIZE, dataset=DATASET, rknn_batch_size=None)
-    if ret != 0:
-        print('Build model failed!')
-        exit(ret)
-    print('done')
-    
-    # Export RKNN model
-    print('--> Export RKNN model')
-    ret = rknn.export_rknn(RKNN_MODEL)
-    if ret != 0:
-        print('Export RKNN model failed!')
-        exit(ret)
-    print('done')
-
-    #init runtime
-    print('--> Init runtime environment')
-    ret = rknn.init_runtime(target='rk3588')
-    if ret != 0:
-        print('Init runtime environment failed!')
-        exit(ret)
-    print('done')
-
-    #precision check
-    print('--> Precision check')
-    ret = rknn.accuracy_analysis(inputs=["lena.png"], target='rk3588')
-    if ret != 0:
-        print('Precision check failed!')
-        exit(ret)
-    print('done')
-    
-    
-    
-
-
-import argparse
-# python convert.py <decoder|encoder|vision|all>
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("model", type=str, help="Model to convert")
-    parser.add_argument("--check", action="store_true", help="Check model")
-    args = parser.parse_args()
-    if args.model == "decoder":
-        convert_decoder()
-    elif args.model == "encoder":
-        convert_encoder()
-    # elif args.model == "embed":   # embed is faster with cpu
-    #     convert_embed()
-    elif args.model == "vision":
-        if args.check:
-            check_vision_model()
-        else:
-            convert_vision()
-    elif args.model == "all":
-        convert_decoder()
-        convert_encoder()
-        # convert_embed()
-        convert_vision()
-    else:
-        print("Invalid model")
-        exit(1)