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get fusible subgraph test
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‎graph_net/config/get_fusible_subgraph_sample_list.txt‎

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samples/timm/crossvit_small_240.in1k
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samples/timm/poolformerv2_s12.sail_in1k
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samples/timm/regnety_080.pycls_in1k
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samples/timm/dla46x_c.in1k
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samples/timm/mobilenetv1_100.ra4_e3600_r224_in1k
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samples/timm/efficientnetv2_rw_s.ra2_in1k
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samples/timm/vit_base_patch16_rope_ape_224.naver_in1k
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samples/timm/fastvit_t8.apple_dist_in1k
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samples/timm/test_byobnet.r160_in1k
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samples/timm/mambaout_base.in1k
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samples/timm/davit_small
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samples/timm/resnet61q.ra2_in1k
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samples/timm/coat_tiny
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samples/timm/regnetx_004.pycls_in1k
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samples/timm/convnextv2_large.fcmae
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samples/timm/regnety_640.seer
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samples/timm/repvit_m1_1.dist_300e_in1k
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samples/timm/tinynet_d.in1k
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samples/timm/resnetrs270.tf_in1k
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samples/timm/cait_m48_448
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samples/timm/legacy_seresnet50.in1k
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samples/timm/tinynet_a.in1k
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samples/timm/convnext_small.fb_in1k
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samples/timm/vit_huge_patch14_clip_quickgelu_224.dfn5b
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samples/timm/dpn131.mx_in1k
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samples/timm/convnextv2_large.fcmae_ft_in1k
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samples/timm/convnextv2_small
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samples/timm/repvit_m1.dist_in1k
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samples/timm/cs3darknet_s
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samples/timm/resnet50d.a1_in1k
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samples/timm/dm_nfnet_f6
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samples/timm/coatnet_1_rw_224
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samples/timm/lcnet_050.ra2_in1k
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samples/timm/efficientnet_em.ra2_in1k
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samples/timm/dpn48b
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samples/timm/semnasnet_075.rmsp_in1k
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samples/timm/skresnet34.ra_in1k
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samples/timm/crossvit_15_dagger_240.in1k
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samples/timm/mnasnet_100.rmsp_in1k
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samples/timm/mobilenetv3_rw.rmsp_in1k
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samples/timm/xception65p.ra3_in1k
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samples/timm/coatnet_0_rw_224
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samples/timm/eca_nfnet_l3
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samples/timm/deit3_base_patch16_224.fb_in1k
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samples/timm/mambaout_base_short_rw.sw_e500_in1k
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samples/timm/mobilenetv4_conv_small.e1200_r224_in1k
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samples/timm/xception71.tf_in1k
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samples/timm/dla60.in1k
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samples/timm/repghostnet_130.in1k
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samples/timm/mambaout_base_plus_rw.sw_e150_in12k
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samples/timm/poolformerv2_s36.sail_in1k
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samples/timm/deit3_huge_patch14_224.fb_in1k
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samples/timm/vit_base_patch32_clip_224.datacompxl
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samples/timm/poolformer_m48.sail_in1k
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samples/timm/regnety_006.pycls_in1k
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samples/timm/starnet_s4.in1k
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samples/timm/poolformer_m36.sail_in1k
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samples/timm/vit_huge_patch14_gap_224.in1k_ijepa
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samples/timm/efficientnet_b3.ra2_in1k
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samples/timm/mobilenetv3_large_150d.ra4_e3600_r256_in1k
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samples/timm/hgnetv2_b0.ssld_stage1_in22k_in1k
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samples/timm/convnextv2_huge.fcmae
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samples/timm/davit_huge
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samples/timm/regnetx_004_tv.tv2_in1k
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samples/timm/dla34.in1k
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samples/timm/convnext_xlarge.fb_in22k
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samples/timm/resmlp_12_224.fb_dino
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samples/timm/fasternet_t1.in1k
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samples/timm/resnetblur50.bt_in1k
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samples/timm/res2net50d.in1k
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samples/timm/vit_base_patch32_224.augreg_in1k
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samples/timm/mambaout_base_wide_rw.sw_e500_in1k
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samples/timm/vgg19_bn.tv_in1k
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samples/timm/vit_small_patch16_rope_ape_224.naver_in1k
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samples/timm/hardcorenas_b.miil_green_in1k
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samples/timm/vgg16.tv_in1k
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samples/timm/xception41p.ra3_in1k
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samples/timm/efficientnet_lite0.ra_in1k
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samples/timm/regnetv_064.ra3_in1k
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samples/timm/regnety_320.pycls_in1k
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samples/timm/convnext_pico.d1_in1k
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samples/timm/repvit_m1_0.dist_300e_in1k
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samples/timm/resnet50c.gluon_in1k
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samples/timm/mobileone_s4.apple_in1k
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samples/timm/ghostnet_100.in1k
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samples/timm/deit_base_distilled_patch16_384
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samples/timm/dpn68b.mx_in1k
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samples/timm/dla60_res2next
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samples/timm/resnet101d.gluon_in1k
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samples/timm/eva02_large_patch14_clip_224.merged2b
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samples/timm/fasternet_m.in1k
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samples/timm/mobilenetv2_110d.ra_in1k
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samples/timm/regnetx_064.pycls_in1k
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samples/timm/cspresnet50.ra_in1k
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samples/timm/resmlp_24_224.fb_dino
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samples/timm/mobileone_s3.apple_in1k
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samples/timm/mobileone_s2.apple_in1k
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samples/timm/res2net101d
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samples/timm/hardcorenas_f.miil_green_in1k
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samples/timm/hrnet_w18_ssld.paddle_in1k

‎graph_net/config/small_sample_list_for_get_fusible_subgraph.txt‎

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#samples/timm/crossvit_small_240.in1k
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#samples/timm/poolformerv2_s12.sail_in1k
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#samples/timm/regnety_080.pycls_in1k
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#samples/timm/dla46x_c.in1k
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#samples/timm/mobilenetv1_100.ra4_e3600_r224_in1k
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samples/timm/efficientnetv2_rw_s.ra2_in1k
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#samples/timm/vit_base_patch16_rope_ape_224.naver_in1k
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#samples/timm/fastvit_t8.apple_dist_in1k
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#samples/timm/test_byobnet.r160_in1k
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#samples/timm/mambaout_base.in1k

‎graph_net/test/graph_decompose_and_look_for_fully_fusionable_subgraph_test.sh‎

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‎graph_net/test/new_graph_decompose_and_look_for_fully_fusible_subgraph_test.sh‎

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#!/bin/bash
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GRAPH_NET_ROOT=$(python3 -c "import graph_net; import os; print(
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os.path.dirname(graph_net.__file__))")
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# input model path
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MODEL_NAME=resnet18
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MODEL_PATH_IN_SAMPLES=/timm/$MODEL_NAME
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# INPUT_MODEL_LIST=$GRAPH_NET_ROOT/config/get_fusible_subgraph_sample_list.txt
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INPUT_MODEL_LIST=$GRAPH_NET_ROOT/config/small_sample_list_for_get_fusible_subgraph.txt
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OUTPUT_DIR="/tmp/find_fully_fusible_output"
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config_json_str=$(cat <<EOF
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{
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"handler_path": "$GRAPH_NET_ROOT/torch/fully_fusible_subgraph_extractor.py",
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"handler_class_name":"FullyFusibleSubgraphExtractor",
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"handler_config": {
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"model_path_prefix": "$GRAPH_NET_ROOT/../",
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"output_dir": "$OUTPUT_DIR",
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"split_positions": [],
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"group_head_and_tail": false,
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"chain_style": false,
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"max_step": 3,
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"min_step": 2,
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"max_nodes": 4
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}
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}
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EOF
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)
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CONFIG=$(echo $config_json_str | base64 -w 0)
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# python3 -m graph_net.model_path_handler --model-path $GRAPH_NET_ROOT/../samples/$MODEL_PATH_IN_SAMPLES --handler-config=$CONFIG
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python3 -m graph_net.model_path_handler --model-path-list $INPUT_MODEL_LIST --handler-config=$CONFIG
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# 1. 读取总输入数量 (通过计算输入模型列表文件中的行数)
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if [ ! -f "$INPUT_MODEL_LIST" ]; then
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echo "input dir $INPUT_MODEL_LIST not exist."
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TOTAL_INPUT_COUNT=0
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else
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# wc -l 计算行数, awk '{print $1}' 移除文件名
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TOTAL_INPUT_COUNT=$(wc -l < "$INPUT_MODEL_LIST")
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fi
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# 2. 统计已产出文件夹数量
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if [ ! -d "$OUTPUT_DIR" ]; then
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echo "output dir $OUTPUT_DIR not exist."
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PRODUCED_COUNT=0
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else
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# 查找 OUTPUT_DIR 下的一级子目录数量
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# -mindepth 1: 忽略父目录本身
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# -maxdepth 1: 只查找一级目录
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# -type d: 只查找目录 (文件夹)
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PRODUCED_COUNT=$(find "$OUTPUT_DIR" -mindepth 1 -maxdepth 1 -type d | wc -l)
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fi
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echo "Total Input = $TOTAL_INPUT_COUNT"
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echo "Total Output = $PRODUCED_COUNT"
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# 3. 计算产出率 (使用 bc 进行浮点数运算)
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if [ "$TOTAL_INPUT_COUNT" -gt 0 ]; then
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# scale=2 设置小数点后两位精度
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PRODUCTION_RATE=$(echo "scale=2; ($PRODUCED_COUNT * 100) / $TOTAL_INPUT_COUNT" | bc)
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echo "Production Rate = ${PRODUCTION_RATE}%"
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fi

‎graph_net/torch/check_if_a_given_model_is_fully_fusionable.py‎

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‎graph_net/torch/constraint_util.py‎

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import sys
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import os
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import graph_net
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import logging
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logger = logging.getLogger(__name__)
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class NaiveDataInputPredicator:
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decorator_config = b64_encoded_bytes.decode("utf-8")
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cmd = f"{sys.executable} -m graph_net.torch.run_model --model-path {model_path} --decorator-config {decorator_config}"
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return os.system(cmd) == 0
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class FusibleSubgraphPredicator:
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def __init__(self, config=None):
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if config is None:
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config = {}
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self.config = config
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def __call__(self, model_path):
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import json
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import base64
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json_string = json.dumps(self.config)
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json_bytes = json_string.encode("utf-8")
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b64_encoded_bytes = base64.b64encode(json_bytes)
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predicator_config = b64_encoded_bytes.decode("utf-8")
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cmd = f"{sys.executable} -m graph_net.model_path_handler --model-path {model_path} --handler-config {predicator_config}"
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return os.system(cmd) == 0

‎graph_net/torch/decompose_util.py‎

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yield arg.stop
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yield arg.step
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else:
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assert isinstance(arg, (int, bool, float, str, type(None))), f"{type(arg)=}"
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assert isinstance(
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arg, (int, bool, float, str, type(...), type(None))
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), f"{type(arg)=}"
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def get_args_node_and_self_node(node):
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for arg in node.args:

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