# coding=utf-8
from __future__ import absolute_import, print_function
from suanpan.app import app
from suanpan.app.arguments import Model, Bool
from arguments import CFGModel
@app.param(Bool(key="usePredWeight", default=True))
@app.output(Model(key="model", type=CFGModel))
def SPRetinanetR101FPN(context):
args = context.args
if args.usePredWeight:
args.model.load_model(
"./detectron2/configs/COCO-Detection/retinanet_R_101_FPN_3x.yaml",
"common/model/detectron2/ImageNetPretrained/MSRA/model_final_59f53c.pkl",
)
else:
args.model.load_model(
"./detectron2/configs/COCO-Detection/retinanet_R_101_FPN_3x.yaml",
"",
)
return args.model
if __name__ == "__main__":
SPRetinanetR101FPN()
from pm4py.algo.discovery.dfg import factory as dfg_miner
from pm4py.objects.conversion.log.factory import apply as df_to_log
from suanpan.app import app
from suanpan.app.arguments import Json, Csv, String, Bool
from utils.csv import convert_df_pm_format
from utils.dfg import dfg_vis, find_start_end
@app.input(Csv(key="inputData1", alias="inputData"))
@app.param(
String(key="param1",
alias="measure",
default="performance",
help="performance, frequency"))
@app.param(Bool(key="param2", alias="hideStartEndNode", default=False))
@app.output(Json(key="outputData1", alias="outputData"))
def SPDFG(context):
args = context.args
df = convert_df_pm_format(args.inputData)
log = df_to_log(df)
dfg = dfg_miner.apply(log, variant=args.measure)
params = {}
if not args.hideStartEndNode:
start, end = find_start_end(dfg)
params = {"start_activities": start, "end_activities": end}
outputData = dfg_vis(dfg, log=log, parameters=params, measure=args.measure)
return outputData
# coding=utf-8
from __future__ import absolute_import, print_function
import os
from suanpan.app.arguments import Folder, String, Int, Bool
from suanpan.app import app
@app.input(Folder(key="inputData"))
@app.input(Folder(key="alignedDir"))
@app.input(Folder(key="modelDir"))
@app.param(Bool(key="__edit", default=False))
@app.param(
String(
key="modelName",
default="SAE",
help="AVATAR, DF, H64, H128, LIAEF128, SAE, DEV_FANSEG",
))
@app.param(Int(key="forceGpuIdx", default=-1))
@app.param(Bool(key="cpuOnly", default=False))
@app.output(Folder(key="outputData"))
def SPConvertLab(context):
args = context.args
convert_args = {
"input_dir": args.inputData,
"output_dir": args.outputData,
"aligned_dir": args.alignedDir,
"model_dir": args.modelDir,
"model_name": args.modelName,
}
from suanpan.app.arguments import Folder, String, Bool
from suanpan.app import app
from mainscripts import Extractor
@app.input(Folder(key="inputData"))
@app.param(
String(
key="faceType",
default="full_face",
help=
"['half_face', 'full_face', 'head', 'full_face_no_align', 'mark_only']",
))
@app.param(String(key="detector", default="s3fd", help="['dlib','mt','s3fd']"))
@app.param(Bool(key="multiGpu", default=False))
@app.param(Bool(key="cpuOnly", default=False))
@app.output(Folder(key="outputData"))
def SPExtractFaceLab(context):
args = context.args
Extractor.main(
args.inputData,
args.outputData,
debug_dir=None,
detector=args.detector,
manual_fix=False,
manual_output_debug_fix=False,
manual_window_size=1368,
image_size=256,
face_type=args.faceType,
from __future__ import absolute_import, print_function
import os
from suanpan.app.arguments import Folder, String, Int, Bool
from suanpan.app import app
from mainscripts import VideoEd
from utils import get_all_files
@app.input(Folder(key="inputData"))
@app.input(Folder(key="referenceFolder"))
@app.param(String(key="fileName", default="media.mp4"))
@app.param(String(key="ext", default="png", help="jpg png"))
@app.param(Int(key="fps", default=25))
@app.param(Int(key="bitrate", default=16))
@app.param(Bool(key="lossless", default=False))
@app.output(Folder(key="outputData"))
def SPVideoFromSequence(context):
args = context.args
if args.referenceFolder:
referenceFile = get_all_files(args.referenceFolder)
VideoEd.video_from_sequence(
args.inputData,
os.path.join(args.outputData, args.fileName),
referenceFile[0],
args.ext,
args.fps,
args.bitrate,
args.lossless,
)
@app.input(Folder(key="inputData1"))
@app.input(Folder(key="alignments1"))
@app.input(Folder(key="inputData2"))
@app.input(Folder(key="alignments2"))
@app.param(Int(key="batchSize", default=4, help="64 (2, 256)"))
@app.param(
String(
key="trainer",
default="original",
help=
"original dfaker dfl-h128 dfl-sae iae lightweight realface unbalanced villain",
))
@app.param(Int(key="iterations", default=10000, help="1000000 (0, 5000000)"))
@app.param(Int(key="__gpu", default=0))
@app.param(Bool(key="allowGrowth", default=False))
@app.param(Bool(key="warpToLandmarks", default=False))
@app.param(Bool(key="noFlip", default=False))
@app.param(Bool(key="noAugmentColor", default=False))
@app.output(Folder(key="outputModel"))
def SPTrain(context):
args = context.args
PARSER = cli.FullHelpArgumentParser()
TRAIN = cli.TrainArgs(
PARSER, "train",
"This command trains the model for the two faces A and B")
argsTransfer = [
"--input-A",
args.inputData1,
