From 2c1f8f2bf6dfb106eec5e5cc94c88f88eb031124 Mon Sep 17 00:00:00 2001 From: Jeremy Hummel Date: Wed, 14 Aug 2019 09:35:52 -0700 Subject: [PATCH] updated CLI options for converter --- converters/ConverterMasked.py | 7 +++++-- models/Model_SAE/Model.py | 8 +++++--- 2 files changed, 10 insertions(+), 5 deletions(-) diff --git a/converters/ConverterMasked.py b/converters/ConverterMasked.py index 8cbe4ca..1653e2a 100644 --- a/converters/ConverterMasked.py +++ b/converters/ConverterMasked.py @@ -8,6 +8,7 @@ import imagelib from facelib import FaceType, FANSegmentator, LandmarksProcessor from interact import interact as io from joblib import SubprocessFunctionCaller +from samplelib.SampleProcessor import ColorTransferMode from utils.pickle_utils import AntiPickler from .Converter import Converter @@ -116,8 +117,10 @@ class ConverterMasked(Converter): 1.0 + io.input_int("Choose output face scale modifier [-50..50] (skip:0) : ", 0) * 0.01, 0.5, 1.5) if self.mode != 'raw': - self.color_transfer_mode = io.input_str( - "Apply color transfer to predicted face? Choose mode ( rct/lct skip:None ) : ", None, ['rct', 'lct']) + self.color_transfer_mode = np.clip(io.input_int( + "Apply color transfer to predicted face? (0) None, (1) LCT, (2) RCT, (3) RCT-c, (4) RCT-p, " + "(5) RCT-pc, (6) mRTC, (7) mRTC-c, (8) mRTC-p, (9) mRTC-pc ?:help skip:%s) : ", ColorTransferMode.NONE), + ColorTransferMode.NONE, ColorTransferMode.MASKED_RCT_PAPER_CLIP) self.super_resolution = io.input_bool("Apply super resolution? (y/n ?:help skip:n) : ", False, help_message="Enhance details by applying DCSCN network.") diff --git a/models/Model_SAE/Model.py b/models/Model_SAE/Model.py index 8941a08..6330cbd 100644 --- a/models/Model_SAE/Model.py +++ b/models/Model_SAE/Model.py @@ -123,13 +123,15 @@ class SAEModel(ModelBase): 0.0, 100.0) default_apply_random_ct = ColorTransferMode.NONE if is_first_run else self.options.get('apply_random_ct', ColorTransferMode.NONE) - self.options['apply_random_ct'] = io.input_int( - "Apply random color transfer to src faceset? (0) None, (1) LCT, (2) RCT, (3) RCT-c, (4) RCT-p, (5) RCT-pc, (6) mRTC, (7) mRTC-c, (8) mRTC-p, (9) mRTC-pc ?:help skip:%s) : " % (default_apply_random_ct), + self.options['apply_random_ct'] = np.clip(io.input_int( + "Apply random color transfer to src faceset? (0) None, (1) LCT, (2) RCT, (3) RCT-c, (4) RCT-p, " + "(5) RCT-pc, (6) mRTC, (7) mRTC-c, (8) mRTC-p, (9) mRTC-pc ?:help skip:%s) : " % default_apply_random_ct, default_apply_random_ct, help_message="Increase variativity of src samples by apply LCT color transfer from random dst " "samples. It is like 'face_style' learning, but more precise color transfer and without " "risk of model collapse, also it does not require additional GPU resources, " - "but the training time may be longer, due to the src faceset is becoming more diverse.") + "but the training time may be longer, due to the src faceset is becoming more diverse."), + ColorTransferMode.NONE, ColorTransferMode.MASKED_RCT_PAPER_CLIP) if nnlib.device.backend != 'plaidML': # todo https://github.com/plaidml/plaidml/issues/301 default_clipgrad = False if is_first_run else self.options.get('clipgrad', False)