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updated CLI options for converter

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This commit is contained in:
Jeremy Hummel 2019-08-14 09:35:52 -07:00
commit 2c1f8f2bf6
2 changed files with 10 additions and 5 deletions
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7
converters/ConverterMasked.py
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@ -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.")

8
models/Model_SAE/Model.py
View file

@ -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)