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Merge pull request #42 from faceshiftlabs/feat/ping-pong-2
Feat/ping pong 2
This commit is contained in:
Jeremy Hummel
GitHub
commit
1dff2283d1
6 changed files with 121 additions and 108 deletions
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@ -14,6 +14,7 @@ import imagelib
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from interact import interact as io
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from nnlib import nnlib
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from samplelib import SampleGeneratorBase
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from samplelib.SampleGeneratorPingPong import PingPongOptions, Paddle
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from utils import Path_utils, std_utils
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from utils.cv2_utils import *
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@ -67,7 +68,7 @@ class ModelBase(object):
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self.debug = debug
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self.is_training_mode = (training_data_src_path is not None and training_data_dst_path is not None)
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self.paddle = 'pong'
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self.paddle = Paddle.PONG
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self.iter = 0
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self.options = {}
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@ -210,15 +211,18 @@ class ModelBase(object):
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self.onInitializeOptions(self.iter == 0, ask_override)
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self.ping_pong_options = PingPongOptions(enabled=self.options['ping_pong'],
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iterations=self.ping_pong_iter,
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model_iter=self.iter,
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paddle=self.paddle,
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batch_cap=self.batch_size)
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nnlib.import_all(self.device_config)
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self.keras = nnlib.keras
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self.K = nnlib.keras.backend
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self.onInitialize()
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self.options['batch_size'] = self.batch_size
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self.paddle = self.options.get('paddle', 'ping')
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if self.debug or self.batch_size == 0:
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self.batch_size = 1
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@ -413,7 +417,7 @@ class ModelBase(object):
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def save(self):
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self.options['batch_size'] = self.batch_size
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self.options['paddle'] = self.paddle
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self.options['paddle'] = self.ping_pong_options.paddle
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summary_path = self.get_strpath_storage_for_file('summary.txt')
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Path( summary_path ).write_text(self.model_summary_text)
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self.onSave()
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@ -542,12 +546,11 @@ class ModelBase(object):
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return [ generator.generate_next() for generator in self.generator_list]
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def train_one_iter(self):
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if self.iter == 1 and self.options.get('ping_pong', False):
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self.set_batch_size(1)
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self.paddle = 'ping'
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elif not self.options.get('ping_pong', False) and self.batch_cap != self.batch_size:
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self.set_batch_size(self.batch_cap)
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# if self.iter == 1 and self.options.get('ping_pong', False):
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# self.set_batch_size(1)
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# self.paddle = 'ping'
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# elif not self.options.get('ping_pong', False) and self.batch_cap != self.batch_size:
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# self.set_batch_size(self.batch_cap)
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sample = self.generate_next_sample()
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iter_time = time.time()
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losses = self.onTrainOneIter(sample, self.generator_list)
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@ -574,21 +577,6 @@ class ModelBase(object):
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img = (np.concatenate ( [preview_lh, preview], axis=0 ) * 255).astype(np.uint8)
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cv2_imwrite (filepath, img )
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if self.iter % self.ping_pong_iter == 0 and self.iter != 0 and self.options.get('ping_pong', False):
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if self.batch_size == self.batch_cap:
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self.paddle = 'pong'
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if self.batch_size > self.batch_cap:
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self.set_batch_size(self.batch_cap)
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self.paddle = 'pong'
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if self.batch_size == 1:
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self.paddle = 'ping'
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if self.paddle == 'ping':
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self.save()
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self.set_batch_size(self.batch_size + 1)
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else:
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self.save()
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self.set_batch_size(self.batch_size - 1)
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self.iter += 1
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return self.iter, iter_time, self.batch_size
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@ -487,7 +487,8 @@ class SAEModel(ModelBase):
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'apply_ct': apply_random_ct} for i in range(ms_count)] + \
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[{'types': (t.IMG_TRANSFORMED, face_type, t.MODE_M),
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'resolution': resolution // (2 ** i)} for i in
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range(ms_count)]
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range(ms_count)],
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ping_pong=self.ping_pong_options,
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),
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SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size,
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@ -500,7 +501,8 @@ class SAEModel(ModelBase):
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range(ms_count)] + \
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[{'types': (t.IMG_TRANSFORMED, face_type, t.MODE_M),
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'resolution': resolution // (2 ** i)} for i in
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range(ms_count)])
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range(ms_count)],
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ping_pong=self.ping_pong_options,)
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])
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# override
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@ -540,38 +542,8 @@ class SAEModel(ModelBase):
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# override
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def set_batch_size(self, batch_size):
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self.batch_size = batch_size
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self.set_training_data_generators(None)
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self.set_training_data_generators([
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SampleGeneratorFace(training_data_src_path,
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sort_by_yaw_target_samples_path=training_data_dst_path if sort_by_yaw else None,
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random_ct_samples_path=training_data_dst_path if apply_random_ct != ColorTransferMode.NONE else None,
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debug=self.is_debug(), batch_size=self.batch_size,
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sample_process_options=SampleProcessor.Options(random_flip=self.random_flip,
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scale_range=np.array([-0.05,
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0.05]) + self.src_scale_mod / 100.0),
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output_sample_types=[{'types': (
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t.IMG_WARPED_TRANSFORMED, face_type, t_mode_bgr),
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'resolution': resolution, 'apply_ct': apply_random_ct}] + \
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[{'types': (t.IMG_TRANSFORMED, face_type, t_mode_bgr),
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'resolution': resolution // (2 ** i),
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'apply_ct': apply_random_ct} for i in range(ms_count)] + \
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[{'types': (t.IMG_TRANSFORMED, face_type, t.MODE_M),
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'resolution': resolution // (2 ** i)} for i in
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range(ms_count)]
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),
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SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size,
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sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, ),
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output_sample_types=[{'types': (
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t.IMG_WARPED_TRANSFORMED, face_type, t_mode_bgr),
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'resolution': resolution}] + \
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[{'types': (t.IMG_TRANSFORMED, face_type, t_mode_bgr),
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'resolution': resolution // (2 ** i)} for i in
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range(ms_count)] + \
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[{'types': (t.IMG_TRANSFORMED, face_type, t.MODE_M),
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'resolution': resolution // (2 ** i)} for i in
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range(ms_count)])
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])
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for generators in self.get_training_data_generators():
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generators.update_batch(batch_size)
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# override
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def onTrainOneIter(self, generators_samples, generators_list):
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@ -5,8 +5,9 @@ import cv2
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import numpy as np
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from facelib import LandmarksProcessor
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from samplelib import (SampleGeneratorBase, SampleLoader, SampleProcessor,
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from samplelib import (SampleGeneratorBase, SampleLoader, SampleProcessor, SampleGeneratorPingPong,
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SampleType)
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from samplelib.SampleGeneratorPingPong import PingPongOptions, SampleGeneratorPingPong
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from utils import iter_utils
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@ -19,12 +20,12 @@ output_sample_types = [
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'''
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class SampleGeneratorFace(SampleGeneratorBase):
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class SampleGeneratorFace(SampleGeneratorPingPong):
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def __init__(self, samples_path, debug, batch_size, sort_by_yaw=False, sort_by_yaw_target_samples_path=None,
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random_ct_samples_path=None, sample_process_options=SampleProcessor.Options(),
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output_sample_types=[], add_sample_idx=False, generators_count=2, generators_random_seed=None,
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**kwargs):
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super().__init__(samples_path, debug, batch_size)
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ping_pong=PingPongOptions(), **kwargs):
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super().__init__(samples_path, debug, batch_size=batch_size, ping_pong=ping_pong)
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self.sample_process_options = sample_process_options
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self.output_sample_types = output_sample_types
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self.add_sample_idx = add_sample_idx
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@ -64,6 +65,7 @@ class SampleGeneratorFace(SampleGeneratorBase):
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def __next__(self):
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self.generator_counter += 1
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generator = self.generators[self.generator_counter % len(self.generators)]
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super().__next__()
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return next(generator)
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def batch_func(self, param):
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50
samplelib/SampleGeneratorPingPong.py
Normal file
50
samplelib/SampleGeneratorPingPong.py
Normal file
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@ -0,0 +1,50 @@
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from enum import Enum
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from samplelib import SampleGeneratorBase
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class Paddle(Enum):
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PING = 'ping' # Ascending
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PONG = 'pong' # Descending
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class PingPongOptions:
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def __init__(self, enabled=False, iterations=1000, model_iter=1, paddle=Paddle.PING, batch_cap=1):
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self.enabled = enabled
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self.iterations = iterations
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self.model_iter = model_iter
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self.paddle = paddle
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self.batch_cap = batch_cap
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class SampleGeneratorPingPong(SampleGeneratorBase):
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def __init__(self, *args, batch_size, ping_pong=PingPongOptions()):
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self.ping_pong = ping_pong
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super().__init__(*args, batch_size)
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def __next__(self):
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if self.ping_pong.enabled and self.ping_pong.model_iter % self.ping_pong.iterations == 0 \
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and self.ping_pong.model_iter != 0:
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# If batch size is greater then batch cap, set it to batch cap
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if self.batch_size > self.ping_pong.batch_cap:
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self.batch_size = self.ping_pong.batch_cap
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# If we are at the batch cap, switch to PONG (descend)
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if self.batch_size == self.ping_pong.batch_cap:
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self.paddle = Paddle.PONG
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# Else if we are at 1, switch to PING (ascend)
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elif self.batch_size == 1:
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self.paddle = Paddle.PING
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# If PING (ascending) increase the batch size
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if self.paddle is Paddle.PING:
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self.batch_size += 1
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# Else decrease the batch size
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else:
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self.batch_size -= 1
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self.ping_pong.model_iter += 1
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super().__next__()
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@ -4,5 +4,6 @@ from .SampleLoader import SampleLoader
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from .SampleProcessor import SampleProcessor
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from .SampleGeneratorBase import SampleGeneratorBase
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from .SampleGeneratorFace import SampleGeneratorFace
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from .SampleGeneratorPingPong import SampleGeneratorPingPong
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from .SampleGeneratorFaceTemporal import SampleGeneratorFaceTemporal
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from .SampleGeneratorImageTemporal import SampleGeneratorImageTemporal
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