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iperov
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1 changed files with 178 additions and 8 deletions
182
__dev/test.py
182
__dev/test.py
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@ -1,5 +1,5 @@
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import os
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os.environ['force_plaidML'] = '1'
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#os.environ['force_plaidML'] = '1'
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import sys
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import argparse
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@ -11,6 +11,7 @@ import numpy as np
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import cv2
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import time
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import multiprocessing
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import threading
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import traceback
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from tqdm import tqdm
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from utils.DFLPNG import DFLPNG
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@ -303,10 +304,41 @@ def get_transform_mat (image_landmarks, output_size, scale=1.0):
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#
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# #alignments[ source_filename_stem ].append (dflimg.get_source_landmarks())
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# alignments.append (dflimg.get_source_landmarks())
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import mathlib
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import string
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def main():
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import ffmpeg
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path = Path('D:/deepfacelab/test')
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input_path = str(path / 'input.mp4')
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#stream = ffmpeg.input(str(path / 'input.mp4') )
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#stream = ffmpeg.hflip(stream)
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#stream = ffmpeg.output(stream, str(path / 'output.mp4') )
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#ffmpeg.run(stream)
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(
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ffmpeg
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.input( str(path / 'input.mp4'))
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.hflip()
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.output( str(path / 'output.mp4'), r="23000/1001" )
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.run()
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)
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#probe = ffmpeg.probe(str(path / 'input.mp4'))
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#out, _ = (
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# ffmpeg
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# .input( input_path )
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# .output('pipe:', format='rawvideo', pix_fmt='rgb24')
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# .run(capture_stdout=True)
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#)
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#video = (
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# np
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# .frombuffer(out, np.uint8)
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# .reshape([-1, height, width, 3])
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#)
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import code
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code.interact(local=dict(globals(), **locals()))
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from nnlib import nnlib
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exec( nnlib.import_all( device_config=nnlib.device.Config() ), locals(), globals() )
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@ -1000,7 +1032,7 @@ O[i0, i1, i2, i3: (1 + 1 - 1)/1, (64 + 1 - 1)/1, (64 + 2 - 1)/2, (1 + 1 - 1)/1]
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for fs in filter_sizes:
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h = w = 128
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h_f = w_f = fs
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str = 2
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stri = 2
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#print "Testing for", imsize, fs, stri, pad
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#tf.reset_default_graph()
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@ -1008,9 +1040,9 @@ O[i0, i1, i2, i3: (1 + 1 - 1)/1, (64 + 1 - 1)/1, (64 + 2 - 1)/2, (1 + 1 - 1)/1]
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W = tf.constant(np.ones([h_f, w_f, c_in, h_f*w_f*c_in]), tf.float32)
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Z = tf.nn.conv2d(X, W, strides=[1, str, str, 1], padding="VALID")
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Z_manual = manual_conv(X, W, strides=[1, str, str, 1], padding="VALID")
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Z_2 = extract_image_patches (X, (fs,fs), (str,str), padding="VALID")
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Z = tf.nn.conv2d(X, W, strides=[1, stri, stri, 1], padding="VALID")
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Z_manual = manual_conv(X, W, strides=[1, stri, stri, 1], padding="VALID")
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Z_2 = extract_image_patches (X, (fs,fs), (stri,stri), padding="VALID")
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import code
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code.interact(local=dict(globals(), **locals()))
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#
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@ -1442,3 +1474,141 @@ if __name__ == "__main__":
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main()
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sys.exit()
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import sys
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import numpy as np
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def main():
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import tensorflow
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####import keras
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keras = tensorflow.keras
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K = keras.backend
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KL = keras.layers
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bgr_shape = (64, 64, 3)
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inp = KL.Input(bgr_shape)
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outp = K.mean(inp)
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flow = K.function([inp],[outp])
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flow ( [np.zeros ( (1,64,64,3), dtype=np.float32 )] )
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if __name__ == "__main__":
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main()
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sys.exit()
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import numpy as np
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import tensorflow as tf
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keras = tf.keras
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KL = keras.layers
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K = keras.backend
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bgr_shape = (128, 128, 3)
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#batch_size = 132 #max -TF.keras-tf.1.11.0-cuda 9
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batch_size = 90 #max -TF.keras-tf.1.13.1-cuda 10
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class PixelShuffler(keras.layers.Layer):
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def __init__(self, size=(2, 2), data_format=None, **kwargs):
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super(PixelShuffler, self).__init__(**kwargs)
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self.size = size
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def call(self, inputs):
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input_shape = K.int_shape(inputs)
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if len(input_shape) != 4:
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raise ValueError('Inputs should have rank ' +
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str(4) +
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'; Received input shape:', str(input_shape))
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batch_size, h, w, c = input_shape
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if batch_size is None:
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batch_size = -1
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rh, rw = self.size
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oh, ow = h * rh, w * rw
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oc = c // (rh * rw)
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out = K.reshape(inputs, (batch_size, h, w, rh, rw, oc))
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out = K.permute_dimensions(out, (0, 1, 3, 2, 4, 5))
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out = K.reshape(out, (batch_size, oh, ow, oc))
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return out
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def compute_output_shape(self, input_shape):
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if len(input_shape) != 4:
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raise ValueError('Inputs should have rank ' +
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str(4) +
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'; Received input shape:', str(input_shape))
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height = input_shape[1] * self.size[0] if input_shape[1] is not None else None
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width = input_shape[2] * self.size[1] if input_shape[2] is not None else None
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channels = input_shape[3] // self.size[0] // self.size[1]
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if channels * self.size[0] * self.size[1] != input_shape[3]:
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raise ValueError('channels of input and size are incompatible')
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return (input_shape[0],
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height,
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width,
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channels)
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def get_config(self):
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config = {'size': self.size}
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base_config = super(PixelShuffler, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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def upscale (dim):
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def func(x):
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return PixelShuffler()((KL.Conv2D(dim * 4, kernel_size=3, strides=1, padding='same')(x)))
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return func
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inp = KL.Input(bgr_shape)
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x = inp
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x = KL.Conv2D(128, 5, strides=2, padding='same')(x)
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x = KL.Conv2D(256, 5, strides=2, padding='same')(x)
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x = KL.Conv2D(512, 5, strides=2, padding='same')(x)
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x = KL.Conv2D(1024, 5, strides=2, padding='same')(x)
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x = KL.Dense(1024)(KL.Flatten()(x))
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x = KL.Dense(8 * 8 * 1024)(x)
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x = KL.Reshape((8, 8, 1024))(x)
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x = upscale(512)(x)
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x = upscale(256)(x)
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x = upscale(128)(x)
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x = upscale(64)(x)
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x = KL.Conv2D(3, 5, strides=1, padding='same')(x)
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model = keras.models.Model ([inp], [x])
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model.compile(optimizer=keras.optimizers.Adam(lr=5e-5, beta_1=0.5, beta_2=0.999), loss='mae')
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training_data = np.zeros ( (batch_size,128,128,3) )
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loss = model.train_on_batch( [training_data], [training_data] )
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print ("FINE")
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import code
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code.interact(local=dict(globals(), **locals()))
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#import keras
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#batch_size = 84 #max -keras-tf.1.11.0-cuda 9
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#batch_size = 80 #max -keras-tf.1.13.1-cuda 10
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#inp = KL.Input(bgr_shape)
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#inp2 = KL.Input(bgr_shape)
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#outp = model(inp)
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#loss = K.mean(K.abs(outp-inp2))
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#train_func = K.function ([inp,inp2],[loss], keras.optimizers.Adam(lr=5e-5, beta_1=0.5, beta_2=0.999).get_updates(loss, model.trainable_weights) )
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#
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#loss = train_func( [training_data, training_data] )
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