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https://github.com/iperov/DeepFaceLab.git
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e1da9c56b4
* test * _ * _ * upd dev_poseest * SAE: finally collapses are fixed * fix batch size help
182 lines
7.1 KiB
Python
182 lines
7.1 KiB
Python
import os
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import pickle
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from functools import partial
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from pathlib import Path
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import cv2
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import numpy as np
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from interact import interact as io
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from nnlib import nnlib
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"""
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PoseEstimator estimates pitch, yaw, roll, from FAN aligned face.
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trained on https://www.umdfaces.io
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based on https://arxiv.org/pdf/1901.06778.pdf HYBRID COARSE-FINE CLASSIFICATION FOR HEAD POSE ESTIMATION
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"""
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class PoseEstimator(object):
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VERSION = 1
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def __init__ (self, resolution, face_type_str, load_weights=True, weights_file_root=None, training=False):
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exec( nnlib.import_all(), locals(), globals() )
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self.angles = [90, 45, 30, 10, 2]
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self.alpha_cat_losses = [0.07,0.05,0.03,0.01,0.01]
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self.class_nums = [ angle+1 for angle in self.angles ]
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self.model = PoseEstimator.BuildModel(resolution, class_nums=self.class_nums)
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if weights_file_root is not None:
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weights_file_root = Path(weights_file_root)
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else:
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weights_file_root = Path(__file__).parent
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self.weights_path = weights_file_root / ('PoseEst_%d_%s.h5' % (resolution, face_type_str) )
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if load_weights:
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self.model.load_weights (str(self.weights_path))
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inp_t, = self.model.inputs
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bins_t = self.model.outputs
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inp_pitch_t = Input ( (1,) )
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inp_yaw_t = Input ( (1,) )
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inp_roll_t = Input ( (1,) )
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inp_bins_t = []
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for class_num in self.class_nums:
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inp_bins_t += [ Input ((class_num,)), Input ((class_num,)), Input ((class_num,)) ]
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loss_pitch = []
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loss_yaw = []
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loss_roll = []
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for i,class_num in enumerate(self.class_nums):
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a = self.alpha_cat_losses[i]
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loss_pitch += [ a*K.categorical_crossentropy( inp_bins_t[i*3+0], bins_t[i*3+0] ) ]
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loss_yaw += [ a*K.categorical_crossentropy( inp_bins_t[i*3+1], bins_t[i*3+1] ) ]
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loss_roll += [ a*K.categorical_crossentropy( inp_bins_t[i*3+2], bins_t[i*3+2] ) ]
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idx_tensor = K.constant( np.array([idx for idx in range(self.class_nums[0])], dtype=K.floatx() ) )
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pitch_t, yaw_t, roll_t = K.sum ( bins_t[0] * idx_tensor, 1), K.sum ( bins_t[1] * idx_tensor, 1), K.sum ( bins_t[2] * idx_tensor, 1)
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reg_alpha = 0.02
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reg_pitch_loss = reg_alpha * K.mean(K.square( inp_pitch_t - pitch_t), -1)
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reg_yaw_loss = reg_alpha * K.mean(K.square( inp_yaw_t - yaw_t), -1)
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reg_roll_loss = reg_alpha * K.mean(K.square( inp_roll_t - roll_t), -1)
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pitch_loss = reg_pitch_loss + sum(loss_pitch)
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yaw_loss = reg_yaw_loss + sum(loss_yaw)
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roll_loss = reg_roll_loss + sum(loss_roll)
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opt = Adam(lr=0.001, tf_cpu_mode=0)
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if training:
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self.train = K.function ([inp_t, inp_pitch_t, inp_yaw_t, inp_roll_t] + inp_bins_t,
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[K.mean(pitch_loss),K.mean(yaw_loss),K.mean(roll_loss)], opt.get_updates( [pitch_loss,yaw_loss,roll_loss], self.model.trainable_weights) )
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self.view = K.function ([inp_t], [pitch_t, yaw_t, roll_t] )
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def __enter__(self):
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return self
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def __exit__(self, exc_type=None, exc_value=None, traceback=None):
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return False #pass exception between __enter__ and __exit__ to outter level
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def save_weights(self):
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self.model.save_weights (str(self.weights_path))
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def train_on_batch(self, imgs, pitch_yaw_roll):
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pyr = pitch_yaw_roll+1
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feed = [imgs]
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for i, (angle, class_num) in enumerate(zip(self.angles, self.class_nums)):
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c = np.round(pyr * (angle / 2) ).astype(K.floatx())
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inp_pitch = c[:,0:1]
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inp_yaw = c[:,1:2]
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inp_roll = c[:,2:3]
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if i == 0:
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feed += [inp_pitch, inp_yaw, inp_roll]
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inp_pitch_bins = keras.utils.to_categorical(inp_pitch, class_num )
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inp_yaw_bins = keras.utils.to_categorical(inp_yaw, class_num )
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inp_roll_bins = keras.utils.to_categorical(inp_roll, class_num )
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feed += [inp_pitch_bins, inp_yaw_bins, inp_roll_bins]
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#import code
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#code.interact(local=dict(globals(), **locals()))
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pitch_loss,yaw_loss,roll_loss = self.train(feed)
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return pitch_loss,yaw_loss,roll_loss
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def extract (self, input_image, is_input_tanh=False):
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if is_input_tanh:
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raise NotImplemented("is_input_tanh")
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input_shape_len = len(input_image.shape)
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if input_shape_len == 3:
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input_image = input_image[np.newaxis,...]
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pitch, yaw, roll = self.view( [input_image] )
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result = np.concatenate( (pitch[...,np.newaxis], yaw[...,np.newaxis], roll[...,np.newaxis]), -1 )
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result = np.clip ( result / (self.angles[0] / 2) - 1, -1.0, 1.0 )
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if input_shape_len == 3:
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result = result[0]
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return result
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@staticmethod
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def BuildModel ( resolution, class_nums):
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exec( nnlib.import_all(), locals(), globals() )
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inp = Input ( (resolution,resolution,3) )
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x = inp
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x = PoseEstimator.Flow(class_nums=class_nums)(x)
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model = Model(inp,x)
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return model
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@staticmethod
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def Flow(class_nums):
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exec( nnlib.import_all(), locals(), globals() )
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def func(input):
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x = input
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# resnet50 = keras.applications.ResNet50(include_top=False, weights=None, input_shape=K.int_shape(x)[1:], pooling='avg')
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# x = resnet50(x)
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# output = []
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# for class_num in class_nums:
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# pitch = Dense(class_num, activation='softmax')(x)
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# yaw = Dense(class_num, activation='softmax')(x)
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# roll = Dense(class_num, activation='softmax')(x)
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# output += [pitch,yaw,roll]
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# return output
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x = Conv2D(64, kernel_size=11, strides=4, padding='same', activation='relu')(x)
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x = MaxPooling2D( (3,3), strides=2 )(x)
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x = Conv2D(192, kernel_size=5, strides=1, padding='same', activation='relu')(x)
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x = MaxPooling2D( (3,3), strides=2 )(x)
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x = Conv2D(384, kernel_size=3, strides=1, padding='same', activation='relu')(x)
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x = Conv2D(256, kernel_size=3, strides=1, padding='same', activation='relu')(x)
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x = Conv2D(256, kernel_size=3, strides=1, padding='same', activation='relu')(x)
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x = MaxPooling2D( (3,3), strides=2 )(x)
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x = Flatten()(x)
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x = Dense(1024, activation='relu')(x)
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x = Dropout(0.5)(x)
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x = Dense(1024, activation='relu')(x)
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output = []
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for class_num in class_nums:
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pitch = Dense(class_num, activation='softmax')(x)
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yaw = Dense(class_num, activation='softmax')(x)
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roll = Dense(class_num, activation='softmax')(x)
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output += [pitch,yaw,roll]
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return output
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return func
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