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SAE collapse fix (#245)
* test * _ * _ * upd dev_poseest * SAE: finally collapses are fixed * fix batch size help
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4 changed files with 87 additions and 70 deletions
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@ -12,6 +12,7 @@ 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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@ -19,9 +20,12 @@ class PoseEstimator(object):
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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.class_num = 91
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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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self.model = PoseEstimator.BuildModel(resolution, class_num=self.class_num)
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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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@ -33,42 +37,44 @@ class PoseEstimator(object):
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if load_weights:
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self.model.load_weights (str(self.weights_path))
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idx_tensor = np.array([idx for idx in range(self.class_num)], dtype=K.floatx() )
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idx_tensor = K.constant(idx_tensor)
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inp_t, = self.model.inputs
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pitch_bins_t, yaw_bins_t, roll_bins_t = self.model.outputs
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bins_t = self.model.outputs
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pitch_t, yaw_t, roll_t = K.sum ( pitch_bins_t * idx_tensor, 1), K.sum ( yaw_bins_t * idx_tensor, 1), K.sum ( roll_bins_t * idx_tensor, 1)
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inp_pitch_bins_t = Input ( (self.class_num,) )
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inp_pitch_t = Input ( (1,) )
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inp_yaw_bins_t = Input ( (self.class_num,) )
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inp_yaw_t = Input ( (1,) )
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inp_roll_bins_t = Input ( (self.class_num,) )
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inp_roll_t = Input ( (1,) )
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alpha = 0.001
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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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pitch_loss = K.categorical_crossentropy(inp_pitch_bins_t, pitch_bins_t) \
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+ alpha * K.mean(K.square( inp_pitch_t - pitch_t), -1)
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loss_pitch = []
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loss_yaw = []
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loss_roll = []
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yaw_loss = K.categorical_crossentropy(inp_yaw_bins_t, yaw_bins_t) \
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+ alpha * K.mean(K.square( inp_yaw_t - yaw_t), -1)
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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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roll_loss = K.categorical_crossentropy(inp_roll_bins_t, roll_bins_t) \
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+ alpha * K.mean(K.square( inp_roll_t - roll_t), -1)
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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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loss = K.mean( pitch_loss + yaw_loss + roll_loss )
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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=2)
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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_bins_t, inp_pitch_t, inp_yaw_bins_t, inp_yaw_t, inp_roll_bins_t, inp_roll_t],
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[loss], opt.get_updates(loss, self.model.trainable_weights) )
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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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@ -82,19 +88,27 @@ class PoseEstimator(object):
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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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c = ( (pitch_yaw_roll+1) * 45.0 ).astype(np.int).astype(K.floatx())
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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, self.class_num )
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inp_yaw_bins = keras.utils.to_categorical(inp_yaw, self.class_num )
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inp_roll_bins = keras.utils.to_categorical(inp_roll, self.class_num )
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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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loss, = self.train( [imgs, inp_pitch_bins, inp_pitch, inp_yaw_bins, inp_yaw, inp_roll_bins, inp_roll] )
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return loss
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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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@ -106,7 +120,7 @@ class PoseEstimator(object):
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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 / 45.0 - 1, -1.0, 1.0 )
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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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@ -114,28 +128,31 @@ class PoseEstimator(object):
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return result
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@staticmethod
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def BuildModel ( resolution, class_num):
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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_num=class_num)(x)
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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_num):
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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='imagenet', input_shape=K.int_shape(x)[1:], pooling='avg')
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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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# pitch = Dense(class_num, activation='softmax', name='pitch')(x)
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# yaw = Dense(class_num, activation='softmax', name='yaw')(x)
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# roll = Dense(class_num, activation='softmax', name='roll')(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 [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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@ -153,10 +170,13 @@ class PoseEstimator(object):
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x = Dropout(0.5)(x)
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x = Dense(1024, activation='relu')(x)
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pitch = Dense(class_num, activation='softmax', name='pitch')(x)
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yaw = Dense(class_num, activation='softmax', name='yaw')(x)
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roll = Dense(class_num, activation='softmax', name='roll')(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 [pitch, yaw, roll]
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return output
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return func
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@ -95,7 +95,7 @@ class ModelBase(object):
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if ask_batch_size and (self.iter == 0 or ask_override):
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default_batch_size = 0 if self.iter == 0 else self.options.get('batch_size',0)
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self.options['batch_size'] = max(0, io.input_int("Batch_size (?:help skip:%d) : " % (default_batch_size), default_batch_size, help_message="Larger batch size is better for NN's generalization, but it can cause Out of Memory error and increases risk of model collapse during training. Tune this value for your videocard manually."))
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self.options['batch_size'] = max(0, io.input_int("Batch_size (?:help skip:%d) : " % (default_batch_size), default_batch_size, help_message="Larger batch size is better for NN's generalization, but it can cause Out of Memory error. Tune this value for your videocard manually."))
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else:
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self.options['batch_size'] = self.options.get('batch_size', 0)
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@ -45,17 +45,16 @@ class Model(ModelBase):
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f = SampleProcessor.TypeFlags
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face_type = f.FACE_TYPE_FULL if self.options['face_type'] == 'f' else f.FACE_TYPE_HALF
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normalize_vgg = False
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self.set_training_data_generators ([
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SampleGeneratorFace(self.training_data_src_path, debug=self.is_debug(), batch_size=self.batch_size,
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SampleGeneratorFace(self.training_data_src_path, debug=self.is_debug(), batch_size=self.batch_size, generators_count=4,
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sample_process_options=SampleProcessor.Options( rotation_range=[0,0], motion_blur = [25, 1] ), #random_flip=True,
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output_sample_types=[ [f.TRANSFORMED | face_type | f.MODE_BGR_SHUFFLE | f.OPT_APPLY_MOTION_BLUR, self.resolution, {'normalize_vgg':normalize_vgg} ],
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output_sample_types=[ [f.TRANSFORMED | face_type | f.MODE_BGR_SHUFFLE | f.OPT_APPLY_MOTION_BLUR, self.resolution ],
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[f.PITCH_YAW_ROLL],
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]),
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SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size,
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SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size, generators_count=4,
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sample_process_options=SampleProcessor.Options( rotation_range=[0,0] ), #random_flip=True,
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output_sample_types=[ [f.TRANSFORMED | face_type | f.MODE_BGR_SHUFFLE, self.resolution, {'normalize_vgg':normalize_vgg} ],
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output_sample_types=[ [f.TRANSFORMED | face_type | f.MODE_BGR_SHUFFLE, self.resolution ],
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[f.PITCH_YAW_ROLL],
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])
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])
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@ -68,9 +67,9 @@ class Model(ModelBase):
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def onTrainOneIter(self, generators_samples, generators_list):
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target_src, pitch_yaw_roll = generators_samples[0]
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loss = self.pose_est.train_on_batch( target_src, pitch_yaw_roll )
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pitch_loss,yaw_loss,roll_loss = self.pose_est.train_on_batch( target_src, pitch_yaw_roll )
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return ( ('loss', loss), )
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return ( ('pitch_loss', pitch_loss), ('yaw_loss', yaw_loss), ('roll_loss', roll_loss) )
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#override
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def onGetPreview(self, generators_samples):
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@ -249,8 +249,6 @@ class SAEModel(ModelBase):
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psd_target_dst_masked_ar = [ pred_src_dst_sigm_ar[i]*target_dstm_sigm_ar[i] for i in range(len(pred_src_dst_sigm_ar))]
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psd_target_dst_anti_masked_ar = [ pred_src_dst_sigm_ar[i]*target_dstm_anti_sigm_ar[i] for i in range(len(pred_src_dst_sigm_ar))]
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alpha_rec = 100
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if self.is_training_mode:
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self.src_dst_opt = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999, tf_cpu_mode=self.options['optimizer_mode']-1)
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self.src_dst_mask_opt = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999, tf_cpu_mode=self.options['optimizer_mode']-1)
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@ -265,9 +263,9 @@ class SAEModel(ModelBase):
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src_dst_mask_loss_train_weights = self.encoder.trainable_weights + self.decoder_srcm.trainable_weights + self.decoder_dstm.trainable_weights
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if not self.options['pixel_loss']:
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src_loss_batch = sum([ ( alpha_rec*K.square( dssim(kernel_size=int(resolution/11.6),max_value=1.0)( target_src_masked_ar_opt[i], pred_src_src_masked_ar_opt[i] ) )) for i in range(len(target_src_masked_ar_opt)) ])
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src_loss_batch = sum([ 10*dssim(kernel_size=int(resolution/11.6),max_value=1.0)( target_src_masked_ar_opt[i], pred_src_src_masked_ar_opt[i]) for i in range(len(target_src_masked_ar_opt)) ])
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else:
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src_loss_batch = sum([ K.mean ( alpha_rec*K.square( target_src_masked_ar_opt[i] - pred_src_src_masked_ar_opt[i] ), axis=[1,2,3]) for i in range(len(target_src_masked_ar_opt)) ])
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src_loss_batch = sum([ K.mean ( 50*K.square( target_src_masked_ar_opt[i] - pred_src_src_masked_ar_opt[i] ), axis=[1,2,3]) for i in range(len(target_src_masked_ar_opt)) ])
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src_loss = K.mean(src_loss_batch)
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@ -279,15 +277,15 @@ class SAEModel(ModelBase):
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bg_style_power = self.options['bg_style_power'] / 100.0
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if bg_style_power != 0:
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if not self.options['pixel_loss']:
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bg_loss = K.mean( (alpha_rec*bg_style_power)*K.square(dssim(kernel_size=int(resolution/11.6),max_value=1.0)( psd_target_dst_anti_masked_ar[-1], target_dst_anti_masked_ar[-1] )))
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bg_loss = K.mean( (10*bg_style_power)*dssim(kernel_size=int(resolution/11.6),max_value=1.0)( psd_target_dst_anti_masked_ar[-1], target_dst_anti_masked_ar[-1] ))
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else:
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bg_loss = K.mean( (alpha_rec*bg_style_power)*K.square( psd_target_dst_anti_masked_ar[-1] - target_dst_anti_masked_ar[-1] ))
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bg_loss = K.mean( (50*bg_style_power)*K.square( psd_target_dst_anti_masked_ar[-1] - target_dst_anti_masked_ar[-1] ))
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src_loss += bg_loss
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if not self.options['pixel_loss']:
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dst_loss_batch = sum([ ( alpha_rec*K.square(dssim(kernel_size=int(resolution/11.6),max_value=1.0)( target_dst_masked_ar_opt[i], pred_dst_dst_masked_ar_opt[i] ) )) for i in range(len(target_dst_masked_ar_opt)) ])
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dst_loss_batch = sum([ 10*dssim(kernel_size=int(resolution/11.6),max_value=1.0)(target_dst_masked_ar_opt[i], pred_dst_dst_masked_ar_opt[i]) for i in range(len(target_dst_masked_ar_opt)) ])
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else:
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dst_loss_batch = sum([ K.mean ( alpha_rec*K.square( target_dst_masked_ar_opt[i] - pred_dst_dst_masked_ar_opt[i] ), axis=[1,2,3]) for i in range(len(target_dst_masked_ar_opt)) ])
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dst_loss_batch = sum([ K.mean ( 50*K.square( target_dst_masked_ar_opt[i] - pred_dst_dst_masked_ar_opt[i] ), axis=[1,2,3]) for i in range(len(target_dst_masked_ar_opt)) ])
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dst_loss = K.mean(dst_loss_batch)
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