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pruned_model.py
Source:pruned_model.py 
1import tensorflow as tf2from tensorflow import keras3from tensorflow.python.keras.engine.base_layer import Layer4from tensorflow.keras.preprocessing.image import ImageDataGenerator5import numpy as np6import os7import cv28from tensorflow.python.framework import ops9from tensorflow.python.ops import math_ops10import tensorflow_model_optimization as tfmot11from tensorflow.keras.callbacks import TensorBoard12from tensorflow_model_optimization.python.core.sparsity.keras import pruning_wrapper13from tensorflow_model_optimization.python.core.quantization.keras import quantize_annotate14#tf.config.list_physical_devices('GPU')15# mish Activation16class Mish(Layer):17 '''18 Mish Activation Function.19 Examples:20 >>> X_input = Input(input_shape)21 >>> X = Mish()(X_input)22 '''23 def __init__(self, **kwargs):24 super(Mish, self).__init__(**kwargs)25 self.supports_masking = True26 def call(self, inputs):27 return inputs * keras.backend.tanh(keras.backend.log(keras.backend.exp(inputs)+1))28 def get_config(self):29 base_config = super(Mish, self).get_config()30 return {**base_config}31 def compute_output_shape(self, input_shape):32 return input_shape33class Sigmoid(Layer):34 def __init__(self, **kwargs):35 super(Sigmoid, self).__init__(**kwargs)36 self.supports_masking = True37 def call(self, inputs):38 return keras.backend.sigmoid(inputs)39 def get_config(self):40 base_config = super(Sigmoid, self).get_config()41 return {**base_config}42 def compute_output_shape(self, input_shape):43 return input_shape44# customize DNN modules45def backbone(input, start_step, end_step, prune, decay):46 conv = keras.layers.Conv2D(16, (3, 3), strides=(2, 2), padding='same')(input)47 bn = keras.layers.BatchNormalization(axis=-1, momentum=0.9)(conv)48 act = Mish()(bn)49 res = prune(keras.layers.Conv2D(16, (1, 1), strides=(2, 2), padding='same'), decay(0.02, 0.08, start_step, end_step))(act)50 bn_res = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.05, 0.15, start_step, end_step))(res)51 act_res = Mish()(bn_res)52 dws = prune(keras.layers.SeparableConv2D(16, (3, 3), padding='same'), decay(0.02, 0.08, start_step, end_step))(act)53 bn = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.05, 0.15, start_step, end_step))(dws)54 act = Mish()(bn)55 dws = prune(keras.layers.SeparableConv2D(16, (3, 3), padding='same'), decay(0.05, 0.15, start_step, end_step))(act)56 bn = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.1, 0.2, start_step, end_step))(dws)57 act = Mish()(bn)58 mp = keras.layers.MaxPooling2D((3, 3), strides=2, padding='same')(act)59 mp = keras.layers.add([mp, act_res])60 mp = Mish()(mp)61 conv = prune(keras.layers.Conv2D(32, (1, 1), strides=1, padding='same'), decay(0.15, 0.3, start_step, end_step))(mp)62 bn = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.2, 0.4, start_step, end_step))(conv)63 act = Mish()(bn)64 conv = prune(keras.layers.Conv2D(64, (3, 3), strides=1, padding='same'), decay(0.15, 0.3, start_step, end_step))(bn)65 bn = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.2, 0.4, start_step, end_step))(conv)66 act = Mish()(bn)67 conv_1 = prune(keras.layers.Conv2D(32, (1, 1), strides=1, padding='same'), decay(0.15, 0.3, start_step, end_step))(mp)68 bn_1 = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.2, 0.4, start_step, end_step))(conv_1)69 act_1 = Mish()(bn_1)70 conv_1 = prune(keras.layers.Conv2D(32, (5, 1), strides=1, padding='same'), decay(0.15, 0.3, start_step, end_step))(act_1)71 bn_1 = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.2, 0.4, start_step, end_step))(conv_1)72 act_1 = Mish()(bn_1)73 conv_1 = prune(keras.layers.Conv2D(32, (1, 5), strides=1, padding='same'), decay(0.15, 0.3, start_step, end_step))(act_1)74 bn_1 = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.2, 0.4, start_step, end_step))(conv_1)75 act_1 = Mish()(bn_1)76 conv_1 = prune(keras.layers.Conv2D(64, (3, 3), strides=1, padding='same'), decay(0.15, 0.3, start_step, end_step))(act_1)77 bn_1 = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.2, 0.4, start_step, end_step))(conv_1)78 act_1 = Mish()(bn_1)79 concat = tf.keras.layers.concatenate(inputs=[act, act_1], axis=3)80 return concat81def neck_module1(ip, start_step, end_step, prune, decay):82 input = Mish()(ip)83 conv = prune(keras.layers.Conv2D(32, (1, 1), strides=1, padding='same'), decay(0.2, 0.4, start_step, end_step))(input)84 bn = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.4, 0.6, start_step, end_step))(conv)85 act = Mish()(bn)86 conv = prune(keras.layers.Conv2D(64, (1, 3), strides=1, padding='same'), decay(0.2, 0.4, start_step, end_step))(act)87 bn = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.4, 0.6, start_step, end_step))(conv)88 act = Mish()(bn)89 conv = prune(keras.layers.Conv2D(32, (3, 1), strides=1, padding='same'), decay(0.2, 0.4, start_step, end_step))(act)90 bn = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.4, 0.6, start_step, end_step))(conv)91 act = Mish()(bn)92 conv_1 = prune(keras.layers.Conv2D(32, (1, 1), strides=1, padding='same'), decay(0.3, 0.5, start_step, end_step))(input)93 bn_1 = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.5, 0.7, start_step, end_step))(conv_1)94 act_1 = Mish()(bn_1)95 conv_1 = prune(keras.layers.Conv2D(64, (1, 3), strides=1, padding='same'), decay(0.3, 0.5, start_step, end_step))(act_1)96 bn_1 = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.5, 0.7, start_step, end_step))(conv_1)97 act_1 = Mish()(bn_1)98 conv_1 = prune(keras.layers.Conv2D(64, (3, 1), strides=1, padding='same'), decay(0.3, 0.5, start_step, end_step))(act_1)99 bn_1 = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.5, 0.7, start_step, end_step))(conv_1)100 act_1 = Mish()(bn_1)101 conv_1 = prune(keras.layers.Conv2D(64, (1, 3), strides=1, padding='same'), decay(0.3, 0.5, start_step, end_step))(act_1)102 bn_1 = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.5, 0.7, start_step, end_step))(conv_1)103 act_1 = Mish()(bn_1)104 conv_1 = prune(keras.layers.Conv2D(32, (3, 1), strides=1, padding='same'), decay(0.3, 0.5, start_step, end_step))(act_1)105 bn_1 = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.5, 0.7, start_step, end_step))(conv_1)106 act_1 = Mish()(bn_1)107 mp = keras.layers.MaxPooling2D((2, 2), strides=1, padding='same')(input)108 conv_2 = prune(keras.layers.Conv2D(32, (1, 1), strides=1, padding='same'), decay(0.3, 0.5, start_step, end_step))(mp)109 bn_2 = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.5, 0.7, start_step, end_step))(conv_2)110 act_2 = Mish()(bn_2)111 conv_3 = prune(keras.layers.Conv2D(32, (1, 1), strides=1, padding='same'), decay(0.3, 0.5, start_step, end_step))(input)112 bn_3 = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.5, 0.7, start_step, end_step))(conv_3)113 act_3 = Mish()(bn_3)114 concat = keras.layers.concatenate(inputs=[act, act_1, act_2, act_3], axis=3)115 sum = keras.layers.add([concat, input])116 return sum117def neck_module2(ip, start_step, end_step, prune, decay):118 input = Mish()(ip)119 dws = prune(keras.layers.SeparableConv2D(64, (3, 3), padding='same'), decay(0.4, 0.7, start_step, end_step))(input)120 bn = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.6, 0.8, start_step, end_step))(dws)121 act = Mish()(bn)122 dws = prune(keras.layers.SeparableConv2D(128, (3, 3), padding='same'), decay(0.4, 0.7, start_step, end_step))(act)123 bn = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.6, 0.8, start_step, end_step))(dws)124 act = Mish()(bn)125 dws = prune(keras.layers.SeparableConv2D(192, (3, 3), padding='same'), decay(0.4, 0.7, start_step, end_step))(act)126 bn = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.6, 0.8, start_step, end_step))(dws)127 act = Mish()(bn)128 dws_c = prune(keras.layers.SeparableConv2D(64, (3, 3), padding='same'), decay(0.4, 0.7, start_step, end_step))(input)129 bn_c = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.6, 0.8, start_step, end_step))(dws_c)130 act_c = Mish()(bn_c)131 concat = keras.layers.concatenate(inputs=[input, act_c], axis=3)132 sum = keras.layers.add([concat, act])133 act_sum = Mish()(sum)134 mp = keras.layers.MaxPooling2D((3, 3), strides=2, padding='same')(act_sum)135 conv = prune(keras.layers.Conv2D(128, (1, 1), strides=1, padding='same'), decay(0.6, 0.8, start_step, end_step))(mp)136 bn = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.8, 0.9, start_step, end_step))(conv)137 act = Mish()(bn)138 conv = prune(keras.layers.Conv2D(128, (1, 3), strides=1, padding='same'), decay(0.6, 0.8, start_step, end_step))(act)139 bn = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.8, 0.9, start_step, end_step))(conv)140 act = Mish()(bn)141 conv = prune(keras.layers.Conv2D(256, (3, 1), strides=1, padding='same'), decay(0.6, 0.8, start_step, end_step))(act)142 bn = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.8, 0.9, start_step, end_step))(conv)143 act = Mish()(bn)144 conv_01 = prune(keras.layers.Conv2D(96, (1, 3), strides=1, padding='same'), decay(0.6, 0.8, start_step, end_step))(act)145 bn_01 = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.8, 0.9, start_step, end_step))(conv_01)146 act_01 = Mish()(bn_01)147 conv_02 = prune(keras.layers.Conv2D(96, (3, 1), strides=1, padding='same'), decay(0.6, 0.8, start_step, end_step))(act)148 bn_02 = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.8, 0.9, start_step, end_step))(conv_02)149 act_02 = Mish()(bn_02)150 conv_1 = prune(keras.layers.Conv2D(192, (1, 1), strides=1, padding='same'), decay(0.6, 0.8, start_step, end_step))(mp)151 bn_1 = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.8, 0.9, start_step, end_step))(conv_1)152 act_1 = Mish()(bn_1)153 conv_11 = prune(keras.layers.Conv2D(96, (1, 3), strides=1, padding='same'), decay(0.6, 0.8, start_step, end_step))(act_1)154 bn_11 = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.8, 0.9, start_step, end_step))(conv_11)155 act_11 = Mish()(bn_11)156 conv_12 = prune(keras.layers.Conv2D(96, (3, 1), strides=1, padding='same'), decay(0.6, 0.8, start_step, end_step))(act_1)157 bn_12 = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.8, 0.9, start_step, end_step))(conv_12)158 act_12 = Mish()(bn_12)159 concat_1 = keras.layers.concatenate(inputs=[act_01, act_02], axis=3)160 concat_2 = keras.layers.concatenate(inputs=[act_11, act_12], axis=3)161 sum = keras.layers.add([mp, concat_1, concat_2])162 return sum163def head(ip, start_step, end_step, prune, decay):164 input = Mish()(ip)165 dws = prune(keras.layers.SeparableConv2D(256, (3, 3), padding='same'), decay(0.7, 0.9, 0, end_step))(input)166 bn_dws = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.8, 0.9, 0, end_step))(dws)167 act_dws = Mish()(bn_dws)168 dws = prune(keras.layers.SeparableConv2D(384, (3, 3), padding='same'), decay(0.7, 0.9, 0, end_step))(act_dws)169 bn_dws = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.8, 0.9, 0, end_step))(dws)170 act_dws = Mish()(bn_dws)171 conv = prune(keras.layers.Conv2D(384, (1, 1), strides=1, padding='same'), decay(0.7, 0.9, 0, end_step))(input)172 bn = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.8, 0.9, 0, end_step))(conv)173 act = Mish()(bn)174 sum = keras.layers.add([act, act_dws])175 act_sum = Mish()(sum)176 dws = prune(keras.layers.SeparableConv2D(512, (3, 3), padding='same'), decay(0.7, 0.9, 0, end_step))(act_sum)177 bn = prune(keras.layers.BatchNormalization(axis=-1, momentum=0.9), decay(0.8, 0.9, 0, end_step))(dws)178 act = Mish()(bn)179 gap = keras.layers.GlobalAveragePooling2D()(act)180 return gap181def FADNet(input_shape, start_step, end_step, prune, decay):182 img_input = keras.layers.Input(shape=input_shape)183 bb = backbone(img_input, start_step, end_step, prune, decay)184 mp0 = keras.layers.MaxPooling2D((3, 3), strides=2, padding='same')(bb)185 n1 = neck_module1(mp0, start_step, end_step, prune, decay)186 mp1 = keras.layers.MaxPooling2D((3, 3), strides=2, padding='same')(n1)187 n2 = neck_module2(mp1, start_step, end_step, prune, decay)188 mp2 = keras.layers.MaxPooling2D((3, 3), strides=2, padding='same')(n2)189 h = head(mp2, start_step, end_step, prune, decay)190 d2 = prune(keras.layers.Dense(1), decay(0.7, 0.9, start_step, end_step))(h)191 d2 = Sigmoid()(d2)192 model = tf.keras.Model(img_input, d2)193 return model194# Customize early stopping callback195class MyThresholdCallback(tf.keras.callbacks.Callback):196 def __init__(self, tr_threshold, val_threshold):197 super(MyThresholdCallback, self).__init__()198 self.tr_threshold = tr_threshold199 self.val_threshold = val_threshold200 def on_epoch_end(self, epoch, logs=None):201 val_acc = logs["val_accuracy"]202 tr_acc = logs["accuracy"]203 if val_acc >= self.val_threshold or tr_acc >= self.tr_threshold:204 self.model.stop_training = True205# Customize learning rate schedule206class CustomSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):207 def __init__(self, initial_learning_rate, decay_steps, decay_rate, warmup_steps):208 super(CustomSchedule, self).__init__()209 self.initial_learning_rate = initial_learning_rate210 self.decay_steps = decay_steps211 self.decay_rate = decay_rate212 self.warmup_steps = warmup_steps213 def __call__(self, step):214 arg1 = self.initial_learning_rate * (self.decay_rate**((step-self.warmup_steps)/self.decay_steps))215 arg2 = self.initial_learning_rate * step / self.warmup_steps216 return tf.math.minimum(arg1, arg2)217 def get_config(self):218 return {219 "initial_learning_rate": self.initial_learning_rate,220 "decay_steps": self.decay_steps,221 "decay_rate": self.decay_rate,222 "warmup_steps": self.warmup_steps,223 }224def blur(img):225 return (cv2.blur(img,(5,5)))226def main():227 # Start k-fold cross vailidation228 k = 6229 val_acc = 0230 val_loss = 0231 model = None232 select = 1233 # Uncomment this to visualize an example of the learn rate schedule234 # temp_learning_rate_schedule = CustomSchedule(initial_learning_rate=0.001*0.95**50, decay_steps=584, decay_rate=0.95, warmup_steps=0)235 # plt.plot(temp_learning_rate_schedule(tf.range(40000, dtype=tf.float32)))236 # plt.ylabel("Learning Rate")237 # plt.xlabel("Train Step")238 # plt.show()239 fill_modes = ["constant", "reflect", "nearest"]240 rr = [10.0, 15.0, 10.0]241 sr = [10.0, 20.0, 10.0]242 cval = [np.random.random()*255, 0, 0]243 for i in range(3):244 # prefix = 'ks\\k' + str(i) + '\\'245 prefix = 'dataset/'246 # img_width, img_height = 100, 100247 train_data_dir = prefix + 'train/'248 validation_data_dir = prefix + 'test/'249 epochs = 30250 batch_size = 30251 if select == 0:252 select = 1253 elif select == 1:254 select = 2255 elif select == 2:256 select = 0257 print(fill_modes[select])258 # Generate Tensor for input images259 train_datagen = ImageDataGenerator(260 rescale=1. / 255,261 width_shift_range=0.2,262 height_shift_range=0.2,263 brightness_range=(0.7, 1.2),264 rotation_range=rr[select],265 shear_range=sr[select],266 zoom_range=(0.8, 1.3),267 channel_shift_range=60,268 fill_mode = fill_modes[select],269 cval=cval[select],270 horizontal_flip=True,271 vertical_flip=True)272 test_datagen = ImageDataGenerator(rescale=1. / 255,273 brightness_range=(0.9, 1.1),274 zoom_range=(1.0, 1.2),275 channel_shift_range=30,276 horizontal_flip=True)277 # Generate Iterator flow from directory278 train_generator = train_datagen.flow_from_directory(279 train_data_dir,280 # target_size=(img_width, img_height),281 target_size=(320, 320),282 batch_size=batch_size,283 class_mode='binary')284 validation_generator = test_datagen.flow_from_directory(285 validation_data_dir,286 # target_size=(img_width, img_height),287 target_size=(320, 320),288 batch_size=batch_size,289 class_mode='binary')290 # Get input image dimension291 unit_image = train_generator.next()[0]292 shape = (unit_image.shape[1], unit_image.shape[2], unit_image.shape[3])293 # pruning specifications294 poly_decay = tfmot.sparsity.keras.PolynomialDecay295 prune = tfmot.sparsity.keras.prune_low_magnitude296 start_step = 101297 end_step = int(584 * epochs / 2)298 quantize_annotate_layer = tfmot.quantization.keras.quantize_annotate_layer299 quantize_annotate_model = tfmot.quantization.keras.quantize_annotate_model300 quantize_scope = tfmot.quantization.keras.quantize_scope301 # Pruned DNN302 pruned_model = FADNet(shape, start_step, end_step, prune, poly_decay)303 # BP304 decay_exp = 0305 if i > 0:306 decay_exp = 60307 # if i == 5:308 # th = 0.985309 lr_schedule = CustomSchedule(initial_learning_rate=0.0001*(0.98**decay_exp)/(i+1),310 decay_steps=584,311 decay_rate=0.95,312 warmup_steps=584*int((i+1)/2))313 opt = keras.optimizers.Adam(learning_rate=lr_schedule, clipnorm=5)314 bcel = keras.losses.BinaryCrossentropy(label_smoothing=0.1)315 # check for transfer learning316 if os.path.exists("quantized_model.h5"):317 pruned_model.load_weights("quantized_model.h5", by_name=True)318 print(pruned_model.layers[-2].get_weights()[0])319 print("Transfer learning continued")320 pruned_model.summary()321 pruned_model.compile(optimizer=opt, loss=bcel, metrics=['accuracy'])322 # callbacks323 my_callbacks = [324 tfmot.sparsity.keras.UpdatePruningStep(),325 # tfmot.sparsity.keras.PruningSummaries(log_dir='pruned_model'),326 keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True),327 MyThresholdCallback(tr_threshold=0.99, val_threshold=0.992+0.001*i),328 # TensorBoard(log_dir="prune"+str(i),329 # histogram_freq=0,330 # write_graph=True,331 # write_images=False,332 # update_freq="epoch",333 # profile_batch=2),334 keras.callbacks.ModelCheckpoint(filepath='pruned_model.h5',335 save_weights_only=False,336 monitor='val_loss',337 mode='min',338 save_best_only=True)339 ]340 history = pruned_model.fit(train_generator, epochs=epochs, validation_data=validation_generator, callbacks=my_callbacks)341 # visualize compression342 # _, pruned_keras_file = tempfile.mkstemp('.h5')343 # tf.keras.models.save_model(model_for_export, pruned_keras_file, include_optimizer=False)344 # print("Size of gzipped pruned Keras model: %.2f bytes" % (get_gzipped_model_size(pruned_keras_file)))345 print('Finished part ' + str(i + 1))346 # strip pruning layers for final model347 model_for_export = tfmot.sparsity.keras.strip_pruning(pruned_model)348 model_for_export.save("pruned_model_stripped.h5")349 model_for_export.save_weights("pruned_model_weights.h5")350if __name__ == '__main__':...
trapping_water.py
Source:trapping_water.py
1class Solution:2 def trap(self, height):3 if len(height) < 3:4 return []5 my_trap = []6 start_step = 07 while start_step <= len(height) - 3:8 if height[start_step] > height[start_step + 1]:9 j = start_step + 110 trap_sequence = []11 trap_sequence.append(height[start_step])12 while j < len(height) -1 and height[j] < height[start_step]:13 trap_sequence.append(height[j])14 j += 115 if height[j] >= height[start_step]:16 trap_sequence.append(height[j])17 else:18 trap_sequence = []19 if len(trap_sequence) > 0:20 my_trap.append(trap_sequence)21 start_step = j22 else:23 start_step +=124 return self.calculate(my_trap)25 def calculate(self, trap):26 trapped_water = 027 for trap_sequence in trap:28 sum = 029 mark_heigh = trap_sequence[0]30 for j in trap_sequence:31 sum += max(mark_heigh - j, 0)32 trapped_water += sum33 return trapped_water34if __name__ == '__main__':35 s = Solution()...
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