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main.py
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main.py
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import numpy as np
from sklearn.metrics.classification import accuracy_score
import argparse
import keras
from keras.layers.pooling import GlobalMaxPooling2D, GlobalAveragePooling2D, MaxPooling2D, AveragePooling2D
from keras.layers import Dense, Dropout, Conv2D, Flatten, Activation, BatchNormalization, Add
from keras.layers import Input
from keras.models import Model
from VIPPruning import VIPPruning
def layers_to_prune(model):
# Convert index into Conv2D index (required by pruning methods)
idx_Conv2D = 0
output = []
for i in range(0, len(model.layers)):
if isinstance(model.get_layer(index=i), Conv2D):
output.append(idx_Conv2D)
idx_Conv2D = idx_Conv2D + 1
#Exception for VGG-Based architectures
output.pop(-1)
return output
def rebuild_net(model=None, layer_filters=[]):
n_discarded_filters = 0
total_filters = 0
model = model
inp = (model.inputs[0].shape.dims[1].value,
model.inputs[0].shape.dims[2].value,
model.inputs[0].shape.dims[3].value)
H = Input(inp)
inp = H
idxs = []
idx_previous = []
for i in range(0, len(model.layers)+1):
try:
layer = model.get_layer(index=i)
except:
break
config = layer.get_config()
if isinstance(layer, MaxPooling2D):
H = MaxPooling2D.from_config(config)(H)
if isinstance(layer, Dropout):
H = Dropout.from_config(config)(H)
if isinstance(layer, Activation):
H = Activation.from_config(config)(H)
if isinstance(layer, BatchNormalization):
weights = layer.get_weights()
weights[0] = np.delete(weights[0], idx_previous)
weights[1] = np.delete(weights[1], idx_previous)
weights[2] = np.delete(weights[2], idx_previous)
weights[3] = np.delete(weights[3], idx_previous)
H = BatchNormalization(weights=weights)(H)
elif isinstance(layer, Conv2D):
weights = layer.get_weights()
n_filters = weights[0].shape[3]
total_filters = total_filters + n_filters
#idxs = [item for item in layer_filters if item[0] == i][0][1]
idxs = [item for item in layer_filters if item[0] == i]
if len(idxs)!=0:
idxs = idxs[0][1]
weights[0] = np.delete(weights[0], idxs, axis=3)
weights[1] = np.delete(weights[1], idxs)
n_discarded_filters += len(idxs)
if len(idx_previous) != 0:
weights[0] = np.delete(weights[0], idx_previous, axis=2)
config['filters'] = weights[1].shape[0]
H = Conv2D(activation=config['activation'],
activity_regularizer=config['activity_regularizer'],
bias_constraint=config['bias_constraint'],
bias_regularizer=config['bias_regularizer'],
data_format=config['data_format'],
dilation_rate=config['dilation_rate'],
filters=config['filters'],
kernel_constraint=config['kernel_constraint'],
# config=config['config'],
# scale=config['scale'],
kernel_regularizer=config['kernel_regularizer'],
kernel_size=config['kernel_size'],
name=config['name'],
padding=config['padding'],
strides=config['strides'],
trainable=config['trainable'],
use_bias=config['use_bias'],
weights=weights
)(H)
elif isinstance(layer, Flatten):
H = Flatten()(H)
elif isinstance(layer, Dense):
weights = layer.get_weights()
weights[0] = np.delete(weights[0], idx_previous, axis=0)
H = Dense(units=config['units'],
activation=config['activation'],
activity_regularizer=config['activity_regularizer'],
bias_constraint=config['bias_constraint'],
bias_regularizer=config['bias_regularizer'],
kernel_constraint=config['kernel_constraint'],
kernel_regularizer=config['kernel_regularizer'],
name=config['name'],
trainable=config['trainable'],
use_bias=config['use_bias'],
weights=weights)(H)
idxs = []#After the first Dense Layer the methods stop prunining
idx_previous = idxs
#print('Percentage of discarded filters {}'.format(n_discarded_filters / float(total_filters)))
return Model(inp, H)
def count_filters(model):
n_filters = 0
for layer_idx in range(1, len(model.layers)):
layer = model.get_layer(index=layer_idx)
if isinstance(layer, keras.layers.Conv2D) == True:
config = layer.get_config()
n_filters+=config['filters']
return n_filters
def compute_flops(model):
import keras
from keras.applications.mobilenet import DepthwiseConv2D
total_flops =0
flops_per_layer = []
for layer_idx in range(1, len(model.layers)):
layer = model.get_layer(index=layer_idx)
if isinstance(layer, DepthwiseConv2D) is True:
_, output_map_H, output_map_W, current_layer_depth = layer.output_shape
_, _, _, previous_layer_depth = layer.input_shape
kernel_H, kernel_W = layer.kernel_size
#Computed according to https://arxiv.org/pdf/1704.04861.pdf Eq.(5)
flops = (kernel_H * kernel_W * previous_layer_depth * output_map_H * output_map_W) + (previous_layer_depth * current_layer_depth * output_map_W * output_map_H)
total_flops += flops
flops_per_layer.append(flops)
elif isinstance(layer, keras.layers.Conv2D) is True:
_, output_map_H, output_map_W, current_layer_depth = layer.output_shape
_, _, _, previous_layer_depth = layer.input_shape
kernel_H, kernel_W = layer.kernel_size
flops = output_map_H * output_map_W * previous_layer_depth * current_layer_depth * kernel_H * kernel_W
total_flops += flops
flops_per_layer.append(flops)
if isinstance(layer, keras.layers.Dense) is True:
_, current_layer_depth = layer.output_shape
_, previous_layer_depth = layer.input_shape
flops = current_layer_depth * previous_layer_depth
total_flops += flops
flops_per_layer.append(flops)
return total_flops, flops_per_layer
if __name__ == '__main__':
np.random.seed(12227)
parser = argparse.ArgumentParser()
parser.add_argument('--iterations', type=int, default=5)
parser.add_argument('--p', type=float, default=0.05)
parser.add_argument('--epochs', type=int, default=10)
parser.add_argument('--n_components', type=int, default=2)
args = parser.parse_args()
iterations = args.iterations
p = args.p
epochs = args.epochs
n_components = args.n_components
(X_train, y_train), (X_test, y_test) = keras.datasets.cifar10.load_data()
X_train, X_test = X_train.astype('float32')/255, X_test.astype('float32')/255
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)
#The architecture we gonna pruning
input = Input((32, 32, 3))
H = Conv2D(16, (3,3), padding='same')(input)
H = Activation('relu')(H)
H = Conv2D(16, (3, 3))(H)
H = Activation('relu')(H)
H = MaxPooling2D(pool_size=(2, 2))(H)
H = Conv2D(32, (3, 3), padding='same')(H)
H = Activation('relu')(H)
H = Conv2D(32, (3, 3))(H)
H = Activation('relu')(H)
H = MaxPooling2D(pool_size=(2, 2))(H)
H = Flatten()(H)
H = Dense(512)(H)
H = Activation('relu')(H)
H = Dropout(0.5)(H)
H = Dense(10)(H)
H = Activation('softmax')(H)
opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)
cnn_model = keras.models.Model([input], H)
cnn_model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
cnn_model.fit(X_train, y_train, epochs=epochs, batch_size=128, verbose=0)
y_pred = cnn_model.predict(X_test)
acc = accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_pred, axis=1))
n_params = cnn_model.count_params()
n_filters = count_filters(cnn_model)
flops, _ = compute_flops(cnn_model)
print('Original Network. #Parameters [{}] #Filters [{}] FLOPs [{}] Accuracy [{:.4f}]'.format(n_params, n_filters, flops, acc))
layers = layers_to_prune(cnn_model)
for i in range(0, iterations):
pruning_method = VIPPruning(n_comp=n_components, model=cnn_model, representation='max', percentage_discard=p)
# pruning_method = VIPPruning(n_comp=n_components, model=cnn_model,
# representation=MaxPooling2D(pool_size=(2, 2),
# name='vip_net'),
# percentage_discard=p)
idxs = pruning_method.idxs_to_prune(X_train, y_train, layers)
cnn_model = rebuild_net(cnn_model, idxs)
cnn_model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
cnn_model.fit(X_train, y_train, epochs=epochs, batch_size=128, verbose=0)
y_pred = cnn_model.predict(X_test)
acc = accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_pred, axis=1))
n_params = cnn_model.count_params()
n_filters = count_filters(cnn_model)
flops, _ = compute_flops(cnn_model)
print('Iteration [{}] #Parameters [{}] #Filters [{}] FLOPs [{}] Accuracy [{:.4f}]'.format(i, n_params, n_filters, flops, acc))