def feature_extractor(input_net):
net_1 = Conv2D(96, 1, 1)(input_net)
net_1 = ReLU()(net_1)
net_1 = Conv2D(208, 3, 1)(net_1)
net_1 = ReLU()(net_1)
net_2 = MaxPool2D(3, 1)(input_net)
net_2 = Conv2D(64, 1, 1)(net_2)
net_2 = ReLU()(net_2)
concat = concatenate(inputs=[net_1, net_2], axis=3)
pooling_out = MaxPool2D(3, 2)(concat)
return pooling_out
def Depthwise_Separable_Conv2D_BN_Leaky(filters, kernel_size=(3, 3), block_id_str=None):
"""Depthwise Separable Convolution2D."""
if not block_id_str:
block_id_str = str(K.get_uid())
return compose(
DepthwiseConv2D(kernel_size, padding='same', name='conv_dw_' + block_id_str),
BatchNormalization(name='conv_dw_%s_bn' % block_id_str),
LeakyReLU(alpha=0.1, name='conv_dw_%s_leaky_relu' % block_id_str),
Conv2D(filters, (1, 1), padding='same', use_bias=False, strides=(1, 1), name='conv_pw_%s' % block_id_str),
BatchNormalization(name='conv_pw_%s_bn' % block_id_str),
LeakyReLU(alpha=0.1, name='conv_pw_%s_leaky_relu' % block_id_str))
subset='training',
target_size=(200, 200))
valid_gen = datagen.flow_from_dataframe(pd_data,
directory='./images/',
subset='validation',
target_size=(200, 200))
np.random.seed(1000)
#Instantiate an empty model
model = Sequential()
# 1st Convolutional Layer
model.add(
Conv2D(filters=96,
input_shape=(200, 200, 3),
kernel_size=(11, 11),
strides=(4, 4),
padding='valid'))
model.add(Activation('relu'))
# Max Pooling
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'))
# 2nd Convolutional Layer
model.add(
Conv2D(filters=256, kernel_size=(11, 11), strides=(1, 1), padding='valid'))
model.add(Activation('relu'))
# Max Pooling
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'))
# 3rd Convolutional Layer
model.add(
import tensorflow as tf
from tensorflow._api.v1.keras import preprocessing
from tensorflow._api.v1.keras.layers import Input, Dense, Conv2D, MaxPool2D, Dropout, ReLU, BatchNormalization, concatenate, Flatten, GlobalAveragePooling2D
from tensorflow._api.v1.keras.models import Model
# This returns a tensor
inputs = Input(shape=(32, 32, 3))
pre_net = Conv2D(64, (7, 7), strides=(2, 2))
# a layer instance is callable on a tensor, and returns a tensor
x = Dense(64, activation='relu')(inputs)
x = Dense(64, activation='relu')(x)
predictions = Dense(10, activation='softmax')(x)
# This creates a model that includes
# the Input layer and three Dense layers
model = Model(inputs=inputs, outputs=predictions)
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
def DarknetConv2D(*args, **kwargs):
"""Wrapper to set Darknet parameters for Convolution2D."""
darknet_conv_kwargs = {'kernel_regularizer': l2(5e-4)}
darknet_conv_kwargs['padding'] = 'valid' if kwargs.get('strides') == (2, 2) else 'same'
darknet_conv_kwargs.update(kwargs)
return Conv2D(*args, **darknet_conv_kwargs)
def _main(args):
config_path = os.path.expanduser(args.config_path)
weights_path = os.path.expanduser(args.weights_path)
assert config_path.endswith('.cfg'), '{} is not a .cfg file'.format(
config_path)
assert weights_path.endswith(
'.weights'), '{} is not a .weights file'.format(weights_path)
output_path = os.path.expanduser(args.output_path)
assert output_path.endswith(
'.h5'), 'output path {} is not a .h5 file'.format(output_path)
output_root = os.path.splitext(output_path)[0]
# Load weights and config.
print('Loading weights.')
weights_file = open(weights_path, 'rb')
major, minor, revision = np.ndarray(
shape=(3,), dtype='int32', buffer=weights_file.read(12))
if (major * 10 + minor) >= 2 and major < 1000 and minor < 1000:
seen = np.ndarray(shape=(1,), dtype='int64', buffer=weights_file.read(8))
else:
seen = np.ndarray(shape=(1,), dtype='int32', buffer=weights_file.read(4))
print('Weights Header: ', major, minor, revision, seen)
print('Parsing Darknet config.')
unique_config_file = unique_config_sections(config_path)
cfg_parser = configparser.ConfigParser()
cfg_parser.read_file(unique_config_file)
print('Creating Keras model.')
input_layer = Input(shape=(None, None, 3), name='image_input')
prev_layer = input_layer
all_layers = []
weight_decay = float(cfg_parser['net_0']['decay']
) if 'net_0' in cfg_parser.sections() else 5e-4
count = 0
out_index = []
for section in cfg_parser.sections():
print('Parsing section {}'.format(section))
if section.startswith('convolutional'):
filters = int(cfg_parser[section]['filters'])
size = int(cfg_parser[section]['size'])
stride = int(cfg_parser[section]['stride'])
pad = int(cfg_parser[section]['pad'])
activation = cfg_parser[section]['activation']
batch_normalize = 'batch_normalize' in cfg_parser[section]
padding = 'same' if pad == 1 and stride == 1 else 'valid'
# Setting weights.
# Darknet serializes convolutional weights as:
# [bias/beta, [gamma, mean, variance], conv_weights]
prev_layer_shape = K.int_shape(prev_layer)
weights_shape = (size, size, prev_layer_shape[-1], filters)
darknet_w_shape = (filters, weights_shape[2], size, size)
weights_size = np.product(weights_shape)
print('conv2d', 'bn' if batch_normalize else ' ', activation, weights_shape)
conv_bias = np.ndarray(
shape=(filters,),
dtype='float32',
buffer=weights_file.read(filters * 4))
count += filters
if batch_normalize:
bn_weights = np.ndarray(
shape=(3, filters),
dtype='float32',
buffer=weights_file.read(filters * 12))
count += 3 * filters
bn_weight_list = [
bn_weights[0], # scale gamma
conv_bias, # shift beta
bn_weights[1], # running mean
bn_weights[2] # running var
]
conv_weights = np.ndarray(
shape=darknet_w_shape,
dtype='float32',
buffer=weights_file.read(weights_size * 4))
count += weights_size
# DarkNet conv_weights are serialized Caffe-style:
# (out_dim, in_dim, height, width)
# We would like to set these to Tensorflow order:
# (height, width, in_dim, out_dim)
conv_weights = np.transpose(conv_weights, [2, 3, 1, 0])
conv_weights = [conv_weights] if batch_normalize else [
conv_weights, conv_bias
]
# Handle activation.
act_fn = None
if activation == 'leaky':
pass # Add advanced activation later.
elif activation == 'mish':
pass # Add advanced activation later.
elif activation != 'linear':
raise ValueError(
'Unknown activation function `{}` in section {}'.format(
activation, section))
# Create Conv2D layer
if stride > 1:
# Darknet uses left and top padding instead of 'same' mode
prev_layer = ZeroPadding2D(((1, 0), (1, 0)))(prev_layer)
conv_layer = (Conv2D(
filters, (size, size),
strides=(stride, stride),
kernel_regularizer=l2(weight_decay),
use_bias=not batch_normalize,
weights=conv_weights,
activation=act_fn,
padding=padding))(prev_layer)
if batch_normalize:
conv_layer = (BatchNormalization(
weights=bn_weight_list))(conv_layer)
prev_layer = conv_layer
if activation == 'linear':
all_layers.append(prev_layer)
# elif activation == 'mish':
# act_layer = Activation(mish)(prev_layer)
# prev_layer = act_layer
# all_layers.append(act_layer)
elif activation == 'leaky':
act_layer = LeakyReLU(alpha=0.1)(prev_layer)
prev_layer = act_layer
all_layers.append(act_layer)
elif section.startswith('route'):
ids = [int(i) for i in cfg_parser[section]['layers'].split(',')]
layers = [all_layers[i] for i in ids]
if len(layers) > 1:
print('Concatenating route layers:', layers)
concatenate_layer = Concatenate()(layers)
all_layers.append(concatenate_layer)
prev_layer = concatenate_layer
else:
skip_layer = layers[0] # only one layer to route
all_layers.append(skip_layer)
prev_layer = skip_layer
elif section.startswith('maxpool'):
size = int(cfg_parser[section]['size'])
stride = int(cfg_parser[section]['stride'])
all_layers.append(
MaxPooling2D(
pool_size=(size, size),
strides=(stride, stride),
padding='same')(prev_layer))
prev_layer = all_layers[-1]
elif section.startswith('avgpool'):
all_layers.append(
AveragePooling2D()(prev_layer))
prev_layer = all_layers[-1]
elif section.startswith('shortcut'):
index = int(cfg_parser[section]['from'])
activation = cfg_parser[section]['activation']
assert activation == 'linear', 'Only linear activation supported.'
all_layers.append(Add()([all_layers[index], prev_layer]))
prev_layer = all_layers[-1]
elif section.startswith('upsample'):
stride = int(cfg_parser[section]['stride'])
assert stride == 2, 'Only stride=2 supported.'
all_layers.append(UpSampling2D(stride)(prev_layer))
prev_layer = all_layers[-1]
elif section.startswith('reorg'):
block_size = int(cfg_parser[section]['stride'])
assert block_size == 2, 'Only reorg with stride 2 supported.'
all_layers.append(
Lambda(
# space_to_depth_x2,
# output_shape=space_to_depth_x2_output_shape,
lambda x: tf.nn.space_to_depth(x, block_size=2),
name='space_to_depth_x2')(prev_layer))
prev_layer = all_layers[-1]
elif section.startswith('region'):
with open('{}_anchors.txt'.format(output_root), 'w') as f:
print(cfg_parser[section]['anchors'], file=f)
elif section.startswith('yolo'):
out_index.append(len(all_layers) - 1)
all_layers.append(None)
prev_layer = all_layers[-1]
elif (section.startswith('net') or section.startswith('cost') or
section.startswith('softmax')):
pass
else:
raise ValueError(
'Unsupported section header type: {}'.format(section))
# Create and save model.
if len(out_index) == 0: out_index.append(len(all_layers) - 1)
if args.yolo4_reorder:
# reverse the output tensor index for YOLOv4 cfg & weights,
# since it use a different yolo outout order
out_index.reverse()
model = Model(inputs=input_layer, outputs=[all_layers[i] for i in out_index])
print(model.summary())
if args.weights_only:
model.save_weights('{}'.format(output_path))
print('Saved Keras weights to {}'.format(output_path))
else:
model.save('{}'.format(output_path))
print('Saved Keras model to {}'.format(output_path))
# Check to see if all weights have been read.
remaining_weights = len(weights_file.read()) / 4
weights_file.close()
print('Read {} of {} from Darknet weights.'.format(count, count +
remaining_weights))
if remaining_weights > 0:
print('Warning: {} unused weights'.format(remaining_weights))
if args.plot_model:
plot(model, to_file='{}.png'.format(output_root), show_shapes=True)
print('Saved model plot to {}.png'.format(output_root))
return (X, y)
else:
return None
data = load_training_data()
if not data:
data = create_training_data()
X, y = data
X = X / 255.0
print(len(X))
model = Sequential()
model.add(Conv2D(64, (3, 3), input_shape=X.shape[1:]))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# image check
plt.imshow(X_train[0])
plt.show()
print(X_train)
X_train = X_train.reshape(60000, 28, 28, 1)
X_test = X_test.reshape(10000, 28, 28, 1)
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
# CNN
from tensorflow._api.v1.keras.models import Sequential
from tensorflow._api.v1.keras.layers import Dense, Conv2D, Flatten
model = Sequential()
model.add(Conv2D(64, kernel_size=3, activation='relu',
input_shape=(28, 28, 1)))
model.add(Conv2D(32, kernel_size=3, activation='relu'))
model.add(Flatten())
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=3)