def get_weights_path_vgg16():
TF_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5'
weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels.h5',TF_WEIGHTS_PATH,cache_subdir='models')
return weights_path
python类get_file()的实例源码
def get_weights_path_resnet():
TF_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5'
weights_path = get_file('resnet50_weights_tf_dim_ordering_tf_kernels.h5',TF_WEIGHTS_PATH,cache_subdir='models')
return weights_path
def __init__(self, inputs, blocks, weights=None,
trainable=True, name='encoder'):
inverse_pyramid = []
# convolutional block
conv_blocks = blocks[:-1]
for i, block in enumerate(conv_blocks):
if i == 0:
x = block(inputs)
inverse_pyramid.append(x)
elif i < len(conv_blocks) - 1:
x = block(x)
inverse_pyramid.append(x)
else:
x = block(x)
# fully convolutional block
fc_block = blocks[-1]
y = fc_block(x)
inverse_pyramid.append(y)
outputs = list(reversed(inverse_pyramid))
super(Encoder, self).__init__(
inputs=inputs, outputs=outputs)
# load pre-trained weights
if weights is not None:
weights_path = get_file(
'{}_weights_tf_dim_ordering_tf_kernels.h5'.format(name),
weights,
cache_subdir='models')
layer_names = load_weights(self, weights_path)
if K.image_data_format() == 'channels_first':
layer_utils.convert_all_kernels_in_model(self)
# Freezing basenet weights
if trainable is False:
for layer in self.layers:
if layer.name in layer_names:
layer.trainable = False
def fcn_vggbase(input_shape=(None,None,3)):
img_input = Input(shape=input_shape)
x = ZeroPadding2D(padding=(100, 100), name='pad1')(img_input)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(x)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same', name='block1_pool')(x)
# Block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), padding='same', name='block2_pool')(x)
# Block 3
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), padding='same', name='block3_pool')(x)
# Block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), padding='same', name='block4_pool')(x)
# Block 5
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), padding='same',name='block5_pool')(x)
x = Conv2D(filters=4096, kernel_size=(7, 7), W_regularizer=l2(0.00005), activation='relu', padding='valid', name='fc6_lsun')(x)
x = Dropout(0.85)(x)
x = Conv2D(filters=4096, kernel_size=(1, 1), W_regularizer=l2(0.00005), activation='relu', padding='valid', name='fc7_lsun')(x)
x = Dropout(0.85)(x)
x = Conv2D(filters=5, kernel_size=(1, 1), strides=(1,1), kernel_initializer='he_normal', padding='valid', name='lsun_score')(x)
x = Conv2DTranspose(filters=5, kernel_initializer='he_normal', kernel_size=(64, 64), strides=(32, 32), padding='valid',use_bias=False, name='lsun_upscore2')(x)
output = _crop(img_input,offset=(32,32), name='score')(x)
model = Model(img_input, output)
weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5', VGG_WEIGHTS_PATH_NO_TOP, cache_subdir='models')
model.load_weights(weights_path, by_name=True)
return model
def fcn16s_vggbase(input_shape=None, nb_class=None):
img_input = Input(shape=input_shape)
x = ZeroPadding2D(padding=(100, 100), name='pad1')(img_input)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(x)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same', name='block1_pool')(x)
# Block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), padding='same', name='block2_pool')(x)
# Block 3
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), padding='same', name='block3_pool')(x)
# Block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), padding='same', name='block4_pool')(x)
pool4 = x
# Block 5
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), padding='same',name='block5_pool')(x)
x = Conv2D(filters=4096, kernel_size=(7, 7), W_regularizer=l2(0.00005), activation='relu', padding='valid', name='fc6')(x)
x = Dropout(0.85)(x)
x = Conv2D(filters=4096, kernel_size=(1, 1), W_regularizer=l2(0.00005), activation='relu', padding='valid', name='fc7')(x)
x = Dropout(0.85)(x)
x = Conv2D(filters=nb_class, kernel_size=(1, 1), strides=(1,1), kernel_initializer='he_normal', padding='valid', name='p5score')(x)
x = Conv2DTranspose(filters=nb_class, kernel_size=(4,4), strides=(2,2), kernel_initializer='he_normal', padding='valid', name='p5upscore')(x)
pool4 = Conv2D(filters=nb_class, kernel_size=(1,1), kernel_initializer='he_normal', padding='valid', name='pool4_score')(pool4)
pool4_score = _crop(x, offset=(5,5), name='pool4_score2')(pool4)
m = merge([pool4_score,x], mode='sum')
upscore = Conv2DTranspose(filters=nb_class, kernel_size=(32,32), strides=(16,16), padding='valid', name='merged_score')(m)
score = _crop(img_input, offset=(27,27), name='output_score')(upscore)
weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5', VGG_WEIGHTS_PATH_NO_TOP, cache_subdir='models')
mdl = Model(img_input, score, name='fcn16s')
mdl.load_weights(weights_path, by_name=True)
return mdl
def dilated_FCN_addmodule(input_shape=None):
img_input = Input(shape=input_shape)
x = ZeroPadding2D(padding=(100, 100), name='pad1')(img_input)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(x)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same', name='block1_pool')(x)
# Block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), padding='same', name='block2_pool')(x)
# Block 3
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), padding='same', name='block3_pool')(x)
# Block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), padding='same', name='block4_pool')(x)
# Block 5
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), padding='same',name='block5_pool')(x)
x = Conv2D(filters=4096, kernel_initializer='he_normal', kernel_size=(7, 7), activation='relu', padding='valid', name='fc6')(x)
x = Dropout(0.85)(x)
x = Conv2D(filters=4096, kernel_initializer='he_normal', kernel_size=(1, 1), activation='relu', padding='valid', name='fc7')(x)
x = Dropout(0.85)(x)
x = Conv2D(filters=40,kernel_size=(1, 1), strides=(1,1), kernel_initializer='he_normal', padding='valid', name='score_fr')(x)
#x = Cropping2D(cropping=((19, 36),(19, 29)), name='score')(x)
x = ZeroPadding2D(padding=(33,33))(x)
x = Conv2D(2*40, (3,3), kernel_initializer='he_normal',activation='relu', name='dl_conv1')(x)
x = Conv2D(2*40, (3,3), kernel_initializer='he_normal',activation='relu', name='dl_conv2')(x)
x = Conv2D(4*40, (3,3), kernel_initializer='he_normal',dilation_rate=(2,2), activation='relu', name='dl_conv3')(x)
x = Conv2D(8*40, (3,3), kernel_initializer='he_normal',dilation_rate=(4,4), activation='relu', name='dl_conv4')(x)
x = Conv2D(16*40, (3,3), kernel_initializer='he_normal',dilation_rate=(8,8), activation='relu', name='dl_conv5')(x)
x = Conv2D(32*40, (3,3), kernel_initializer='he_normal',dilation_rate=(16,16), activation='relu', name='dl_conv6')(x)
x = Conv2D(32*40, (1,1), kernel_initializer='he_normal',name='dl_conv7')(x)
x = Conv2D(1*40, (1,1), kernel_initializer='he_normal',name='dl_final')(x)
x = Conv2DTranspose(filters=40, kernel_initializer='he_normal', kernel_size=(64, 64), strides=(32, 32), padding='valid',use_bias=False, name='upscore2')(x)
x = CroppingLike2D(img_input, offset='centered', name='score')(x)
mdl = Model(img_input, x, name='dilatedmoduleFCN')
#weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5', VGG_WEIGHTS_PATH_NO_TOP, cache_subdir='models')
mdl.load_weights('logs/model_June13_sgd_60kitr.h5', by_name=True)
return mdl
def dilated_FCN_frontended(input_shape=None, weight_decay=None, nb_classes=40):
img_input = Input(shape=input_shape)
#x = ZeroPadding2D(padding=(100, 100), name='pad1')(img_input)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same', name='block1_pool')(x)
# Block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), padding='same', name='block2_pool')(x)
# Block 3
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), padding='same', name='block3_pool')(x)
# Block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
x = Conv2D(512, (3,3), dilation_rate=(2,2), activation='relu', name='block5_conv1')(x)
x = Conv2D(512, (3,3), dilation_rate=(2,2), activation='relu', name='block5_conv2')(x)
x = Conv2D(512, (3,3), dilation_rate=(2,2), activation='relu', name='block5_conv3')(x)
x = Conv2D(4096, (3,3), kernel_initializer='he_normal', dilation_rate=(4,4), activation='relu', name='fc6')(x)
x = Dropout(0.5, name='drop6')(x)
x = Conv2D(4096, (1,1), kernel_initializer='he_normal', activation='relu', name='fc7')(x)
x = Dropout(0.5, name='drop7')(x)
x = Conv2D(nb_classes, (1,1), kernel_initializer='he_normal', activation='relu', name='fc_final')(x)
#x = Conv2DTranspose(nb_classes, kernel_size=(64,64), strides=(32,32), padding='valid', name='upscore2')(x)
x = ZeroPadding2D(padding=(33,33))(x)
x = Conv2D(2*nb_classes, (3,3), kernel_initializer='he_normal',activation='relu', name='dl_conv1')(x)
x = Conv2D(2*nb_classes, (3,3), kernel_initializer='he_normal',activation='relu', name='dl_conv2')(x)
x = Conv2D(4*nb_classes, (3,3), kernel_initializer='he_normal',dilation_rate=(2,2), activation='relu', name='dl_conv3')(x)
x = Conv2D(8*nb_classes, (3,3), kernel_initializer='he_normal',dilation_rate=(4,4), activation='relu', name='dl_conv4')(x)
x = Conv2D(16*nb_classes, (3,3), kernel_initializer='he_normal',dilation_rate=(8,8), activation='relu', name='dl_conv5')(x)
x = Conv2D(32*nb_classes, (3,3), kernel_initializer='he_normal',dilation_rate=(16,16), activation='relu', name='dl_conv6')(x)
x = Conv2D(32*nb_classes, (1,1), kernel_initializer='he_normal',name='dl_conv7')(x)
x = Conv2D(1*nb_classes, (1,1), kernel_initializer='he_normal',name='dl_final')(x)
x = Conv2DTranspose(nb_classes, kernel_initializer='he_normal', kernel_size=(64,64), strides=(8,8), padding='valid', name='upscore2')(x)
x = CroppingLike2D(img_input, offset='centered', name='score')(x)
#x = Cropping2D(cropping=((19,36), (19,29)), name='score')(x)
mdl = Model(input=img_input, output=x, name='dilated_fcn')
weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5', VGG_WEIGHTS_PATH_NO_TOP, cache_subdir='models')
mdl.load_weights(weights_path, by_name=True)
return mdl
def inception_v4(num_classes, dropout_keep_prob, weights, include_top):
'''
Creates the inception v4 network
Args:
num_classes: number of classes
dropout_keep_prob: float, the fraction to keep before final layer.
Returns:
logits: the logits outputs of the model.
'''
# Input Shape is 299 x 299 x 3 (tf) or 3 x 299 x 299 (th)
if K.image_data_format() == 'channels_first':
inputs = Input((3, 299, 299))
else:
inputs = Input((299, 299, 3))
# Make inception base
x = inception_v4_base(inputs)
# Final pooling and prediction
if include_top:
# 1 x 1 x 1536
x = AveragePooling2D((8,8), padding='valid')(x)
x = Dropout(dropout_keep_prob)(x)
x = Flatten()(x)
# 1536
x = Dense(units=num_classes, activation='softmax')(x)
model = Model(inputs, x, name='inception_v4')
# load weights
if weights == 'imagenet':
if K.image_data_format() == 'channels_first':
if K.backend() == 'tensorflow':
warnings.warn('You are using the TensorFlow backend, yet you '
'are using the Theano '
'image data format convention '
'(`image_data_format="channels_first"`). '
'For best performance, set '
'`image_data_format="channels_last"` in '
'your Keras config '
'at ~/.keras/keras.json.')
if include_top:
weights_path = get_file(
'inception-v4_weights_tf_dim_ordering_tf_kernels.h5',
WEIGHTS_PATH,
cache_subdir='models',
md5_hash='9fe79d77f793fe874470d84ca6ba4a3b')
else:
weights_path = get_file(
'inception-v4_weights_tf_dim_ordering_tf_kernels_notop.h5',
WEIGHTS_PATH_NO_TOP,
cache_subdir='models',
md5_hash='9296b46b5971573064d12e4669110969')
model.load_weights(weights_path, by_name=True)
return model
