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resnet_model.py
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resnet_model.py
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from keras.models import Model
from keras.layers import Conv2D
from keras.layers import BatchNormalization
from keras.layers import Activation
from keras.layers import Add
from keras.layers import ZeroPadding2D
from keras.layers import MaxPooling2D
from keras.layers import Input
from keras.layers import GlobalMaxPooling2D
from keras.layers import Dense
from keras_applications.imagenet_utils import _obtain_input_shape
from keras.utils import get_file
weights_collection = [
# ResNet18
{
'model': 'resnet18',
'dataset': 'imagenet',
'classes': 1000,
'include_top': True,
'url': 'https://github.com/qubvel/classification_models/releases/download/0.0.1/resnet18_imagenet_1000.h5',
'name': 'resnet18_imagenet_1000.h5',
'md5': '64da73012bb70e16c901316c201d9803',
},
{
'model': 'resnet18',
'dataset': 'imagenet',
'classes': 1000,
'include_top': False,
'url': 'https://github.com/qubvel/classification_models/releases/download/0.0.1/resnet18_imagenet_1000_no_top.h5',
'name': 'resnet18_imagenet_1000.h5',
'md5': '318e3ac0cd98d51e917526c9f62f0b50',
},
# ResNet34
{
'model': 'resnet34',
'dataset': 'imagenet',
'classes': 1000,
'include_top': True,
'url': 'https://github.com/qubvel/classification_models/releases/download/0.0.1/resnet34_imagenet_1000.h5',
'name': 'resnet34_imagenet_1000.h5',
'md5': '2ac8277412f65e5d047f255bcbd10383',
},
{
'model': 'resnet34',
'dataset': 'imagenet',
'classes': 1000,
'include_top': False,
'url': 'https://github.com/qubvel/classification_models/releases/download/0.0.1/resnet34_imagenet_1000_no_top.h5',
'name': 'resnet34_imagenet_1000_no_top.h5',
'md5': '8caaa0ad39d927cb8ba5385bf945d582',
},
# ResNet50
{
'model': 'resnet50',
'dataset': 'imagenet',
'classes': 1000,
'include_top': True,
'url': 'https://github.com/qubvel/classification_models/releases/download/0.0.1/resnet50_imagenet_1000.h5',
'name': 'resnet50_imagenet_1000.h5',
'md5': 'd0feba4fc650e68ac8c19166ee1ba87f',
},
{
'model': 'resnet50',
'dataset': 'imagenet',
'classes': 1000,
'include_top': False,
'url': 'https://github.com/qubvel/classification_models/releases/download/0.0.1/resnet50_imagenet_1000_no_top.h5',
'name': 'resnet50_imagenet_1000_no_top.h5',
'md5': 'db3b217156506944570ac220086f09b6',
},
{
'model': 'resnet50',
'dataset': 'imagenet11k-places365ch',
'classes': 11586,
'include_top': True,
'url': 'https://github.com/qubvel/classification_models/releases/download/0.0.1/resnet50_places365_11586.h5',
'name': 'resnet50_places365_11586.h5',
'md5': 'bb8963db145bc9906452b3d9c9917275',
},
{
'model': 'resnet50',
'dataset': 'imagenet11k-places365ch',
'classes': 11586,
'include_top': False,
'url': 'https://github.com/qubvel/classification_models/releases/download/0.0.1/resnet50_imagenet_11586_no_top.h5',
'name': 'resnet50_imagenet_11586_no_top.h5',
'md5': 'd8bf4e7ea082d9d43e37644da217324a',
},
# ResNet101
{
'model': 'resnet101',
'dataset': 'imagenet',
'classes': 1000,
'include_top': True,
'url': 'https://github.com/qubvel/classification_models/releases/download/0.0.1/resnet101_imagenet_1000.h5',
'name': 'resnet101_imagenet_1000.h5',
'md5': '9489ed2d5d0037538134c880167622ad',
},
{
'model': 'resnet101',
'dataset': 'imagenet',
'classes': 1000,
'include_top': False,
'url': 'https://github.com/qubvel/classification_models/releases/download/0.0.1/resnet101_imagenet_1000_no_top.h5',
'name': 'resnet101_imagenet_1000_no_top.h5',
'md5': '1016e7663980d5597a4e224d915c342d',
},
# ResNet152
{
'model': 'resnet152',
'dataset': 'imagenet',
'classes': 1000,
'include_top': True,
'url': 'https://github.com/qubvel/classification_models/releases/download/0.0.1/resnet152_imagenet_1000.h5',
'name': 'resnet152_imagenet_1000.h5',
'md5': '1efffbcc0708fb0d46a9d096ae14f905',
},
{
'model': 'resnet152',
'dataset': 'imagenet',
'classes': 1000,
'include_top': False,
'url': 'https://github.com/qubvel/classification_models/releases/download/0.0.1/resnet152_imagenet_1000_no_top.h5',
'name': 'resnet152_imagenet_1000_no_top.h5',
'md5': '5867b94098df4640918941115db93734',
},
{
'model': 'resnet152',
'dataset': 'imagenet11k',
'classes': 11221,
'include_top': True,
'url': 'https://github.com/qubvel/classification_models/releases/download/0.0.1/resnet152_imagenet11k_11221.h5',
'name': 'resnet152_imagenet11k_11221.h5',
'md5': '24791790f6ef32f274430ce4a2ffee5d',
},
{
'model': 'resnet152',
'dataset': 'imagenet11k',
'classes': 11221,
'include_top': False,
'url': 'https://github.com/qubvel/classification_models/releases/download/0.0.1/resnet152_imagenet11k_11221_no_top.h5',
'name': 'resnet152_imagenet11k_11221_no_top.h5',
'md5': '25ab66dec217cb774a27d0f3659cafb3',
},
# ResNeXt50
{
'model': 'resnext50',
'dataset': 'imagenet',
'classes': 1000,
'include_top': True,
'url': 'https://github.com/qubvel/classification_models/releases/download/0.0.1/resnext50_imagenet_1000.h5',
'name': 'resnext50_imagenet_1000.h5',
'md5': '7c5c40381efb044a8dea5287ab2c83db',
},
{
'model': 'resnext50',
'dataset': 'imagenet',
'classes': 1000,
'include_top': False,
'url': 'https://github.com/qubvel/classification_models/releases/download/0.0.1/resnext50_imagenet_1000_no_top.h5',
'name': 'resnext50_imagenet_1000_no_top.h5',
'md5': '7ade5c8aac9194af79b1724229bdaa50',
},
# ResNeXt101
{
'model': 'resnext101',
'dataset': 'imagenet',
'classes': 1000,
'include_top': True,
'url': 'https://github.com/qubvel/classification_models/releases/download/0.0.1/resnext101_imagenet_1000.h5',
'name': 'resnext101_imagenet_1000.h5',
'md5': '432536e85ee811568a0851c328182735',
},
{
'model': 'resnext101',
'dataset': 'imagenet',
'classes': 1000,
'include_top': False,
'url': 'https://github.com/qubvel/classification_models/releases/download/0.0.1/resnext101_imagenet_1000_no_top.h5',
'name': 'resnext101_imagenet_1000_no_top.h5',
'md5': '91fe0126320e49f6ee607a0719828c7e',
},
]
def find_weights(weights_collection, model_name, dataset, include_top):
w = list(filter(lambda x: x['model'] == model_name, weights_collection))
w = list(filter(lambda x: x['dataset'] == dataset, w))
w = list(filter(lambda x: x['include_top'] == include_top, w))
return w
def load_model_weights(weights_collection, model, dataset, classes, include_top):
weights = find_weights(weights_collection, model.name, dataset, include_top)
if weights:
weights = weights[0]
if include_top and weights['classes'] != classes:
raise ValueError('If using `weights` and `include_top`'
' as true, `classes` should be {}'.format(weights['classes']))
weights_path = get_file(weights['name'],
weights['url'],
cache_subdir='models',
md5_hash=weights['md5'])
model.load_weights(weights_path)
else:
raise ValueError('There is no weights for such configuration: ' +
'model = {}, dataset = {}, '.format(model.name, dataset) +
'classes = {}, include_top = {}.'.format(classes, include_top))
def get_conv_params(**params):
default_conv_params = {
'kernel_initializer': 'glorot_uniform',
'use_bias': False,
'padding': 'valid',
}
default_conv_params.update(params)
return default_conv_params
def get_bn_params(**params):
default_bn_params = {
'axis': 3,
'momentum': 0.99,
'epsilon': 2e-5,
'center': True,
'scale': True,
}
default_bn_params.update(params)
return default_bn_params
def handle_block_names(stage, block):
name_base = 'stage{}_unit{}_'.format(stage + 1, block + 1)
conv_name = name_base + 'conv'
bn_name = name_base + 'bn'
relu_name = name_base + 'relu'
sc_name = name_base + 'sc'
return conv_name, bn_name, relu_name, sc_name
def basic_identity_block(filters, stage, block):
"""The identity block is the block that has no conv layer at shortcut.
# Arguments
kernel_size: default 3, the kernel size of
middle conv layer at main path
filters: list of integers, the filters of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
# Returns
Output tensor for the block.
"""
def layer(input_tensor):
conv_params = get_conv_params()
bn_params = get_bn_params()
conv_name, bn_name, relu_name, sc_name = handle_block_names(stage, block)
x = BatchNormalization(name=bn_name + '1', **bn_params)(input_tensor)
x = Activation('relu', name=relu_name + '1')(x)
x = ZeroPadding2D(padding=(1, 1))(x)
x = Conv2D(filters, (3, 3), name=conv_name + '1', **conv_params)(x)
x = BatchNormalization(name=bn_name + '2', **bn_params)(x)
x = Activation('relu', name=relu_name + '2')(x)
x = ZeroPadding2D(padding=(1, 1))(x)
x = Conv2D(filters, (3, 3), name=conv_name + '2', **conv_params)(x)
x = Add()([x, input_tensor])
return x
return layer
def basic_conv_block(filters, stage, block, strides=(2, 2)):
"""The identity block is the block that has no conv layer at shortcut.
# Arguments
input_tensor: input tensor
kernel_size: default 3, the kernel size of
middle conv layer at main path
filters: list of integers, the filters of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
# Returns
Output tensor for the block.
"""
def layer(input_tensor):
conv_params = get_conv_params()
bn_params = get_bn_params()
conv_name, bn_name, relu_name, sc_name = handle_block_names(stage, block)
x = BatchNormalization(name=bn_name + '1', **bn_params)(input_tensor)
x = Activation('relu', name=relu_name + '1')(x)
shortcut = x
x = ZeroPadding2D(padding=(1, 1))(x)
x = Conv2D(filters, (3, 3), strides=strides, name=conv_name + '1', **conv_params)(x)
x = BatchNormalization(name=bn_name + '2', **bn_params)(x)
x = Activation('relu', name=relu_name + '2')(x)
x = ZeroPadding2D(padding=(1, 1))(x)
x = Conv2D(filters, (3, 3), name=conv_name + '2', **conv_params)(x)
shortcut = Conv2D(filters, (1, 1), name=sc_name, strides=strides, **conv_params)(shortcut)
x = Add()([x, shortcut])
return x
return layer
def conv_block(filters, stage, block, strides=(2, 2)):
"""The identity block is the block that has no conv layer at shortcut.
# Arguments
input_tensor: input tensor
kernel_size: default 3, the kernel size of
middle conv layer at main path
filters: list of integers, the filters of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
# Returns
Output tensor for the block.
"""
def layer(input_tensor):
conv_params = get_conv_params()
bn_params = get_bn_params()
conv_name, bn_name, relu_name, sc_name = handle_block_names(stage, block)
x = BatchNormalization(name=bn_name + '1', **bn_params)(input_tensor)
x = Activation('relu', name=relu_name + '1')(x)
shortcut = x
x = Conv2D(filters, (1, 1), name=conv_name + '1', **conv_params)(x)
x = BatchNormalization(name=bn_name + '2', **bn_params)(x)
x = Activation('relu', name=relu_name + '2')(x)
x = ZeroPadding2D(padding=(1, 1))(x)
x = Conv2D(filters, (3, 3), strides=strides, name=conv_name + '2', **conv_params)(x)
x = BatchNormalization(name=bn_name + '3', **bn_params)(x)
x = Activation('relu', name=relu_name + '3')(x)
x = Conv2D(filters*4, (1, 1), name=conv_name + '3', **conv_params)(x)
shortcut = Conv2D(filters*4, (1, 1), name=sc_name, strides=strides, **conv_params)(shortcut)
x = Add()([x, shortcut])
return x
return layer
def identity_block(filters, stage, block):
"""The identity block is the block that has no conv layer at shortcut.
# Arguments
kernel_size: default 3, the kernel size of
middle conv layer at main path
filters: list of integers, the filters of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
# Returns
Output tensor for the block.
"""
def layer(input_tensor):
conv_params = get_conv_params()
bn_params = get_bn_params()
conv_name, bn_name, relu_name, sc_name = handle_block_names(stage, block)
x = BatchNormalization(name=bn_name + '1', **bn_params)(input_tensor)
x = Activation('relu', name=relu_name + '1')(x)
x = Conv2D(filters, (1, 1), name=conv_name + '1', **conv_params)(x)
x = BatchNormalization(name=bn_name + '2', **bn_params)(x)
x = Activation('relu', name=relu_name + '2')(x)
x = ZeroPadding2D(padding=(1, 1))(x)
x = Conv2D(filters, (3, 3), name=conv_name + '2', **conv_params)(x)
x = BatchNormalization(name=bn_name + '3', **bn_params)(x)
x = Activation('relu', name=relu_name + '3')(x)
x = Conv2D(filters*4, (1, 1), name=conv_name + '3', **conv_params)(x)
x = Add()([x, input_tensor])
return x
return layer
def build_resnet(
repetitions=(2, 2, 2, 2),
include_top=True,
input_tensor=None,
input_shape=None,
classes=1000,
block_type='usual',
class_detector_top=False):
"""
TODO
"""
# Determine proper input shape
input_shape = _obtain_input_shape(input_shape,
default_size=224,
min_size=197,
data_format='channels_last',
require_flatten=include_top)
if input_tensor is None:
img_input = Input(shape=input_shape, name='data')
else:
if not K.is_keras_tensor(input_tensor):
img_input = Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
# get parameters for model layers
no_scale_bn_params = get_bn_params(scale=False)
bn_params = get_bn_params()
conv_params = get_conv_params()
init_filters = 64
if block_type == 'basic':
conv_block = basic_conv_block
identity_block = basic_identity_block
else:
conv_block = usual_conv_block
identity_block = usual_identity_block
# resnet bottom
x = BatchNormalization(name='bn_data', **no_scale_bn_params)(img_input)
x = ZeroPadding2D(padding=(3, 3))(x)
x = Conv2D(init_filters, (7, 7), strides=(2, 2), name='conv0', **conv_params)(x)
x = BatchNormalization(name='bn0', **bn_params)(x)
x = Activation('relu', name='relu0')(x)
x = ZeroPadding2D(padding=(1, 1))(x)
x = MaxPooling2D((3, 3), strides=(2, 2), padding='valid', name='pooling0')(x)
# resnet body
for stage, rep in enumerate(repetitions):
for block in range(rep):
filters = init_filters * (2**stage)
# first block of first stage without strides because we have maxpooling before
if block == 0 and stage == 0:
x = conv_block(filters, stage, block, strides=(1, 1))(x)
elif block == 0:
x = conv_block(filters, stage, block, strides=(2, 2))(x)
else:
x = identity_block(filters, stage, block)(x)
x = BatchNormalization(name='bn1', **bn_params)(x)
x = Activation('relu', name='relu1')(x)
# resnet top
if include_top:
x = GlobalAveragePooling2D(name='pool1')(x)
x = Dense(classes, name='fc1')(x)
x = Activation('softmax', name='softmax')(x)
if class_detector_top:
x = GlobalMaxPooling2D()(x)
x = Dense(1, name='fc1')(x)
x = Activation('sigmoid')(x)
# Ensure that the model takes into account any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = Model(inputs, x)
return model
def ResNet18(input_shape, input_tensor=None, weights=None, classes=1000, include_top=True):
model = build_resnet(input_tensor=input_tensor,
input_shape=input_shape,
repetitions=(2, 2, 2, 2),
classes=classes,
include_top=include_top,
block_type='basic')
model.name = 'resnet18'
if weights:
load_model_weights(weights_collection, model, weights, classes, include_top)
return model
def ResNet34(input_shape, input_tensor=None, weights=None, classes=1000, include_top=True, class_detector_top=False):
model = build_resnet(input_tensor=input_tensor,
input_shape=input_shape,
repetitions=(3, 4, 6, 3),
classes=classes,
include_top=include_top,
block_type='basic',
class_detector_top=class_detector_top)
model.name = 'resnet34'
if weights:
load_model_weights(weights_collection, model, weights, classes, include_top)
return model
def ResNet50(input_shape, input_tensor=None, weights=None, classes=1000, include_top=True):
model = build_resnet(input_tensor=input_tensor,
input_shape=input_shape,
repetitions=(3, 4, 6, 3),
classes=classes,
include_top=include_top)
model.name = 'resnet50'
if weights:
load_model_weights(weights_collection, model, weights, classes, include_top)
return model
def ResNet101(input_shape, input_tensor=None, weights=None, classes=1000, include_top=True):
model = build_resnet(input_tensor=input_tensor,
input_shape=input_shape,
repetitions=(3, 4, 23, 3),
classes=classes,
include_top=include_top)
model.name = 'resnet101'
if weights:
load_model_weights(weights_collection, model, weights, classes, include_top)
return model
def ResNet152(input_shape, input_tensor=None, weights=None, classes=1000, include_top=True):
model = build_resnet(input_tensor=input_tensor,
input_shape=input_shape,
repetitions=(3, 8, 36, 3),
classes=classes,
include_top=include_top)
model.name = 'resnet152'
if weights:
load_model_weights(weights_collection, model, weights, classes, include_top)
return model