#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# A tf.keras implementation of squeezenet with pretrained weights,
# which is ported from https://github.com/rcmalli/keras-squeezenet
#
from keras_applications.imagenet_utils import _obtain_input_shape
from tensorflow.keras.utils import get_source_inputs, get_file
from tensorflow.keras.layers import Conv2D, MaxPool2D, GlobalMaxPooling2D, GlobalAveragePooling2D
from tensorflow.keras.layers import BatchNormalization, Dropout, Lambda
from tensorflow.keras.layers import Input, Activation, concatenate
from tensorflow.keras.models import Model
from tensorflow.keras import backend as K
import warnings
sq1x1 = "squeeze1x1"
exp1x1 = "expand1x1"
exp3x3 = "expand3x3"
relu = "relu_"
WEIGHTS_PATH = "https://github.com/rcmalli/keras-squeezenet/releases/download/v1.0/squeezenet_weights_tf_dim_ordering_tf_kernels.h5"
WEIGHTS_PATH_NO_TOP = "https://github.com/rcmalli/keras-squeezenet/releases/download/v1.0/squeezenet_weights_tf_dim_ordering_tf_kernels_notop.h5"
# Modular function for Fire Node
def fire_module(x, fire_id, squeeze=16, expand=64):
s_id = 'fire' + str(fire_id) + '/'
if K.image_data_format() == 'channels_first':
channel_axis = 1
else:
channel_axis = 3
x = Conv2D(squeeze, (1, 1), padding='valid', name=s_id + sq1x1)(x)
x = Activation('relu', name=s_id + relu + sq1x1)(x)
left = Conv2D(expand, (1, 1), padding='valid', name=s_id + exp1x1)(x)
left = Activation('relu', name=s_id + relu + exp1x1)(left)
right = Conv2D(expand, (3, 3), padding='same', name=s_id + exp3x3)(x)
right = Activation('relu', name=s_id + relu + exp3x3)(right)
x = concatenate([left, right], axis=channel_axis, name=s_id + 'concat')
return x
# Original SqueezeNet from paper.
def SqueezeNet(include_top=True,
input_shape=None,
weights='imagenet',
input_tensor=None,
pooling=None,
classes=1000,
**kwargs):
"""Instantiates the SqueezeNet architecture.
"""
if weights not in {'imagenet', None}:
raise ValueError('The `weights` argument should be either '
'`None` (random initialization) or `imagenet` '
'(pre-training on ImageNet).')
if weights == 'imagenet' and classes != 1000:
raise ValueError('If using `weights` as imagenet with `include_top`'
' as true, `classes` should be 1000')
input_shape = _obtain_input_shape(input_shape,
default_size=227,
min_size=48,
data_format=K.image_data_format(),
require_flatten=include_top)
if input_tensor is None:
img_input = Input(shape=input_shape)
else:
#if not K.is_keras_tensor(input_tensor):
#img_input = Input(tensor=input_tensor, shape=input_shape)
#else:
#img_input = input_tensor
img_input = input_tensor
x = Conv2D(64, (3, 3), strides=(2, 2), padding='valid', name='conv1')(img_input)
x = Activation('relu', name='relu_conv1')(x)
x = MaxPool2D(pool_size=(3, 3), strides=(2, 2), name='pool1')(x)
x = fire_module(x, fire_id=2, squeeze=16, expand=64)
x = fire_module(x, fire_id=3, squeeze=16, expand=64)
x = MaxPool2D(pool_size=(3, 3), strides=(2, 2), name='pool3')(x)
x = fire_module(x, fire_id=4, squeeze=32, expand=128)
x = fire_module(x, fire_id=5, squeeze=32, expand=128)
x = MaxPool2D(pool_size=(3, 3), strides=(2, 2), name='pool5')(x)
x = fire_module(x, fire_id=6, squeeze=48, expand=192)
x = fire_module(x, fire_id=7, squeeze=48, expand=192)
x = fire_module(x, fire_id=8, squeeze=64, expand=256)
x = fire_module(x, fire_id=9, squeeze=64, expand=256)
if include_top:
# It's not obvious where to cut the network...
# Could do the 8th or 9th layer... some work recommends cutting earlier layers.
x = Dropout(0.5, name='drop9')(x)
x = Conv2D(classes, (1, 1), padding='valid', name='conv10')(x)
x = Activation('relu', name='relu_conv10')(x)
x = GlobalAveragePooling2D()(x)
x = Activation('softmax', name='loss')(x)
else:
if pooling == 'avg':
x = GlobalAveragePooling2D()(x)
elif pooling=='max':
x = GlobalMaxPooling2D()(x)
elif pooling==None:
pass
else:
raise ValueError("Unknown argument for 'pooling'=" + pooling)
# 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
model = Model(inputs, x, name='squeezenet')
# load weights
if weights == 'imagenet':
if include_top:
weights_path = get_file('squeezenet_weights_tf_dim_ordering_tf_kernels.h5',
WEIGHTS_PATH,
cache_subdir='models')
else:
weights_path = get_file('squeezenet_weights_tf_dim_ordering_tf_kernels_notop.h5',
WEIGHTS_PATH_NO_TOP,
cache_subdir='models')
model.load_weights(weights_path)
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.')
return model
if __name__ == '__main__':
input_tensor = Input(shape=(None, None, 3), name='image_input')
#model = SqueezeNet(include_top=False, input_shape=(416, 416, 3), weights='imagenet')
model = SqueezeNet(include_top=True, input_tensor=input_tensor, weights='imagenet')
model.summary()
import numpy as np
#from keras_applications.imagenet_utils import preprocess_input, decode_predictions
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from keras_preprocessing import image
model = SqueezeNet()
img = image.load_img('../../example/eagle.jpg', target_size=(227, 227))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
preds = model.predict(x)
print('Predicted:', decode_predictions(preds))
