def perf_test_RASTAweights():
"""
Test the performance of the RASTA weights provide by Lecoultre et al.
"""
dataset = 'RASTA'
sess = tf.Session()
set_session(sess)
tf.keras.backend.set_image_data_format('channels_last')
base_model = resnet_trained(20)
predictions = Dense(25, activation='softmax')(base_model.output)
net_finetuned = Model(inputs=base_model.input, outputs=predictions)
#net_finetuned = custom_resnet() # Ce model a 87 layers
path_to_model = os.path.join(os.sep,'media','gonthier','HDD2','output_exp','rasta_models','resnet_2017_7_31-19_9_45','model.h5')
#ce model a 107 layers
constrNet = 'LResNet50' # For Lecoutre ResNet50 version
model_name = 'Lecoutre2017'
input_name_lucid = 'input_1'
net_finetuned.load_weights(path_to_model) # ,by_name=True
net_finetuned.build((224,224,3))
print(net_finetuned.summary())
print(net_finetuned.predict(np.random.rand(1,224,224,3)))
item_name,path_to_img,default_path_imdb,classes,ext,num_classes,str_val,df_label,\
path_data,Not_on_NicolasPC = get_database(dataset)
sLength = len(df_label[item_name])
classes_vectors = df_label[classes].values
df_label_test = df_label[df_label['set']=='test']
y_test = classes_vectors[df_label['set']=='test',:]
cropCenter = False
randomCrop = False
imSize = 224
predictions = predictionFT_net(net_finetuned,df_test=df_label_test,x_col=item_name,\
y_col=classes,path_im=path_to_img,Net=constrNet,\
cropCenter=cropCenter,randomCrop=randomCrop,\
imSize=imSize)
with sess.as_default():
metrics = evaluationScoreRASTA(y_test,predictions)
top_k_accs,AP_per_class,P_per_class,R_per_class,P20_per_class,F1_per_class,acc_per_class= metrics
for k,top_k_acc in zip([1,3,5],top_k_accs):
print('Top-{0} accuracy : {1:.2f}%'.format(k,top_k_acc*100))
def testDiversVaries():
#tf.keras.backend.clear_session()
#tf.reset_default_graph()
#K.set_learning_phase(0)
sess = tf.Session()
#graph = tf.get_default_graph()
#keras.backend.set_session(sess)
# IMPORTANT: models have to be loaded AFTER SETTING THE SESSION for keras!
# Otherwise, their weights will be unavailable in the threads after the session there has been set
set_session(sess)
original_model = resnet_trained(n_retrain_layers=20)
# Cela va charger un tf.keras model
base_model = resnet_trained(20)
predictions = Dense(25, activation='softmax')(base_model.output)
net_finetuned = Model(inputs=base_model.input, outputs=predictions)
net_finetuned.predict(np.random.rand(1,224,224,3))
trainable_layers_name = []
for original_layer in original_model.layers:
if original_layer.trainable:
trainable_layers_name += [original_layer.name]
#C:\media\gonthier\HDD2\output_exp\rasta_models\resnet_2017_7_31-19_9_45
path_to_model = os.path.join(os.sep,'media','gonthier','HDD2','output_exp','rasta_models','resnet_2017_7_31-19_9_45','model.h5')
constrNet = 'LResNet50' # For Lecoutre ResNet50 version
model_name = 'Lecoutre2017'
input_name_lucid = 'input_1'
tf.keras.backend.set_image_data_format('channels_last')
net_finetuned.load_weights(path_to_model,by_name=True)
net_finetuned.build((224,224,3))
net_finetuned.summary()
net_finetuned.predict(np.random.rand(1,224,224,3))
#net_finetuned = keras.models.load_model(path_to_model,compile=True)
#net_finetuned = load_model(path_to_model,compile=True)
number_of_trainable_layer = 20
#
#list_layer_index_to_print = []
#for layer in model.layers:
# trainable_l = layer.trainable
# name_l = layer.name
# if trainable_l and 'res' in name_l:
# print(name_l,trainable_l)
# num_features = tf.shape(layer.bias).eval(session=sess)[0]
# list_layer_index_to_print += [name_l,np.arange(0,num_features)]
#
#for layer in original_model.layers:
# print(layer)
# trainable_l = layer.trainable
# name_l = layer.name
# if trainable_l and 'res' in name_l:
# print(name_l,trainable_l)
# num_features = tf.shape(layer.bias).eval(session=sess)[0]
# list_layer_index_to_print += [name_l,np.arange(0,num_features)]
#list_weights,list_name_layers = get_weights_and_name_layers_forPurekerasModel(original_model)
list_weights,list_name_layers = CompNet_FT_lucidIm.get_weights_and_name_layers(original_model)
dict_layers_relative_diff,dict_layers_argsort = CompNet_FT_lucidIm.get_gap_between_weights(list_name_layers,\
list_weights,net_finetuned)
layer_considered_for_print_im = []
for layer in net_finetuned.layers:
trainable_l = layer.trainable
name_l = layer.name
print(name_l,trainable_l)
if trainable_l and (name_l in trainable_layers_name):
layer_considered_for_print_im += [name_l]
num_top = 3
list_layer_index_to_print_base_model = []
list_layer_index_to_print = []
#print(layer_considered_for_print_im)
for key in dict_layers_argsort.keys():
#print(key)
if not(key in layer_considered_for_print_im):
continue
for k in range(num_top):
topk = dict_layers_argsort[key][k]
list_layer_index_to_print += [[key,topk]]
list_layer_index_to_print_base_model += [[key,topk]]
print('list_layer_index_to_print',list_layer_index_to_print)
#dict_list_layer_index_to_print_base_model[model_name+suffix] = list_layer_index_to_print_base_model
#dict_layers_relative_diff,dict_layers_argsort = CompNet_FT_lucidIm.get_gap_between_weights(list_name_layers,\
# list_weights,model)
# For the fine-tuned model !!!
path_lucid_model = os.path.join(os.sep,'media','gonthier','HDD2','output_exp','Covdata','Lucid_model')
path = path_lucid_model
if path=='':
os.makedirs('./model', exist_ok=True)
path ='model'
else:
os.makedirs(path, exist_ok=True)
frozen_graph = lucid_utils.freeze_session(sess,
output_names=[out.op.name for out in net_finetuned.outputs])
name_pb = 'tf_graph_'+constrNet+model_name+'.pb'
#nodes_tab = [n.name for n in tf.get_default_graph().as_graph_def().node]
#print(nodes_tab)
tf.io.write_graph(frozen_graph,logdir= path,name= name_pb, as_text=False)
if platform.system()=='Windows':
output_path = os.path.join('CompModifModel',constrNet)
else:
output_path = os.path.join(os.sep,'media','gonthier','HDD2','output_exp','Covdata','CompModifModel',constrNet)
pathlib.Path(output_path).mkdir(parents=True, exist_ok=True)
matplotlib.use('Agg')
output_path_with_model = os.path.join(output_path,model_name)
pathlib.Path(output_path_with_model).mkdir(parents=True, exist_ok=True)
# global sess
# global graph
# with graph.as_default():
# set_session(sess)
# net_finetuned.predict(np.random.rand(1,224,224,3))
net_finetuned.predict(np.random.rand(1,224,224,3))
lucid_utils.print_images(model_path=path_lucid_model+'/'+name_pb,list_layer_index_to_print=list_layer_index_to_print\
,path_output=output_path_with_model,prexif_name=model_name,input_name=input_name_lucid,Net=constrNet)
# For the original one !!!
original_model.predict(np.random.rand(1,224,224,3))
#sess = keras.backend.get_session()
#sess.run()
frozen_graph = lucid_utils.freeze_session(sess,
output_names=[out.op.name for out in original_model.outputs])
name_pb = 'tf_graph_'+constrNet+'PretrainedImageNet.pb'
tf.io.write_graph(frozen_graph,logdir= path,name= name_pb, as_text=False)
lucid_utils.print_images(model_path=path_lucid_model+'/'+name_pb,list_layer_index_to_print=list_layer_index_to_print\
,path_output=output_path_with_model,prexif_name=model_name,input_name=input_name_lucid,Net=constrNet)
