def build_model_densenet121_with_mask():
img_input = Input(INPUT_SHAPE, name='data')
mask_model = model_unet(INPUT_SHAPE)
mask_model.load_weights(
'../output/checkpoints/fish_mask_unet/model_fish_unet2/checkpoint-best-064-0.0476.hdf5'
)
mask = mask_model(img_input)
mask3 = concatenate([mask, mask, mask], axis=3)
masked_image = multiply([img_input, mask3])
base_model = densenet121.DenseNet(
img_input=img_input,
reduction=0.5,
weights_path='../input/densenet121_weights_tf.h5',
classes=1000)
base_model.layers.pop()
base_model.layers.pop()
base_model_output = base_model(masked_image)
species_dense = Dense(len(SPECIES_CLASSES),
activation='softmax',
name='cat_species')(base_model_output)
cover_dense = Dense(len(COVER_CLASSES),
activation='softmax',
name='cat_cover')(base_model_output)
model = Model(inputs=img_input, outputs=[species_dense, cover_dense])
sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd,
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
def build_model_densenet_121():
img_input = Input(INPUT_SHAPE, name='data')
base_model = densenet121.DenseNet(
img_input=img_input,
reduction=0.5,
weights_path='../input/densenet121_weights_tf.h5',
classes=1000)
base_model.layers.pop()
base_model.layers.pop()
species_dense = Dense(len(SPECIES_CLASSES),
activation='softmax',
name='cat_species')(base_model.layers[-1].output)
cover_dense = Dense(len(COVER_CLASSES),
activation='softmax',
name='cat_cover')(base_model.layers[-1].output)
# output = concatenate([species_dense, cover_dense], axis=0)
model = Model(input=img_input, outputs=[species_dense, cover_dense])
sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd,
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
def dense121():
weights_path = '/your path/wh_code/densenet/densenet121_weights_tf.h5'
image_size = (224, 224)
base_model = densenet121.DenseNet(reduction=0.5,
classes=1000,
weights_path=weights_path)
#base_model = densenet161.densenet161_model(img_rows=224, img_cols=224, color_type=3)
model = Model(base_model.input,
GlobalAveragePooling2D()(base_model.output))
gen = ImageDataGenerator(
preprocessing_function=densenet121.preprocess_input)
print(base_model.output)
#gen = ImageDataGenerator()
#train_generator = gen.flow_from_directory("/your path/train_cut2p", image_size, shuffle=False, batch_size=8)#37189
#train_generator = gen.flow_from_directory("/your path/traindata2_pre", image_size, shuffle=False, batch_size=8)#20227
#val_generator = gen.flow_from_directory("/your path/val_cutpre", image_size, shuffle=False, batch_size=8)#3117
#train_generator = gen.flow_from_directory("/your path/train_cut", image_size, shuffle=False, batch_size=8)#18686
#train_generator = gen.flow_from_directory("/your path/train_cut2p", image_size, shuffle=False, batch_size=8)#37189
#train_generator = gen.flow_from_directory("/your path/train_cut", image_size, shuffle=False, batch_size=8)#18686
#train_generator = gen.flow_from_directory("/your path/train",image_size, shuffle=False, batch_size=8)#18686
#train_generator = gen.flow_from_directory("/your path/train_cut3p", image_size, shuffle=False, batch_size=8)#55298
#test_generator = gen.flow_from_directory("/your path/test_cut", image_size, shuffle=False, batch_size=8, class_mode=None)#10593
test_generator = gen.flow_from_directory("/your path/test",
image_size,
shuffle=False,
batch_size=8,
class_mode=None) #10593
#train_generator = gen.flow_from_directory("/your path/train4p", image_size, shuffle=False, batch_size=8)#73049
#train_generator = gen.flow_from_directory("/your path/train_cut4p", image_size, shuffle=False, batch_size=8)#72981
#train_generator = gen.flow_from_directory("/your path/train_cut5p", image_size, shuffle=False, batch_size=8)#90253
#train_generator = gen.flow_from_directory("/your path/train2p", image_size, shuffle=False, batch_size=8)#37201
train_generator = gen.flow_from_directory("/your path/train3p",
image_size,
shuffle=False,
batch_size=8) #55273
#train = model.predict_generator(train_generator,2336,verbose=True)#18686
#train = model.predict_generator(train_generator,4649,verbose=True)#37189
#train = model.predict_generator(train_generator,6913,verbose=True)#55298
#train = model.predict_generator(train_generator,6908,verbose=True)#55261
#val = model.predict_generator(val_generator, 390,verbose=True)#3117
#train = model.predict_generator(train_generator,4651,verbose=True)#37201
train = model.predict_generator(train_generator, 6910,
verbose=True) #55273
#train = model.predict_generator(train_generator,9127,verbose=True)#73014
#train = model.predict_generator(train_generator,9123,verbose=True)#72981
#train = model.predict_generator(train_generator,11282,verbose=True)#90253
test = model.predict_generator(test_generator, 1325, verbose=True) #10593
#print(test .shape)
with h5py.File(fileDir + "wh_code/nocut/224_3p-dense121.h5") as h:
h.create_dataset("train", data=train)
h.create_dataset("test", data=test)
h.create_dataset("label", data=train_generator.classes)
#h.create_dataset("val", data=val)
#h.create_dataset("trainlabel", data=train_generator.classes)
#h.create_dataset("vallabel", data=val_generator.classes)
K.clear_session()
def train_ps_cnn(epochs, datapath, net_type, log_save_path, continue_from_weight, keep_zero_map_rate, update_zero_map_path, update_zero_map_array, lr,lr_s, training_generator, validation_generator, kernel_regu):
normal_classes = 3
continue_from_model = None
kernel_constraint = keras.constraints.NonNeg()
weight_decay = 0
loss_funcs = keras.losses.mean_squared_error
metric_funcs = [mean_angular_error]
input_shape = (14,14,1)
if continue_from_model is None:
if net_type == "DenseNet":
model = densenet121.DenseNet(nb_dense_block=3, growth_rate=32, nb_filter=64, reduction=0.0,
dropout_rate=0.2, weight_decay=1e-4, classes=normal_classes, weights_path=None,
input_shape=input_shape)
model.compile(loss=loss_funcs,
optimizer=keras.optimizers.Adam(),
metrics=metric_funcs)
elif net_type == "ResNet":
model = ResNet(input_shape=input_shape, n=3, classes=normal_classes)
model.compile(loss=loss_funcs,
optimizer=keras.optimizers.Adam(lr=lr),
metrics=metric_funcs)
elif net_type == "Simple":
model = simple_cnn(input_shape,
classes=normal_classes,
weights_path=continue_from_weight,
kernel_regu=kernel_regu,
kernel_constraint=kernel_constraint,
weight_decay=weight_decay)
model.compile(loss=loss_funcs,
optimizer=keras.optimizers.Adam(lr=lr),
metrics=metric_funcs)
elif net_type == "pscnn":
model = get_densenet_2d_channel_last_2dense(input_shape[0], input_shape[1],
classes=normal_classes,
weights_path=continue_from_weight,
kernel_regu=kernel_regu,
kernel_constraint=kernel_constraint,
weight_decay=weight_decay)
model.compile(loss=loss_funcs,
optimizer=keras.optimizers.Adam(lr=lr),
metrics=metric_funcs)
else:
print("No model:", net_type)
else:
model = keras.models.load_model(continue_from_model, custom_objects={"Scale": Scale,
"mean_angular_error": mean_angular_error,
"mean_sqrt_error": mean_sqrt_error,
"ConnectMap": ConnectMap,
"normalize": normalize})
model.compile(loss=loss_funcs,
optimizer=keras.optimizers.Adam(lr=lr),
metrics=metric_funcs)
if keep_zero_map_rate != 1:
ws = model.get_weights()
my_function.update_ZeroMap(ws, keep_zero_map_rate)
model.set_weights(ws)
if update_zero_map_path is not None:
ws = model.get_weights()
my_function.update_ZeroMap_from_npy(ws, update_zero_map_path)
model.set_weights(ws)
if update_zero_map_array is not None:
ws = model.get_weights()
ws[1] = update_zero_map_array.reshape((1, 14, 14, 1))
model.set_weights(ws)
# Prepare model model saving directory.
save_dir = os.path.join(os.getcwd(), 'saved_models')
model_name = net_type + '.{epoch:03d}.weights.h5'
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
if not os.path.isdir(log_save_path):
os.makedirs(log_save_path)
filepath = os.path.join(save_dir, model_name)
# Prepare callbacks for model saving and for learning rate adjustment.
checkpoint = ModelCheckpoint(filepath=filepath,
monitor='val_loss',
verbose=1,
save_best_only=False,
save_weights_only=True)
tensorboard = TensorBoard(log_dir=log_save_path)
call_lists = [tensorboard, checkpoint]
if kernel_regu is not None:
save_connect_map = Connection_map_plot(log_save_path)
call_lists.append(save_connect_map)
if lr_s is not None:
call_lists.append(lr_s)
if continue_from_model is None and continue_from_weight is None:
initial_epoch = 0
else:
initial_epoch = int(os.path.basename(continue_from_weight).split('.')[1])
copyfile(__file__, os.path.join(log_save_path, "note%03d.py" % initial_epoch))
model.fit_generator(generator=training_generator,
validation_data=validation_generator,
steps_per_epoch=len(training_generator),
validation_steps=len(validation_generator),
epochs=epochs,
use_multiprocessing=True,
workers=3,
max_queue_size=10,
verbose=1,
initial_epoch=initial_epoch,
callbacks=call_lists)
model.save(os.path.join(log_save_path, net_type + '.%03d.h5' % epochs))
model.save_weights(os.path.join(log_save_path, net_type + '.%03d.weights.h5' % epochs))