File size: 9,043 Bytes
747451d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 | # /*---------------------------------------------------------------------------------------------
# * Copyright (c) 2022-2023 STMicroelectronics.
# * All rights reserved.
# *
# * This software is licensed under terms that can be found in the LICENSE file in
# * the root directory of this software component.
# * If no LICENSE file comes with this software, it is provided AS-IS.
# *--------------------------------------------------------------------------------------------*/
import os
import tensorflow as tf
from omegaconf import DictConfig
from tensorflow.keras import layers
def change_yolo_model_number_of_classes(model,num_classes,num_anchors):
output_shape = (5 + num_classes)*num_anchors
# If the model already has the correct number of classes -> dont do anything
for outp in model.outputs:
if outp.shape[0] == output_shape:
return model
l = -1
l_list = []
while True:
layer_type = type(model.layers[l])
layer_config = model.layers[l].get_config()
if layer_type in [layers.Conv2D,
layers.Conv2DTranspose,
layers.Conv1D,
layers.Conv1DTranspose,
layers.Dense]:
if layer_type in [layers.Conv2D,layers.Conv2DTranspose,layers.Conv1D,layers.Conv1DTranspose]:
layer_config['filters'] = output_shape
new_layer = layer_type(**layer_config)
outputs = new_layer(model.layers[l-1].output)
else:
layer_config['units'] = output_shape
new_layer = layer_type(**layer_config)
outputs = new_layer(model.layers[l-1].output)
for i,new_l in enumerate(l_list[::-1]):
outputs = new_l(outputs)
return tf.keras.Model(inputs=model.input, outputs=outputs, name=model.name)
else:
l_list.append(layer_type(**layer_config))
l-=1
return None
def search_layer(tensor,model,with_his="output"):
for i,l in enumerate(model.layers): # search which layer has this tensor as output
if not type(l)==layers.InputLayer:
if with_his=="output":
for lo in (l.output if type(l.output)==list else [l.output]):
if tensor is lo:
return i
if with_his=="input":
if tensor in (l.input if type(l.input)==list else [l.input]):
return i
return None
def change_yolo_x_model_number_of_classes(model,num_classes,num_anchors):
model_outputs = model.outputs
concatenate_layers_indexes = []
for o in model_outputs:
concatenate_layers_indexes.append(search_layer(o,model,'output'))
output_tensors_list = [] # list of output tensors of the new model
for c in concatenate_layers_indexes: # for all Yolo_X heads
# concatenate layer infos
concatenate_layer_inputs = model.layers[c].input
concatenate_layer_type = type(model.layers[c])
concatenate_layer_config = model.layers[c].get_config()
new_concatenate_layer_inputs = []
list_of_shapes = [4*num_anchors,1*num_anchors,num_classes*num_anchors]
for i,t in enumerate(concatenate_layer_inputs):
if t.shape[-1] != list_of_shapes[i]:
conv_layer_index = search_layer(t,model,"output")
conv_layer_input = model.layers[conv_layer_index].input
conv_layer_type = type(model.layers[conv_layer_index])
conv_layer_config = model.layers[conv_layer_index].get_config()
# change the number of filters of the Conv2d layer
conv_layer_config['filters'] = list_of_shapes[i]
new_conv_layer = conv_layer_type(**conv_layer_config)
new_concatenate_layer_inputs.append(new_conv_layer(conv_layer_input))
else:
new_concatenate_layer_inputs.append(concatenate_layer_inputs[i])
new_concatenate_layer = concatenate_layer_type(**concatenate_layer_config)
output_tensors_list.append(new_concatenate_layer(new_concatenate_layer_inputs))
return tf.keras.Model(inputs=model.input, outputs=output_tensors_list, name=model.name)
def change_model_input_shape(model,new_inp_shape):
conf = model.get_config()
conf['layers'][0]['config']['batch_shape'] = new_inp_shape
new_model = model.__class__.from_config(conf, custom_objects={})
# iterate over all the layers that we want to get weights from
weights = [layer.get_weights() for layer in model.layers[1:]]
for layer, weight in zip(new_model.layers[1:], weights):
layer.set_weights(weight)
old_inp_shape = model.get_config()['layers'][0]['config']['batch_shape']
return new_model, old_inp_shape
def change_yolo_model_number_of_classes(model,num_classes,num_anchors):
output_shape = (5 + num_classes)*num_anchors
# If the model already has the correct number of classes -> dont do anything
for outp in model.outputs:
if outp.shape[0] == output_shape:
return model
l = -1
l_list = []
while True:
layer_type = type(model.layers[l])
layer_config = model.layers[l].get_config()
if layer_type in [layers.Conv2D,
layers.Conv2DTranspose,
layers.Conv1D,
layers.Conv1DTranspose,
layers.Dense]:
if layer_type in [layers.Conv2D,layers.Conv2DTranspose,layers.Conv1D,layers.Conv1DTranspose]:
layer_config['filters'] = output_shape
new_layer = layer_type(**layer_config)
outputs = new_layer(model.layers[l-1].output)
else:
layer_config['units'] = output_shape
new_layer = layer_type(**layer_config)
outputs = new_layer(model.layers[l-1].output)
for i,new_l in enumerate(l_list[::-1]):
outputs = new_l(outputs)
return tf.keras.Model(inputs=model.input, outputs=outputs, name=model.name)
else:
l_list.append(layer_type(**layer_config))
l-=1
return None
def search_layer(tensor,model,with_his="output"):
for i,l in enumerate(model.layers): # search which layer has this tensor as output
if with_his=="output":
if tensor in (l.output if type(l.output)==list else [l.output]):
return i
if with_his=="input":
if tensor in (l.input if type(l.input)==list else [l.input]):
return i
return None
def change_yolo_x_model_number_of_classes(model,num_classes,num_anchors):
model_outputs = model.outputs
concatenate_layers_indexes = []
for o in model_outputs:
concatenate_layers_indexes.append(search_layer(o,model,'output'))
output_tensors_list = [] # list of output tensors of the new model
for c in concatenate_layers_indexes: # for all Yolo_X heads
# concatenate layer infos
concatenate_layer_inputs = model.layers[c].input
concatenate_layer_type = type(model.layers[c])
concatenate_layer_config = model.layers[c].get_config()
new_concatenate_layer_inputs = []
list_of_shapes = [4*num_anchors,1*num_anchors,num_classes*num_anchors]
for i,t in enumerate(concatenate_layer_inputs):
if t.shape[-1] != list_of_shapes[i]:
conv_layer_index = search_layer(t,model,"output")
conv_layer_input = model.layers[conv_layer_index].input
conv_layer_type = type(model.layers[conv_layer_index])
conv_layer_config = model.layers[conv_layer_index].get_config()
# change the number of filters of the Conv2d layer
conv_layer_config['filters'] = list_of_shapes[i]
new_conv_layer = conv_layer_type(**conv_layer_config)
new_concatenate_layer_inputs.append(new_conv_layer(conv_layer_input))
else:
new_concatenate_layer_inputs.append(concatenate_layer_inputs[i])
new_concatenate_layer = concatenate_layer_type(**concatenate_layer_config)
output_tensors_list.append(new_concatenate_layer(new_concatenate_layer_inputs))
return tf.keras.Model(inputs=model.input, outputs=output_tensors_list, name=model.name)
def ai_runner_invoke(image_processed,ai_runner_interpreter):
def reduce_shape(x): # reduce shape (request by legacy API)
old_shape = x.shape
n_shape = [old_shape[0]]
for v in x.shape[1:len(x.shape) - 1]:
if v != 1:
n_shape.append(v)
n_shape.append(old_shape[-1])
return x.reshape(n_shape)
preds, _ = ai_runner_interpreter.invoke(image_processed)
predictions = []
for x in preds:
x = reduce_shape(x)
predictions.append(x.copy())
return predictions
|