This library allows building deep learning architectures using string notation where string descriptors are decoded to tensorflow blocks: layers (tf.keras.layers.Conv2D) or operations on tensors (tf.concat). Those strings are grouped in python lists.
For example the lists :
['cn_r2_k3_s1_c64_nre',
'cn_r1_k3_s2_c128_nre']...will be decoded to :
tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3), stride=1, activation='tf.nn.relu')
tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3), stride=1, activation='tf.nn.relu')
tf.keras.layers.Conv2D(filters=128, kernel_size=(3, 3), stride=2, activation='tf.nn.relu')Why is the first convolution repeated ? There is an argument ‘r2’ in the notation which means that this block should be repeated 2 times.
I came across this notation in this impressive git repo: pytorch-image-models where the author uses this notation to build stacks of layers in some of his models. This is definitely a very concise method. I find it easier to manage experiments by storing model definitions as text files instead of constantly tweaking Python scripts.
My implementation goes a step further and introduces operations like concatenation, multiplications and any custom function which operates on tensors and can be used in the method def call(self, x) of custom class derived from tf.keras.Model. New block types or operations can be easily registered.
This allows building complex architectures with branches, upscaling, concatenating. All with simple text descriptors.
The folder ./tf_netbuilder/models contains 2 sample models:
- mobilenet_v3.py
- openpose_singlenet.py
Test code is in the folder ./examples.
Definition of the architecture :
model_def = {
'inputs#': [
['img#norm1'] # custom name ‘img#’ for input and normalization -1 to 1
],
'backbone#': [
['select:img#'], # select the input
['cn_bn_r1_k3_s2_c16_nhs'], # conv2d with batch norm, hard_swish
['ir_r1_k3_s1_e1_c16_nre'], # inverted residual with expansion 1, relu
['ir_r1_k3_s2_e4_c24_nre', 'ir_r1_k3_s1_e3_c24_nre'], # inverted residual
['c3#ir_r3_k5_s2_e3_c40_se4_nre'], # custom name ‘c3#’ for the last repeated layer, size 1/8 of input
['ir_r1_k3_s2_e6_c80_nhs'], # inverted residual with expansion 6, hard_swish
['ir_r1_k3_s1_e2.5_c80_nhs'], # inverted residual with expansion 2.5, hard_swish
['ir_r2_k3_s1_e2.3_c80_nhs'], # inverted residual with expansion 2.3, hard_swish
['c4#ir_r2_k3_s1_e6_c112_se4_nhs'], # custom name ‘c4#’ for the last repeated layer, size 1/16 of input
['c5#ir_r3_k5_s2_e6_c160_se4_nhs'], # custom name ‘c5#’ for the last repeated layer, size 1/32 of input
['cn_bn_r1_k1_s1_c960_nhs'], # conv2d with batch norm, hard_swish,...
['avgpool_k7_s1'], # average pooling
['cn_r1_k1_s1_c1280_nhs'], # conv2d with hard_swish
]
}
model_ins = 'inputs#'
model_outs = ['backbone#']Python code:
from tf_netbuilder.builder import NetModule
...
class MobilenetV3(tf.keras.Model):
def __init__(self, num_classes):
self.backbone = NetModule(
net_def=model_def,
inputs_stack_name=model_ins,
output_names=model_outs,
in_chs=[3], name='MobilenetV3')
...
def call(self, inputs):
x = self.backbone(inputs)
...NetBuilder class is the engine of this library. It is nothing else than an implementation of tf.keras.layers.Layer so it can be embedded in your existing custom model. Parameters:
- net_def - dictionary with definition of architecture
- key - stack name (stacks are layer groups that can be referenced from other stacks)
- value - list of lists of blocks (layers, operations)
- inputs_stack - name of a stack containing inputs definition. One stack should be reserved for inputs.
- output_names - list of names of stacks that we want at the output. This is useful if our model contains multiple branches or generally, we need multiple outputs from the model.
- in_chs - number of channels for each input. Note that this is a list because we can provide multiple inputs.
- name - custom name for the module.
Note:
All labels should end with the character ‘#’. This is an indicator that we are dealing with a label and it is also a separator in a block definition.
The first item in any stack should always be the block select. This is a way to set inputs for a stack. It may be model's input or other stack (reference by name).
Individual blocks can have custom name for example c3#ir_r3... This is useful if we want to use the tensor of that layer in an operation like concatenate, upscale, etc.
This is an example of a more complex model with concatenation, upsacling and multiple outputs. The architecture is described in detail here: Single-Network Whole-Body Pose Estimation Although the original implementation uses more stages and VGG as a backbone, I have created a smaller version based on MobilenetV3 and only 3 paf stages and one heatmap stage. Here is the training code
Definition of architecture
model_def = {
'inputs#': [
['img#norm1']
],
# Mobilenet v3 backbone
'backbone#': [
['select:img#'],
['cn_bn_r1_k3_s2_c16_nhs'],
['ir_r1_k3_s1_e1_c16_nre'],
['ir_r1_k3_s2_e4_c24_nre', 'ir_r1_k3_s1_e3_c24_nre'],
['c3#ir_r3_k5_s2_e3_c40_se4_nre'],
['ir_r1_k3_s2_e6_c80_nhs'],
['ir_r1_k3_s1_e2.5_c80_nhs'],
['ir_r2_k3_s1_e2.3_c80_nhs'],
['c4#ir_r2_k3_s1_e6_c112_se4_nhs'],
['upscaled_c4#up_x2:'],
['cnct:c3#:upscaled_c4#']
],
# PAF stages
'stage_0#': [
['select:backbone#'],
['cn2_r5_e1_c192_npre'],
['ir_r1_k1_s1_e1_c256_se4_npre'],
['hd_r1_k1_s1_c38']
],
'stage_1#': [
['select:stage_0#:backbone#'], # select 2 stacks
['cnct:'], # concatenate them
['cn2_r5_e1_c384_npre'],
['ir_r1_k1_s1_e1_c256_se4_npre'],
['hd_r1_k1_s1_c38']
],
'stage_2#': [
['select:stage_1#:backbone#'],
['cnct:'],
['cn2_r5_e1_c384_npre'],
['ir_r1_k1_s1_e1_c512_se4_npre'],
['hd_r1_k1_s1_c38']
],
# Heatmap stages
'stage_3#': [
['select:stage_2#:backbone#'],
['cnct:'],
['cn2_r5_e1_c384_npre'],
['ir_r1_k1_s1_e1_c512_se4_npre'],
['hd_r1_k1_s1_c19']
],
}
model_ins = 'inputs#'
model_outs = ['stage_0#', 'stage_1#', 'stage_2#', 'stage_3#']Python code
from tf_netbuilder.builder import NetModule
...
class OpenPoseSingleNet(tf.keras.Model):
def __init__(self, in_chs):
super(OpenPoseSingleNet, self).__init__()
self.net = NetModule(
net_def=model_def,
inputs_stack_name=model_ins,
output_names=model_outs,
in_chs=[3], name='MobilenetV3')
def call(self, inputs):
x = self.net(inputs)
return x Note:
This model has multiple stacks. How can we distinguish an operation from a layer ? Operation code ends with the character ':' and all its arguments (references to other stacks or blocks) should be separated by ':' as well. For example:
['cnct:c3#:upscaled_c4#']This is a concatenation operation denoted by a code cnct:. It has 2 arguments c3# and upscaled_c4# which are references to other blocks.
Take a look at this special construct:
{
'stage_2#': [
['select:stage_1#:backbone#'],
['cnct:'],
...
]
}Here we select stage_1# and backbone# as input to the stage_2# stack. The operation in the next line cnct: will perform concatenation on the previously selected stacks.
The current implementation contains quite a small set of layers and operations. Only those that I needed in my experiments with pose estimation models. More layers and operation can be easily added. Take a look at the script ./tf_netbuilder/config.py :
...
# registering parsers for arguments of all blocks
NetBuilderConfig.add_parser("n", lambda arg: ACTIVATION_FUNCS[arg], "activation")
NetBuilderConfig.add_parser("s", lambda arg: int(arg), "strides")
NetBuilderConfig.add_parser("c", lambda arg: int(arg), "out_chs")
NetBuilderConfig.add_parser("e", lambda arg: float(eval(arg)), "exp_ratio")
NetBuilderConfig.add_parser("se", lambda arg: int(arg), "se_factor")
NetBuilderConfig.add_parser("k", lambda arg: int(arg), "kernel_size")
NetBuilderConfig.add_parser("bn", lambda arg: True, "batch_norm") # no arg, just the flag
# below is the internal parser and variable determining how many times a given layer should be repeated
NetBuilderConfig.add_parser("r", lambda arg: int(arg), KEY_REPEATS_NUM)
# registering blocks types - layers
NetBuilderConfig.add_block_type("ir", InvertedResidual, prepare_ir_args)
NetBuilderConfig.add_block_type("cn", ConvBnAct, prepare_cn_args)
NetBuilderConfig.add_block_type("cn2", SmallerConv7x7, prepare_cn2_args)
NetBuilderConfig.add_block_type("hd", tf.keras.layers.Conv2D, prepare_hd_args)
NetBuilderConfig.add_block_type("avgpool", tf.keras.layers.AveragePooling2D, prepare_avgpool_args)
NetBuilderConfig.add_block_type("maxpool", tf.keras.layers.MaxPooling2D, prepare_maxpool_args)
# registering operations
NetBuilderConfig.add_operation('cnct', ConcatOper)
NetBuilderConfig.add_operation('mul', MulOper)
NetBuilderConfig.add_operation('up_x2', UpscaleX2)
# registering operations on inputs
NetBuilderConfig.add_input_operation('norm1', NormalizationMinus1Plus1Input)
NetBuilderConfig.add_input_operation('norm05', NormalizationMinus05Plus05Input)
...In short:
- add_parser - parser for a single argument. For example k3 should be parsed to {‘kernel_size’: 3}
- add_block_type - here you can assign a layer class to a string code. Plus, additional function that prepares a set of arguments for a given layer.
- add_operation - code for operation and class implementing that operation.
- add_input_operation - code for special operation on inputs. Usually, this is kind of an input normalization etc.
There is still work going on in this library so I obviously don’t recommend installing it system-wide. You can try it with just a few simple steps:
git clone https://github.com/michalfaber/tf_netbuilder
cd tf_netbuilder
mkdir .venv
virtualenv .venv/tf_netbuilder
source .venv/tf_netbuilder/bin/activate
pip install -r requirements.txt
cd examples
python eval_mobilenet_v3.py
python eval_openpose_singlenet.pyIn a blog post: https://www.worldestimators.com/mobilenet