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David Rotermund
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functions/make_cnn_v2.py
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310
functions/make_cnn_v2.py
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import torch
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import os
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import numpy as np
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import json
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from jsmin import jsmin
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from functions.SoftmaxPower import SoftmaxPower
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def make_cnn(
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network_config_filename: str,
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logger,
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input_shape: torch.Size,
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) -> torch.nn.Sequential:
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with open(network_config_filename, "r") as file_handle:
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network_config = json.loads(jsmin(file_handle.read()))
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# Convolution layer
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conv_out_channel: list[int] = network_config["conv_out_channel"]
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conv_kernel_size: list[int] = network_config["conv_kernel_size"]
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conv_stride_size: list[int] = network_config["conv_stride_size"]
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conv_bias: list[bool] = network_config["conv_bias"]
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conv_padding: list[int | str] = network_config["conv_padding"]
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# Activation function
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activation_function: str = str(network_config["activation_function"])
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l_relu_negative_slope: float = float(network_config["l_relu_negative_slope"])
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# Pooling layer
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pooling_kernel_size: list[int] = network_config["pooling_kernel_size"]
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pooling_stride: list[int] = network_config["pooling_stride"]
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pooling_type: str = str(network_config["pooling_type"])
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# Softmax layer
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softmax_enable: list[bool] = network_config["softmax_enable"]
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softmax_power: float = network_config["softmax_power"]
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softmax_meanmode: bool = network_config["softmax_meanmode"]
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softmax_no_input_mode: bool = network_config["softmax_no_input_mode"]
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# Load pre-trained weights and biases
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path_pretrained_weights_bias: str = str(
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network_config["path_pretrained_weights_bias"]
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)
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train_weights: list[bool] = network_config["train_weights"]
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train_bias: list[bool] = network_config["train_bias"]
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# Output layer
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number_of_classes: int = int(network_config["number_of_classes"])
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assert len(conv_out_channel) == len(conv_kernel_size)
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assert len(conv_out_channel) == len(conv_stride_size)
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assert len(conv_out_channel) + 1 == len(conv_bias)
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assert len(conv_out_channel) == len(conv_padding)
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assert len(conv_out_channel) == len(pooling_kernel_size)
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assert len(conv_out_channel) == len(pooling_stride)
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assert len(conv_out_channel) + 1 == len(train_weights)
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assert len(conv_out_channel) + 1 == len(train_bias)
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assert len(conv_out_channel) == len(softmax_enable)
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assert number_of_classes > 1
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cnn = torch.nn.Sequential()
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temp_image: torch.Tensor = torch.zeros(
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(1, *input_shape), dtype=torch.float32, device=torch.device("cpu")
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)
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logger.info(
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(
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f"Input shape: {int(temp_image.shape[1])}, "
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f"{int(temp_image.shape[2])}, "
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f"{int(temp_image.shape[3])}"
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)
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)
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layer_counter: int = 0
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# Changing structure
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for i in range(0, len(conv_out_channel)):
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# Conv Layer
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if i == 0:
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in_channels: int = int(temp_image.shape[0])
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else:
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in_channels = conv_out_channel[i - 1]
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cnn.append(
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torch.nn.Conv2d(
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in_channels=int(in_channels),
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out_channels=int(conv_out_channel[i]),
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kernel_size=int(conv_kernel_size[i]),
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stride=int(conv_stride_size[i]),
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bias=bool(conv_bias[i]),
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padding=conv_padding[i],
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)
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)
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# Load bias
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if bool(conv_bias[i]) and bool(train_bias[i]) is False:
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filename_load = os.path.join(
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path_pretrained_weights_bias, str(f"bias_{i}.npy")
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)
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logger.info(f"Replace bias in conv2d {i} with {filename_load}")
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temp = np.load(filename_load)
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assert torch.equal(
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torch.Tensor(temp.shape),
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torch.Tensor(cnn[-1]._parameters["bias"].shape),
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)
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cnn[-1]._parameters["bias"] = torch.tensor(
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temp,
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dtype=cnn[-1]._parameters["bias"].dtype,
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requires_grad=False,
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device=cnn[-1]._parameters["bias"].device,
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)
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# Load weights
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if train_weights[i] is False:
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filename_load = os.path.join(
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path_pretrained_weights_bias, str(f"weights_{i}.npy")
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)
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logger.info(f"Replace weights in conv2d {i} with {filename_load}")
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temp = np.load(filename_load)
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assert torch.equal(
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torch.Tensor(temp.shape),
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torch.Tensor(cnn[-1]._parameters["weight"].shape),
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)
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cnn[-1]._parameters["weight"] = torch.tensor(
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temp,
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dtype=cnn[-1]._parameters["weight"].dtype,
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requires_grad=False,
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device=cnn[-1]._parameters["weight"].device,
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)
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cnn[-1].train_bias = True
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if bool(conv_bias[i]) and bool(train_bias[i]) is False:
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cnn[-1].train_bias = False
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cnn[-1].train_weights = train_weights[i]
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temp_image = cnn[layer_counter](temp_image)
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logger.info(
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(
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f"After layer {layer_counter} (Convolution 2D layer): {int(temp_image.shape[1])}, "
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f"{int(temp_image.shape[2])}, "
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f"{int(temp_image.shape[3])}, "
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f"train bias: {cnn[-1].train_bias}, "
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f"train weights: {cnn[-1].train_weights}, "
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)
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)
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layer_counter += 1
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# Activation Function
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setting_understood = False
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if activation_function.upper() == str("relu").upper():
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cnn.append(torch.nn.ReLU())
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setting_understood = True
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elif activation_function.upper() == str("leaky relu").upper():
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cnn.append(torch.nn.LeakyReLU(negative_slope=l_relu_negative_slope))
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setting_understood = True
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elif activation_function.upper() == str("tanh").upper():
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cnn.append(torch.nn.Tanh())
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setting_understood = True
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elif activation_function.upper() == str("none").upper():
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setting_understood = True
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assert setting_understood
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cnn[-1].train_bias = False
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cnn[-1].train_weights = False
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temp_image = cnn[layer_counter](temp_image)
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logger.info(
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(
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f"After layer {layer_counter} (Activation function): {int(temp_image.shape[1])}, "
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f"{int(temp_image.shape[2])}, "
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f"{int(temp_image.shape[3])}, "
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f"train bias: {cnn[-1].train_bias}, "
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f"train weights: {cnn[-1].train_weights} "
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)
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)
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layer_counter += 1
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if (pooling_kernel_size[i] > 0) and (pooling_stride[i] > 0):
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setting_understood = False
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if pooling_type.upper() == str("max").upper():
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cnn.append(
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torch.nn.MaxPool2d(
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kernel_size=pooling_kernel_size[i], stride=pooling_stride[i]
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)
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)
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setting_understood = True
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elif pooling_type.upper() == str("average").upper():
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cnn.append(
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torch.nn.AvgPool2d(
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kernel_size=pooling_kernel_size[i], stride=pooling_stride[i]
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)
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)
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setting_understood = True
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elif pooling_type.upper() == str("none").upper():
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setting_understood = True
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assert setting_understood
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cnn[-1].train_bias = False
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cnn[-1].train_weights = False
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temp_image = cnn[layer_counter](temp_image)
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logger.info(
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(
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f"After layer {layer_counter} (Pooling layer): {int(temp_image.shape[1])}, "
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f"{int(temp_image.shape[2])}, "
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f"{int(temp_image.shape[3])}, "
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f"train bias: {cnn[-1].train_bias}, "
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f"train weights: {cnn[-1].train_weights} "
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)
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)
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layer_counter += 1
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if softmax_enable[i]:
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cnn.append(
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SoftmaxPower(
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dim=1,
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power=float(softmax_power),
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mean_mode=bool(softmax_meanmode),
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no_input_mode=bool(softmax_no_input_mode),
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)
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)
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cnn[-1].train_bias = False
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cnn[-1].train_weights = False
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temp_image = cnn[layer_counter](temp_image)
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logger.info(
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(
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f"After layer {layer_counter}: {int(temp_image.shape[1])}, "
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f"{int(temp_image.shape[2])}, "
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f"{int(temp_image.shape[3])}, "
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f"train bias: {cnn[-1].train_bias}, "
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f"train weights: {cnn[-1].train_weights} "
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)
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)
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layer_counter += 1
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# Output layer
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cnn.append(
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torch.nn.Conv2d(
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in_channels=int(temp_image.shape[1]),
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out_channels=int(number_of_classes),
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kernel_size=(int(temp_image.shape[2]), int(temp_image.shape[3])),
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stride=1,
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bias=bool(conv_bias[-1]),
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)
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)
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# Load bias
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if bool(conv_bias[-1]) and bool(train_bias[-1]) is False:
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filename_load = os.path.join(
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path_pretrained_weights_bias, str(f"bias_{len(conv_out_channel)}.npy")
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)
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logger.info(
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f"Replace bias in conv2d {len(conv_out_channel)} with {filename_load}"
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)
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temp = np.load(filename_load)
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assert torch.equal(
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torch.Tensor(temp.shape),
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torch.Tensor(cnn[-1]._parameters["bias"].shape),
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)
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cnn[-1]._parameters["bias"] = torch.tensor(
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temp,
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dtype=cnn[-1]._parameters["bias"].dtype,
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requires_grad=False,
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device=cnn[-1]._parameters["bias"].device,
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)
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# Load weights
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if train_weights[-1] is False:
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filename_load = os.path.join(
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path_pretrained_weights_bias, str(f"weights_{len(conv_out_channel)}.npy")
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)
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logger.info(
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f"Replace weights in conv2d {len(conv_out_channel)} with {filename_load}"
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)
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temp = np.load(filename_load)
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assert torch.equal(
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torch.Tensor(temp.shape),
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torch.Tensor(cnn[-1]._parameters["weight"].shape),
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)
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cnn[-1]._parameters["weight"] = torch.tensor(
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temp,
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dtype=cnn[-1]._parameters["weight"].dtype,
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requires_grad=False,
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device=cnn[-1]._parameters["weight"].device,
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)
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cnn[-1].train_bias = True
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if bool(conv_bias[i]) and bool(train_bias[i]) is False:
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cnn[-1].train_bias = False
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cnn[-1].train_weights = train_weights[i]
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temp_image = cnn[layer_counter](temp_image)
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logger.info(
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(
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f"After layer {layer_counter} (Output layer): {int(temp_image.shape[1])}, "
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f"{int(temp_image.shape[2])}, "
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f"{int(temp_image.shape[3])}, "
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f"train bias: {cnn[-1].train_bias}, "
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f"train weights: {cnn[-1].train_weights} "
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)
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)
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layer_counter += 1
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# Output layer needs to have a x and y dim of 1
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assert int(temp_image.shape[2]) == 1
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assert int(temp_image.shape[3]) == 1
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return cnn
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