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| import logging, os | |
| logging.disable(logging.WARNING) | |
| os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" | |
| import tensorflow as tf | |
| """This is the configuration file. | |
| """ | |
| ################################################################################ | |
| # Settings for Basic Operaters | |
| ################################################################################ | |
| conf_basic_ops = dict() | |
| # kernel_initializer for convolutions and transposed convolutions | |
| # If None, the default initializer is the Glorot (Xavier) normal initializer. | |
| conf_basic_ops['kernel_initializer'] = tf.glorot_uniform_initializer() | |
| # momentum for batch normalization | |
| conf_basic_ops['momentum'] = 0.997 | |
| # epsilon for batch normalization | |
| conf_basic_ops['epsilon'] = 1e-5 | |
| # String options: 'relu', 'relu6' | |
| conf_basic_ops['relu_type'] = 'relu' | |
| ################################################################################ | |
| # Settings for Attention Modules | |
| ################################################################################ | |
| # Set the attention in same_gto | |
| conf_attn_same = dict() | |
| # Define the relationship between total_key_filters and output_filters. | |
| # total_key_filters = output_filters // key_ratio | |
| conf_attn_same['key_ratio'] = 1 | |
| # Define the relationship between total_value_filters and output_filters. | |
| # total_key_filters = output_filters // value_ratio | |
| conf_attn_same['value_ratio'] = 1 | |
| # number of heads | |
| conf_attn_same['num_heads'] = 2 | |
| # dropout rate, 0.0 means no dropout | |
| conf_attn_same['dropout_rate'] = 0.0 | |
| # whether to use softmax on attention_weights | |
| conf_attn_same['use_softmax'] = False | |
| # whether to use bias terms in input/output transformations | |
| conf_attn_same['use_bias'] = True | |
| # Set the attention in up_gto | |
| conf_attn_up = dict() | |
| conf_attn_up['key_ratio'] = 1 | |
| conf_attn_up['value_ratio'] = 1 | |
| conf_attn_up['num_heads'] = 2 | |
| conf_attn_up['dropout_rate'] = 0 | |
| conf_attn_up['use_softmax'] = False | |
| conf_attn_up['use_bias'] = True | |
| # Set the attention in down_gto | |
| conf_attn_down = dict() | |
| conf_attn_down['key_ratio'] = 1 | |
| conf_attn_down['value_ratio'] = 1 | |
| conf_attn_down['num_heads'] = 2 | |
| conf_attn_down['dropout_rate'] = 0.0 | |
| conf_attn_down['use_softmax'] = False | |
| conf_attn_down['use_bias'] = True | |
| ################################################################################ | |
| # Describing the U-net | |
| ################################################################################ | |
| conf_unet = dict() | |
| """ | |
| Describe your U-Net under the following framework: | |
| ******************************************************************************************** | |
| layers | output_filters | |
| | | |
| first_convolution + encoding_block_1 (same) | first_output_filters | |
| + encoding_block_i, i = 2, 3, ..., depth. (down) | first_output_filters*(2**(i-1)) | |
| + bottom_block | first_output_filters*(2**(depth-1)) | |
| + decoding_block_j, j = depth-1, depth-2, ..., 1 (up) | first_output_filters*(2**(j-1)) | |
| + output_layer | |
| ******************************************************************************************** | |
| Specifically, | |
| encoding_block_1 (same) = one or more res_block | |
| encoding_block_i (down) = downsampling + zero or more res_block, i = 2, 3, ..., depth-1 | |
| encoding_block_depth (down) = downsampling | |
| bottom_block = a combination of same_gto and res_block | |
| decoding_block_j (up) = upsampling + zero or more res_block, j = depth-1, depth-2, ..., 1 | |
| Identity skip connections are between the output of encoding_block_i and | |
| the output of upsampling in decoding_block_i, i = 1, 2, ..., depth-1. | |
| The combination method could be 'add' or 'concat'. | |
| """ | |
| # Set U-Net depth. | |
| conf_unet['depth'] = 3 | |
| # Set the output_filters for first_convolution and encoding_block_1 (same). | |
| conf_unet['first_output_filters'] = 96 | |
| # Set the encoding block sizes, i.e., number of res_block in encoding_block_i, i = 1, 2, ..., depth. | |
| # It is an integer list whose length equals to depth. | |
| # The first entry should be positive since encoding_block_1 = one or more res_block. | |
| # The last entry should be zero since encoding_block_depth (down) = downsampling. | |
| conf_unet['encoding_block_sizes'] = [1, 1, 0] | |
| # Set the decoding block sizes, i.e., number of res_block in decoding_block_j, j = depth-1, depth-2, ..., 1. | |
| # It is an integer list whose length equals to depth-1. | |
| conf_unet['decoding_block_sizes'] = [1, 1] | |
| # Set the downsampling methods for each encoding_block_i, i = 2, 3, ..., depth. | |
| # It is an string list whose length equals to depth-1. | |
| # String options: 'down_gto_v1', 'down_gto_v2', 'down_res_block', 'convolution' | |
| conf_unet['downsampling'] = ['convolution', 'convolution'] | |
| # Set the combination method for identity skip connections | |
| # String options: 'add', 'concat' | |
| conf_unet['skip_method'] = 'concat' | |
| # Set the output layer | |
| # Check | |
| assert conf_unet['depth'] == len(conf_unet['encoding_block_sizes']) | |
| assert conf_unet['encoding_block_sizes'][0] > 0 | |
| assert conf_unet['encoding_block_sizes'][-1] == 0 | |
| assert conf_unet['depth'] == len(conf_unet['decoding_block_sizes']) + 1 | |
| assert conf_unet['depth'] == len(conf_unet['downsampling']) + 1 | |
| assert conf_unet['skip_method'] in ['add', 'concat'] |