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import numpy as np
import tensorflow as tf
class Parameters:
# data level
image_count = 3670
image_size = 384
batch_size = 12
num_grad_accumulation = 8
class_number = 5
val_split = 0.2
autotune = tf.data.AUTOTUNE
# hparams
epochs = 10
lr_sched = "cosine_restart"
lr_base = 0.016
lr_min = 0
lr_decay_epoch = 2.4
lr_warmup_epoch = 5
lr_decay_factor = 0.97
scaled_lr = lr_base * (batch_size / 256.0)
scaled_lr_min = lr_min * (batch_size / 256.0)
num_validation_sample = int(image_count * val_split)
num_training_sample = image_count - num_validation_sample
train_step = int(np.ceil(num_training_sample / float(batch_size)))
total_steps = train_step * epochs
params = Parameters()
patch_size = (2, 2) # 4-by-4 sized patches
dropout_rate = 0.5 # Dropout rate
num_heads = 8 # Attention heads
embed_dim = 64 # Embedding dimension
num_mlp = 128 # MLP layer size
qkv_bias = True # Convert embedded patches to query, key, and values with a learnable additive value
window_size = 2 # Size of attention window
shift_size = 1 # Size of shifting window
image_dimension = 24 # Initial image size / Input size of the transformer model
num_patch_x = image_dimension // patch_size[0]
num_patch_y = image_dimension // patch_size[1]
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