qurator-spk/sbb_pixelwise_segmentation
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transformer patch size is dynamic now.

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vahidrezanezhad 2024-06-12 13:26:27 +02:00
parent 2aa216e388
commit f1fd74c7eb
3 changed files with 75 additions and 30 deletions
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28
config_params.json
View file

@ -1,42 +1,44 @@
{
"backbone_type" : "nontransformer",
"task": "classification",
"backbone_type" : "transformer",
"task": "binarization",
"n_classes" : 2,
"n_epochs" : 20,
"input_height" : 448,
"input_width" : 448,
"n_epochs" : 1,
"input_height" : 224,
"input_width" : 672,
"weight_decay" : 1e-6,
"n_batch" : 6,
"n_batch" : 1,
"learning_rate": 1e-4,
"f1_threshold_classification": 0.8,
"patches" : true,
"pretraining" : true,
"augmentation" : false,
"flip_aug" : false,
"blur_aug" : false,
"scaling" : true,
"degrading": false,
"brightening": false,
"binarization" : false,
"scaling_bluring" : false,
"scaling_binarization" : false,
"scaling_flip" : false,
"rotation": false,
"rotation_not_90": false,
"transformer_num_patches_xy": [28, 28],
"transformer_patchsize": 1,
"transformer_num_patches_xy": [7, 7],
"transformer_patchsize_x": 3,
"transformer_patchsize_y": 1,
"transformer_projection_dim": 192,
"blur_k" : ["blur","guass","median"],
"scales" : [0.6, 0.7, 0.8, 0.9, 1.1, 1.2, 1.4],
"brightness" : [1.3, 1.5, 1.7, 2],
"degrade_scales" : [0.2, 0.4],
"flip_index" : [0, 1, -1],
"thetha" : [10, -10],
"classification_classes_name" : {"0":"apple", "1":"orange"},
"continue_training": false,
"index_start" : 0,
"dir_of_start_model" : " ",
"weighted_loss": false,
"is_loss_soft_dice": false,
"data_is_provided": false,
"dir_train": "./train",
"dir_eval": "./eval",
"dir_output": "./output"
"dir_train": "/home/vahid/Documents/test/training_data_sample_binarization",
"dir_eval": "/home/vahid/Documents/test/eval",
"dir_output": "/home/vahid/Documents/test/out"
}

47
models.py
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@ -6,25 +6,49 @@ from tensorflow.keras import layers
from tensorflow.keras.regularizers import l2
mlp_head_units = [2048, 1024]
projection_dim = 64
#projection_dim = 64
transformer_layers = 8
num_heads = 4
resnet50_Weights_path = './pretrained_model/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
IMAGE_ORDERING = 'channels_last'
MERGE_AXIS = -1
transformer_units = [
projection_dim * 2,
projection_dim,
] # Size of the transformer layers
def mlp(x, hidden_units, dropout_rate):
for units in hidden_units:
x = layers.Dense(units, activation=tf.nn.gelu)(x)
x = layers.Dropout(dropout_rate)(x)
return x
class Patches(layers.Layer):
def __init__(self, patch_size_x, patch_size_y):#__init__(self, **kwargs):#:__init__(self, patch_size):#__init__(self, **kwargs):
super(Patches, self).__init__()
self.patch_size_x = patch_size_x
self.patch_size_y = patch_size_y
def call(self, images):
#print(tf.shape(images)[1],'images')
#print(self.patch_size,'self.patch_size')
batch_size = tf.shape(images)[0]
patches = tf.image.extract_patches(
images=images,
sizes=[1, self.patch_size_y, self.patch_size_x, 1],
strides=[1, self.patch_size_y, self.patch_size_x, 1],
rates=[1, 1, 1, 1],
padding="VALID",
)
patch_dims = patches.shape[-1]
patches = tf.reshape(patches, [batch_size, -1, patch_dims])
return patches
def get_config(self):
config = super().get_config().copy()
config.update({
'patch_size_x': self.patch_size_x,
'patch_size_y': self.patch_size_y,
})
return config
class Patches_old(layers.Layer):
def __init__(self, patch_size):#__init__(self, **kwargs):#:__init__(self, patch_size):#__init__(self, **kwargs):
super(Patches, self).__init__()
self.patch_size = patch_size
@ -369,8 +393,13 @@ def resnet50_unet(n_classes, input_height=224, input_width=224, task="segmentati
return model
def vit_resnet50_unet(n_classes, patch_size, num_patches, input_height=224, input_width=224, task="segmentation", weight_decay=1e-6, pretraining=False):
def vit_resnet50_unet(n_classes, patch_size_x, patch_size_y, num_patches, projection_dim = 64, input_height=224, input_width=224, task="segmentation", weight_decay=1e-6, pretraining=False):
inputs = layers.Input(shape=(input_height, input_width, 3))
transformer_units = [
projection_dim * 2,
projection_dim,
] # Size of the transformer layers
IMAGE_ORDERING = 'channels_last'
bn_axis=3
@ -414,7 +443,7 @@ def vit_resnet50_unet(n_classes, patch_size, num_patches, input_height=224, inpu
#patch_size_y = input_height / x.shape[1]
#patch_size_x = input_width / x.shape[2]
#patch_size = patch_size_x * patch_size_y
patches = Patches(patch_size)(x)
patches = Patches(patch_size_x, patch_size_y)(x)
# Encode patches.
encoded_patches = PatchEncoder(num_patches, projection_dim)(patches)
@ -434,7 +463,7 @@ def vit_resnet50_unet(n_classes, patch_size, num_patches, input_height=224, inpu
# Skip connection 2.
encoded_patches = layers.Add()([x3, x2])
encoded_patches = tf.reshape(encoded_patches, [-1, x.shape[1], x.shape[2], 64])
encoded_patches = tf.reshape(encoded_patches, [-1, x.shape[1], x.shape[2] , int( projection_dim / (patch_size_x * patch_size_y) )])
v1024_2048 = Conv2D( 1024 , (1, 1), padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay))(encoded_patches)
v1024_2048 = (BatchNormalization(axis=bn_axis))(v1024_2048)

30
train.py
View file

@ -70,8 +70,10 @@ def config_params():
brightness = None # Brighten image for augmentation.
flip_index = None # Flip image for augmentation.
continue_training = False # Set to true if you would like to continue training an already trained a model.
transformer_patchsize = None # Patch size of vision transformer patches.
transformer_patchsize_x = None # Patch size of vision transformer patches.
transformer_patchsize_y = None
transformer_num_patches_xy = None # Number of patches for vision transformer.
transformer_projection_dim = 64 # Transformer projection dimension
index_start = 0 # Index of model to continue training from. E.g. if you trained for 3 epochs and last index is 2, to continue from model_1.h5, set "index_start" to 3 to start naming model with index 3.
dir_of_start_model = '' # Directory containing pretrained encoder to continue training the model.
is_loss_soft_dice = False # Use soft dice as loss function. When set to true, "weighted_loss" must be false.
@ -92,7 +94,7 @@ def run(_config, n_classes, n_epochs, input_height,
brightening, binarization, blur_k, scales, degrade_scales,
brightness, dir_train, data_is_provided, scaling_bluring,
scaling_brightness, scaling_binarization, rotation, rotation_not_90,
thetha, scaling_flip, continue_training, transformer_patchsize,
thetha, scaling_flip, continue_training, transformer_projection_dim, transformer_patchsize_x, transformer_patchsize_y,
transformer_num_patches_xy, backbone_type, flip_index, dir_eval, dir_output,
pretraining, learning_rate, task, f1_threshold_classification, classification_classes_name):
@ -212,15 +214,27 @@ def run(_config, n_classes, n_epochs, input_height,
if backbone_type=='nontransformer':
model = resnet50_unet(n_classes, input_height, input_width, task, weight_decay, pretraining)
elif backbone_type=='transformer':
num_patches = transformer_num_patches_xy[0]*transformer_num_patches_xy[1]
num_patches_x = transformer_num_patches_xy[0]
num_patches_y = transformer_num_patches_xy[1]
num_patches = num_patches_x * num_patches_y
if not (num_patches == (input_width / 32) * (input_height / 32)):
print("Error: transformer num patches error. Parameter transformer_num_patches_xy should be set to (input_width/32) = {} and (input_height/32) = {}".format(int(input_width / 32), int(input_height / 32)) )
##if not (num_patches == (input_width / 32) * (input_height / 32)):
##print("Error: transformer num patches error. Parameter transformer_num_patches_xy should be set to (input_width/32) = {} and (input_height/32) = {}".format(int(input_width / 32), int(input_height / 32)) )
##sys.exit(1)
#if not (transformer_patchsize == 1):
#print("Error: transformer patchsize error. Parameter transformer_patchsizeshould set to 1" )
#sys.exit(1)
if (input_height != (num_patches_y * transformer_patchsize_y * 32) ):
print("Error: transformer_patchsize_y or transformer_num_patches_xy height value error . input_height should be equal to ( transformer_num_patches_xy height value * transformer_patchsize_y * 32)")
sys.exit(1)
if not (transformer_patchsize == 1):
print("Error: transformer patchsize error. Parameter transformer_patchsizeshould set to 1" )
if (input_width != (num_patches_x * transformer_patchsize_x * 32) ):
print("Error: transformer_patchsize_x or transformer_num_patches_xy width value error . input_width should be equal to ( transformer_num_patches_xy width value * transformer_patchsize_x * 32)")
sys.exit(1)
model = vit_resnet50_unet(n_classes, transformer_patchsize, num_patches, input_height, input_width, task, weight_decay, pretraining)
if (transformer_projection_dim % (transformer_patchsize_y * transformer_patchsize_x)) != 0:
print("Error: transformer_projection_dim error. The remainder when parameter transformer_projection_dim is divided by (transformer_patchsize_y*transformer_patchsize_x) should be zero")
sys.exit(1)
model = vit_resnet50_unet(n_classes, transformer_patchsize_x, transformer_patchsize_y, num_patches, transformer_projection_dim, input_height, input_width, task, weight_decay, pretraining)
#if you want to see the model structure just uncomment model summary.
#model.summary()