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#!/usr/bin/env python
# coding: utf-8
# In[2]:
from tensorflow.keras.layers import *
import pandas as pd
import numpy as np
from tensorflow.keras.models import *
from keras.optimizers import Adam
import cv2
import tensorflow as tf
from keras.callbacks import *
from tensorflow.keras.utils import to_categorical
from keras.preprocessing.text import Tokenizer
from sklearn.preprocessing import LabelBinarizer, MultiLabelBinarizer
import matplotlib.pyplot as plt
from keras.activations import swish
from keras.preprocessing.image import *
from tensorflow.image import extract_patches
# In[3]:
num_patches=224//3
# In[3]:
class patches(Layer):
def __init__(self,patch_size ):
self.patch_size=patch_size
def __call__(self, x):
assert x.shape[1]%self.patch_size==0, 'Patch_size should be divisible'
if len(list(tf.shape(x)))==2:
x=tf.expand_dims(x, axis=-1)
if len(list(tf.shape(x)))==3:
x=tf.expand_dims(x, axis=0)
patch=extract_patches(images=x,strides=[1, self.patch_size, self.patch_size, 1] ,sizes=[1, self.patch_size, self.patch_size, 1],rates=[1, 1, 1,1], padding='VALID')
return patch
# In[9]:
def encoder(x, dim=32,pos:bool=True):
lin_proj=Dense(dim, activation='relu')
if pos:
pos_Emb=Embedding(x.shape[1], dim)
position=tf.range(0, x.shape[1])
return lin_proj(x)+pos_Emb(position)
else:
return lin_proj(x)
def Mlp(x, n:int=8, dim=32):
x=GlobalAveragePooling1D()(x)
for i in range(n): #7
x=Dense(dim, activation='relu')(x)
x=Dense(dim, activation='relu')(x)
return x
# In[79]:
class PaViT:
def __init__(self, shape=(224, 224, 3),num_heads=12, patch_size=32, dim=126, pos_emb:bool =False,
mlp_it=8, attn_drop:int= .3, dropout:bool=True):
self.dropout=dropout
self.shape=shape
self.num_heads=num_heads
self.patch_size=patch_size
self.dim=dim
self.attn_drop=attn_drop
self.pos_emb=pos_emb
self.mlp_it=mlp_it
def model(self, output_class=None, output=15, activation='softmax'):
inp=Input(shape=self.shape, name='Input')
patch=patches(patch_size=self.patch_size)(inp)
reshape=Reshape((-1, patch.shape[-1]))(patch)
encode=encoder(reshape, dim=self.dim, pos=True)
x=BatchNormalization()(encode)
drop=None
if self.attn_drop:
drop=self.attn_drop
attn=MultiHeadAttention(num_heads=self.num_heads, key_dim=self.dim, dropout=drop)(x,x) #12
mlp=Mlp(x,n=self.mlp_it, dim=self.dim)
add=Add()([mlp, attn])
norm=BatchNormalization()(add)
if self.dropout:
norm=Dropout(.3)(norm)
flat=Flatten()(norm)
if not output_class:
out=Dense(output, activation=activation)(flat)
else:
out=output_class(flat)
self.without_head=Model(inp, norm)
return Model(inp, out)
def remove_head(self):
try:
return self.without_head
except:
print('Cant load model without last layer. \nInitialize model first')
model=PaViT()
mox=model.model()
mox.summary()
#mox.load_weights('C:\\Users\\Emmanuel\\Downloads\\PAVIT_weights.h5')
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