inference_resnet.py

import tensorflow as tf
gpu_devices = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(gpu_devices[0], True)
from keras.applications import resnet
import tensorflow.keras.layers as L
import os 

from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
import matplotlib.pyplot as plt
from typing import Tuple
from huggingface_hub import snapshot_download
from labels import lookup_170
import numpy as np 


REPO_ID='Serrelab/fossil_classification_models'
snapshot_download(repo_id=REPO_ID, token=os.environ.get('READ_TOKEN'),repo_type='model',local_dir='model_classification')


def get_model(base_arch='Nasnet',weights='imagenet',input_shape=(600,600,3),classes=64500):

    if base_arch == 'Nasnet':
        base_model = tf.keras.applications.NASNetLarge(
                                     input_shape=input_shape,
                                    include_top=False,
                                    weights=weights,
                                    input_tensor=None,
                                    pooling=None,
                                    
                            )
    elif base_arch == 'Resnet50v2':
        base_model = tf.keras.applications.ResNet50V2(weights=weights, 
                                    include_top=False, 
                                    pooling='avg',
                                    input_shape=input_shape)
    elif base_arch == 'Resnet50v2_finer':
        base_model = tf.keras.applications.ResNet50V2(weights=weights, 
                                    include_top=False, 
                                    pooling='avg',
                                    input_shape=input_shape)
        base_model = resnet.stack2(base_model.output, 512, 2, name="conv6")
        base_model = resnet.stack2(base_model, 512, 2, name="conv7")
        base_model = tf.keras.Model(base_model.input,base_model)        
                             
    model = tf.keras.Sequential([
        base_model,
        L.Dense(classes,activation='softmax')
    ])
    
    
    
    model.compile(optimizer='adam',
                  loss='categorical_crossentropy',
                  )
    
    return model


def get_triplet_model(input_shape = (600, 600, 3),
                      embedding_units = 256,
                      embedding_depth = 2,
                      backbone_class=tf.keras.applications.ResNet50V2,
                      nb_classes = 19,load_weights=False,finer_model=False,backbone_name ='Resnet50v2'):

    
    backbone = backbone_class(input_shape=input_shape, include_top=False)
    if load_weights:
        model = get_model(backbone_name,input_shape=input_shape)
        model.load_weights('/users/irodri15/data/irodri15/Fossils/Models/pretrained-herbarium/Resnet50v2_NO_imagenet_None_best_1600.h5')
        trw = model.layers[0].get_weights()
        backbone.set_weights(trw)
    if finer_model: 
        base_model = resnet.stack2(backbone.output, 512, 2, name="conv6")
        base_model = resnet.stack2(base_model, 512, 2, name="conv7")
        backbone = tf.keras.Model(backbone.input,base_model)  

    features = GlobalAveragePooling2D()(backbone.output)

    embedding_head = features
    for embed_i in range(embedding_depth):
        embedding_head = Dense(embedding_units, activation="relu" if embed_i < embedding_depth-1 else "linear")(embedding_head)
    embedding_head = tf.nn.l2_normalize(embedding_head, -1, epsilon=1e-5)

    logits_head = Dense(nb_classes)(features)

    model = tf.keras.Model(backbone.input, [embedding_head, logits_head])
    model.compile(loss='cce',metrics=['accuracy'])
    #model.summary()

    return model

load_size = 600
crop_size = 600
def _clever_crop(img: tf.Tensor,
                 target_size: Tuple[int]=(128,128),
                 grayscale: bool=False
                 ) -> tf.Tensor:
    """[summary]
    Args:
        img (tf.Tensor): [description]
        target_size (Tuple[int], optional): [description]. Defaults to (128,128).
        grayscale (bool, optional): [description]. Defaults to False.
    Returns:
        tf.Tensor: [description]
    """    
    maxside = tf.math.maximum(tf.shape(img)[0],tf.shape(img)[1])
    minside = tf.math.minimum(tf.shape(img)[0],tf.shape(img)[1])
    new_img = img
             
    if tf.math.divide(maxside,minside) > 1.2:
        repeating = tf.math.floor(tf.math.divide(maxside,minside))  
        new_img = img
        if tf.math.equal(tf.shape(img)[1],minside):
            for _ in range(int(repeating)):
                new_img = tf.concat((new_img, img), axis=1) 

        if tf.math.equal(tf.shape(img)[0],minside):
            for _ in range(int(repeating)):
                new_img = tf.concat((new_img, img), axis=0)
            new_img = tf.image.rot90(new_img) 
    else:
        new_img = img  
        repeating = 0     
    img = tf.image.resize(new_img, target_size)
    if grayscale:
        img = tf.image.rgb_to_grayscale(img)
        img = tf.image.grayscale_to_rgb(img)
        
    return img,repeating

def preprocess(img,size=600):
  img = np.array(img, np.float32) / 255.0
  img = tf.image.resize(img, (size, size))
  return np.array(img, np.float32)


def select_top_n(preds,n=10):
    top_n = np.argsort(preds)[-n:][::-1]
    return top_n


def parse_results(top_n,logits):
    results = {}
    for n in top_n:
        label = lookup_170[n]
        results[label] = float(logits[n])
    return results 

def inference_resnet_finer(x,type_model,size=576,n_classes=170,n_top=10):
    
    model = get_triplet_model(input_shape = (size, size, 3),
                        embedding_units = 256,
                        embedding_depth = 2,
                        backbone_class=tf.keras.applications.ResNet50V2,
                        nb_classes = n_classes,load_weights=False,finer_model=True,backbone_name ='Resnet50v2')
    if type_model=='Mummified 170':
        model.load_weights('model_classification/mummified-170.h5')
    elif type_model=='Rock 170':
        model.load_weights('model_classification/rock-170.h5')
    else:
        return 'Error' 
    cropped = _clever_crop(x,(size,size))[0]
    prep = preprocess(cropped,size=size)
    logits = tf.nn.softmax(model.predict(np.array([prep]))[1][0]).cpu().numpy()
    top_n = select_top_n(logits,n=n_top)
    
    return parse_results(top_n,logits)