Merge branch 'main' of https://huggingface.co/imessam/OrientationClassifier

README.md

@@ -1 +1,15 @@
-# Orientation Classifier
+# Orientation Classifier
+---
+
+language:
+- en
+tags:
+- image-classification
+license: apache-2.0
+datasets:
+- cifar10
+metrics:
+- accuracy
+- f1
+
+---

model.py

@@ -1,44 +0,0 @@
-import os
-import numpy as np
-import tensorflow as tf
-from tensorflow.keras.layers import *
-
-#os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
-
-
-
-class OrientationClassifier(tf.keras.Model):
-    
-
-    def __init__(self,input_shape,hidden_dim,no_classes=1,isTrainable = False):
-        super().__init__()
-        resnet = tf.keras.applications.resnet50.ResNet50(include_top=False,input_shape=input_shape)
-        resnet.trainable = isTrainable
-
-        self.featureExtractor = tf.keras.models.Sequential([
-            resnet,
-            GlobalAveragePooling2D()
-        ])
-
-        self.classifier = tf.keras.models.Sequential([
-            Dense(hidden_dim,activation = 'relu'),
-            BatchNormalization(),
-            
-            Dense(hidden_dim/2,activation = 'relu'),
-            BatchNormalization(),
-            
-            Dense(hidden_dim/4,activation = 'relu'),
-            BatchNormalization(),
-            
-            Dense(no_classes,activation = 'sigmoid')
-        ])
-
-
-    def call(self, inputs):
-        
-        x = self.featureExtractor(inputs)
-        
-        preds = self.classifier(x)
-        
-        return preds
-

transforms.py

@@ -1,32 +0,0 @@
-import tensorflow as tf
-import numpy as np
-
-
-
-def normalize(image,label):
-    
-    return tf.cast(image, tf.float32) / 255., label
-
-
-def resize(image,label):
-    
-    return tf.image.resize(image,size=(100,100)), label
-    
-
-def flip(image,label):
-    
-    new_label = tf.random.uniform(shape=(), minval=0, maxval=2, dtype=tf.int32)
-    
-    img = image
-    
-    if new_label == 1:
-        img = tf.image.flip_up_down(img) 
-    
-    return img,new_label
-
-
-    
-    
-
-
-    

utils.py

@@ -1,203 +0,0 @@
-import numpy as np
-import tensorflow as tf
-import matplotlib.pyplot as plt
-import gc
-
-
-classes = ["UN-FLIPPED","FLIPPED"]
-
-
-
-
-def showSample(data):
-
-    
-    for i,(image,label) in enumerate(data.unbatch().as_numpy_iterator()):
-        
-        print(classes[label])
-        plt.imshow(image)
-        plt.show()
-        
-        if i == 3:
-            break
-            
-    tf.keras.backend.clear_session()
-    gc.collect()
-        
-def checkBalance(data):
-    
-    
-    labels = []
-    
-    for i,(image,label) in enumerate(data.unbatch().as_numpy_iterator()):
-        
-        labels.append(label)
- 
-    
-    values,count = np.unique(np.array(labels), return_counts=True)
-    
-    for i,value in enumerate(values):
-        print(f"{classes[value]} : {count[i]}")
-    
-    tf.keras.backend.clear_session()
-    gc.collect()
-    
-        
-def prepareData(data, transforms, batch_size, isTrain= False, size=None):
-    
-    ds = data
-    
-    for transform in transforms:
-        ds = ds.map(transform)
-    ds = ds.cache()
-    ds = ds.batch(batch_size)
-    ds = ds.prefetch(tf.data.AUTOTUNE)
-    
-    if isTrain:
-        ds = ds.shuffle(size)
-        
-    return ds
-
-
-
-
-def train(data,classifier,params,optimizer,loss,epochs,modelName = "OrientationClassifier",callback = None, runs=3):
-    
-    train,val = data
-    
-    history = []
-    losses = []
-    accs = []
-    
-    callbacks = []
-
-    if callback is not None:
-        callbacks.append(callback)
-    
-    for i in range(runs):
-        print(f"######## run {i+1} ##########")
-        
-        callbacks.append(tf.keras.callbacks.ModelCheckpoint(
-        filepath=f"models/{modelName} run {(i+1)}",
-        save_weights_only=True,
-        monitor='val_loss',
-        mode='min',
-        save_best_only=True
-    ))
-        
-        model = classifier(*params)
-        tf.random.set_seed(i+1)
-                
-        model.compile(optimizer=optimizer,loss=loss,metrics="accuracy")
-        hist = model.fit(train,epochs=epochs,validation_data=val,callbacks=callbacks)
-        
-        losses.append(np.amin(hist.history["val_loss"]))
-        accs.append(np.amax(hist.history["val_accuracy"]))
-        history.append(hist)
-        tf.keras.backend.clear_session()
-
-   
-    idx = np.argmin(losses)
-    bestModel = model
-    bestModel.load_weights(f"models/{modelName} run {(idx+1)}")
-    loss,acc = bestModel.evaluate(val)
-    
-    print(f"best model is on run {idx+1} with validation loss : {loss} and validation accuracy : {acc}")
-    del model
-    
-    tf.keras.backend.clear_session()
-    gc.collect()
-  
-
-    return history
-
-
-
-
-
-def plotHistory(histories):
-    
-    for i,history in enumerate(histories):
-        
-        print(f"history on run {i+1} : ")
-    
-        # summarize history for accuracy
-        plt.plot(history.history['accuracy'])
-        plt.plot(history.history['val_accuracy'])
-        plt.title('model accuracy')
-        plt.ylabel('accuracy')
-        plt.xlabel('epoch')
-        plt.legend(['train', 'val'], loc='upper left')
-        plt.show()
-
-        # summarize history for loss
-        plt.plot(history.history['loss'])
-        plt.plot(history.history['val_loss'])
-        plt.title('model loss')
-        plt.ylabel('loss')
-        plt.xlabel('epoch')
-        plt.legend(['train', 'val'], loc='upper left')
-        plt.show()
-
-        
-        
-def getMetrics(true,preds,thresh=0.5):
-    
-    acc = 0
-    TP = 0
-    TN = 0
-    FP = 0
-    FN = 0
-    recall = 0
-    precision = 0
-    f1 = 0
-    
-    logits = np.array(preds>thresh,dtype = np.int)
-    
-    for i,label in enumerate(iter(true)):
-        
-        if label == 1:
-            if logits[i] == 1:
-                TP += 1
-            else:
-                FN += 1
-        else:
-            if logits[i] == 1:
-                FP += 1
-            else:
-                TN += 1
-    
-    confusion_matrix = [[TP,FN],[FP,TN]]
-    acc = (TP + TN)/(TP+TN+FP+FN)
-    recall = TP/(TP+FN)
-    precision = TP/(TP+FP)
-    f1 = 2 * ((precision*recall)/(precision+recall))
-    
-    s = ""
-    for i in range(2):
-        for j in range(2):
-            s+=f"{confusion_matrix[i][j]}\t"
-        s+="\n"
-    
-    print(f"Accuracy : {acc} , recall : {recall} , precision : {precision} , f1 : {f1} , Confusion Matrix : \n{s}")
-    
-    return acc,confusion_matrix,recall,precision,f1   
-
-
-
-def loadWeights(model,optimizer,loss,weights_path):
-    
-    model.load_weights(weights_path)
-    model.compile(optimizer = optimizer,loss = loss)
-    
-    return model
-
-
-def evaluate(model,data):
-    
-    images = data.map(lambda image,label:image)
-    true = data.map(lambda image,label:label).unbatch()
-
-    preds = model.predict(images)
-
-    return getMetrics(true,preds)