InvalidArgumentError: Graph execution error:in yolov8

Below is the code for objection detection using yolo

import os
import xml.etree.ElementTree as ET
import tensorflow as tf
import keras_cv
import requests
import zipfile
import tensorflow as tf
import sys
print(sys.path)
sys.path.append(r'C:UsersDELLDownloadskeras-yolo3-master')
from ultralytics import YOLO 
from tqdm.auto import tqdm
from tensorflow import keras
from keras_cv import bounding_box
from keras_cv import visualization


import tensorflow as tf


SPLIT_RATIO = 0.2
BATCH_SIZE = 8
LEARNING_RATE = 0.0001
EPOCH = 30
GLOBAL_CLIPNORM = 10.0
IMG_SIZE = (640,640)

class_ids = [
    "motorcycle",
    "aeroplane",
    "face",
  
]
class_mapping = dict(zip(range(len(class_ids)), class_ids))
 
import os

# Example paths using raw string literal
path_annot = r"dataset15/annotations"
path_images = r"dataset15/documents"

# List of class names (assuming these are the names of your 13 classes)

# Get all XML file paths in path_annot and sort them
xml_files = sorted(
    [
        os.path.join(path_annot, class_name, file_name)
        for class_name in class_ids
        for file_name in os.listdir(os.path.join(path_annot, class_name))
        if file_name.endswith(".xml")
    ]
)

# Get all JPEG image file paths in path_images and sort them
jpg_files = sorted(
    [
        os.path.join(path_images, class_name, file_name)
        for class_name in class_ids
        for file_name in os.listdir(os.path.join(path_images, class_name))
        if file_name.endswith(".jpg")
    ]
)

print("XML Files:", xml_files)
print("JPEG Files:", jpg_files)

import os
import xml.etree.ElementTree as ET

def parse_annotation(xml_file):
    tree = ET.parse(xml_file)
    root = tree.getroot()

    image_name = root.find("filename").text
   # Example if images are organized in class subdirectories
    class_name = root.find("folder").text  # or however the class name is stored in the XML
    image_path = os.path.join(path_images, class_name, image_name)

    boxes = []
    classes = []
    for obj in root.iter("object"):
        cls = obj.find("name").text
        classes.append(cls)

        bbox = obj.find("bndbox")
        xmin = float(bbox.find("xmin").text)
        ymin = float(bbox.find("ymin").text)
        xmax = float(bbox.find("xmax").text)
        ymax = float(bbox.find("ymax").text)
        boxes.append([xmin, ymin, xmax, ymax])

    class_ids = [
        list(class_mapping.keys())[list(class_mapping.values()).index(cls)]
        for cls in classes
    ]
    return image_path, boxes, class_ids


image_paths = []
bbox = []
classes = []
for xml_file in tqdm(xml_files):
    image_path, boxes, class_ids = parse_annotation(xml_file)
    image_paths.append(image_path)
    bbox.append(boxes)
    classes.append(class_ids)

bbox = tf.ragged.constant(bbox)
classes = tf.ragged.constant(classes)
image_paths = tf.ragged.constant(image_paths)
 
data = tf.data.Dataset.from_tensor_slices((image_paths, classes, bbox))    
    
num_val = int(len(xml_files) * SPLIT_RATIO)


# Split the dataset into train and validation sets
val_data = data.take(num_val)
train_data = data.skip(num_val)

def load_image(image_path):
    image = tf.io.read_file(image_path)
    image = tf.image.decode_jpeg(image, channels=3)
    return image


def load_dataset(image_path, classes, bbox):
    # Read Image
    image = load_image(image_path)
    bounding_boxes = {
        "classes": tf.cast(classes, dtype=tf.float32),
        "boxes": bbox,
    }
    return {"images": tf.cast(image, tf.float32), "bounding_boxes": bounding_boxes}

augmenter = keras.Sequential(
    layers=[
        keras_cv.layers.RandomFlip(mode="horizontal", bounding_box_format="xyxy"),
        keras_cv.layers.RandomShear(
            x_factor=0.2, y_factor=0.2, bounding_box_format="xyxy"
        ),
        keras_cv.layers.JitteredResize(
            target_size=(640, 640), scale_factor=(1, 1), bounding_box_format="xyxy"
        ),
    ]
)

train_ds = train_data.map(load_dataset, num_parallel_calls=tf.data.AUTOTUNE)
train_ds = train_ds.shuffle(BATCH_SIZE * 4)
train_ds = train_ds.ragged_batch(BATCH_SIZE, drop_remainder=True)
train_ds = train_ds.map(augmenter, num_parallel_calls=tf.data.AUTOTUNE)

resizing = keras_cv.layers.JitteredResize(
    target_size=(640, 640),
    scale_factor=(1, 1),
    bounding_box_format="xyxy",
)

val_ds = val_data.map(load_dataset, num_parallel_calls=tf.data.AUTOTUNE)
val_ds = val_ds.shuffle(BATCH_SIZE * 4)
val_ds = val_ds.ragged_batch(BATCH_SIZE, drop_remainder=True)
val_ds = val_ds.map(resizing, num_parallel_calls=tf.data.AUTOTUNE)


def dict_to_tuple(inputs):
    return inputs["images"], inputs["bounding_boxes"]


train_ds = train_ds.map(dict_to_tuple, num_parallel_calls=tf.data.AUTOTUNE)
train_ds = train_ds.prefetch(tf.data.AUTOTUNE)

val_ds = val_ds.map(dict_to_tuple, num_parallel_calls=tf.data.AUTOTUNE)
val_ds = val_ds.prefetch(tf.data.AUTOTUNE)

backbone = keras_cv.models.YOLOV8Backbone.from_preset(
    "yolo_v8_l_backbone_coco"  # We will use yolov8 small backbone with coco weights
)

backbone = backbone
yolo = keras_cv.models.YOLOV8Detector(
    num_classes=len(class_mapping),
    bounding_box_format="xyxy",
    backbone=backbone,
    fpn_depth=3,
)
 
yolo.summary()

optimizer = tf.keras.optimizers.Adam(
    learning_rate=LEARNING_RATE,
    global_clipnorm=GLOBAL_CLIPNORM,
)
 
yolo.compile(
    optimizer=optimizer, classification_loss="binary_crossentropy", box_loss="ciou"
)

import pycocotools
print("pycocotools is installed correctly.")
class EvaluateCOCOMetricsCallback(keras.callbacks.Callback):
    def __init__(self, data, save_path):
        super().__init__()
        self.data = data
        self.metrics = keras_cv.metrics.BoxCOCOMetrics(
            bounding_box_format="xyxy",
            evaluate_freq=1e9,
        )

        self.save_path = save_path
        self.best_map = -1.0

    def on_epoch_end(self, epoch, logs):
        self.metrics.reset_state()
        for batch in self.data:
            images, y_true = batch[0], batch[1]
            y_pred = self.model.predict(images, verbose=0)
            self.metrics.update_state(y_true, y_pred)

        metrics = self.metrics.result(force=True)
        logs.update(metrics)

        current_map = metrics["MaP"]
        if current_map > self.best_map:
            self.best_map = current_map
            self.model.save(self.save_path)  # Save the model when mAP improves

        return logs

from tensorflow.keras.models import save_model

    
yolo.fit(
    train_ds,
    validation_data=val_ds,
    epochs=3,
    callbacks=[EvaluateCOCOMetricsCallback(val_ds, "F:GPU6model.h5")],
)

This first code works properly but in this … but in this training and validation images are cropped this code works on 640*640 and my original image size is 2000 * 880 so i modified this code to following 2nd code in which traning and validation images are not cropped

import os
import xml.etree.ElementTree as ET
import tensorflow as tf
import keras_cv
import requests
import zipfile
import tensorflow as tf
import sys
print(sys.path)
#sys.path.append(r'C:UsersDELLDownloadskeras-yolo3-master')
from ultralytics import YOLO 
from tqdm.auto import tqdm
from tensorflow import keras
from keras_cv import bounding_box
from keras_cv import visualization


import tensorflow as tf


import os
import xml.etree.ElementTree as ET
import tensorflow as tf
from ultralytics import YOLO 
from tqdm.auto import tqdm
from tensorflow import keras
import matplotlib.pyplot as plt
import matplotlib.patches as patches

SPLIT_RATIO = 0.2
BATCH_SIZE = 8
LEARNING_RATE = 0.001
EPOCH = 3
GLOBAL_CLIPNORM = 10.0
IMG_SIZE = (640, 640)

class_mapping = dict(zip(range(len(class_ids)), class_ids))

path_annot = r"dataset15/annotations"
path_images = r"dataset15/documents"

import os
import xml.etree.ElementTree as ET
import tensorflow as tf
import keras_cv
import requests
import zipfile
import tensorflow as tf
import sys
print(sys.path)
#sys.path.append(r'C:UsersDELLDownloadskeras-yolo3-master')
from ultralytics import YOLO 
from tqdm.auto import tqdm
from tensorflow import keras
from keras_cv import bounding_box
from keras_cv import visualization


import tensorflow as tf


import os
import xml.etree.ElementTree as ET
import tensorflow as tf
from ultralytics import YOLO 
from tqdm.auto import tqdm
from tensorflow import keras
import matplotlib.pyplot as plt
import matplotlib.patches as patches

SPLIT_RATIO = 0.2
BATCH_SIZE = 8
LEARNING_RATE = 0.001
EPOCH = 30
GLOBAL_CLIPNORM = 10.0
IMG_SIZE = (640, 640)

class_ids = [
    "aeroplane",
    "motorcycle",
    "face",
  
    
]
class_mapping = dict(zip(range(len(class_ids)), class_ids))

path_annot = r"dataset15/annotations"
path_images = r"dataset15/documents"

xml_files = sorted(
    [
        os.path.join(path_annot, class_name, file_name)
        for class_name in class_ids
        for file_name in os.listdir(os.path.join(path_annot, class_name))
        if file_name.endswith(".xml")
    ]
)

jpg_files = sorted(
    [
        os.path.join(path_images, class_name, file_name)
        for class_name in class_ids
        for file_name in os.listdir(os.path.join(path_images, class_name))
        if file_name.endswith(".jpg")
    ]
)

print("XML Files:", xml_files)
print("JPEG Files:", jpg_files)

def parse_annotation(xml_file):
    tree = ET.parse(xml_file)
    root = tree.getroot()

    image_name = root.find("filename").text
    class_name = root.find("folder").text
    image_path = os.path.join(path_images, class_name, image_name)

    boxes = []
    classes = []
    for obj in root.iter("object"):
        cls = obj.find("name").text
        classes.append(cls)

        bbox = obj.find("bndbox")
        xmin = float(bbox.find("xmin").text)
        ymin = float(bbox.find("ymin").text)
        xmax = float(bbox.find("xmax").text)
        ymax = float(bbox.find("ymax").text)
        boxes.append([xmin, ymin, xmax, ymax])

    class_ids = [
        list(class_mapping.keys())[list(class_mapping.values()).index(cls)]
        for cls in classes
    ]
    return image_path, boxes, class_ids

image_paths = []
bbox = []
classes = []
for xml_file in tqdm(xml_files):
    image_path, boxes, class_ids = parse_annotation(xml_file)
    image_paths.append(image_path)
    bbox.append(boxes)
    classes.append(class_ids)

bbox = tf.ragged.constant(bbox)
classes = tf.ragged.constant(classes)
image_paths = tf.ragged.constant(image_paths)

data = tf.data.Dataset.from_tensor_slices((image_paths, classes, bbox))

num_val = int(len(xml_files) * SPLIT_RATIO)

val_data = data.take(num_val)
train_data = data.skip(num_val)

def load_image(image_path):
    image = tf.io.read_file(image_path)
    image = tf.image.decode_jpeg(image, channels=3)
    return image

def resize_and_crop_image(image, target_height, target_width):
    original_shape = tf.shape(image)
    image = tf.image.resize(image, (target_height, target_width))
    image = tf.image.resize_with_crop_or_pad(image, target_height, target_width)
    return image, original_shape

def resize_and_crop_bboxes(bboxes, original_shape, target_height, target_width):
    height, width = original_shape[0], original_shape[1]
    scale_y = target_height / height
    scale_x = target_width / width

    bboxes = bboxes * [scale_x, scale_y, scale_x, scale_y]
    return bboxes

def load_dataset(image_path, classes, bbox):
    image = load_image(image_path)
    image, original_shape = resize_and_crop_image(image, IMG_SIZE[0], IMG_SIZE[1])
    bbox = resize_and_crop_bboxes(bbox, original_shape, IMG_SIZE[0], IMG_SIZE[1])
    bounding_boxes = {
        "classes": tf.cast(classes, dtype=tf.float32),
        "boxes": bbox,
    }
    return {"images": tf.cast(image, tf.float32), "bounding_boxes": bounding_boxes}

train_ds = train_data.map(load_dataset, num_parallel_calls=tf.data.AUTOTUNE)
train_ds = train_ds.shuffle(BATCH_SIZE * 4)
train_ds = train_ds.ragged_batch(BATCH_SIZE, drop_remainder=True)

val_ds = val_data.map(load_dataset, num_parallel_calls=tf.data.AUTOTUNE)
val_ds = val_ds.shuffle(BATCH_SIZE * 4)
val_ds = val_ds.ragged_batch(BATCH_SIZE, drop_remainder=True)

def dict_to_tuple(inputs):
    return inputs["images"], inputs["bounding_boxes"]

train_ds = train_ds.map(dict_to_tuple, num_parallel_calls=tf.data.AUTOTUNE)
train_ds = train_ds.prefetch(tf.data.AUTOTUNE)

val_ds = val_ds.map(dict_to_tuple, num_parallel_calls=tf.data.AUTOTUNE)
val_ds = val_ds.prefetch(tf.data.AUTOTUNE)

for element in train_ds.take(1):
    print(element)

def plot_image_with_bboxes(image, bboxes, classes):
    fig, ax = plt.subplots(1)
    ax.imshow(image.numpy().astype("uint8"))
    for bbox, cls in zip(bboxes, classes):
        xmin, ymin, xmax, ymax = bbox
        width = xmax - xmin
        height = ymax - ymin
        rect = patches.Rectangle((xmin, ymin), width, height, linewidth=2, edgecolor='r', facecolor='none')
        ax.add_patch(rect)
        ax.text(xmin, ymin, class_mapping[cls], bbox=dict(facecolor='yellow', alpha=0.5))
    plt.show()

def visualize_dataset(dataset, num_samples=5):
    for images, bounding_boxes in dataset.take(1):
        for i in range(num_samples):
            image = images[i]
            bboxes = bounding_boxes['boxes'][i].numpy()
            classes = bounding_boxes['classes'][i].numpy()
            plot_image_with_bboxes(image, bboxes, classes)

print("Training dataset samples:")
visualize_dataset(train_ds)

print("Validation dataset samples:")
visualize_dataset(val_ds)

for images, bounding_boxes in train_ds.take(1):
    print("Train batch shape:", images.shape)
    print("Bounding boxes shape:", bounding_boxes['boxes'].shape)
    print("Classes shape:", bounding_boxes['classes'].shape)

for images, bounding_boxes in val_ds.take(1):
    print("Validation batch shape:", images.shape)
    print("Bounding boxes shape:", bounding_boxes['boxes'].shape)
    print("Classes shape:", bounding_boxes['classes'].shape)
    
backbone = keras_cv.models.YOLOV8Backbone.from_preset(
    "yolo_v8_l_backbone_coco"  # We will use yolov8 small backbone with coco weights
)
for images, labels in train_ds.take(1):
    print("Image batch shape:", images.shape)
    print("Labels keys:", labels.keys())
    for key, value in labels.items():
        print(f"Key: {key}, Value shape: {value.shape}")
        
backbone = backbone
yolo = keras_cv.models.YOLOV8Detector(
    num_classes=len(class_mapping),
    bounding_box_format="xyxy",
    backbone=backbone,
    fpn_depth=3,
)
 
yolo.summary()

optimizer = tf.keras.optimizers.Adam(
    learning_rate=LEARNING_RATE,
    global_clipnorm=GLOBAL_CLIPNORM,
)
 
yolo.compile(
    optimizer=optimizer, classification_loss="binary_crossentropy", box_loss="ciou"
)

import pycocotools
print("pycocotools is installed correctly.")
class EvaluateCOCOMetricsCallback(keras.callbacks.Callback):
    def __init__(self, data, save_path):
        super().__init__()
        self.data = data
        self.metrics = keras_cv.metrics.BoxCOCOMetrics(
            bounding_box_format="xyxy",
            evaluate_freq=1e9,
        )

        self.save_path = save_path
        self.best_map = -1.0

    def on_epoch_end(self, epoch, logs):
        self.metrics.reset_state()
        for batch in self.data:
            images, y_true = batch[0], batch[1]
            y_pred = self.model.predict(images, verbose=0)
            self.metrics.update_state(y_true, y_pred)

        metrics = self.metrics.result(force=True)
        logs.update(metrics)

        current_map = metrics["MaP"]
        if current_map > self.best_map:
            self.best_map = current_map
            self.model.save(self.save_path)  # Save the model when mAP improves

        return logs

from tensorflow.keras.models import save_model

    
yolo.fit(
    train_ds,
    validation_data=val_ds,
    epochs=3,
    callbacks=[EvaluateCOCOMetricsCallback(val_ds, "model.h5")],
)

inthis code training and validation images are properly visua;ize but this code gives following error: InvalidArgumentError Traceback (most recent call last)
Cell In[2], line 328
323 return logs
325 from tensorflow.keras.models import save_model
–> 328 yolo.fit(
329 train_ds,
330 validation_data=val_ds,
331 epochs=3,
332 callbacks=[EvaluateCOCOMetricsCallback(val_ds, “model.h5”)],
333 )

File ~/anaconda3/envs/cvlab/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py:70, in filter_traceback..error_handler(*args, **kwargs)
67 filtered_tb = _process_traceback_frames(e.traceback)
68 # To get the full stack trace, call:
69 # tf.debugging.disable_traceback_filtering()
—> 70 raise e.with_traceback(filtered_tb) from None
71 finally:
72 del filtered_tb

File ~/anaconda3/envs/cvlab/lib/python3.11/site-packages/tensorflow/python/eager/execute.py:53, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
51 try:
52 ctx.ensure_initialized()
—> 53 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
54 inputs, attrs, num_outputs)
55 except core._NotOkStatusException as e:
56 if name is not None:

InvalidArgumentError: Graph execution error:

assertion failed: [Last row partition does not match flat_values.] [Condition x == y did not hold element-wise:] [x (strided_slice_4:0) = ] [4] [y (strided_slice_5:0) = ] [8]
[[{{node assert_equal_1/Assert/AssertGuard/Assert}}]]
[[IteratorGetNext]] [Op:__inference_train_function_66844]

  • python
  • tensorflow
  • keras
  • deep-learning
  • yolo

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Filed under: Kiến thức lập trình - @ 16:04

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Trang chủ Giới thiệu Sinh nhật bé trai Sinh nhật bé gái Tổ chức sự kiện Biểu diễn giải trí Dịch vụ khác Trang trí tiệc cưới Tổ chức khai trương Tư vấn dịch vụ Thư viện ảnh Tin tức - sự kiện Liên hệ Chú hề sinh nhật Trang trí YEAR END PARTY công ty Trang trí tất niên cuối năm Trang trí tất niên xu hướng mới nhất Trang trí sinh nhật bé trai Hải Đăng Trang trí sinh nhật bé Khánh Vân Trang trí sinh nhật Bích Ngân Trang trí sinh nhật bé Thanh Trang Thuê ông già Noel phát quà Biểu diễn xiếc khỉ Xiếc quay đĩa
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