问我将repo从Github导入到Colab，当我运行它时没有响应

EN

import os
os.chdir("/content/drive/uygulama")
!git clone https://github.com/xuannianz/EfficientDet.git
!python3 /content/drive/uygulama/EfficientDet/inference.py

2020-04-03 18:16:19.781211: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-04-03 18:16:21.705357: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-04-03 18:16:21.718914: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-03 18:16:21.719461: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: 
pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0
coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s
2020-04-03 18:16:21.719526: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-04-03 18:16:21.719593: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-04-03 18:16:21.719625: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-04-03 18:16:21.719657: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-04-03 18:16:21.719687: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-04-03 18:16:21.720621: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-04-03 18:16:21.720704: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-04-03 18:16:21.720808: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-03 18:16:21.721346: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-03 18:16:21.721859: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2020-04-03 18:16:21.727125: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2200000000 Hz
2020-04-03 18:16:21.727305: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1d65100 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-04-03 18:16:21.727352: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-04-03 18:16:21.816983: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-03 18:16:21.817861: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1d652c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-04-03 18:16:21.817893: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0
2020-04-03 18:16:21.818109: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-03 18:16:21.818693: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: 
pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0
coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s
2020-04-03 18:16:21.818752: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-04-03 18:16:21.818776: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-04-03 18:16:21.818804: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-04-03 18:16:21.818827: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-04-03 18:16:21.818846: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-04-03 18:16:21.818891: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-04-03 18:16:21.818909: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-04-03 18:16:21.819005: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-03 18:16:21.819632: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-03 18:16:21.820138: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2020-04-03 18:16:21.820201: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-04-03 18:16:22.362078: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-04-03 18:16:22.362140: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2020-04-03 18:16:22.362153: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2020-04-03 18:16:22.362389: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-03 18:16:22.363088: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-03 18:16:22.363704: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
2020-04-03 18:16:22.363748: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14974 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)

import cv2
import json
import numpy as np
import os
import time
import glob

from model import efficientdet
from utils import preprocess_image, postprocess_boxes
from utils.draw_boxes import draw_boxes


def main():
    os.environ['CUDA_VISIBLE_DEVICES'] = '0' #ekran kartı olsaydı 0 -> 1 olacaktı.

    phi = 0 # çözünürlük indisi. phi ne ile eğittiyseniz ona göre ayarlayacaksınız.
    weighted_bifpn = True
    model_path = '/content/drive/uygulama/EfficientDet/efficientdet-d0.h5' #modelin pathi ve kendisi. 
    #çözünürlük olarak 512 yi mi seçtik indisi 0 yaparak: evet.
    image_sizes = (512, 640, 768, 896, 1024, 1280, 1408)
    image_size = image_sizes[phi]
    # coco classes
    classes = {value['id'] - 1: value['name'] for value in json.load(open('/content/drive/uygulama/EfficientDet/coco_90.json', 'r')).values()} # bu datasetler 90 farklı nesneye göre 
    #oluşturulmuş datasetler siz eğer sadece araba, kamyon gibi şeyleri almak istiyorsanız. coco_90.json bu dosyaya gidip aradığınız nesnenin labelına bakacaksınız.
    # örneğin car 3 bisiklet 2 bunu bir if else yapısı ile aşağıda ayrıştıracaksınız.
    num_classes = 90
    score_threshold = 0.3 # modelin verdiği score'un eşiklenmesi. Örneğin bir nesneye 0.2 dediyse onu almayacak.
    #anladım bunun bir formülü var mı deneme yanılma mı? : Modelin başarısına göre siz ayarlayacaksınız. Deneme yanılma biraz.
    colors = [np.random.randint(0, 256, 3).tolist() for _ in range(num_classes)]
    _, model = efficientdet(phi=phi,
                            weighted_bifpn=weighted_bifpn, #false yapıp dene
                            num_classes=num_classes,
                            score_threshold=score_threshold)
    model.load_weights(model_path, by_name=True)

    cap = cv2.VideoCapture('t.mp4')

    while cap.isOpened():
        ret,image = cap.read()
        src_image = image.copy()
        # BGR -> RGB
        image = image[:, :, ::-1]
        h, w = image.shape[:2]

        image, scale = preprocess_image(image, image_size=image_size)
        # run network
        start = time.time()
        boxes, scores, labels = model.predict_on_batch([np.expand_dims(image, axis=0)]) # modelin çalıştığı yer
        boxes, scores, labels = np.squeeze(boxes), np.squeeze(scores), np.squeeze(labels)
        print(time.time() - start)
        boxes = postprocess_boxes(boxes=boxes, scale=scale, height=h, width=w)

        # select indices which have a score above the threshold
        indices = np.where(scores[:] > score_threshold)[0] # eşiklemenin yapıldığı yer.

        # select those detections
        boxes = boxes[indices]
        labels = labels[indices]
        #buradaki labels hangi cisme ait olduğunu gösterir. Bu değer 3 ise o arabadır. Umarım anlatabilmişimdir.
        #şöyle mi olacak? if label == 3 ise alt kısmı gerçkleştir?: Aynen öyle. Tabi bu sizin kodlama becerinize bağlı olarak uğraştırabilir. :)
        # 0.93 yazan confidence değeri.
        # kodlama becerisi derken? basit bir if else yapısı gibi duruyor. o yüzden sordum? : Tabi basit. Ama indisleri iyi seçmek lazım. Gözünüz korkmasın.

        if labels[0]==2 or labels[0]==3:
            draw_boxes(src_image, boxes, scores, labels, colors, classes)

            cv2.namedWindow('image', cv2.WINDOW_NORMAL)
            cv2.imshow('image', src_image)
            cv2.waitKey(0)
    cap.release()
    cv2.destroyAllWindows()  

if __name__ == '__main__':
    main()