commit 7852cccb5dd074eef9f30057416d0eaa32f39cdf from: Negasonic69 <148595374+Richard7987@users.noreply.github.com> via: GitHub date: Wed Oct 23 00:53:11 2024 UTC Add files via upload commit - /dev/null commit + 7852cccb5dd074eef9f30057416d0eaa32f39cdf blob - /dev/null blob + 38e8c03a2fd1447d8925bc002028a464ad01d5b4 (mode 644) --- /dev/null +++ README.md @@ -0,0 +1,13 @@ +# Human-Detection-Using-Drone-During-Disasters + +This project focuses on detecting humans in disaster scenarios using three different methods. The system is designed to send a message to a predefined number whenever a human is detected. The detection methods include: + +Live Camera Feed: Utilizes the laptop's live camera to detect humans in real-time. +AI-Generated Dataset: Employs a custom dataset simulating people trapped in earthquake scenarios to train AI models for human detection. +Drone Footage: Analyzes video recorded by a DJI Naza M Lite drone to identify humans in the footage. +We used Vonage for sending messages and Visual Studio for project development. The objective of this project is to enhance disaster response times by promptly alerting rescuers to the presence of humans in disaster-stricken areas. + +In each scenario, the software sends an alert message to a predefined number upon detecting a human, ensuring a faster response in disaster situations. + + + blob - /dev/null blob + cec1768c7a4023a89c124f011c41bfc6b8edf264 (mode 644) --- /dev/null +++ coco.names @@ -0,0 +1 @@ +person \ No newline at end of file blob - /dev/null blob + 759151b8b9dbb287ce5a9f9a3470681c07710708 (mode 644) Binary files /dev/null and frozen_inference_graph.pb differ blob - /dev/null blob + 1504d776d9d0a6bee9d66b7f9c3c22ffdaa7f264 (mode 644) --- /dev/null +++ main.py @@ -0,0 +1,72 @@ +import cv2 +import cvzone +from collections import deque + +thres = 0.55 # Umbral de confianza +nmsThres = 0.2 # Umbral de NMS (Non-Maxima Suppression) + +cap = cv2.VideoCapture('v1.mp4') +cap.set(3, 640) +cap.set(4, 480) + +# Carga los nombres de las clases +classNames = [] +classFile = 'coco.names' +with open(classFile, 'rt') as f: + classNames = f.read().strip().split('\n') + +# Cargar la configuración y los pesos de la red +configPath = 'ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt' +weightsPath = "frozen_inference_graph.pb" + +net = cv2.dnn_DetectionModel(weightsPath, configPath) +net.setInputSize(320, 320) +net.setInputScale(1.0 / 127.5) +net.setInputMean((127.5, 127.5, 127.5)) +net.setInputSwapRB(True) + +# Inicializa el contador de personas y una lista de seguimiento +person_count = 0 +trackers = [] # Lista de trackers activos +max_distance = 50 # Distancia máxima entre dos detecciones para considerar que es la misma persona +detections = deque(maxlen=20) # Cola para almacenar las últimas detecciones de personas + +while True: + success, img = cap.read() + if not success: + break # Sale del bucle si no se pueden leer más frames + + # Detección de objetos + classIds, confs, bbox = net.detect(img, confThreshold=thres, nmsThreshold=nmsThres) + + # Verifica si la detección no está vacía + if len(classIds) != 0: + for classId, conf, box in zip(classIds.flatten(), confs.flatten(), bbox): + if 0 < classId <= len(classNames): + if classNames[classId - 1].lower() == "person": + # Verifica si la persona ya fue contada en los últimos frames + x, y, w, h = box + detected = False + + for prev_box in detections: + prev_x, prev_y, prev_w, prev_h = prev_box + distance = ((x - prev_x) ** 2 + (y - prev_y) ** 2) ** 0.5 + if distance < max_distance: + detected = True + break + + if not detected: + person_count += 1 # Incrementa el contador solo si no ha sido detectada recientemente + detections.append(box) # Agrega la nueva detección a la cola + + # Dibuja el cuadro alrededor de la persona + cvzone.cornerRect(img, box) + cv2.putText(img, f'PERSON {round(conf * 100, 2)}%', + (box[0] + 10, box[1] + 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, + 1, (0, 255, 0), 2) + + cv2.imshow("Image", img) + cv2.waitKey(1) + +# Al finalizar el video, muestra el conteo de personas +print(f'Cantidad total de personas detectadas: {person_count}') blob - /dev/null blob + 2fb4408bf540d9571945aca7fd11486987c3c429 (mode 644) --- /dev/null +++ on_dataset.py @@ -0,0 +1,71 @@ +import os +import cv2 +import numpy as np + +# Load YOLO +net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg") +classes = [] +with open("coco.names", "r") as f: + classes = [line.strip() for line in f.readlines()] + +layer_names = net.getLayerNames() +output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()] + +# Load dataset +dataset_path = r"YOUR_PATH" +image_files = [file for file in os.listdir(dataset_path) if file.endswith((".jpg", ".jpeg"))] + +# Loop through images in the dataset +for image_file in image_files: + # Read image + image = cv2.imread(os.path.join(dataset_path, image_file)) + if image is None: + print(f"Error: Unable to read image file '{image_file}'. Skipping...") + continue + height, width, channels = image.shape + + # Detecting objects + blob = cv2.dnn.blobFromImage(image, 0.00392, (416, 416), (0, 0, 0), True, crop=False) + net.setInput(blob) + outs = net.forward(output_layers) + + # Showing information on the screen + class_ids = [] + confidences = [] + boxes = [] + for out in outs: + for detection in out: + scores = detection[5:] + class_id = np.argmax(scores) + confidence = scores[class_id] + if confidence > 0.5: # Confidence threshold + # Object detected + center_x = int(detection[0] * width) + center_y = int(detection[1] * height) + w = int(detection[2] * width) + h = int(detection[3] * height) + + # Rectangle coordinates + x = int(center_x - w / 2) + y = int(center_y - h / 2) + + boxes.append([x, y, w, h]) + confidences.append(float(confidence)) + class_ids.append(class_id) + + indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4) # Non-maximum suppression + + font = cv2.FONT_HERSHEY_PLAIN + for i in range(len(boxes)): + if i in indexes: + x, y, w, h = boxes[i] + label = str(classes[class_ids[i]]) + color = (255, 0, 0) + cv2.rectangle(image, (x, y), (x + w, y + h), color, 2) + cv2.putText(image, label, (x, y + 30), font, 3, color, 3) + print(f"Object detected: {label} (Confidence: {confidences[i]})") + + # Show image + #cv2.imshow("Image", image) + #cv2.waitKey(0) + #cv2.destroyAllWindows() blob - /dev/null blob + 2e069e45aa9bda19ee64aefe964ec96466cd6dd2 (mode 644) Binary files /dev/null and output.avi differ blob - /dev/null blob + 514860d693735bdc4f0ed2dd62a8fa9b38377e5b (mode 644) --- /dev/null +++ recorded vedio.py @@ -0,0 +1,70 @@ +import cv2 +import cvzone +from sms import send_msg + +thres = 0.55 +nmsThres = 0.2 + +# Path to your video file +video_path = r"YOUR_PATH" + +classNames = [] +classFile = 'coco.names' +with open(classFile, 'rt') as f: + classNames = f.read().split('\n') + +configPath = 'ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt' +weightsPath = "frozen_inference_graph.pb" + +net = cv2.dnn_DetectionModel(weightsPath, configPath) +net.setInputSize(320, 320) +net.setInputScale(1.0 / 127.5) +net.setInputMean((127.5, 127.5, 127.5)) +net.setInputSwapRB(True) + +# Open the video file +cap = cv2.VideoCapture(video_path) + +# Get the video's frame width and height +frame_width = int(cap.get(3)) +frame_height = int(cap.get(4)) + +# Specify the codec and create VideoWriter object +out = cv2.VideoWriter('output.avi', cv2.VideoWriter_fourcc(*'MJPG'), 10, (frame_width, frame_height)) + +sms_sent = False # Flag variable to track SMS sent status + +while cap.isOpened(): + ret, frame = cap.read() + if not ret: + break + + classIds, confs, bbox = net.detect(frame, confThreshold=thres, nmsThreshold=nmsThres) + + # Send SMS only once for the first frame + if not sms_sent: + try: + send_msg() + sms_sent = True # Set flag to True once SMS is sent + except Exception as e: + print("Error sending SMS:", e) + + if len(classIds) != 0: + for classId, conf, box in zip(classIds.flatten(), confs.flatten(), bbox): + if classId == 0: # 0 corresponds to the class "person" in COCO dataset + cv2.rectangle(frame, (box[0], box[1]), (box[0] + box[2], box[1] + box[3]), (0, 255, 0), 2) + cv2.putText(frame, f'{classNames[classId - 1].upper()} {round(conf * 100, 2)}', + (box[0] + 10, box[1] + 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, + 1, (0, 255, 0), 2) + + # Write the frame into the output video + out.write(frame) + + cv2.imshow("Video", frame) + if cv2.waitKey(1) & 0xFF == ord('q'): + break + +# Release everything if job is finished +cap.release() +out.release() +cv2.destroyAllWindows() blob - /dev/null blob + da855960e27b80ee0856fca67a8b4b6fbbf1ba03 (mode 644) --- /dev/null +++ single_img.py @@ -0,0 +1,56 @@ +# +# sending sms only once per image in dataset + + +import cv2 +import os +import cvzone +from sms import send_msg +thres = 0.55 +nmsThres = 0.2 + +# Path to your dataset directory containing images +dataset_path = r"YOUR_PATH" + +classNames = [] +classFile = 'coco.names' +with open(classFile, 'rt') as f: + classNames = f.read().split('\n') +print(classNames) + +configPath = 'ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt' +weightsPath = "frozen_inference_graph.pb" + +net = cv2.dnn_DetectionModel(weightsPath, configPath) +net.setInputSize(320, 320) +net.setInputScale(1.0 / 127.5) +net.setInputMean((127.5, 127.5, 127.5)) +net.setInputSwapRB(True) + +# Get a list of image files in the dataset directory +image_files = [file for file in os.listdir(dataset_path) if file.endswith((".jpg", ".jpeg"))] + +sms_sent = False # Flag variable to track SMS sent status + +for image_file in image_files: + img = cv2.imread(os.path.join(dataset_path, image_file)) + classIds, confs, bbox = net.detect(img, confThreshold=thres, nmsThreshold=nmsThres) + + # Send SMS only once for each image + if not sms_sent: + try: + send_msg() + sms_sent = True # Set flag to True once SMS is sent + except Exception as e: + print("Error sending SMS:", e) + + for classId, conf, box in zip(classIds.flatten(), confs.flatten(), bbox): + cvzone.cornerRect(img, box) + cv2.putText(img, f'{classNames[classId - 1].upper()} {round(conf * 100, 2)}', + (box[0] + 10, box[1] + 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, + 1, (0, 255, 0), 2) + + cv2.imshow("Image", img) + cv2.waitKey(0) + +cv2.destroyAllWindows() blob - /dev/null blob + c983cda0a21f3a1244f6e5af2066e8b667ec84e2 (mode 644) --- /dev/null +++ sms.py @@ -0,0 +1,24 @@ +import os + +import vonage + + +def send_msg(): + # Credenciales y número de destino desde variables de entorno, nunca en el código: + # VONAGE_API_KEY, VONAGE_API_SECRET, SMS_TO (ej. 52XXXXXXXXXX) + client = vonage.Client(key=os.environ["VONAGE_API_KEY"], secret=os.environ["VONAGE_API_SECRET"]) + sms = vonage.Sms(client) + responseData = sms.send_message( + { + "from": "Vonage APIs", + "to": os.environ["SMS_TO"], + "text": "Se a detectado una persona", + } + ) + + if responseData["messages"][0]["status"] == "0": + print("Message sent successfully.") + else: + print(f"Message failed with error: {responseData['messages'][0]['error-text']}") +if __name__=='__main__': + send_msg() blob - /dev/null blob + 64bedd739162d744f7973da2e6ed07eb14976bb3 (mode 644) --- /dev/null +++ ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt @@ -0,0 +1,8399 @@ +node { + name: "normalized_input_image_tensor" + op: "Placeholder" + attr { + key: "dtype" + value { + type: DT_FLOAT + } + } + attr { + key: "shape" + value { + shape { + dim { + size: 1 + } + dim { + size: 320 + } + dim { + size: 320 + } + dim { + size: 3 + } + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/Conv/Conv2D" + op: "Conv2D" + input: "normalized_input_image_tensor" + input: "FeatureExtractor/MobilenetV3/Conv/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr 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input: "FeatureExtractor/MobilenetV3/expanded_conv_1/project/BatchNorm/FusedBatchNormV3" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_2/expand/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_1/output" + input: "FeatureExtractor/MobilenetV3/expanded_conv_2/expand/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_2/expand/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_2/expand/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_2/expand/BatchNorm/gamma" + input: 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+ } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_2/depthwise/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_2/depthwise/depthwise" + input: "FeatureExtractor/MobilenetV3/expanded_conv_2/depthwise/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_2/depthwise/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_2/depthwise/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_2/depthwise/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_2/depthwise/Relu" + op: "Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_2/depthwise/BatchNorm/FusedBatchNormV3" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_2/project/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_2/depthwise/Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_2/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_2/project/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_2/project/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_2/project/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_2/project/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_2/project/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_2/project/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_2/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/expanded_conv_2/project/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_1/output" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_3/input" + op: "Identity" + input: "FeatureExtractor/MobilenetV3/expanded_conv_2/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_3/expand/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/input" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/expand/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_3/expand/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/expand/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/expand/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/expand/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/expand/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/expand/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_3/expand/Relu" + op: "Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/expand/BatchNorm/FusedBatchNormV3" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_3/depthwise/depthwise" + op: "DepthwiseConv2dNative" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/expand/Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 2 + i: 2 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_3/depthwise/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/depthwise/depthwise" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/depthwise/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/depthwise/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/depthwise/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/depthwise/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_3/depthwise/Relu" + op: "Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/depthwise/BatchNorm/FusedBatchNormV3" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/Mean" + op: "Mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/depthwise/Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/Mean/reduction_indices" + attr { + key: "keep_dims" + value { + b: true + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/Conv/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/Mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/Conv/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/Conv/BiasAdd" + op: "BiasAdd" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/Conv/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/Conv/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/Conv/Relu" + op: "Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/Conv/BiasAdd" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/Conv_1/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/Conv/Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/Conv_1/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/Conv_1/BiasAdd" + op: "BiasAdd" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/Conv_1/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/Conv_1/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/Conv_1/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/Conv_1/BiasAdd" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/Conv_1/add/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/Conv_1/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/Conv_1/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/Conv_1/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/Conv_1/Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/Conv_1/mul/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/depthwise/Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/Conv_1/mul" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_3/project/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/squeeze_excite/mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_3/project/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/project/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/project/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/project/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/project/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/project/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_3/output" + op: "Identity" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/project/BatchNorm/FusedBatchNormV3" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_4/expand/Conv2D" + op: "Conv2D" + input: 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"FeatureExtractor/MobilenetV3/expanded_conv_4/expand/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_4/expand/Relu" + op: "Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/expand/BatchNorm/FusedBatchNormV3" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_4/depthwise/depthwise" + op: "DepthwiseConv2dNative" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/expand/Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + 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"FeatureExtractor/MobilenetV3/expanded_conv_4/depthwise/Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/squeeze_excite/Mean/reduction_indices" + attr { + key: "keep_dims" + value { + b: true + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_4/squeeze_excite/Conv/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/squeeze_excite/Mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/squeeze_excite/Conv/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_4/squeeze_excite/Conv/BiasAdd" + op: "BiasAdd" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/squeeze_excite/Conv/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/squeeze_excite/Conv/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_4/squeeze_excite/Conv/Relu" + op: "Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/squeeze_excite/Conv/BiasAdd" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_4/squeeze_excite/Conv_1/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/squeeze_excite/Conv/Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/squeeze_excite/Conv_1/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_4/squeeze_excite/Conv_1/BiasAdd" + op: "BiasAdd" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/squeeze_excite/Conv_1/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/squeeze_excite/Conv_1/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_4/squeeze_excite/Conv_1/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/squeeze_excite/Conv_1/BiasAdd" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/squeeze_excite/Conv_1/add/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_4/squeeze_excite/Conv_1/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/squeeze_excite/Conv_1/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_4/squeeze_excite/Conv_1/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/squeeze_excite/Conv_1/Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/squeeze_excite/Conv_1/mul/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_4/squeeze_excite/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/depthwise/Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/squeeze_excite/Conv_1/mul" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_4/project/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/squeeze_excite/mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_4/project/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/project/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/project/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/project/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/project/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/project/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_4/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/project/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_3/output" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_5/input" + op: "Identity" + input: "FeatureExtractor/MobilenetV3/expanded_conv_4/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_5/expand/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/input" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/expand/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_5/expand/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/expand/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/expand/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/expand/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/expand/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/expand/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_5/expand/Relu" + op: "Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/expand/BatchNorm/FusedBatchNormV3" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_5/depthwise/depthwise" + op: "DepthwiseConv2dNative" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/expand/Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_5/depthwise/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/depthwise/depthwise" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/depthwise/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/depthwise/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/depthwise/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/depthwise/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_5/depthwise/Relu" + op: "Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/depthwise/BatchNorm/FusedBatchNormV3" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/Mean" + op: "Mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/depthwise/Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/Mean/reduction_indices" + attr { + key: "keep_dims" + value { + b: true + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/Conv/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/Mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/Conv/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/Conv/BiasAdd" + op: "BiasAdd" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/Conv/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/Conv/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/Conv/Relu" + op: "Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/Conv/BiasAdd" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/Conv_1/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/Conv/Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/Conv_1/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/Conv_1/BiasAdd" + op: "BiasAdd" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/Conv_1/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/Conv_1/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/Conv_1/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/Conv_1/BiasAdd" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/Conv_1/add/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/Conv_1/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/Conv_1/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/Conv_1/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/Conv_1/Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/Conv_1/mul/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/depthwise/Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/Conv_1/mul" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_5/project/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/squeeze_excite/mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_5/project/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/project/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/project/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/project/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/project/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/project/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_5/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/project/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/input" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_6/input" + op: "Identity" + input: "FeatureExtractor/MobilenetV3/expanded_conv_5/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_6/expand/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/input" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/expand/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_6/expand/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/expand/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/expand/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/expand/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/expand/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/expand/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_6/expand/hard_swish/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/expand/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/expand/hard_swish/add/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_6/expand/hard_swish/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/expand/hard_swish/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_6/expand/hard_swish/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/expand/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/expand/hard_swish/Relu6" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_6/expand/hard_swish/mul_1" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/expand/hard_swish/mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/expand/hard_swish/mul_1/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_6/depthwise/depthwise" + op: "DepthwiseConv2dNative" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/expand/hard_swish/mul_1" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 2 + i: 2 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_6/depthwise/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/depthwise/depthwise" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/depthwise/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/depthwise/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/depthwise/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/depthwise/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_6/depthwise/hard_swish/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/depthwise/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/depthwise/hard_swish/add/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_6/depthwise/hard_swish/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/depthwise/hard_swish/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_6/depthwise/hard_swish/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/depthwise/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/depthwise/hard_swish/Relu6" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_6/depthwise/hard_swish/mul_1" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/depthwise/hard_swish/mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/depthwise/hard_swish/mul_1/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_6/project/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/depthwise/hard_swish/mul_1" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_6/project/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/project/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/project/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/project/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/project/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/project/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_6/output" + op: "Identity" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/project/BatchNorm/FusedBatchNormV3" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_7/expand/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_6/output" + input: "FeatureExtractor/MobilenetV3/expanded_conv_7/expand/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + 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} + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_8/expand/hard_swish/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/expanded_conv_8/expand/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_8/expand/hard_swish/add/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_8/expand/hard_swish/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_8/expand/hard_swish/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_8/expand/hard_swish/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_8/expand/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_8/expand/hard_swish/Relu6" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_8/expand/hard_swish/mul_1" + op: "Mul" + input: 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attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_8/project/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_8/project/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_8/project/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_8/project/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_8/project/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_8/project/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_8/add" + op: "AddV2" + input: 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"FeatureExtractor/MobilenetV3/expanded_conv_9/expand/hard_swish/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_9/expand/hard_swish/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_9/expand/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_9/expand/hard_swish/Relu6" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_9/expand/hard_swish/mul_1" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_9/expand/hard_swish/mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_9/expand/hard_swish/mul_1/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_9/depthwise/depthwise" + op: "DepthwiseConv2dNative" + input: "FeatureExtractor/MobilenetV3/expanded_conv_9/expand/hard_swish/mul_1" + input: "FeatureExtractor/MobilenetV3/expanded_conv_9/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 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"FeatureExtractor/MobilenetV3/expanded_conv_9/depthwise/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_9/depthwise/hard_swish/add/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_9/depthwise/hard_swish/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_9/depthwise/hard_swish/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_9/depthwise/hard_swish/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_9/depthwise/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_9/depthwise/hard_swish/Relu6" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_9/depthwise/hard_swish/mul_1" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_9/depthwise/hard_swish/mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_9/depthwise/hard_swish/mul_1/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_9/project/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_9/depthwise/hard_swish/mul_1" + input: "FeatureExtractor/MobilenetV3/expanded_conv_9/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_9/project/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_9/project/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_9/project/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_9/project/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_9/project/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_9/project/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_9/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/expanded_conv_9/project/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_9/input" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_10/input" + op: "Identity" + input: "FeatureExtractor/MobilenetV3/expanded_conv_9/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_10/expand/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/input" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/expand/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_10/expand/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/expand/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/expand/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/expand/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/expand/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/expand/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_10/expand/hard_swish/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/expand/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/expand/hard_swish/add/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_10/expand/hard_swish/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/expand/hard_swish/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_10/expand/hard_swish/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/expand/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/expand/hard_swish/Relu6" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_10/expand/hard_swish/mul_1" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/expand/hard_swish/mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/expand/hard_swish/mul_1/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_10/depthwise/depthwise" + op: "DepthwiseConv2dNative" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/expand/hard_swish/mul_1" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_10/depthwise/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/depthwise/depthwise" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/depthwise/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/depthwise/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/depthwise/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/depthwise/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_10/depthwise/hard_swish/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/depthwise/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/depthwise/hard_swish/add/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_10/depthwise/hard_swish/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/depthwise/hard_swish/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_10/depthwise/hard_swish/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/depthwise/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/depthwise/hard_swish/Relu6" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_10/depthwise/hard_swish/mul_1" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/depthwise/hard_swish/mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/depthwise/hard_swish/mul_1/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/Mean" + op: "Mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/depthwise/hard_swish/mul_1" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/Mean/reduction_indices" + attr { + key: "keep_dims" + value { + b: true + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/Conv/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/Mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/Conv/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/Conv/BiasAdd" + op: "BiasAdd" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/Conv/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/Conv/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/Conv/Relu" + op: "Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/Conv/BiasAdd" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/Conv_1/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/Conv/Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/Conv_1/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/Conv_1/BiasAdd" + op: "BiasAdd" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/Conv_1/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/Conv_1/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/Conv_1/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/Conv_1/BiasAdd" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/Conv_1/add/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/Conv_1/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/Conv_1/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/Conv_1/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/Conv_1/Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/Conv_1/mul/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/depthwise/hard_swish/mul_1" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/Conv_1/mul" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_10/project/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/squeeze_excite/mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_10/project/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/project/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/project/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/project/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/project/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/project/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_10/output" + op: "Identity" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/project/BatchNorm/FusedBatchNormV3" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_11/expand/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/output" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/expand/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_11/expand/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/expand/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/expand/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/expand/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/expand/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/expand/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_11/expand/hard_swish/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/expand/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/expand/hard_swish/add/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_11/expand/hard_swish/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/expand/hard_swish/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_11/expand/hard_swish/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/expand/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/expand/hard_swish/Relu6" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_11/expand/hard_swish/mul_1" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/expand/hard_swish/mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/expand/hard_swish/mul_1/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_11/depthwise/depthwise" + op: "DepthwiseConv2dNative" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/expand/hard_swish/mul_1" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_11/depthwise/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/depthwise/depthwise" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/depthwise/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/depthwise/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/depthwise/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/depthwise/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_11/depthwise/hard_swish/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/depthwise/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/depthwise/hard_swish/add/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_11/depthwise/hard_swish/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/depthwise/hard_swish/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_11/depthwise/hard_swish/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/depthwise/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/depthwise/hard_swish/Relu6" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_11/depthwise/hard_swish/mul_1" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/depthwise/hard_swish/mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/depthwise/hard_swish/mul_1/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/Mean" + op: "Mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/depthwise/hard_swish/mul_1" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/Mean/reduction_indices" + attr { + key: "keep_dims" + value { + b: true + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/Conv/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/Mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/Conv/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/Conv/BiasAdd" + op: "BiasAdd" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/Conv/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/Conv/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/Conv/Relu" + op: "Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/Conv/BiasAdd" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/Conv_1/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/Conv/Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/Conv_1/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/Conv_1/BiasAdd" + op: "BiasAdd" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/Conv_1/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/Conv_1/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/Conv_1/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/Conv_1/BiasAdd" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/Conv_1/add/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/Conv_1/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/Conv_1/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/Conv_1/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/Conv_1/Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/Conv_1/mul/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/depthwise/hard_swish/mul_1" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/Conv_1/mul" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_11/project/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/squeeze_excite/mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_11/project/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/project/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/project/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/project/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/project/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/project/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_11/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/project/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_10/output" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_12/input" + op: "Identity" + input: "FeatureExtractor/MobilenetV3/expanded_conv_11/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_12/expand/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/input" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/expand/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_12/expand/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/expand/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/expand/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/expand/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/expand/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/expand/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_12/expand/hard_swish/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/expand/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/expand/hard_swish/add/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_12/expand/hard_swish/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/expand/hard_swish/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_12/expand/hard_swish/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/expand/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/expand/hard_swish/Relu6" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_12/expand/hard_swish/mul_1" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/expand/hard_swish/mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/expand/hard_swish/mul_1/y" +} +node { + name: "BoxPredictor_0/ClassPredictor_depthwise/depthwise" + op: "DepthwiseConv2dNative" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/expand/hard_swish/mul_1" + input: "BoxPredictor_0/ClassPredictor_depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "BoxPredictor_0/ClassPredictor_depthwise/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "BoxPredictor_0/ClassPredictor_depthwise/depthwise" + input: "BoxPredictor_0/ClassPredictor_depthwise/BatchNorm/gamma" + input: "BoxPredictor_0/ClassPredictor_depthwise/BatchNorm/beta" + input: "BoxPredictor_0/ClassPredictor_depthwise/BatchNorm/moving_mean" + input: "BoxPredictor_0/ClassPredictor_depthwise/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "BoxPredictor_0/ClassPredictor_depthwise/Relu6" + op: "Relu6" + input: "BoxPredictor_0/ClassPredictor_depthwise/BatchNorm/FusedBatchNormV3" +} +node { + name: "BoxPredictor_0/ClassPredictor/Conv2D" + op: "Conv2D" + input: "BoxPredictor_0/ClassPredictor_depthwise/Relu6" + input: "BoxPredictor_0/ClassPredictor/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "BoxPredictor_0/ClassPredictor/BiasAdd" + op: "BiasAdd" + input: "BoxPredictor_0/ClassPredictor/Conv2D" + input: "BoxPredictor_0/ClassPredictor/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } +} +node { + name: "BoxPredictor_0/BoxEncodingPredictor_depthwise/depthwise" + op: "DepthwiseConv2dNative" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/expand/hard_swish/mul_1" + input: "BoxPredictor_0/BoxEncodingPredictor_depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "BoxPredictor_0/BoxEncodingPredictor_depthwise/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "BoxPredictor_0/BoxEncodingPredictor_depthwise/depthwise" + input: "BoxPredictor_0/BoxEncodingPredictor_depthwise/BatchNorm/gamma" + input: "BoxPredictor_0/BoxEncodingPredictor_depthwise/BatchNorm/beta" + input: "BoxPredictor_0/BoxEncodingPredictor_depthwise/BatchNorm/moving_mean" + input: "BoxPredictor_0/BoxEncodingPredictor_depthwise/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "BoxPredictor_0/BoxEncodingPredictor_depthwise/Relu6" + op: "Relu6" + input: "BoxPredictor_0/BoxEncodingPredictor_depthwise/BatchNorm/FusedBatchNormV3" +} +node { + name: "BoxPredictor_0/BoxEncodingPredictor/Conv2D" + op: "Conv2D" + input: "BoxPredictor_0/BoxEncodingPredictor_depthwise/Relu6" + input: "BoxPredictor_0/BoxEncodingPredictor/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "loc_pred_transposed" + value { + b: true + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "BoxPredictor_0/BoxEncodingPredictor/BiasAdd" + op: "BiasAdd" + input: "BoxPredictor_0/BoxEncodingPredictor/Conv2D" + input: "BoxPredictor_0/BoxEncodingPredictor/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_12/depthwise/depthwise" + op: "DepthwiseConv2dNative" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/expand/hard_swish/mul_1" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 2 + i: 2 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_12/depthwise/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/depthwise/depthwise" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/depthwise/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/depthwise/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/depthwise/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/depthwise/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_12/depthwise/hard_swish/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/depthwise/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/depthwise/hard_swish/add/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_12/depthwise/hard_swish/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/depthwise/hard_swish/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_12/depthwise/hard_swish/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/depthwise/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/depthwise/hard_swish/Relu6" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_12/depthwise/hard_swish/mul_1" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/depthwise/hard_swish/mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/depthwise/hard_swish/mul_1/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/Mean" + op: "Mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/depthwise/hard_swish/mul_1" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/Mean/reduction_indices" + attr { + key: "keep_dims" + value { + b: true + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/Conv/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/Mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/Conv/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/Conv/BiasAdd" + op: "BiasAdd" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/Conv/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/Conv/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/Conv/Relu" + op: "Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/Conv/BiasAdd" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/Conv_1/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/Conv/Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/Conv_1/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/Conv_1/BiasAdd" + op: "BiasAdd" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/Conv_1/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/Conv_1/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/Conv_1/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/Conv_1/BiasAdd" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/Conv_1/add/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/Conv_1/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/Conv_1/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/Conv_1/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/Conv_1/Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/Conv_1/mul/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/depthwise/hard_swish/mul_1" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/Conv_1/mul" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_12/project/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/squeeze_excite/mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_12/project/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/project/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/project/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/project/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/project/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/project/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_12/output" + op: "Identity" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/project/BatchNorm/FusedBatchNormV3" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_13/expand/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/output" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/expand/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_13/expand/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/expand/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/expand/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/expand/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/expand/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/expand/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_13/expand/hard_swish/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/expand/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/expand/hard_swish/add/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_13/expand/hard_swish/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/expand/hard_swish/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_13/expand/hard_swish/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/expand/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/expand/hard_swish/Relu6" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_13/expand/hard_swish/mul_1" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/expand/hard_swish/mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/expand/hard_swish/mul_1/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_13/depthwise/depthwise" + op: "DepthwiseConv2dNative" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/expand/hard_swish/mul_1" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_13/depthwise/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/depthwise/depthwise" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/depthwise/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/depthwise/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/depthwise/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/depthwise/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_13/depthwise/hard_swish/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/depthwise/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/depthwise/hard_swish/add/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_13/depthwise/hard_swish/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/depthwise/hard_swish/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_13/depthwise/hard_swish/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/depthwise/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/depthwise/hard_swish/Relu6" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_13/depthwise/hard_swish/mul_1" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/depthwise/hard_swish/mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/depthwise/hard_swish/mul_1/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/Mean" + op: "Mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/depthwise/hard_swish/mul_1" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/Mean/reduction_indices" + attr { + key: "keep_dims" + value { + b: true + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/Conv/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/Mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/Conv/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/Conv/BiasAdd" + op: "BiasAdd" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/Conv/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/Conv/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/Conv/Relu" + op: "Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/Conv/BiasAdd" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/Conv_1/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/Conv/Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/Conv_1/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/Conv_1/BiasAdd" + op: "BiasAdd" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/Conv_1/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/Conv_1/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/Conv_1/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/Conv_1/BiasAdd" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/Conv_1/add/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/Conv_1/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/Conv_1/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/Conv_1/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/Conv_1/Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/Conv_1/mul/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/depthwise/hard_swish/mul_1" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/Conv_1/mul" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_13/project/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/squeeze_excite/mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_13/project/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/project/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/project/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/project/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/project/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/project/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_13/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/project/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_12/output" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_14/input" + op: "Identity" + input: "FeatureExtractor/MobilenetV3/expanded_conv_13/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_14/expand/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/input" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/expand/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_14/expand/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/expand/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/expand/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/expand/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/expand/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/expand/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_14/expand/hard_swish/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/expand/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/expand/hard_swish/add/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_14/expand/hard_swish/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/expand/hard_swish/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_14/expand/hard_swish/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/expand/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/expand/hard_swish/Relu6" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_14/expand/hard_swish/mul_1" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/expand/hard_swish/mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/expand/hard_swish/mul_1/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_14/depthwise/depthwise" + op: "DepthwiseConv2dNative" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/expand/hard_swish/mul_1" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_14/depthwise/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/depthwise/depthwise" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/depthwise/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/depthwise/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/depthwise/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/depthwise/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_14/depthwise/hard_swish/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/depthwise/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/depthwise/hard_swish/add/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_14/depthwise/hard_swish/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/depthwise/hard_swish/add" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_14/depthwise/hard_swish/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/depthwise/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/depthwise/hard_swish/Relu6" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_14/depthwise/hard_swish/mul_1" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/depthwise/hard_swish/mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/depthwise/hard_swish/mul_1/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_14/squeeze_excite/Mean" + op: "Mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/depthwise/hard_swish/mul_1" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/squeeze_excite/Mean/reduction_indices" + attr { + key: "keep_dims" + value { + b: true + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_14/squeeze_excite/Conv/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/squeeze_excite/Mean" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/squeeze_excite/Conv/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_14/squeeze_excite/Conv/BiasAdd" + op: "BiasAdd" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/squeeze_excite/Conv/Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/squeeze_excite/Conv/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_14/squeeze_excite/Conv/Relu" + op: "Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/squeeze_excite/Conv/BiasAdd" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_14/squeeze_excite/Conv_1/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/squeeze_excite/Conv/Relu" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/squeeze_excite/Conv_1/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: 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"FeatureExtractor/MobilenetV3/expanded_conv_14/squeeze_excite/Conv_1/mul/y" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_14/squeeze_excite/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/depthwise/hard_swish/mul_1" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/squeeze_excite/Conv_1/mul" +} +node { + name: "FeatureExtractor/MobilenetV3/expanded_conv_14/project/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/squeeze_excite/mul" + input: "FeatureExtractor/MobilenetV3/expanded_conv_14/project/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: 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value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/Conv_1/hard_swish/add" + op: "AddV2" + input: "FeatureExtractor/MobilenetV3/Conv_1/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/Conv_1/hard_swish/add/y" +} +node { + name: "FeatureExtractor/MobilenetV3/Conv_1/hard_swish/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV3/Conv_1/hard_swish/add" +} +node { + name: "FeatureExtractor/MobilenetV3/Conv_1/hard_swish/mul" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/Conv_1/BatchNorm/FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/Conv_1/hard_swish/Relu6" +} +node { + name: "FeatureExtractor/MobilenetV3/Conv_1/hard_swish/mul_1" + op: "Mul" + input: "FeatureExtractor/MobilenetV3/Conv_1/hard_swish/mul" + input: "FeatureExtractor/MobilenetV3/Conv_1/hard_swish/mul_1/y" +} +node { + name: "BoxPredictor_1/ClassPredictor_depthwise/depthwise" + op: "DepthwiseConv2dNative" + input: "FeatureExtractor/MobilenetV3/Conv_1/hard_swish/mul_1" + input: "BoxPredictor_1/ClassPredictor_depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "BoxPredictor_1/ClassPredictor_depthwise/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "BoxPredictor_1/ClassPredictor_depthwise/depthwise" + input: "BoxPredictor_1/ClassPredictor_depthwise/BatchNorm/gamma" + input: "BoxPredictor_1/ClassPredictor_depthwise/BatchNorm/beta" + input: "BoxPredictor_1/ClassPredictor_depthwise/BatchNorm/moving_mean" + input: "BoxPredictor_1/ClassPredictor_depthwise/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { 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"BoxPredictor_1/BoxEncodingPredictor_depthwise/depthwise" + op: "DepthwiseConv2dNative" + input: "FeatureExtractor/MobilenetV3/Conv_1/hard_swish/mul_1" + input: "BoxPredictor_1/BoxEncodingPredictor_depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "BoxPredictor_1/BoxEncodingPredictor_depthwise/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "BoxPredictor_1/BoxEncodingPredictor_depthwise/depthwise" + input: "BoxPredictor_1/BoxEncodingPredictor_depthwise/BatchNorm/gamma" + input: "BoxPredictor_1/BoxEncodingPredictor_depthwise/BatchNorm/beta" + input: "BoxPredictor_1/BoxEncodingPredictor_depthwise/BatchNorm/moving_mean" + input: 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"FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/layer_17_1_Conv2d_2_1x1_256/Conv2D" + input: "FeatureExtractor/MobilenetV3/layer_17_1_Conv2d_2_1x1_256/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/layer_17_1_Conv2d_2_1x1_256/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/layer_17_1_Conv2d_2_1x1_256/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/layer_17_1_Conv2d_2_1x1_256/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/layer_17_1_Conv2d_2_1x1_256/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV3/layer_17_1_Conv2d_2_1x1_256/BatchNorm/FusedBatchNormV3" +} +node { + name: "FeatureExtractor/MobilenetV3/layer_17_2_Conv2d_2_3x3_s2_512_depthwise/depthwise" + op: "DepthwiseConv2dNative" + input: 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"FeatureExtractor/MobilenetV3/layer_17_2_Conv2d_2_3x3_s2_512_depthwise/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/layer_17_2_Conv2d_2_3x3_s2_512_depthwise/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV3/layer_17_2_Conv2d_2_3x3_s2_512_depthwise/BatchNorm/FusedBatchNormV3" +} +node { + name: "FeatureExtractor/MobilenetV3/layer_17_2_Conv2d_2_3x3_s2_512/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV3/layer_17_2_Conv2d_2_3x3_s2_512_depthwise/Relu6" + input: "FeatureExtractor/MobilenetV3/layer_17_2_Conv2d_2_3x3_s2_512/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/layer_17_2_Conv2d_2_3x3_s2_512/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "FeatureExtractor/MobilenetV3/layer_17_2_Conv2d_2_3x3_s2_512/Conv2D" + input: "FeatureExtractor/MobilenetV3/layer_17_2_Conv2d_2_3x3_s2_512/BatchNorm/gamma" + input: "FeatureExtractor/MobilenetV3/layer_17_2_Conv2d_2_3x3_s2_512/BatchNorm/beta" + input: "FeatureExtractor/MobilenetV3/layer_17_2_Conv2d_2_3x3_s2_512/BatchNorm/moving_mean" + input: "FeatureExtractor/MobilenetV3/layer_17_2_Conv2d_2_3x3_s2_512/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "FeatureExtractor/MobilenetV3/layer_17_2_Conv2d_2_3x3_s2_512/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV3/layer_17_2_Conv2d_2_3x3_s2_512/BatchNorm/FusedBatchNormV3" +} +node { + name: "BoxPredictor_2/ClassPredictor_depthwise/depthwise" + op: "DepthwiseConv2dNative" + input: "FeatureExtractor/MobilenetV3/layer_17_2_Conv2d_2_3x3_s2_512/Relu6" + input: "BoxPredictor_2/ClassPredictor_depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "BoxPredictor_2/ClassPredictor_depthwise/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "BoxPredictor_2/ClassPredictor_depthwise/depthwise" + input: "BoxPredictor_2/ClassPredictor_depthwise/BatchNorm/gamma" + input: "BoxPredictor_2/ClassPredictor_depthwise/BatchNorm/beta" + input: "BoxPredictor_2/ClassPredictor_depthwise/BatchNorm/moving_mean" + input: "BoxPredictor_2/ClassPredictor_depthwise/BatchNorm/moving_variance" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "epsilon" + value { + f: 0.001 + } + } + attr { + key: "U" + value { + type: DT_FLOAT + } + } +} +node { + name: "BoxPredictor_2/ClassPredictor_depthwise/Relu6" + op: "Relu6" + input: "BoxPredictor_2/ClassPredictor_depthwise/BatchNorm/FusedBatchNormV3" +} +node { + name: "BoxPredictor_2/ClassPredictor/Conv2D" + op: "Conv2D" + input: "BoxPredictor_2/ClassPredictor_depthwise/Relu6" + input: "BoxPredictor_2/ClassPredictor/weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "explicit_paddings" + value { + list { + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "BoxPredictor_2/ClassPredictor/BiasAdd" + op: "BiasAdd" + input: "BoxPredictor_2/ClassPredictor/Conv2D" + input: "BoxPredictor_2/ClassPredictor/biases" + attr { + key: "data_format" + value { + s: "NHWC" + } + } +} +node { + name: "BoxPredictor_2/BoxEncodingPredictor_depthwise/depthwise" + op: "DepthwiseConv2dNative" + input: "FeatureExtractor/MobilenetV3/layer_17_2_Conv2d_2_3x3_s2_512/Relu6" + input: "BoxPredictor_2/BoxEncodingPredictor_depthwise/depthwise_weights" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "dilations" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "BoxPredictor_2/BoxEncodingPredictor_depthwise/BatchNorm/FusedBatchNormV3" + op: "FusedBatchNormV3" + input: "BoxPredictor_2/BoxEncodingPredictor_depthwise/depthwise" + input: 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value { + tensor { + dtype: DT_INT32 + int_val: 0 + int_val: -1 + int_val: 91 + tensor_shape { + dim { + size: 3 + } + } + } + } + } +} +node { + name: "ClassPredictor/concat3d" + op: "Reshape" + input: "ClassPredictor/concat" + input: "ClassPredictor/concat3d/shape" +} +node { + name: "ClassPredictor/concat/sigmoid" + op: "Sigmoid" + input: "ClassPredictor/concat3d" +} +node { + name: "ClassPredictor/concat/sigmoid/Flatten" + op: "Flatten" + input: "ClassPredictor/concat/sigmoid" +} +node { + name: "detection_out" + op: "DetectionOutput" + input: "BoxEncodingPredictor/concat" + input: "ClassPredictor/concat/sigmoid/Flatten" + input: "PriorBox/concat" + attr { + key: "background_label_id" + value { + i: 0 + } + } + attr { + key: "code_type" + value { + s: "CENTER_SIZE" + } + } + attr { + key: "confidence_threshold" + value { + f: 1e-08 + } + } + attr { + key: "keep_top_k" + value { + i: 100 + } + } + attr { + key: "nms_threshold" + value { + f: 0.6 + } + } + attr { + key: "num_classes" + value { + i: 91 + } + } + attr { + key: "share_location" + value { + b: true + } + } + attr { + key: "top_k" + value { + i: 100 + } + } +} blob - /dev/null blob + 3a52305187ed1dfff0fdbe909d0ac2252e72d116 (mode 644) Binary files /dev/null and v1.mp4 differ blob - /dev/null blob + 1f3f3b8107eda44d374ab76886468d779e91ef9f (mode 644) --- /dev/null +++ yolov3.cfg @@ -0,0 +1,788 @@ +[net] +# Testing +# batch=1 +# subdivisions=1 +# Training +batch=64 +subdivisions=16 +width=608 +height=608 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +###################### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 61 + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 36 + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1