Inference on test data

[crystal0523]

Hi, I would like to inference on test data, but it seems that the repository didn't include that file. Is there any references could help me with that?

[ajithvcoder]

@crystal0523 if it helps please close the issue

Use below command to run the file and code as visuvalize_single_image.py

!python visualize_single_image.py --image_dir="frames" --model_path="../coco_resnet_50_map_0_335_state_dict.pt"

import torch
import numpy as np
import time
import os
import csv
import cv2
import argparse
from retinanet.model import resnet50

def load_classes(csv_reader):
    result = {}

    for line, row in enumerate(csv_reader):
        line += 1

        try:
            class_name, class_id = row
        except ValueError:
            raise(ValueError('line {}: format should be \'class_name,class_id\''.format(line)))
        class_id = int(class_id)

        if class_name in result:
            raise ValueError('line {}: duplicate class name: \'{}\''.format(line, class_name))
        result[class_name] = class_id
    return result


# Draws a caption above the box in an image
def draw_caption(image, box, caption):
    b = np.array(box).astype(int)
    cv2.putText(image, caption, (b[0], b[1] - 10), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 0), 2)
    cv2.putText(image, caption, (b[0], b[1] - 10), cv2.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1)


def detect_image(image_path, model_path, class_list):

    # with open(class_list, 'r') as f:
    #     classes = load_classes(csv.reader(f, delimiter=','))
    #classes = {1:'person',2:'bicycle'}
    classes= {0: u'__background__', 
 1: u'person',
 2: u'bicycle',
 3: u'car',
 4: u'motorcycle',
 5: u'airplane',
 6: u'bus',
 7: u'train',
 8: u'truck',
 9: u'boat',
 10: u'traffic light',
 11: u'fire hydrant',
 12: u'stop sign',
 13: u'parking meter',
 14: u'bench',
 15: u'bird',
 16: u'cat',
 17: u'dog',
 18: u'horse',
 19: u'sheep',
 20: u'cow',
 21: u'elephant',
 22: u'bear',
 23: u'zebra',
 24: u'giraffe',
 25: u'backpack',
 26: u'umbrella',
 27: u'handbag',
 28: u'tie',
 29: u'suitcase',
 30: u'frisbee',
 31: u'skis',
 32: u'snowboard',
 33: u'sports ball',
 34: u'kite',
 35: u'baseball bat',
 36: u'baseball glove',
 37: u'skateboard',
 38: u'surfboard',
 39: u'tennis racket',
 40: u'bottle',
 41: u'wine glass',
 42: u'cup',
 43: u'fork',
 44: u'knife',
 45: u'spoon',
 46: u'bowl',
 47: u'banana',
 48: u'apple',
 49: u'sandwich',
 50: u'orange',
 51: u'broccoli',
 52: u'carrot',
 53: u'hot dog',
 54: u'pizza',
 55: u'donut',
 56: u'cake',
 57: u'chair',
 58: u'couch',
 59: u'potted plant',
 60: u'bed',
 61: u'dining table',
 62: u'toilet',
 63: u'tv',
 64: u'laptop',
 65: u'mouse',
 66: u'remote',
 67: u'keyboard',
 68: u'cell phone',
 69: u'microwave',
 70: u'oven',
 71: u'toaster',
 72: u'sink',
 73: u'refrigerator',
 74: u'book',
 75: u'clock',
 76: u'vase',
 77: u'scissors',
 78: u'teddy bear',
 79: u'hair drier',
 80: u'toothbrush'}
    #1: u'person',
    #2: u'bicycle'

    labels = {}
    for key, value in classes.items():
        labels[value] = key
    #model = resnet18(num_classes=2)
    #model.load_state_dict(torch.load(model_path,map_location=torch.device('cpu')))
    #model = torch.load(model_path,map_location=torch.device('cpu'))

    retinanet = resnet50(num_classes=80,)
    retinanet.load_state_dict(torch.load(model_path,map_location=torch.device('cpu')))
    model = retinanet
    if torch.cuda.is_available():
        model = model.cuda()

    model.training = False
    model.eval()

    for img_name in os.listdir(image_path):

        image = cv2.imread(os.path.join(image_path, img_name))
        if image is None:
            continue
        image_orig = image.copy()

        rows, cols, cns = image.shape

        smallest_side = min(rows, cols)

        # rescale the image so the smallest side is min_side
        min_side = 608
        max_side = 1024
        scale = min_side / smallest_side

        # check if the largest side is now greater than max_side, which can happen
        # when images have a large aspect ratio
        largest_side = max(rows, cols)

        if largest_side * scale > max_side:
            scale = max_side / largest_side

        # resize the image with the computed scale
        image = cv2.resize(image, (int(round(cols * scale)), int(round((rows * scale)))))
        rows, cols, cns = image.shape

        pad_w = 32 - rows % 32
        pad_h = 32 - cols % 32

        new_image = np.zeros((rows + pad_w, cols + pad_h, cns)).astype(np.float32)
        new_image[:rows, :cols, :] = image.astype(np.float32)
        image = new_image.astype(np.float32)
        image /= 255
        image -= [0.485, 0.456, 0.406]
        image /= [0.229, 0.224, 0.225]
        image = np.expand_dims(image, 0)
        image = np.transpose(image, (0, 3, 1, 2))

        with torch.no_grad():

            image = torch.from_numpy(image)
            if torch.cuda.is_available():
                image = image.cuda()

            st = time.time()
            print(image.shape, image_orig.shape, scale)
            scores, classification, transformed_anchors = model(image.cuda().float())
            print('Elapsed time: {}'.format(time.time() - st))
            idxs = np.where(scores.cpu() > 0.5)

            for j in range(idxs[0].shape[0]):
                bbox = transformed_anchors[idxs[0][j], :]

                x1 = int(bbox[0] / scale)
                y1 = int(bbox[1] / scale)
                x2 = int(bbox[2] / scale)
                y2 = int(bbox[3] / scale)
                #label_name = labels[int(classification[idxs[0][j]])]
                print(int(classification[idxs[0][j]]))
                label_name = str(int(classification[idxs[0][j]]))
                print(bbox, classification.shape)
                score = scores[j]
                caption = '{} {:.3f}'.format(label_name, score)
                # draw_caption(img, (x1, y1, x2, y2), label_name)
                draw_caption(image_orig, (x1, y1, x2, y2), caption)
                cv2.rectangle(image_orig, (x1, y1), (x2, y2), color=(0, 0, 255), thickness=2)

            # cv2.imshow('detections', image_orig)
            # cv2.waitKey(0)
            cv2.imwrite("newvideoresults/"+img_name,image_orig)


if __name__ == '__main__':

    parser = argparse.ArgumentParser(description='Simple script for visualizing result of training.')

    parser.add_argument('--image_dir', help='Path to directory containing images')
    parser.add_argument('--model_path', help='Path to model')
    parser.add_argument('--class_list', help='Path to CSV file listing class names (see README)')

    parser = parser.parse_args()

    detect_image(parser.image_dir, parser.model_path, parser.class_list)

[MuhammadJahanara-1995]

Hi, I would like to inference on test data, but it seems that the repository didn't include that file. Is there any references could help me with that?

[ArashJalalian]

I trained my model using a custom data on GPU, now I want to use that model to detect objects on a CPU. But the above code gives an error that CUDA is not available on scores, classification, transformed_anchors = model(image.cuda().float()) line.

I think the issue is image.cuda().float() I tried to pass the image as it's but still no luck.

The trained model works fine when I detect objects using GPU.

[ErezYosef]

Hi @ArashJalalian,
I added a fix to your issue #210.
Hope it will be merged soon and help you with the error.