1. Advisor
  2. datasets
  3. functions
  4. datasets.dataset_utils.int64_feature
View all datasets analysis

How to use the datasets.dataset_utils.int64_feature function in datasets

To help you get started, we’ve selected a few datasets examples, based on popular ways it is used in public projects.

Secure your code as it's written. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately.

github charliememory / Disentangled-Person-Image-Generation / datasets / convert_RCV.py View on Github external
return None

    example = tf.train.Example(features=tf.train.Features(feature={
            'image_name_0': dataset_utils.bytes_feature(pairs[i][0]),
            'image_name_1': dataset_utils.bytes_feature(pairs[i][1]),
            'image_raw_0': dataset_utils.bytes_feature(image_raw_0),
            'image_raw_1': dataset_utils.bytes_feature(image_raw_1),
            'label': dataset_utils.int64_feature(labels[i]),
            'id_0': dataset_utils.int64_feature(id_map[id_0]),
            'id_1': dataset_utils.int64_feature(id_map[id_1]),
            'cam_0': dataset_utils.int64_feature(-1),
            'cam_1': dataset_utils.int64_feature(-1),
            'image_format': dataset_utils.bytes_feature(_IMG_PATTERN),
            'image_height': dataset_utils.int64_feature(height),
            'image_width': dataset_utils.int64_feature(width),
            'real_data': dataset_utils.int64_feature(1),
            'attrs_0': dataset_utils.int64_feature(attrs_0),
            'attrs_1': dataset_utils.int64_feature(attrs_1),
            'pose_peaks_0_rcv': dataset_utils.float_feature(pose_peaks_0_rcv.flatten().tolist()),
            'pose_peaks_1_rcv': dataset_utils.float_feature(pose_peaks_1_rcv.flatten().tolist()),
            'pose_mask_r4_0': dataset_utils.int64_feature(pose_mask_r4_0.astype(np.int64).flatten().tolist()),
            'pose_mask_r4_1': dataset_utils.int64_feature(pose_mask_r4_1.astype(np.int64).flatten().tolist()),
            'pose_mask_r8_0': dataset_utils.int64_feature(pose_mask_r8_0.astype(np.int64).flatten().tolist()),
            'pose_mask_r8_1': dataset_utils.int64_feature(pose_mask_r8_1.astype(np.int64).flatten().tolist()),
            'pose_mask_r10_0': dataset_utils.int64_feature(pose_mask_r10_0.astype(np.int64).flatten().tolist()),
            'pose_mask_r10_1': dataset_utils.int64_feature(pose_mask_r10_1.astype(np.int64).flatten().tolist()),

            'shape': dataset_utils.int64_feature(shape_0),
            
            'indices_r4_0': dataset_utils.int64_feature(np.array(indices_r4_0).astype(np.int64).flatten().tolist()),
            'values_r4_0': dataset_utils.float_feature(np.array(values_r4_0).astype(np.float).flatten().tolist()),
            'indices_r4_1': dataset_utils.int64_feature(np.array(indices_r4_1).astype(np.int64).flatten().tolist()),
github charliememory / Disentangled-Person-Image-Generation / datasets / convert_RCV.py View on Github external
#     _visualizePose(roi_mask_list_0[8], scipy.misc.imread(img_path_0))
        #     _visualizePose(roi_mask_list_0[9], scipy.misc.imread(img_path_0))
        # pdb.set_trace()
    else:
        return None

    example = tf.train.Example(features=tf.train.Features(feature={
            'image_name_0': dataset_utils.bytes_feature(pairs[i][0]),
            'image_name_1': dataset_utils.bytes_feature(pairs[i][1]),
            'image_raw_0': dataset_utils.bytes_feature(image_raw_0),
            'image_raw_1': dataset_utils.bytes_feature(image_raw_1),
            'label': dataset_utils.int64_feature(labels[i]),
            'id_0': dataset_utils.int64_feature(id_map[id_0]),
            'id_1': dataset_utils.int64_feature(id_map[id_1]),
            'cam_0': dataset_utils.int64_feature(-1),
            'cam_1': dataset_utils.int64_feature(-1),
            'image_format': dataset_utils.bytes_feature(_IMG_PATTERN),
            'image_height': dataset_utils.int64_feature(height),
            'image_width': dataset_utils.int64_feature(width),
            'real_data': dataset_utils.int64_feature(1),
            'attrs_0': dataset_utils.int64_feature(attrs_0),
            'attrs_1': dataset_utils.int64_feature(attrs_1),
            'pose_peaks_0_rcv': dataset_utils.float_feature(pose_peaks_0_rcv.flatten().tolist()),
            'pose_peaks_1_rcv': dataset_utils.float_feature(pose_peaks_1_rcv.flatten().tolist()),
            'pose_mask_r4_0': dataset_utils.int64_feature(pose_mask_r4_0.astype(np.int64).flatten().tolist()),
            'pose_mask_r4_1': dataset_utils.int64_feature(pose_mask_r4_1.astype(np.int64).flatten().tolist()),
            'pose_mask_r8_0': dataset_utils.int64_feature(pose_mask_r8_0.astype(np.int64).flatten().tolist()),
            'pose_mask_r8_1': dataset_utils.int64_feature(pose_mask_r8_1.astype(np.int64).flatten().tolist()),
            'pose_mask_r10_0': dataset_utils.int64_feature(pose_mask_r10_0.astype(np.int64).flatten().tolist()),
            'pose_mask_r10_1': dataset_utils.int64_feature(pose_mask_r10_1.astype(np.int64).flatten().tolist()),

            'shape': dataset_utils.int64_feature(shape_0),
github Shun14 / TextBoxes_plusplus_Tensorflow / datasets / xml_to_tfrecords.py View on Github external
'image/filename': bytes_feature(filename.encode('utf-8')),
            'image/object/bbox/xmin': float_feature(xmin),
            'image/object/bbox/xmax': float_feature(xmax),
            'image/object/bbox/ymin': float_feature(ymin),
            'image/object/bbox/ymax': float_feature(ymax),
            'image/object/bbox/x1': float_feature(x1),
            'image/object/bbox/y1': float_feature(y1),
            'image/object/bbox/x2': float_feature(x2),
            'image/object/bbox/y2': float_feature(y2),
            'image/object/bbox/x3': float_feature(x3),
            'image/object/bbox/y3': float_feature(y3),
            'image/object/bbox/x4': float_feature(x4),
            'image/object/bbox/y4': float_feature(y4),
            'image/object/bbox/label': int64_feature(labels),
            'image/object/bbox/label_text': bytes_feature(labels_text),
            'image/object/bbox/difficult': int64_feature(difficult),
            'image/object/bbox/truncated': int64_feature(truncated),
            'image/object/bbox/ignored': int64_feature(ignored),
            'image/format': bytes_feature(image_format),
            'image/encoded': bytes_feature(image_data)}))
    return example
github charliememory / Pose-Guided-Person-Image-Generation / datasets / convert_DF.py View on Github external
return None

    example = tf.train.Example(features=tf.train.Features(feature={
            'image_name_0': dataset_utils.bytes_feature(pairs[i][0]),
            'image_name_1': dataset_utils.bytes_feature(pairs[i][1]),
            'image_raw_0': dataset_utils.bytes_feature(image_raw_0),
            'image_raw_1': dataset_utils.bytes_feature(image_raw_1),
            'label': dataset_utils.int64_feature(labels[i]),
            'id_0': dataset_utils.int64_feature(id_map[id_0]),
            'id_1': dataset_utils.int64_feature(id_map[id_1]),
            'cam_0': dataset_utils.int64_feature(-1),
            'cam_1': dataset_utils.int64_feature(-1),
            'image_format': dataset_utils.bytes_feature('jpg'),
            'image_height': dataset_utils.int64_feature(height),
            'image_width': dataset_utils.int64_feature(width),
            'real_data': dataset_utils.int64_feature(1),
            'attrs_0': dataset_utils.int64_feature(attrs_0),
            'attrs_1': dataset_utils.int64_feature(attrs_1),
            'pose_peaks_0': dataset_utils.float_feature(pose_peaks_0.flatten().tolist()),
            'pose_peaks_1': dataset_utils.float_feature(pose_peaks_1.flatten().tolist()),
            'pose_peaks_0_rcv': dataset_utils.float_feature(pose_peaks_0_rcv.flatten().tolist()),
            'pose_peaks_1_rcv': dataset_utils.float_feature(pose_peaks_1_rcv.flatten().tolist()),
            # 'pose_dense_r4_0': dataset_utils.int64_feature(pose_dense_r4_0.astype(np.int64).flatten().tolist()),
            # 'pose_dense_r4_1': dataset_utils.int64_feature(pose_dense_r4_1.astype(np.int64).flatten().tolist()),
            'pose_mask_r4_0': dataset_utils.int64_feature(pose_mask_r4_0.astype(np.int64).flatten().tolist()),
            'pose_mask_r4_1': dataset_utils.int64_feature(pose_mask_r4_1.astype(np.int64).flatten().tolist()),
            'pose_mask_r8_0': dataset_utils.int64_feature(pose_mask_r8_0.astype(np.int64).flatten().tolist()),
            'pose_mask_r8_1': dataset_utils.int64_feature(pose_mask_r8_1.astype(np.int64).flatten().tolist()),

            'shape': dataset_utils.int64_feature(shape_0),

            # 'indices_r6_v4_0': dataset_utils.int64_feature(np.array(indices_r6_v4_0).astype(np.int64).flatten().tolist()),
github charliememory / Pose-Guided-Person-Image-Generation / datasets / convert_market.py View on Github external
pose_peaks_1_rcv[ii][0] = p[0][1]
                pose_peaks_1_rcv[ii][1] = p[0][0]
                pose_peaks_1_rcv[ii][2] = 1
        pose_subs_0 = subsets_dic[pairs[idx][0]][0].tolist()
        pose_subs_1 = subsets_dic[pairs[idx][1]][0].tolist()
    else:
        return None


    example = tf.train.Example(features=tf.train.Features(feature={
            'image_name_0': dataset_utils.bytes_feature(pairs[idx][0]),
            'image_name_1': dataset_utils.bytes_feature(pairs[idx][1]),
            'image_raw_0': dataset_utils.bytes_feature(image_raw_0),
            'image_raw_1': dataset_utils.bytes_feature(image_raw_1),
            'label': dataset_utils.int64_feature(labels[idx]),
            'id_0': dataset_utils.int64_feature(id_map[id_0]),
            'id_1': dataset_utils.int64_feature(id_map[id_1]),
            'cam_0': dataset_utils.int64_feature(int(cam_0)),
            'cam_1': dataset_utils.int64_feature(int(cam_1)),
            'image_format': dataset_utils.bytes_feature('jpg'),
            'image_height': dataset_utils.int64_feature(height),
            'image_width': dataset_utils.int64_feature(width),
            'real_data': dataset_utils.int64_feature(1),
            'attrs_0': dataset_utils.int64_feature(attrs_0),
            'attrs_1': dataset_utils.int64_feature(attrs_1),
            'attrs_w2v25_0': dataset_utils.float_feature(attrs_w2v25_0),
            'attrs_w2v25_1': dataset_utils.float_feature(attrs_w2v25_1),
            'attrs_w2v50_0': dataset_utils.float_feature(attrs_w2v50_0),
            'attrs_w2v50_1': dataset_utils.float_feature(attrs_w2v50_1),
            'attrs_w2v100_0': dataset_utils.float_feature(attrs_w2v100_0),
            'attrs_w2v100_1': dataset_utils.float_feature(attrs_w2v100_1),
            'attrs_w2v150_0': dataset_utils.float_feature(attrs_w2v150_0),
github charliememory / Pose-Guided-Person-Image-Generation / datasets / convert_market.py View on Github external
return None


    example = tf.train.Example(features=tf.train.Features(feature={
            'image_name_0': dataset_utils.bytes_feature(pairs[idx][0]),
            'image_name_1': dataset_utils.bytes_feature(pairs[idx][1]),
            'image_raw_0': dataset_utils.bytes_feature(image_raw_0),
            'image_raw_1': dataset_utils.bytes_feature(image_raw_1),
            'label': dataset_utils.int64_feature(labels[idx]),
            'id_0': dataset_utils.int64_feature(id_map[id_0]),
            'id_1': dataset_utils.int64_feature(id_map[id_1]),
            'cam_0': dataset_utils.int64_feature(int(cam_0)),
            'cam_1': dataset_utils.int64_feature(int(cam_1)),
            'image_format': dataset_utils.bytes_feature('jpg'),
            'image_height': dataset_utils.int64_feature(height),
            'image_width': dataset_utils.int64_feature(width),
            'real_data': dataset_utils.int64_feature(1),
            'attrs_0': dataset_utils.int64_feature(attrs_0),
            'attrs_1': dataset_utils.int64_feature(attrs_1),
            'attrs_w2v25_0': dataset_utils.float_feature(attrs_w2v25_0),
            'attrs_w2v25_1': dataset_utils.float_feature(attrs_w2v25_1),
            'attrs_w2v50_0': dataset_utils.float_feature(attrs_w2v50_0),
            'attrs_w2v50_1': dataset_utils.float_feature(attrs_w2v50_1),
            'attrs_w2v100_0': dataset_utils.float_feature(attrs_w2v100_0),
            'attrs_w2v100_1': dataset_utils.float_feature(attrs_w2v100_1),
            'attrs_w2v150_0': dataset_utils.float_feature(attrs_w2v150_0),
            'attrs_w2v150_1': dataset_utils.float_feature(attrs_w2v150_1),
            'pose_peaks_0': dataset_utils.float_feature(pose_peaks_0.flatten().tolist()),
            'pose_peaks_1': dataset_utils.float_feature(pose_peaks_1.flatten().tolist()),
            'pose_peaks_0_rcv': dataset_utils.float_feature(pose_peaks_0_rcv.flatten().tolist()),
            'pose_peaks_1_rcv': dataset_utils.float_feature(pose_peaks_1_rcv.flatten().tolist()),
            'pose_mask_r4_0': dataset_utils.int64_feature(pose_mask_r4_0.astype(np.int64).flatten().tolist()),
github charliememory / Pose-Guided-Person-Image-Generation / datasets / convert_DF.py View on Github external
pose_subs_1 = subsets_dic[pairs[i][1]][0].tolist()
    else:
        return None

    example = tf.train.Example(features=tf.train.Features(feature={
            'image_name_0': dataset_utils.bytes_feature(pairs[i][0]),
            'image_name_1': dataset_utils.bytes_feature(pairs[i][1]),
            'image_raw_0': dataset_utils.bytes_feature(image_raw_0),
            'image_raw_1': dataset_utils.bytes_feature(image_raw_1),
            'label': dataset_utils.int64_feature(labels[i]),
            'id_0': dataset_utils.int64_feature(id_map[id_0]),
            'id_1': dataset_utils.int64_feature(id_map[id_1]),
            'cam_0': dataset_utils.int64_feature(-1),
            'cam_1': dataset_utils.int64_feature(-1),
            'image_format': dataset_utils.bytes_feature('jpg'),
            'image_height': dataset_utils.int64_feature(height),
            'image_width': dataset_utils.int64_feature(width),
            'real_data': dataset_utils.int64_feature(1),
            'attrs_0': dataset_utils.int64_feature(attrs_0),
            'attrs_1': dataset_utils.int64_feature(attrs_1),
            'pose_peaks_0': dataset_utils.float_feature(pose_peaks_0.flatten().tolist()),
            'pose_peaks_1': dataset_utils.float_feature(pose_peaks_1.flatten().tolist()),
            'pose_peaks_0_rcv': dataset_utils.float_feature(pose_peaks_0_rcv.flatten().tolist()),
            'pose_peaks_1_rcv': dataset_utils.float_feature(pose_peaks_1_rcv.flatten().tolist()),
            # 'pose_dense_r4_0': dataset_utils.int64_feature(pose_dense_r4_0.astype(np.int64).flatten().tolist()),
            # 'pose_dense_r4_1': dataset_utils.int64_feature(pose_dense_r4_1.astype(np.int64).flatten().tolist()),
            'pose_mask_r4_0': dataset_utils.int64_feature(pose_mask_r4_0.astype(np.int64).flatten().tolist()),
            'pose_mask_r4_1': dataset_utils.int64_feature(pose_mask_r4_1.astype(np.int64).flatten().tolist()),
            'pose_mask_r8_0': dataset_utils.int64_feature(pose_mask_r8_0.astype(np.int64).flatten().tolist()),
            'pose_mask_r8_1': dataset_utils.int64_feature(pose_mask_r8_1.astype(np.int64).flatten().tolist()),

            'shape': dataset_utils.int64_feature(shape_0),
github Shun14 / TextBoxes_plusplus_Tensorflow / datasets / xml_to_tfrecords.py View on Github external
'image/object/bbox/xmin': float_feature(xmin),
            'image/object/bbox/xmax': float_feature(xmax),
            'image/object/bbox/ymin': float_feature(ymin),
            'image/object/bbox/ymax': float_feature(ymax),
            'image/object/bbox/x1': float_feature(x1),
            'image/object/bbox/y1': float_feature(y1),
            'image/object/bbox/x2': float_feature(x2),
            'image/object/bbox/y2': float_feature(y2),
            'image/object/bbox/x3': float_feature(x3),
            'image/object/bbox/y3': float_feature(y3),
            'image/object/bbox/x4': float_feature(x4),
            'image/object/bbox/y4': float_feature(y4),
            'image/object/bbox/label': int64_feature(labels),
            'image/object/bbox/label_text': bytes_feature(labels_text),
            'image/object/bbox/difficult': int64_feature(difficult),
            'image/object/bbox/truncated': int64_feature(truncated),
            'image/object/bbox/ignored': int64_feature(ignored),
            'image/format': bytes_feature(image_format),
            'image/encoded': bytes_feature(image_data)}))
    return example
github Shun14 / TextBoxes_plusplus_Tensorflow / datasets / xml_to_tfrecords.py View on Github external
'image/channels': int64_feature(shape[2]),
            'image/shape': int64_feature(shape),
            'image/filename': bytes_feature(filename.encode('utf-8')),
            'image/object/bbox/xmin': float_feature(xmin),
            'image/object/bbox/xmax': float_feature(xmax),
            'image/object/bbox/ymin': float_feature(ymin),
            'image/object/bbox/ymax': float_feature(ymax),
            'image/object/bbox/x1': float_feature(x1),
            'image/object/bbox/y1': float_feature(y1),
            'image/object/bbox/x2': float_feature(x2),
            'image/object/bbox/y2': float_feature(y2),
            'image/object/bbox/x3': float_feature(x3),
            'image/object/bbox/y3': float_feature(y3),
            'image/object/bbox/x4': float_feature(x4),
            'image/object/bbox/y4': float_feature(y4),
            'image/object/bbox/label': int64_feature(labels),
            'image/object/bbox/label_text': bytes_feature(labels_text),
            'image/object/bbox/difficult': int64_feature(difficult),
            'image/object/bbox/truncated': int64_feature(truncated),
            'image/object/bbox/ignored': int64_feature(ignored),
            'image/format': bytes_feature(image_format),
            'image/encoded': bytes_feature(image_data)}))
    return example
github charliememory / Pose-Guided-Person-Image-Generation / datasets / convert_DF.py View on Github external
example = tf.train.Example(features=tf.train.Features(feature={
            'image_name_0': dataset_utils.bytes_feature(pairs[i][0]),
            'image_name_1': dataset_utils.bytes_feature(pairs[i][1]),
            'image_raw_0': dataset_utils.bytes_feature(image_raw_0),
            'image_raw_1': dataset_utils.bytes_feature(image_raw_1),
            'label': dataset_utils.int64_feature(labels[i]),
            'id_0': dataset_utils.int64_feature(id_map[id_0]),
            'id_1': dataset_utils.int64_feature(id_map[id_1]),
            'cam_0': dataset_utils.int64_feature(-1),
            'cam_1': dataset_utils.int64_feature(-1),
            'image_format': dataset_utils.bytes_feature('jpg'),
            'image_height': dataset_utils.int64_feature(height),
            'image_width': dataset_utils.int64_feature(width),
            'real_data': dataset_utils.int64_feature(1),
            'attrs_0': dataset_utils.int64_feature(attrs_0),
            'attrs_1': dataset_utils.int64_feature(attrs_1),
            'pose_peaks_0': dataset_utils.float_feature(pose_peaks_0.flatten().tolist()),
            'pose_peaks_1': dataset_utils.float_feature(pose_peaks_1.flatten().tolist()),
            'pose_peaks_0_rcv': dataset_utils.float_feature(pose_peaks_0_rcv.flatten().tolist()),
            'pose_peaks_1_rcv': dataset_utils.float_feature(pose_peaks_1_rcv.flatten().tolist()),
            # 'pose_dense_r4_0': dataset_utils.int64_feature(pose_dense_r4_0.astype(np.int64).flatten().tolist()),
            # 'pose_dense_r4_1': dataset_utils.int64_feature(pose_dense_r4_1.astype(np.int64).flatten().tolist()),
            'pose_mask_r4_0': dataset_utils.int64_feature(pose_mask_r4_0.astype(np.int64).flatten().tolist()),
            'pose_mask_r4_1': dataset_utils.int64_feature(pose_mask_r4_1.astype(np.int64).flatten().tolist()),
            'pose_mask_r8_0': dataset_utils.int64_feature(pose_mask_r8_0.astype(np.int64).flatten().tolist()),
            'pose_mask_r8_1': dataset_utils.int64_feature(pose_mask_r8_1.astype(np.int64).flatten().tolist()),

            'shape': dataset_utils.int64_feature(shape_0),

            # 'indices_r6_v4_0': dataset_utils.int64_feature(np.array(indices_r6_v4_0).astype(np.int64).flatten().tolist()),
            # 'values_r6_v4_0': dataset_utils.float_feature(np.array(values_r6_v4_0).astype(np.float).flatten().tolist()),

datasets

HuggingFace community-driven open-source library of datasets

GitHub
Apache-2.0
Latest version published 20 days ago

Package Health Score

97 / 100
Full package analysis

Popular datasets functions

Similar packages