Beispiel #1
0
def inference_roicnn(images, keep_prob, deconv = False, layer=2, feat=[2, 4]):

    _print_tensor_size(images, 'inference_roicnn')
    assert isinstance(keep_prob, object)

    if not layer == len(feat):
        print('Make sure you have defined the feature map size for each layer.')
        return

    #images2 = rsvp_quick_inference.inference_augment_s_filter(images)
    #

    # add noise
    #images2 = tf.cond(keep_prob < .999999, lambda: images + tf.truncated_normal(images.get_shape(), mean = 0.0, stddev = (lr /FLAGS.learning_rate) * (lr /FLAGS.learning_rate) * (lr /FLAGS.learning_rate) * 1.0), lambda: images)  # .8 was working well with .25 dropout and .992 or .994 decay

    # local st
    conv_tensor = rsvp_quick_inference.inference_local_st5_filter(images, 'conv0', out_feat=feat[0])
    pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(conv_tensor, 2)
    for l in range(1, layer):
        conv_tensor = rsvp_quick_inference.inference_local_st5_filter \
                (pool_tensor, 'conv' + str(l), in_feat=feat[l - 1], out_feat=feat[l])
        pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(conv_tensor, 'pool' + str(l), 2)

    logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob)

    if deconv:
        for l in range(layer-1, 0, -1):
            conv_tensor = rsvp_quick_inference.inference_local_st5_unfilter \
                (pool_tensor, 'conv' + str(l), in_feat=feat[l - 1], out_feat=feat[l])
            pool_tensor = rsvp_quick_inference.inference_unpooling_s_filter(conv_tensor, 'pool' + str(l), 2)
    else:
        logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob)

    assert isinstance(logits, object)
    return logits
Beispiel #2
0
def inference_roi_ts_cnn(images, keep_prob, layer=2, feat=[2, 4]):

    _print_tensor_size(images, 'inference_roi_ts_cnn')
    assert isinstance(keep_prob, object)

    if not layer == len(feat):
        print(
            'Make sure you have defined the feature map size for each layer.')
        return

    # local st
    conv_tensor = rsvp_quick_inference.inference_roi_global_ts_filter(
        images, 'conv0', out_feat=feat[0])
    pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(conv_tensor,
                                                                  kwidth=1)
    for l in range(1, layer):
        conv_tensor = rsvp_quick_inference.inference_roi_s_filter\
            (pool_tensor, 'conv'+str(l), in_feat=feat[l-1], out_feat=feat[l])
        pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(
            conv_tensor, kwidth=1)

    logits = rsvp_quick_inference.inference_fully_connected_1layer(
        pool_tensor, keep_prob)

    assert isinstance(logits, object)
    return logits
Beispiel #3
0
def inference_roicnn(images, keep_prob, deconv=False, layer=2, feat=[2, 4]):

    _print_tensor_size(images, 'inference_roicnn')
    assert isinstance(keep_prob, object)

    if not layer == len(feat):
        print(
            'Make sure you have defined the feature map size for each layer.')
        return

    #images2 = rsvp_quick_inference.inference_augment_s_filter(images)
    #

    # add noise
    #images2 = tf.cond(keep_prob < .999999, lambda: images + tf.truncated_normal(images.get_shape(), mean = 0.0, stddev = (lr /FLAGS.learning_rate) * (lr /FLAGS.learning_rate) * (lr /FLAGS.learning_rate) * 1.0), lambda: images)  # .8 was working well with .25 dropout and .992 or .994 decay

    # local st
    conv_tensor = rsvp_quick_inference.inference_local_st5_filter(
        images, 'conv0', out_feat=feat[0])
    pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(
        conv_tensor, 2)
    for l in range(1, layer):
        conv_tensor = rsvp_quick_inference.inference_local_st5_filter \
                (pool_tensor, 'conv' + str(l), in_feat=feat[l - 1], out_feat=feat[l])
        pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(
            conv_tensor, 'pool' + str(l), 2)

    logits = rsvp_quick_inference.inference_fully_connected_1layer(
        pool_tensor, keep_prob)

    if deconv:
        for l in range(layer - 1, 0, -1):
            conv_tensor = rsvp_quick_inference.inference_local_st5_unfilter \
                (pool_tensor, 'conv' + str(l), in_feat=feat[l - 1], out_feat=feat[l])
            pool_tensor = rsvp_quick_inference.inference_unpooling_s_filter(
                conv_tensor, 'pool' + str(l), 2)
    else:
        logits = rsvp_quick_inference.inference_fully_connected_1layer(
            pool_tensor, keep_prob)

    assert isinstance(logits, object)
    return logits
def inference_roi_ts_cnn_1layer(images, keep_prob, feat=[2]):

    _print_tensor_size(images, 'inference_roi_ts_cnn')
    assert isinstance(keep_prob, object)

    # local st
    conv_tensor = rsvp_quick_inference.inference_roi_global_ts_filter(images, 'conv0', out_feat=feat[0])
    pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(conv_tensor, kwidth=1)
    logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob)

    assert isinstance(logits, object)
    return logits
Beispiel #5
0
def inference_roi_ts_cnn(images, keep_prob, layer=2, feat=[2, 4]):

    _print_tensor_size(images, 'inference_roi_ts_cnn')
    assert isinstance(keep_prob, object)

    if not layer == len(feat):
        print('Make sure you have defined the feature map size for each layer.')
        return

    # local st
    conv_tensor = rsvp_quick_inference.inference_roi_global_ts_filter(images, 'conv0', out_feat=feat[0])
    pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(conv_tensor, kwidth=1)
    for l in range(1, layer):
        conv_tensor = rsvp_quick_inference.inference_roi_s_filter\
            (pool_tensor, 'conv'+str(l), in_feat=feat[l-1], out_feat=feat[l])
        pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(conv_tensor, kwidth=1)

    logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob)

    assert isinstance(logits, object)
    return logits
def inference_roicnn_1layer(images, keep_prob, feat=[2]):

    _print_tensor_size(images, 'inference_roicnn')
    assert isinstance(keep_prob, object)

    # local st
    conv_tensor = rsvp_quick_inference.inference_local_st5_filter(
        images, 'conv0', out_feat=feat[0])
    pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(conv_tensor)
    logits = rsvp_quick_inference.inference_fully_connected_1layer(
        pool_tensor, keep_prob)

    assert isinstance(logits, object)
    return logits