Esempio n. 1
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def validate(val_data, val_dataset, net, ctx):
    if isinstance(ctx, mx.Context):
        ctx = [ctx]

    val_metric.reset()

    from tqdm import tqdm
    for batch in tqdm(val_data):
        data, scale, center, score, imgid = val_batch_fn(batch, ctx)

        outputs = [net(X) for X in data]
        if opt.flip_test:
            data_flip = [nd.flip(X, axis=3) for X in data]
            outputs_flip = [net(X) for X in data_flip]
            outputs_flipback = [flip_heatmap(o, val_dataset.joint_pairs, shift=True) for o in outputs_flip]
            outputs = [(o + o_flip)/2 for o, o_flip in zip(outputs, outputs_flipback)]

        if len(outputs) > 1:
            outputs_stack = nd.concat(*[o.as_in_context(mx.cpu()) for o in outputs], dim=0)
        else:
            outputs_stack = outputs[0].as_in_context(mx.cpu())

        preds, maxvals = get_final_preds(outputs_stack, center.asnumpy(), scale.asnumpy())
        val_metric.update(preds, maxvals, score, imgid)

    res = val_metric.get()
    return
Esempio n. 2
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def validate(val_data, val_dataset, net, ctx):
    if isinstance(ctx, mx.Context):
        ctx = [ctx]

    val_metric.reset()

    from tqdm import tqdm
    for batch in tqdm(val_data):
        data, scale, center, score, imgid = val_batch_fn(batch, ctx)

        outputs = [net(X) for X in data]
        if opt.flip_test:
            data_flip = [nd.flip(X, axis=3) for X in data]
            outputs_flip = [net(X) for X in data_flip]
            outputs_flipback = [flip_heatmap(o, val_dataset.joint_pairs, shift=True) for o in outputs_flip]
            outputs = [(o + o_flip)/2 for o, o_flip in zip(outputs, outputs_flipback)]

        if len(outputs) > 1:
            outputs_stack = nd.concat(*[o.as_in_context(mx.cpu()) for o in outputs], dim=0)
        else:
            outputs_stack = outputs[0].as_in_context(mx.cpu())

        preds, maxvals = get_final_preds(outputs_stack, center.asnumpy(), scale.asnumpy())
        val_metric.update(preds, maxvals, score, imgid)

    res = val_metric.get()
    return
Esempio n. 3
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def heatmap_to_coord(heatmaps, bbox_list):
    center_list = []
    scale_list = []
    for i, bbox in enumerate(bbox_list):
        x0 = bbox[0]
        y0 = bbox[1]
        x1 = bbox[2]
        y1 = bbox[3]
        w = (x1 - x0) / 2
        h = (y1 - y0) / 2
        center_list.append(np.array([x0 + w, y0 + h]))
        scale_list.append(np.array([w, h]))

    coords, maxvals = get_final_preds(heatmaps, center_list, scale_list)
    return coords, maxvals
Esempio n. 4
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def validate(val_data, val_dataset, net, ctx):
    if isinstance(ctx, mx.Context):
        ctx = [ctx]

    val_metric.reset()

    from tqdm import tqdm
    for batch in tqdm(val_data):
        data, scale, center, score, imgid = val_batch_fn(batch, ctx)

        outputs = [net(X) for X in data]
        if opt.flip_test:
            data_flip = [nd.flip(X, axis=3) for X in data]
            outputs_flip = [net(X) for X in data_flip]
            outputs_flipback = [
                flip_heatmap(o, val_dataset.joint_pairs, shift=True)
                for o in outputs_flip
            ]
            outputs = [(o + o_flip) / 2
                       for o, o_flip in zip(outputs, outputs_flipback)]

        if opt.dsnt:
            outputs = [net_dsnt(X)[0] for X in outputs]

        if len(outputs) > 1:
            outputs_stack = nd.concat(
                *[o.as_in_context(mx.cpu()) for o in outputs], dim=0)
        else:
            outputs_stack = outputs[0].as_in_context(mx.cpu())

        if opt.dsnt:
            preds = (outputs_stack - 0.5) * scale.expand_dims(
                axis=1) + center.expand_dims(axis=1)
            maxvals = nd.ones(preds.shape[0:2] + (1, ))
        else:
            preds, maxvals = get_final_preds(outputs_stack, center.asnumpy(),
                                             scale.asnumpy())
        val_metric.update(preds, maxvals, score, imgid)

    metric_name, metric_score = val_metric.get()
    print("Inference Completed! %s = %.4f" % (metric_name, metric_score))
    return