Python torch.bincount() Examples

def KMeans(x_i, c_j, Nits = 10, ranges = None):

    D = x_i.shape[1]
    for i in range(10):
        # Points -> Nearest cluster
        labs_i = nn_search(x_i, c_j, ranges = ranges)
        # Class cardinals:
        Ncl = torch.bincount(labs_i.view(-1)).type(dtype)
        # Compute the cluster centroids with torch.bincount:
        for d in range(D):  # Unfortunately, vector weights are not supported...
            c_j[:, d] = torch.bincount(labs_i.view(-1), weights=x_i[:, d]) / Ncl
    
    return c_j, labs_i


##############################################
# On the subject
# ~~~~~~~~~~~~~~~~~~~~~~~~
#
# For new subject (unlabelled), we perform a simple Kmean
# on R^60 to obtain a cluster of the data.
# 

def split(data, batch):
    node_slice = torch.cumsum(torch.from_numpy(np.bincount(batch)), 0)
    node_slice = torch.cat([torch.tensor([0]), node_slice])

    row, _ = data.edge_index
    edge_slice = torch.cumsum(torch.from_numpy(np.bincount(batch[row])), 0)
    edge_slice = torch.cat([torch.tensor([0]), edge_slice])

    # Edge indices should start at zero for every graph.
    data.edge_index -= node_slice[batch[row]].unsqueeze(0)
    data.__num_nodes__ = torch.bincount(batch).tolist()

    slices = {'edge_index': edge_slice}
    if data.x is not None:
        slices['x'] = node_slice
    if data.edge_attr is not None:
        slices['edge_attr'] = edge_slice
    if data.y is not None:
        if data.y.size(0) == batch.size(0):
            slices['y'] = node_slice
        else:
            slices['y'] = torch.arange(0, batch[-1] + 2, dtype=torch.long)

    return data, slices 

def __init__(self, centroids, assignments, bias, in_features, out_features):
        super(PQLinear, self).__init__()
        self.block_size = centroids.size(1)
        self.n_centroids = centroids.size(0)
        self.in_features = in_features
        self.out_features = out_features
        # check compatibility
        if self.in_features % self.block_size != 0:
            raise ValueError("Wrong PQ sizes")
        if len(assignments) % self.out_features != 0:
            raise ValueError("Wrong PQ sizes")
        # define parameters
        self.centroids = nn.Parameter(centroids, requires_grad=True)
        self.register_buffer("assignments", assignments)
        self.register_buffer("counts", torch.bincount(assignments).type_as(centroids))
        if bias is not None:
            self.bias = nn.Parameter(bias)
        else:
            self.register_parameter("bias", None) 

def forward(self, simmat, dlens, dtoks, qtoks):
        BATCH, CHANNELS, QLEN, DLEN = simmat.shape

        # +1e-5 to nudge scores of 1 to above threshold
        bins = ((simmat + 1.00001) / 2. * (self.bins - 1)).int()
        weights = ((dtoks != -1).reshape(BATCH, 1, DLEN).expand(BATCH, QLEN, DLEN) * \
                      (qtoks != -1).reshape(BATCH, QLEN, 1).expand(BATCH, QLEN, DLEN)).float()
        # apparently no way to batch this... https://discuss.pytorch.org/t/histogram-function-in-pytorch/5350
        bins, weights = bins.cpu(), weights.cpu() # WARNING: this line (and the similar line below) improve performance tenfold when on GPU
        histogram = []
        for superbins, w in zip(bins, weights):
            result = []
            for b in superbins:
                result.append(torch.stack([torch.bincount(q, x, self.bins) for q, x in zip(b, w)], dim=0))
            result = torch.stack(result, dim=0)
            histogram.append(result)
        histogram = torch.stack(histogram, dim=0)
        histogram = histogram.to(simmat.device) # WARNING: this line (and the similar line above) improve performance tenfold when on GPU
        return histogram 

def update(self, output: Sequence[torch.Tensor]) -> None:
        self._check_shape(output)
        y_pred, y = output

        self._num_examples += y_pred.shape[0]

        # target is (batch_size, ...)
        y_pred = torch.argmax(y_pred, dim=1).flatten()
        y = y.flatten()

        target_mask = (y >= 0) & (y < self.num_classes)
        y = y[target_mask]
        y_pred = y_pred[target_mask]

        indices = self.num_classes * y + y_pred
        m = torch.bincount(indices, minlength=self.num_classes ** 2).reshape(self.num_classes, self.num_classes)
        self.confusion_matrix += m.to(self.confusion_matrix) 

def forward(self, simmat, dtoks, qtoks):
        # THIS IS SLOW ... Any way to make this faster? Maybe it's not worth doing on GPU?
        BATCH, CHANNELS, QLEN, DLEN = simmat.shape
        # +1e-5 to nudge scores of 1 to above threshold
        bins = ((simmat + 1.000001) / 2. * (self.bins - 1)).int()
        # set weights of 0 for padding (in both query and doc dims)
        weights = ((dtoks != -1).reshape(BATCH, 1, DLEN).expand(BATCH, QLEN, DLEN) * \
                  (qtoks != -1).reshape(BATCH, QLEN, 1).expand(BATCH, QLEN, DLEN)).float()

        # no way to batch this... loses gradients here. https://discuss.pytorch.org/t/histogram-function-in-pytorch/5350
        bins, weights = bins.cpu(), weights.cpu()
        histogram = []
        for superbins, w in zip(bins, weights):
            result = []
            for b in superbins:
                result.append(torch.stack([torch.bincount(q, x, self.bins) for q, x in zip(b, w)], dim=0))
            result = torch.stack(result, dim=0)
            histogram.append(result)
        histogram = torch.stack(histogram, dim=0)

        # back to GPU
        histogram = histogram.to(simmat.device)
        return (histogram.float() + 1e-5).log() 

def _torch_hist(label_true, label_pred, n_class):
    """Calculates the confusion matrix for the labels
    
    Args:
        label_true ([type]): [description]
        label_pred ([type]): [description]
        n_class ([type]): [description]
    
    Returns:
        [type]: [description]
    """
    
    assert len(label_true.shape) == 1, "Labels need to be 1D"
    assert len(label_pred.shape) == 1, "Predictions need to be 1D"
    mask = (label_true >= 0) & (label_true < n_class)
    hist = torch.bincount(n_class * label_true[mask] + label_pred[mask], minlength=n_class ** 2).reshape(
        n_class, n_class
    )
    return hist 

def __init__(self, centroids, assignments, bias, in_features, out_features):
        super(PQLinear, self).__init__()
        self.block_size = centroids.size(1)
        self.n_centroids = centroids.size(0)
        self.in_features = in_features
        self.out_features = out_features
        # check compatibility
        if self.in_features % self.block_size != 0:
            raise ValueError("Wrong PQ sizes")
        if len(assignments) % self.out_features != 0:
            raise ValueError("Wrong PQ sizes")
        # define parameters
        self.centroids = nn.Parameter(centroids, requires_grad=True)
        self.register_buffer("assignments", assignments)
        self.register_buffer("counts", torch.bincount(assignments).type_as(centroids))
        if bias is not None:
            self.bias = nn.Parameter(bias)
        else:
            self.register_parameter("bias", None) 

def test_main():
    '''
    Test the unified segmenter.
    '''
    from PIL import Image
    testim = Image.open('script/testdata/test_church_242.jpg')
    tensor_im = (torch.from_numpy(numpy.asarray(testim)).permute(2, 0, 1)
            .float() / 255 * 2 - 1)[None, :, :, :].cuda()
    segmenter = UnifiedParsingSegmenter()
    seg = segmenter.segment_batch(tensor_im)
    bc = torch.bincount(seg.view(-1))
    labels, cats = segmenter.get_label_and_category_names()
    for label in bc.nonzero()[:,0]:
        if label.item():
            # What is the prediction for this class?
            pred, mask = segmenter.predict_single_class(tensor_im, label.item())
            assert mask.sum().item() == bc[label].item()
            assert len(((seg == label).max(1)[0] - mask).nonzero()) == 0
            inside_pred = pred[mask].mean().item()
            outside_pred = pred[~mask].mean().item()
            print('%s (%s, #%d): %d pixels, pred %.2g inside %.2g outside' %
                (labels[label.item()] + (label.item(), bc[label].item(),
                    inside_pred, outside_pred))) 

def __init__(self, centroids, assignments, num_embeddings, embedding_dim,
                     padding_idx=None, max_norm=None, norm_type=2.,
                     scale_grad_by_freq=False, sparse=False, _weight=None):
        super(PQEmbedding, self).__init__()
        self.block_size = centroids.size(1)
        self.n_centroids = centroids.size(0)
        self.num_embeddings = num_embeddings
        self.embedding_dim = embedding_dim
        if padding_idx is not None:
            if padding_idx > 0:
                assert padding_idx < self.num_embeddings, 'Padding_idx must be within num_embeddings'
            elif padding_idx < 0:
                assert padding_idx >= -self.num_embeddings, 'Padding_idx must be within num_embeddings'
                padding_idx = self.num_embeddings + padding_idx
        self.padding_idx = padding_idx
        self.max_norm = max_norm
        self.norm_type = norm_type
        self.scale_grad_by_freq = scale_grad_by_freq
        self.sparse = sparse
        # check compatibility
        if self.embedding_dim % self.block_size != 0:
            raise ValueError("Wrong PQ sizes")
        if len(assignments) % self.num_embeddings != 0:
            raise ValueError("Wrong PQ sizes")
        # define parameters
        self.centroids = nn.Parameter(centroids, requires_grad=True)
        self.register_buffer("assignments", assignments)
        self.register_buffer("counts", torch.bincount(assignments).type_as(centroids)) 

def update(self, a, b):
        n = self.num_classes
        if self.mat is None:
            self.mat = torch.zeros((n, n), dtype=torch.int64, device=a.device)
        with torch.no_grad():
            k = (a >= 0) & (a < n)
            inds = n * a[k].to(torch.int64) + b[k]
            self.mat += torch.bincount(inds, minlength=n**2).reshape(n, n)# this line can take long 

def confusion_matrix(
        pred: torch.Tensor,
        target: torch.Tensor,
        normalize: bool = False,
) -> torch.Tensor:
    """
    Computes the confusion matrix C where each entry C_{i,j} is the number of observations
    in group i that were predicted in group j.

    Args:
        pred: estimated targets
        target: ground truth labels
        normalize: normalizes confusion matrix

    Return:
        Tensor, confusion matrix C [num_classes, num_classes ]

    Example:

        >>> x = torch.tensor([1, 2, 3])
        >>> y = torch.tensor([0, 2, 3])
        >>> confusion_matrix(x, y)
        tensor([[0., 1., 0., 0.],
                [0., 0., 0., 0.],
                [0., 0., 1., 0.],
                [0., 0., 0., 1.]])
    """
    num_classes = get_num_classes(pred, target, None)

    unique_labels = target.view(-1) * num_classes + pred.view(-1)

    bins = torch.bincount(unique_labels, minlength=num_classes ** 2)
    cm = bins.reshape(num_classes, num_classes).squeeze().float()

    if normalize:
        cm = cm / cm.sum(-1)

    return cm 

def __call__(self, net, dl, n_classes):
        ## evaluate
        hist = torch.zeros(n_classes, n_classes).cuda().detach()
        if dist.is_initialized() and dist.get_rank() != 0:
            diter = enumerate(dl)
        else:
            diter = enumerate(tqdm(dl))
        for i, (imgs, label) in diter:
            N, _, H, W = label.shape
            label = label.squeeze(1).cuda()
            size = label.size()[-2:]
            imgs = imgs.cuda()
            logits = net(imgs)[0]
            logits = F.interpolate(logits, size=size,
                    mode='bilinear', align_corners=True)
            probs = torch.softmax(logits, dim=1)
            preds = torch.argmax(probs, dim=1)
            keep = label != self.ignore_label
            hist += torch.bincount(
                label[keep] * n_classes + preds[keep],
                minlength=n_classes ** 2
                ).view(n_classes, n_classes)
        if dist.is_initialized():
            dist.all_reduce(hist, dist.ReduceOp.SUM)
        ious = hist.diag() / (hist.sum(dim=0) + hist.sum(dim=1) - hist.diag())
        miou = ious.mean()
        return miou.item() 

def update(self, a, b):
        n = self.num_classes
        if self.mat is None:
            self.mat = torch.zeros((n, n), dtype=torch.int64, device=a.device)
        with torch.no_grad():
            k = (a >= 0) & (a < n)
            inds = n * a[k].to(torch.int64) + b[k]
            self.mat += torch.bincount(inds, minlength=n**2).reshape(n, n) 

def _get_labels_distribution(predicts, coefficients):
    predicts = _get_predicts(predicts, coefficients)
    labels = predicts.argmax(dim=-1)
    counter = torch.bincount(labels, minlength=predicts.shape[1])
    return counter 

def update_p_base(e_idx, data_p, data_e):
        """ base function to update each p to the centroids of its cluster

        Args:
            e_idx (pytorch Tensor): assignment of e to p
            data_p (pytorch Tensor): cluster centroids, p
            data_e (pytorch Tensor): empirical samples, e
            p0 (pytorch Tensor): iteration index

        Returns:
            p0 (pytorch Tensor): new p
            max_change_pct (float): max_change
        """

        p0 = torch.zeros(data_p.shape).double()
        num_p = data_p.shape[0]

        max_change_pct = 0.0
        # update p to the centroid of its clustered e samples
        bincount = torch.bincount(e_idx, minlength=num_p).double()
        if 0 in bincount:
            print('Empty cluster found, optimal transport probably did not converge\n'
                  'Try larger lr or max_iter after checking the measures.')
            # return False
        eps = 1e-8
        for i in range(data_p.shape[1]):
            # update p to the centroid of their correspondences one dimension at a time
            p_target = torch.bincount(e_idx, weights=data_e[:, i], minlength=num_p).double() / (bincount+eps)
            change_pct = torch.max(torch.abs((data_p[:, i] - p_target) / (data_p[:, i])+eps))
            max_change_pct = max(max_change_pct, change_pct)
            p0[:, i] = p_target

        # replace nan by original data TODO replace nan by nn barycenter?
        mask = torch.isnan(p0).any(dim=1)
        p0[mask] = data_p[mask].clone()

        return p0, max_change_pct 

def update_p_base(e_idx, data_p, data_e):
        """ base function to update each p to the centroids of its cluster

        Args:
            e_idx (pytorch Tensor): assignment of e to p
            data_p (pytorch Tensor): cluster centroids, p
            data_e (pytorch Tensor): empirical samples, e
            p0 (pytorch Tensor): iteration index

        Returns:
            p0 (pytorch Tensor): new p
            max_change_pct (float): max_change
        """

        p0 = torch.zeros(data_p.shape).double().to(data_p.device)
        num_p = data_p.shape[0]

        max_change_pct = 0.0
        # update p to the centroid of its clustered e samples
        bincount = torch.bincount(e_idx, minlength=num_p).double().to(data_p.device)
        if 0 in bincount:
            print('Empty cluster found, optimal transport probably did not converge\n'
                  'Try a different lr or max_iter after checking the measures.')
            # return False
        eps = 1e-8
        for i in range(data_p.shape[1]):
            # update p to the centroid of their correspondences one dimension at a time
            p_target = torch.bincount(e_idx, weights=data_e[:, i], minlength=num_p).double().to(data_p.device) / (bincount+eps)
            change_pct = torch.max(torch.abs((data_p[:, i] - p_target) / (data_p[:, i])+eps))
            max_change_pct = max(max_change_pct, change_pct)
            p0[:, i] = p_target

        # replace nan by original data TODO replace nan by nn barycenter?
        mask = torch.isnan(p0).any(dim=1)
        p0[mask] = data_p[mask].clone()

        return p0, max_change_pct 

def KMeans(x, K=10, Niter=10, verbose=True):
    N, D = x.shape  # Number of samples, dimension of the ambient space

    # Define our KeOps CUDA kernel:
    nn_search = generic_argmin(  # Argmin reduction for generic formulas:
        'SqDist(x,y)',           # A simple squared L2 distance
        'ind = Vi(1)',           # Output one index per "line" (reduction over "j")
        'x = Vi({})'.format(D),  # 1st arg: one point per "line"
        'y = Vj({})'.format(D))  # 2nd arg: one point per "column"
    
    # K-means loop:
    # - x  is the point cloud, 
    # - cl is the vector of class labels
    # - c  is the cloud of cluster centroids
    start = time.time()

    # Simplistic random initialization for the cluster centroids:
    perm = torch.randperm(N)
    idx = perm[:K]
    c = x[idx, :].clone()  

    for i in range(Niter):
        cl  = nn_search(x,c).view(-1)  # Points -> Nearest cluster
        Ncl = torch.bincount(cl).type(dtype)  # Class weights
        for d in range(D):  # Compute the cluster centroids with torch.bincount:
            c[:, d] = torch.bincount(cl, weights=x[:, d]) / Ncl
    if use_cuda: torch.cuda.synchronize()
    end = time.time()
    if verbose: print("KMeans performed in {:.3f}s.".format(end-start))

    return cl, c 

def fast_hist(label_true, label_pred):
    n_class = settings.N_CLASSES
    mask = (label_true >= 0) & (label_true < n_class)
    hist = torch.bincount(
        n_class * label_true[mask].int() + label_pred[mask].int(),
        minlength=n_class ** 2,
    ).reshape(n_class, n_class)
    return hist 

def region_based_classification_single(self, sample, radius):
        """

        :param sample: one sample (1*channel*H*W)
        :param radius:
        :return:
        """
        self.model.eval()

        assert sample.shape[0] == 1, "the sample parameter should be one example in numpy format"
        copy_sample = np.copy(sample)

        with torch.no_grad():
            copy_sample = torch.from_numpy(copy_sample).to(self.device)

            # prepare the hypercube samples (size=num_points) for the sample (size=1)
            hypercube_samples = copy_sample.repeat(self.num_points, 1, 1, 1).to(self.device).float()
            random_space = torch.Tensor(*hypercube_samples.size()).to(self.device).float()
            random_space.uniform_(-radius, radius)
            hypercube_samples = torch.clamp(hypercube_samples + random_space, min=0.0, max=1.0)

            # predicting for hypercube samples
            hypercube_preds = self.model(hypercube_samples)
            hypercube_labels = torch.max(hypercube_preds, dim=1)[1]

            # voting for predicted labels
            bin_count = torch.bincount(hypercube_labels)
            rc_label = torch.max(bin_count, dim=0)[1]

            return rc_label.cpu().numpy() 

def _fast_hist(true, pred, num_classes):
    mask = (true >= 0) & (true < num_classes)
    hist = torch.bincount(
        num_classes * true[mask] + pred[mask],
        minlength=num_classes ** 2,
    ).reshape(num_classes, num_classes).float()
    return hist 

def forward(self, inputs: torch.Tensor) -> torch.Tensor:
        """
        # Parameters

        inputs : `torch.Tensor`
            Shape `(batch_size, timesteps, sequence_length)` of word ids
            representing the current batch.

        # Returns

        `torch.Tensor`
            The bag-of-words representations for the input sequence, shape
            `(batch_size, vocab_size)`
        """
        bag_of_words_vectors = []

        mask = get_text_field_mask({"tokens": {"tokens": inputs}})
        if self._ignore_oov:
            # also mask out positions corresponding to oov
            mask &= inputs != self._oov_idx
        for document, doc_mask in zip(inputs, mask):
            document = torch.masked_select(document, doc_mask)
            vec = torch.bincount(document, minlength=self.vocab_size).float()
            vec = vec.view(1, -1)
            bag_of_words_vectors.append(vec)
        bag_of_words_output = torch.cat(bag_of_words_vectors, 0)

        if self._projection:
            projection = self._projection
            bag_of_words_output = projection(bag_of_words_output)
        return bag_of_words_output 

def energy_spectrum(vel):
    """
    Compute energy spectrum given a velocity field
    :param vel: tensor of shape (N, 3, res, res, res)
    :return spec: tensor of shape(N, res/2)
    :return k: tensor of shape (res/2,), frequencies corresponding to spec
    """
    device = vel.device
    res = vel.shape[-2:]

    assert(res[0] == res[1])
    r = res[0]
    k_end = int(r/2)
    vel_ = pad_rfft3(vel, onesided=False) # (N, 3, res, res, res, 2)
    uu_ = (torch.norm(vel_, dim=-1) / r**3)**2
    e_ = torch.sum(uu_, dim=1)  # (N, res, res, res)
    k = fftfreqs(res).to(device) # (3, res, res, res)
    rad = torch.norm(k, dim=0) # (res, res, res)
    k_bin = torch.arange(k_end, device=device).float()+1
    bins = torch.zeros(k_end+1).to(device)
    bins[1:-1] = (k_bin[1:]+k_bin[:-1])/2
    bins[-1] = k_bin[-1]
    bins = bins.unsqueeze(0)
    bins[1:] += 1e-3
    inds = searchsorted(bins, rad.flatten().unsqueeze(0)).squeeze().int()
    # bincount = torch.histc(inds.cpu(), bins=bins.shape[1]+1).to(device)
    bincount = torch.bincount(inds)
    asort = torch.argsort(inds.squeeze())
    sorted_e_ = e_.view(e_.shape[0], -1)[:, asort]
    csum_e_ = torch.cumsum(sorted_e_, dim=1)
    binloc = torch.cumsum(bincount, dim=0).long()-1
    spec_ = csum_e_[:,binloc[1:]] - csum_e_[:,binloc[:-1]]
    spec_ = spec_[:, :-1]
    spec_ = spec_ * 2 * np.pi * (k_bin.float()**2) / bincount[1:-1].float()
    return spec_, k_bin


##################### COMPUTE STATS ########################### 

def __init__(self, centroids, assignments, num_embeddings, embedding_dim,
                     padding_idx=None, max_norm=None, norm_type=2.,
                     scale_grad_by_freq=False, sparse=False, _weight=None):
        super(PQEmbedding, self).__init__()
        self.block_size = centroids.size(1)
        self.n_centroids = centroids.size(0)
        self.num_embeddings = num_embeddings
        self.embedding_dim = embedding_dim
        if padding_idx is not None:
            if padding_idx > 0:
                assert padding_idx < self.num_embeddings, 'Padding_idx must be within num_embeddings'
            elif padding_idx < 0:
                assert padding_idx >= -self.num_embeddings, 'Padding_idx must be within num_embeddings'
                padding_idx = self.num_embeddings + padding_idx
        self.padding_idx = padding_idx
        self.max_norm = max_norm
        self.norm_type = norm_type
        self.scale_grad_by_freq = scale_grad_by_freq
        self.sparse = sparse
        # check compatibility
        if self.embedding_dim % self.block_size != 0:
            raise ValueError("Wrong PQ sizes")
        if len(assignments) % self.num_embeddings != 0:
            raise ValueError("Wrong PQ sizes")
        # define parameters
        self.centroids = nn.Parameter(centroids, requires_grad=True)
        self.register_buffer("assignments", assignments)
        self.register_buffer("counts", torch.bincount(assignments).type_as(centroids)) 

def update(self, a, b):
        n = self.num_classes
        if self.mat is None:
            self.mat = torch.zeros((n, n), dtype=torch.int64, device=a.device)
        with torch.no_grad():
            k = (a >= 0) & (a < n)
            inds = n * a[k].to(torch.int64) + b[k]
            self.mat += torch.bincount(inds, minlength=n ** 2).reshape(n, n) 

def fast_hist(label_true, label_pred):
    n_class = settings.N_CLASSES
    mask = (label_true >= 0) & (label_true < n_class)
    hist = torch.bincount(
        n_class * label_true[mask].int() + label_pred[mask].int(),
        minlength=n_class ** 2,
    ).reshape(n_class, n_class)
    return hist 

def compute_present_locations(args, corpus, cache_filename,
        model, segmenter, classnum, full_sample):
    # Phase 1.  Identify a set of locations where there are doorways.
    # Segment the image and find featuremap pixels that maximize the number
    # of doorway pixels under the featuremap pixel.
    if all(k in corpus for k in ['present_indices',
            'object_present_sample', 'object_present_location',
            'object_location_popularity', 'weighted_mean_present_feature']):
        return
    progress = default_progress()
    feature_shape = model.feature_shape[args.layer][2:]
    num_locations = numpy.prod(feature_shape).item()
    num_units = model.feature_shape[args.layer][1]
    with torch.no_grad():
        weighted_feature_sum = torch.zeros(num_units).cuda()
        object_presence_scores = []
        for [zbatch] in progress(
                torch.utils.data.DataLoader(TensorDataset(full_sample),
                batch_size=args.inference_batch_size, num_workers=10,
                pin_memory=True),
                desc="Object pool"):
            zbatch = zbatch.cuda()
            tensor_image = model(zbatch)
            segmented_image = segmenter.segment_batch(tensor_image,
                    downsample=2)
            mask = (segmented_image == classnum).max(1)[0]
            score = torch.nn.functional.adaptive_avg_pool2d(
                    mask.float(), feature_shape)
            object_presence_scores.append(score.cpu())
            feat = model.retained_layer(args.layer)
            weighted_feature_sum += (feat * score[:,None,:,:]).view(
                    feat.shape[0],feat.shape[1], -1).sum(2).sum(0)
        object_presence_at_feature = torch.cat(object_presence_scores)
        object_presence_at_image, object_location_in_image = (
                object_presence_at_feature.view(args.search_size, -1).max(1))
        best_presence_scores, best_presence_images = torch.sort(
                -object_presence_at_image)
        all_present_indices = torch.sort(
                best_presence_images[:(args.train_size+args.eval_size)])[0]
        corpus.present_indices = all_present_indices[:args.train_size]
        corpus.object_present_sample = full_sample[corpus.present_indices]
        corpus.object_present_location = object_location_in_image[
                corpus.present_indices]
        corpus.object_location_popularity = torch.bincount(
            corpus.object_present_location,
            minlength=num_locations)
        corpus.weighted_mean_present_feature = (weighted_feature_sum.cpu() / (
            1e-20 + object_presence_at_feature.view(-1).sum()))
        corpus.eval_present_indices = all_present_indices[-args.eval_size:]
        corpus.eval_present_sample = full_sample[corpus.eval_present_indices]
        corpus.eval_present_location = object_location_in_image[
                corpus.eval_present_indices]

    if cache_filename:
        numpy.savez(cache_filename, **corpus) 

def __call__(self, net):
        ## evaluate
        n_classes = self.cfg.n_classes
        ignore_label = self.cfg.ignore_label
        if dist.is_initialized() and dist.get_rank()!=0:
            diter = enumerate(self.dl)
        else:
            diter = enumerate(tqdm(self.dl))
        hist = torch.zeros(n_classes, n_classes).cuda()
        for i, (imgs, label) in diter:
            label = label.squeeze(1).cuda()
            N, H, W = label.shape
            probs = torch.zeros((N, n_classes, H, W)).cuda()
            probs.requires_grad = False
            for sc in self.cfg.eval_scales:
                new_hw = [int(H*sc), int(W*sc)]
                with torch.no_grad():
                    im = F.interpolate(imgs, new_hw, mode='bilinear', align_corners=True)
                    im = im.cuda()
                    out = net(im)
                    out = F.interpolate(out, (H, W), mode='bilinear', align_corners=True)
                    prob = F.softmax(out, 1)
                    probs += prob
                    if self.cfg.eval_flip:
                        out = net(torch.flip(im, dims=(3,)))
                        out = torch.flip(out, dims=(3,))
                        out = F.interpolate(out, (H, W), mode='bilinear',
                                align_corners=True)
                        prob = F.softmax(out, 1)
                        probs += prob
                    del out, prob
            torch.cuda.empty_cache()
            preds = torch.argmax(probs, dim=1)
            keep = label != ignore_label
            hist += torch.bincount(
                label[keep] * n_classes + preds[keep],
                minlength=n_classes ** 2
                ).view(n_classes, n_classes)
        if dist.is_initialized():
            dist.all_reduce(hist, dist.ReduceOp.SUM)
        ious = hist.diag() / (hist.sum(dim=0) + hist.sum(dim=1) - hist.diag())
        miou = ious.mean()
        return miou.item() 

def __init__(
        self,
        centroids,
        assignments,
        bias,
        in_channels,
        out_channels,
        kernel_size,
        stride=1,
        padding=0,
        dilation=1,
        groups=1,
        padding_mode="zeros",
    ):
        super(PQConv2d, self).__init__()
        self.block_size = centroids.size(1)
        self.n_centroids = centroids.size(0)
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = _pair(kernel_size)
        self.stride = _pair(stride)
        self.padding = _pair(padding)
        self.dilation = _pair(dilation)
        self.groups = groups
        self.padding_mode = padding_mode
        # check compatibility
        if in_channels // groups * np.prod(self.kernel_size) % self.block_size != 0:
            raise ValueError("Wrong PQ sizes")
        if len(assignments) % out_channels != 0:
            raise ValueError("Wrong PQ sizes")
        if in_channels % groups != 0:
            raise ValueError("in_channels must be divisible by groups")
        if out_channels % groups != 0:
            raise ValueError("out_channels must be divisible by groups")
        # define parameters
        self.centroids = nn.Parameter(centroids, requires_grad=True)
        self.register_buffer("assignments", assignments)
        self.register_buffer("counts", torch.bincount(assignments).type_as(centroids))
        if bias is not None:
            self.bias = nn.Parameter(bias)
        else:
            self.register_parameter("bias", None)
        # register hook for averaging gradients per centroids instead of summing
        self.centroids.register_hook(lambda x: x / self.counts[:, None]) 

def __init__(
        self,
        centroids,
        assignments,
        bias,
        in_channels,
        out_channels,
        kernel_size,
        stride=1,
        padding=0,
        dilation=1,
        groups=1,
        padding_mode="zeros",
    ):
        super(PQConv2d, self).__init__()
        self.block_size = centroids.size(1)
        self.n_centroids = centroids.size(0)
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = _pair(kernel_size)
        self.stride = _pair(stride)
        self.padding = _pair(padding)
        self.dilation = _pair(dilation)
        self.groups = groups
        self.padding_mode = padding_mode
        # check compatibility
        if in_channels // groups * np.prod(self.kernel_size) % self.block_size != 0:
            raise ValueError("Wrong PQ sizes")
        if len(assignments) % out_channels != 0:
            raise ValueError("Wrong PQ sizes")
        if in_channels % groups != 0:
            raise ValueError("in_channels must be divisible by groups")
        if out_channels % groups != 0:
            raise ValueError("out_channels must be divisible by groups")
        # define parameters
        self.centroids = nn.Parameter(centroids, requires_grad=True)
        self.register_buffer("assignments", assignments)
        self.register_buffer("counts", torch.bincount(assignments).type_as(centroids))
        if bias is not None:
            self.bias = nn.Parameter(bias)
        else:
            self.register_parameter("bias", None)
        # register hook for averaging gradients per centroids instead of summing
        self.centroids.register_hook(lambda x: x / self.counts[:, None])