Code release

utils/visdial_metrics.py

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+"""
+A Metric observes output of certain model, for example, in form of logits or
+scores, and accumulates a particular metric with reference to some provided
+targets. In context of VisDial, we use Recall (@ 1, 5, 10), Mean Rank, Mean
+Reciprocal Rank (MRR) and Normalized Discounted Cumulative Gain (NDCG).
+
+Each ``Metric`` must atleast implement three methods:
+    - ``observe``, update accumulated metric with currently observed outputs
+      and targets.
+    - ``retrieve`` to return the accumulated metric., an optionally reset
+      internally accumulated metric (this is commonly done between two epochs
+      after validation).
+    - ``reset`` to explicitly reset the internally accumulated metric.
+
+Caveat, if you wish to implement your own class of Metric, make sure you call
+``detach`` on output tensors (like logits), else it will cause memory leaks.
+"""
+import torch
+import torch.distributed as dist
+import numpy as np
+
+def scores_to_ranks(scores: torch.Tensor):
+    """Convert model output scores into ranks."""
+    batch_size, num_rounds, num_options = scores.size()
+    scores = scores.view(-1, num_options)
+
+    # sort in descending order - largest score gets highest rank
+    sorted_ranks, ranked_idx = scores.sort(1, descending=True)
+
+    # i-th position in ranked_idx specifies which score shall take this
+    # position but we want i-th position to have rank of score at that
+    # position, do this conversion
+    ranks = ranked_idx.clone().fill_(0)
+    for i in range(ranked_idx.size(0)):
+        for j in range(num_options):
+            ranks[i][ranked_idx[i][j]] = j
+    # convert from 0-99 ranks to 1-100 ranks
+    ranks += 1
+    ranks = ranks.view(batch_size, num_rounds, num_options)
+    return ranks
+
+class SparseGTMetrics(object):
+    """
+    A class to accumulate all metrics with sparse ground truth annotations.
+    These include Recall (@ 1, 5, 10), Mean Rank and Mean Reciprocal Rank.
+    """
+
+    def __init__(self):
+        self._rank_list = []
+        self._rank_list_rnd = []
+        self.num_rounds = None
+
+    def observe(
+        self, predicted_scores: torch.Tensor, target_ranks: torch.Tensor
+    ):
+        predicted_scores = predicted_scores.detach()
+
+        # shape: (batch_size, num_rounds, num_options)
+        predicted_ranks = scores_to_ranks(predicted_scores)
+        batch_size, num_rounds, num_options = predicted_ranks.size()
+        self.num_rounds = num_rounds
+        # collapse batch dimension
+        predicted_ranks = predicted_ranks.view(
+            batch_size * num_rounds, num_options
+        )
+
+        # shape: (batch_size * num_rounds, )
+        target_ranks = target_ranks.view(batch_size * num_rounds).long()
+
+        # shape: (batch_size * num_rounds, )
+        predicted_gt_ranks = predicted_ranks[
+            torch.arange(batch_size * num_rounds), target_ranks
+        ]
+        self._rank_list.extend(list(predicted_gt_ranks.cpu().numpy()))
+        
+        predicted_gt_ranks_rnd = predicted_gt_ranks.view(batch_size, num_rounds)
+        #  predicted gt ranks
+        self._rank_list_rnd.append(predicted_gt_ranks_rnd.cpu().numpy())
+
+    def retrieve(self, reset: bool = True):
+        num_examples = len(self._rank_list)
+        if num_examples > 0:
+            # convert to numpy array for easy calculation.
+            __rank_list = torch.tensor(self._rank_list).float()
+            metrics = {
+                "r@1": torch.mean((__rank_list <= 1).float()).item(),
+                "r@5": torch.mean((__rank_list <= 5).float()).item(),
+                "r@10": torch.mean((__rank_list <= 10).float()).item(),
+                "mean": torch.mean(__rank_list).item(),
+                "mrr": torch.mean(__rank_list.reciprocal()).item()
+            }
+            # add round metrics
+            _rank_list_rnd = np.concatenate(self._rank_list_rnd)
+            _rank_list_rnd = _rank_list_rnd.astype(float)
+            r_1_rnd = np.mean(_rank_list_rnd <= 1, axis=0)
+            r_5_rnd = np.mean(_rank_list_rnd <= 5, axis=0)
+            r_10_rnd = np.mean(_rank_list_rnd <= 10, axis=0)
+            mean_rnd = np.mean(_rank_list_rnd, axis=0)
+            mrr_rnd = np.mean(np.reciprocal(_rank_list_rnd), axis=0)
+
+            for rnd in range(1, self.num_rounds + 1):
+                metrics["r_1" + "_round_" + str(rnd)] = r_1_rnd[rnd-1]
+                metrics["r_5" + "_round_" + str(rnd)] = r_5_rnd[rnd-1]
+                metrics["r_10" + "_round_" + str(rnd)] = r_10_rnd[rnd-1]
+                metrics["mean" + "_round_" + str(rnd)] = mean_rnd[rnd-1]
+                metrics["mrr" + "_round_" + str(rnd)] = mrr_rnd[rnd-1]
+        else:
+            metrics = {}
+
+        if reset:
+            self.reset()
+        return metrics
+
+    def reset(self):
+        self._rank_list = []
+        self._rank_list_rnd = []
+
+class NDCG(object):
+    def __init__(self):
+        self._ndcg_numerator = 0.0
+        self._ndcg_denominator = 0.0
+
+    def observe(
+        self, predicted_scores: torch.Tensor, target_relevance: torch.Tensor
+    ):
+        """
+        Observe model output scores and target ground truth relevance and
+        accumulate NDCG metric.
+
+        Parameters
+        ----------
+        predicted_scores: torch.Tensor
+            A tensor of shape (batch_size, num_options), because dense
+            annotations are available for 1 randomly picked round out of 10.
+        target_relevance: torch.Tensor
+            A tensor of shape same as predicted scores, indicating ground truth
+            relevance of each answer option for a particular round.
+        """
+        predicted_scores = predicted_scores.detach()
+
+        # shape: (batch_size, 1, num_options)
+        predicted_scores = predicted_scores.unsqueeze(1)
+        predicted_ranks = scores_to_ranks(predicted_scores)
+
+        # shape: (batch_size, num_options)
+        predicted_ranks = predicted_ranks.squeeze(1)
+        batch_size, num_options = predicted_ranks.size()
+        
+        k = torch.sum(target_relevance != 0, dim=-1)
+
+        # shape: (batch_size, num_options)
+        _, rankings = torch.sort(predicted_ranks, dim=-1)
+        # Sort relevance in descending order so highest relevance gets top rnk.
+        _, best_rankings = torch.sort(
+            target_relevance, dim=-1, descending=True
+        )
+
+        # shape: (batch_size, )
+        batch_ndcg = []
+        for batch_index in range(batch_size):
+            num_relevant = k[batch_index]
+            dcg = self._dcg(
+                rankings[batch_index][:num_relevant],
+                target_relevance[batch_index],
+            )
+            best_dcg = self._dcg(
+                best_rankings[batch_index][:num_relevant],
+                target_relevance[batch_index],
+            )
+            batch_ndcg.append(dcg / best_dcg)
+
+        self._ndcg_denominator += batch_size
+        self._ndcg_numerator += sum(batch_ndcg)
+
+    def _dcg(self, rankings: torch.Tensor, relevance: torch.Tensor):
+        sorted_relevance = relevance[rankings].cpu().float()
+        discounts = torch.log2(torch.arange(len(rankings)).float() + 2)
+        return torch.sum(sorted_relevance / discounts, dim=-1)
+
+    def retrieve(self, reset: bool = True):
+        if self._ndcg_denominator > 0:
+            metrics = {
+                "ndcg": float(self._ndcg_numerator / self._ndcg_denominator)
+            }
+        else:
+            metrics = {}
+
+        if reset:
+            self.reset()
+        return metrics
+
+    def reset(self):
+        self._ndcg_numerator = 0.0
+        self._ndcg_denominator = 0.0
+
+class SparseGTMetricsParallel(object):
+    """
+    A class to accumulate all metrics with sparse ground truth annotations.
+    These include Recall (@ 1, 5, 10), Mean Rank and Mean Reciprocal Rank.
+    """
+
+    def __init__(self, gpu_rank):
+        self.rank_1 = 0
+        self.rank_5 = 0
+        self.rank_10 = 0
+        self.ranks = 0
+        self.reciprocal = 0
+        self.count = 0
+        self.gpu_rank = gpu_rank
+        self.img_ids = []
+
+    def observe(
+        self, img_id: list, predicted_scores: torch.Tensor, target_ranks: torch.Tensor
+    ):
+        if img_id in self.img_ids:
+            return
+        else:
+            self.img_ids.append(img_id)
+
+        predicted_scores = predicted_scores.detach()
+
+        # shape: (batch_size, num_rounds, num_options)
+        predicted_ranks = scores_to_ranks(predicted_scores)
+        batch_size, num_rounds, num_options = predicted_ranks.size()
+        self.num_rounds = num_rounds
+        # collapse batch dimension
+        predicted_ranks = predicted_ranks.view(
+            batch_size * num_rounds, num_options
+        )
+
+        # shape: (batch_size * num_rounds, )
+        target_ranks = target_ranks.view(batch_size * num_rounds).long()
+
+        # shape: (batch_size * num_rounds, )
+        predicted_gt_ranks = predicted_ranks[
+            torch.arange(batch_size * num_rounds), target_ranks
+        ]
+
+        self.rank_1 += (predicted_gt_ranks <= 1).sum().item()
+        self.rank_5 += (predicted_gt_ranks <= 5).sum().item()
+        self.rank_10 += (predicted_gt_ranks <= 10).sum().item()
+        self.ranks += predicted_gt_ranks.sum().item()
+        self.reciprocal += predicted_gt_ranks.float().reciprocal().sum().item()
+        self.count += batch_size * num_rounds
+
+    def retrieve(self):
+        if self.count > 0:
+            # retrieve data from all gpu
+            # define tensor on GPU, count and total is the result at each GPU
+            t = torch.tensor([self.rank_1, self.rank_5, self.rank_10, self.ranks, self.reciprocal, self.count], dtype=torch.float32, device=f'cuda:{self.gpu_rank}')
+            dist.barrier()  # synchronizes all processes
+            dist.all_reduce(t, op=torch.distributed.ReduceOp.SUM,)  # Reduces the tensor data across all machines in such a way that all get the final result.
+            t = t.tolist()
+            self.rank_1, self.rank_5, self.rank_10, self.ranks, self.reciprocal, self.count = t
+
+            # convert to numpy array for easy calculation.
+            metrics = {
+                "r@1": self.rank_1 / self.count,
+                "r@5": self.rank_5 / self.count,
+                "r@10": self.rank_10 / self.count,
+                "mean": self.ranks / self.count,
+                "mrr": self.reciprocal / self.count,
+                "tot_rnds": self.count,
+            }
+
+        else:
+            metrics = {}
+
+        return metrics
+
+    def get_count(self):
+        return int(self.count)
+
+class NDCGParallel(NDCG):
+    def __init__(self, gpu_rank):
+        super(NDCGParallel, self).__init__()
+        self.gpu_rank = gpu_rank
+        self.img_ids = []
+        self.count = 0
+
+    def observe(
+        self, img_id: int, predicted_scores: torch.Tensor, target_relevance: torch.Tensor
+    ):
+        """
+        Observe model output scores and target ground truth relevance and
+        accumulate NDCG metric.
+
+        Parameters
+        ----------
+        predicted_scores: torch.Tensor
+            A tensor of shape (batch_size, num_options), because dense
+            annotations are available for 1 randomly picked round out of 10.
+        target_relevance: torch.Tensor
+            A tensor of shape same as predicted scores, indicating ground truth
+            relevance of each answer option for a particular round.
+        """
+        if img_id in self.img_ids:
+            return
+        else:
+            self.img_ids.append(img_id)
+            self.count += 1
+
+        super(NDCGParallel, self).observe(predicted_scores, target_relevance)
+
+
+    def retrieve(self):
+        if self._ndcg_denominator > 0:
+            # define tensor on GPU, count and total is the result at each GPU
+            t = torch.tensor([self._ndcg_numerator, self._ndcg_denominator, self.count], dtype=torch.float32, device=f'cuda:{self.gpu_rank}')
+            dist.barrier()  # synchronizes all processes
+            dist.all_reduce(t, op=torch.distributed.ReduceOp.SUM,)  # Reduces the tensor data across all machines in such a way that all get the final result.
+            t = t.tolist()
+            self._ndcg_numerator, self._ndcg_denominator, self.count = t
+            metrics = {
+                "ndcg": float(self._ndcg_numerator / self._ndcg_denominator)
+            }
+        else:
+            metrics = {}
+        return metrics
+
+    def get_count(self):
+        return int(self.count)