<gh_stars>1-10
import torch
import numpy as np
import pandas as pd
from tqdm import tqdm

import util

NEG_INF = -1e9


class Predictor(object):
    """Predictor class for a single model."""
    def __init__(self, model, device):

        self.model = model
        self.device = device

    def predict(self, loader):
        self.model.eval()
        probs = []
        gt = []
        if loader.dataset.return_info_dict:
            paths = []
        with tqdm(total=len(loader.dataset)) as progress_bar:
            for data in loader:
                with torch.no_grad():
                    if loader.dataset.study_level:
                        if loader.dataset.return_info_dict:
                            inputs, targets, info_dict, mask = data
                        else:
                            inputs, targets, mask = data

                        # Fuse batch size `b` and study length `s`
                        b, s, c, h, w = inputs.size()
                        inputs = inputs.view(-1, c, h, w)

                        # Predict
                        logits = self.model(inputs.to(self.device))
                        logits = logits.view(b, s, -1)

                        # Mask padding to negative infinity
                        ignore_where = (mask == 0).unsqueeze(-1)
                        ignore_where = ignore_where.repeat(1, 1,
                                                           logits.size(-1))
                        ignore_where = ignore_where.to(self.device)
                        logits = torch.where(ignore_where,
                                             torch.full_like(logits, NEG_INF),
                                             logits)
                        batch_logits, _ = torch.max(logits, 1)

                    else:
                        if loader.dataset.return_info_dict:
                            inputs, targets, info_dict = data
                        else:
                            inputs, targets = data

                        batch_logits = self.model(inputs.to(self.device))

                    if self.model.module.model_uncertainty:
                        batch_probs =\
                            util.uncertain_logits_to_probs(batch_logits)
                    else:
                        batch_probs = torch.sigmoid(batch_logits)

                probs.append(batch_probs.cpu())
                gt.append(targets)
                if loader.dataset.return_info_dict:
                    paths.extend(info_dict['paths'])
                progress_bar.update(targets.size(0))

        probs_concat = np.concatenate(probs)
        gt_concat = np.concatenate(gt)
        tasks = self.model.module.tasks
        probs_df = pd.DataFrame({task: probs_concat[:, i]
                                 for i, task in enumerate(tasks)})
        gt_df = pd.DataFrame({task: gt_concat[:, i]
                              for i, task in enumerate(tasks)})

        self.model.train()

        if loader.dataset.return_info_dict:
            return probs_df, gt_df, paths

        return probs_df, gt_df

import torch
import numpy as np
import pandas as pd
from tqdm import tqdm

import util

NEG_INF = -1e9


class Predictor(object):
    """Predictor class for a single model."""
    def __init__(self, model, device):

        self.model = model
        self.device = device

    def predict(self, loader):
        self.model.eval()
        probs = []
        gt = []
        if loader.dataset.return_info_dict:
            paths = []
        with tqdm(total=len(loader.dataset)) as progress_bar:
            for data in loader:
                with torch.no_grad():
                    if loader.dataset.study_level:
                        if loader.dataset.return_info_dict:
                            inputs, targets, info_dict, mask = data
                        else:
                            inputs, targets, mask = data

                        # Fuse batch size `b` and study length `s`
                        b, s, c, h, w = inputs.size()
                        inputs = inputs.view(-1, c, h, w)

                        # Predict
                        logits = self.model(inputs.to(self.device))
                        logits = logits.view(b, s, -1)

                        # Mask padding to negative infinity
                        ignore_where = (mask == 0).unsqueeze(-1)
                        ignore_where = ignore_where.repeat(1, 1,
                                                           logits.size(-1))
                        ignore_where = ignore_where.to(self.device)
                        logits = torch.where(ignore_where,
                                             torch.full_like(logits, NEG_INF),
                                             logits)
                        batch_logits, _ = torch.max(logits, 1)

                    else:
                        if loader.dataset.return_info_dict:
                            inputs, targets, info_dict = data
                        else:
                            inputs, targets = data

                        batch_logits = self.model(inputs.to(self.device))

                    if self.model.module.model_uncertainty:
                        batch_probs =\
                            util.uncertain_logits_to_probs(batch_logits)
                    else:
                        batch_probs = torch.sigmoid(batch_logits)

                probs.append(batch_probs.cpu())
                gt.append(targets)
                if loader.dataset.return_info_dict:
                    paths.extend(info_dict['paths'])
                progress_bar.update(targets.size(0))

        probs_concat = np.concatenate(probs)
        gt_concat = np.concatenate(gt)
        tasks = self.model.module.tasks
        probs_df = pd.DataFrame({task: probs_concat[:, i]
                                 for i, task in enumerate(tasks)})
        gt_df = pd.DataFrame({task: gt_concat[:, i]
                              for i, task in enumerate(tasks)})

        self.model.train()

        if loader.dataset.return_info_dict:
            return probs_df, gt_df, paths

        return probs_df, gt_df
Predictor class for a single model. # Fuse batch size `b` and study length `s` # Predict # Mask padding to negative infinity