engine_finetune.py

# -*- coding: utf-8 -*-
# Author: Gaojian Wang@ZJUICSR; TongWu@ZJUICSR
# --------------------------------------------------------
# This source code is licensed under the Attribution-NonCommercial 4.0 International License.
# You can find the license in the LICENSE file in the root directory of this source tree.
# --------------------------------------------------------

import numpy as np
import torch
import torch.nn.functional as F

import util.misc as misc
from util.metrics import *


@torch.no_grad()
def test_two_class(data_loader, model, device):
    criterion = torch.nn.CrossEntropyLoss()

    # switch to evaluation mode
    model.eval()

    frame_labels = np.array([])  # int label
    frame_preds = np.array([])  # pred logit
    frame_y_preds = np.array([])  # pred int
    video_names_list = list()

    for batch in data_loader:
        images = batch[0]  # torch.Size([BS, C, H, W])
        target = batch[1]  # torch.Size([BS])
        video_name = batch[-1]  # list[BS]
        images = images.to(device, non_blocking=True)
        target = target.to(device, non_blocking=True)

        output = model(images).to(device, non_blocking=True)  # modified
        loss = criterion(output, target)

        frame_pred = (F.softmax(output, dim=1)[:, 1].detach().cpu().numpy())
        frame_preds = np.append(frame_preds, frame_pred)
        frame_y_pred = np.argmax(output.detach().cpu().numpy(), axis=1)
        frame_y_preds = np.append(frame_y_preds, frame_y_pred)

        frame_label = (target.detach().cpu().numpy())
        frame_labels = np.append(frame_labels, frame_label)
        video_names_list.extend(list(video_name))

    # video-level metrics:
    frame_labels_list = frame_labels.tolist()
    frame_preds_list = frame_preds.tolist()
    video_label_list, video_pred_list, video_y_pred_list = get_video_level_label_pred(frame_labels_list, video_names_list, frame_preds_list)

    return frame_preds_list, video_pred_list


@torch.no_grad()
def test_multi_class(data_loader, model, device):
    criterion = torch.nn.CrossEntropyLoss()

    # switch to evaluation mode
    model.eval()

    frame_labels = np.array([])  # int label
    frame_preds = np.empty((0, 4))  # pred logit, initialize as 2D array with 4 columns for 4 classes
    frame_y_preds = np.array([])  # pred int
    video_names_list = list()

    for batch in data_loader:
        images = batch[0]  # torch.Size([BS, C, H, W])
        target = batch[1]  # torch.Size([BS])
        video_name = batch[-1]  # list[BS]
        images = images.to(device, non_blocking=True)
        target = target.to(device, non_blocking=True)

        output = model(images).to(device, non_blocking=True)
        loss = criterion(output, target)

        frame_pred = F.softmax(output, dim=1).detach().cpu().numpy()
        frame_preds = np.append(frame_preds, frame_pred, axis=0)
        frame_y_pred = np.argmax(output.detach().cpu().numpy(), axis=1)
        frame_y_preds = np.append(frame_y_preds, frame_y_pred)

        frame_label = target.detach().cpu().numpy()
        frame_labels = np.append(frame_labels, frame_label)
        video_names_list.extend(list(video_name))

    # video-level metrics:
    frame_labels_list = frame_labels.tolist()
    frame_preds_list = frame_preds.tolist()
    video_label_list, video_pred_list, video_y_pred_list = get_video_level_label_pred_multi_class(frame_labels_list, video_names_list, frame_preds_list)

    return frame_preds_list, video_pred_list


# @torch.no_grad()
# def test_multi_class(data_loader, model, device):
#     criterion = torch.nn.CrossEntropyLoss()
#
#     # switch to evaluation mode
#     model.eval()
#
#     frame_labels = np.array([])  # int label
#     frame_preds = np.array([])  # pred logit
#     frame_y_preds = np.array([])  # pred int
#     video_names_list = list()
#
#     for batch in data_loader:
#         images = batch[0]  # torch.Size([BS, C, H, W])
#         target = batch[1]  # torch.Size([BS])
#         video_name = batch[-1]  # list[BS]
#         images = images.to(device, non_blocking=True)
#         target = target.to(device, non_blocking=True)
#
#         output = model(images).to(device, non_blocking=True)
#         loss = criterion(output, target)
#
#         frame_pred = F.softmax(output, dim=1).detach().cpu().numpy()
#         frame_preds = np.append(frame_preds, frame_pred, axis=0)
#         frame_y_pred = np.argmax(output.detach().cpu().numpy(), axis=1)
#         frame_y_preds = np.append(frame_y_preds, frame_y_pred)
#
#         frame_label = target.detach().cpu().numpy()
#         frame_labels = np.append(frame_labels, frame_label)
#         video_names_list.extend(list(video_name))
#
#     # video-level metrics:
#     frame_labels_list = frame_labels.tolist()
#     frame_preds_list = frame_preds.tolist()
#     video_label_list, video_pred_list, video_y_pred_list = get_video_level_label_pred_multi_class(frame_labels_list, video_names_list, frame_preds_list)
#
#     return frame_preds_list, video_pred_list