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# Copyright 2021 - Valeo Comfort and Driving Assistance - Oriane Siméoni @ valeo.ai
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Code adapted from previous method LOST: https://github.com/valeoai/LOST
"""
import os
import time
import torch
import torch.nn as nn
import numpy as np
from tqdm import tqdm
from misc import bbox_iou, get_bbox_from_segmentation_labels
def evaluation_unsupervised_object_discovery(
dataset,
model,
evaluation_mode: str = 'single', # choices are ["single", "multi"]
output_dir:str = "outputs",
no_hards:bool = False,
):
assert evaluation_mode == "single"
sigmoid = nn.Sigmoid()
# ----------------------------------------------------
# Loop over images
preds_dict = {}
cnt = 0
corloc = np.zeros(len(dataset.dataloader))
start_time = time.time()
pbar = tqdm(dataset.dataloader)
for im_id, inp in enumerate(pbar):
# ------------ IMAGE PROCESSING -------------------------------------------
img = inp[0]
init_image_size = img.shape
# Get the name of the image
im_name = dataset.get_image_name(inp[1])
# Pass in case of no gt boxes in the image
if im_name is None:
continue
# Padding the image with zeros to fit multiple of patch-size
size_im = (
img.shape[0],
int(np.ceil(img.shape[1] / model.vit_patch_size) * model.vit_patch_size),
int(np.ceil(img.shape[2] / model.vit_patch_size) * model.vit_patch_size),
)
paded = torch.zeros(size_im)
paded[:, : img.shape[1], : img.shape[2]] = img
img = paded
# # Move to gpu
img = img.cuda(non_blocking=True)
# Size for transformers
w_featmap = img.shape[-2] // model.vit_patch_size
h_featmap = img.shape[-1] // model.vit_patch_size
# ------------ GROUND-TRUTH -------------------------------------------
gt_bbxs, gt_cls = dataset.extract_gt(inp[1], im_name)
if gt_bbxs is not None:
# Discard images with no gt annotations
# Happens only in the case of VOC07 and VOC12
if gt_bbxs.shape[0] == 0 and no_hards:
continue
outputs = model.forward_step(img[None, :, :, :])
preds = (sigmoid(outputs[0].detach()) > 0.5).float().squeeze().cpu().numpy()
# get bbox
pred = get_bbox_from_segmentation_labels(
segmenter_predictions=preds,
scales=[model.vit_patch_size, model.vit_patch_size],
initial_image_size=init_image_size[1:],
)
# ------------ Visualizations -------------------------------------------
# Save the prediction
preds_dict[im_name] = pred
# Compare prediction to GT boxes
ious = bbox_iou(torch.from_numpy(pred), torch.from_numpy(gt_bbxs))
if torch.any(ious >= 0.5):
corloc[im_id] = 1
cnt += 1
if cnt % 50 == 0:
pbar.set_description(f"Found {int(np.sum(corloc))}/{cnt}")
# Evaluate
print(f"corloc: {100*np.sum(corloc)/cnt:.2f} ({int(np.sum(corloc))}/{cnt})")
result_file = os.path.join(output_dir, 'uod_results.txt')
with open(result_file, 'w') as f:
f.write('corloc,%.1f,,\n'%(100*np.sum(corloc)/cnt))
print('File saved at %s'%result_file) |