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fast-reid
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TelBotDev 2021-12-29 22:40:42 +08:00 committed by GitHub
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10
demo/README.md
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@ -7,4 +7,12 @@ You can run this command to get cosine similarites between different images
```bash
cd demo/
sh run_demo.sh
```
```
What is more, you can use this command to make thing more interesting
```bash
export CUDA_VISIBLE_DEVICES=0
python3 demo/visualize_result.py --config-file ./configs/VeRi/sbs_R50-ibn.yml --actmap --dataset-name 'VeRi' --output logs/veri/sbs_R50-ibn/eval --opts MODEL.WEIGHTS logs/veri/sbs_R50-ibn/model_best.pth
```
![4](https://user-images.githubusercontent.com/77771760/123026335-90dd8780-d40e-11eb-8a8d-1683dc19a05a.jpg)
where `--actmap` is used to add activation map upon the original image.

44
demo/predictor.py
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@ -9,6 +9,9 @@ import bisect
from collections import deque
import cv2
import numpy as np
import torch.nn.functional as F
import torch
import torch.multiprocessing as mp
@ -57,6 +60,31 @@ class FeatureExtractionDemo(object):
predictions = self.predictor(image)
return predictions
def get_actmap(self, features, sz):
"""
:param features: (1, 2048, 16, 8) activation map
:return:
"""
features = (features ** 2).sum(1) # (1, 16, 8)
b, h, w = features.size()
features = features.view(b, h * w)
features = F.normalize(features, p=2, dim=1)
acts = features.view(b, h, w)
all_acts = []
for i in range(b):
act = acts[i].numpy()
act = cv2.resize(act, (sz[1], sz[0]))
# act = 255 * (act - act.max()) / (act.max() - act.min() + 1e-12)
act = 255 * (act - act.min()) / (act.max() - act.min() + 1e-12)
act = np.uint8(np.floor(act))
act = cv2.applyColorMap(act, cv2.COLORMAP_JET)
all_acts.append(act)
return all_acts
def run_on_loader(self, data_loader):
if self.parallel:
buffer_size = self.predictor.default_buffer_size
@ -78,8 +106,22 @@ class FeatureExtractionDemo(object):
yield predictions, batch["targets"].cpu().numpy(), batch["camids"].cpu().numpy()
else:
for batch in data_loader:
# add hook here to get features: start
act_outputs = []
def hook_fns_forward(module, input, output):
act_outputs.append(output.cpu())
handle = self.predictor.model.backbone.register_forward_hook(hook_fns_forward)
# add hook here to get features: end
predictions = self.predictor(batch["images"])
yield predictions, batch["targets"].cpu().numpy(), batch["camids"].cpu().numpy()
# add hook here to get features: start
handle.remove()
sz = list(batch["images"].shape[-2:])
acts = self.get_actmap(act_outputs[0], sz)
# add hook here to get features: end
yield predictions, batch["targets"].cpu().numpy(), batch["camids"].cpu().numpy(), acts
class AsyncPredictor:

15
demo/visualize_result.py
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@ -55,6 +55,11 @@ def get_parser():
action='store_true',
help='if use multiprocess for feature extraction.'
)
parser.add_argument(
'--actmap',
action='store_true',
help='if use activation map to overlap the image.'
)
parser.add_argument(
"--dataset-name",
help="a test dataset name for visualizing ranking list."
@ -72,6 +77,7 @@ def get_parser():
)
parser.add_argument(
"--num-vis",
type=int,
default=100,
help="number of query images to be visualized",
)
@ -87,6 +93,7 @@ def get_parser():
)
parser.add_argument(
"--max-rank",
type=int,
default=10,
help="maximum number of rank list to be visualized",
)
@ -109,10 +116,12 @@ if __name__ == '__main__':
feats = []
pids = []
camids = []
for (feat, pid, camid) in tqdm.tqdm(demo.run_on_loader(test_loader), total=len(test_loader)):
acts_list = []
for (feat, pid, camid, acts) in tqdm.tqdm(demo.run_on_loader(test_loader), total=len(test_loader)):
feats.append(feat)
pids.extend(pid)
camids.extend(camid)
acts_list.extend(acts)
feats = torch.cat(feats, dim=0)
q_feat = feats[:num_query]
@ -131,7 +140,7 @@ if __name__ == '__main__':
logger.info("Finish computing APs for all query images!")
visualizer = Visualizer(test_loader.dataset)
visualizer.get_model_output(all_ap, distmat, q_pids, g_pids, q_camids, g_camids)
visualizer.get_model_output(all_ap, distmat, q_pids, g_pids, q_camids, g_camids, acts_list)
logger.info("Start saving ROC curve ...")
fpr, tpr, pos, neg = visualizer.vis_roc_curve(args.output)
@ -140,5 +149,5 @@ if __name__ == '__main__':
logger.info("Saving rank list result ...")
query_indices = visualizer.vis_rank_list(args.output, args.vis_label, args.num_vis,
args.rank_sort, args.label_sort, args.max_rank)
args.rank_sort, args.label_sort, args.max_rank, args.actmap)
logger.info("Finish saving rank list results!")

71
fastreid/utils/visualizer.py
View File

@ -7,6 +7,8 @@
import os
import pickle
import random
import cv2
import torch.nn.functional as F
import matplotlib.pyplot as plt
import numpy as np
@ -23,7 +25,7 @@ class Visualizer:
def __init__(self, dataset):
self.dataset = dataset
def get_model_output(self, all_ap, dist, q_pids, g_pids, q_camids, g_camids):
def get_model_output(self, all_ap, dist, q_pids, g_pids, q_camids, g_camids, acts=None):
self.all_ap = all_ap
self.dist = dist
self.sim = 1 - dist
@ -36,6 +38,8 @@ class Visualizer:
self.matches = (g_pids[self.indices] == q_pids[:, np.newaxis]).astype(np.int32)
self.num_query = len(q_pids)
if acts: self.acts = acts
def get_matched_result(self, q_index):
q_pid = self.q_pids[q_index]
@ -65,7 +69,19 @@ class Visualizer:
query_img = np.rollaxis(np.asarray(query_img.numpy(), dtype=np.uint8), 0, 3)
plt.clf()
ax = fig.add_subplot(1, max_rank + 1, 1)
ax.imshow(query_img)
# ax.imshow(query_img)
# added: show acts
if actmap:
query_acts = self.acts[q_idx]
overlapped = query_img*0.3 + query_acts*0.7
overlapped[overlapped > 255] = 255
overlapped = overlapped.astype(np.uint8)
ax.imshow(overlapped)
# added: show acts
else:
ax.imshow(query_img)
ax.set_title('{:.4f}/cam{}'.format(self.all_ap[q_idx], cam_id))
ax.axis("off")
for i in range(max_rank):
@ -89,27 +105,21 @@ class Visualizer:
ax.add_patch(plt.Rectangle(xy=(0, 0), width=gallery_img.shape[1] - 1,
height=gallery_img.shape[0] - 1,
edgecolor=(0, 0, 1), fill=False, linewidth=5))
ax.imshow(gallery_img)
# added: show acts
if actmap:
gallery_acts = self.acts[g_idx]
overlapped = gallery_img*0.3 + gallery_acts*0.7
overlapped[overlapped > 255] = 255
overlapped = overlapped.astype(np.uint8)
ax.imshow(overlapped)
# added: show acts
else:
ax.imshow(gallery_img)
ax.set_title(f'{self.sim[q_idx, sort_idx[i]]:.3f}/{label}/cam{cam_id}')
ax.axis("off")
# if actmap:
# act_outputs = []
#
# def hook_fns_forward(module, input, output):
# act_outputs.append(output.cpu())
#
# all_imgs = np.stack(all_imgs, axis=0) # (b, 3, h, w)
# all_imgs = torch.from_numpy(all_imgs).float()
# # normalize
# all_imgs = all_imgs.sub_(self.mean).div_(self.std)
# sz = list(all_imgs.shape[-2:])
# handle = m.base.register_forward_hook(hook_fns_forward)
# with torch.no_grad():
# _ = m(all_imgs.cuda())
# handle.remove()
# acts = self.get_actmap(act_outputs[0], sz)
# for i in range(top + 1):
# axes.flat[i].imshow(acts[i], alpha=0.3, cmap='jet')
if vis_label:
label_indice = np.where(cmc == 1)[0]
if label_sort == "ascending": label_indice = label_indice[::-1]
@ -257,22 +267,3 @@ class Visualizer:
# plt.xticks(np.arange(0.1, 1.0, 0.1))
# plt.title('positive and negative pair distribution')
# return fig
# def get_actmap(self, features, sz):
# """
# :param features: (1, 2048, 16, 8) activation map
# :return:
# """
# features = (features ** 2).sum(1) # (1, 16, 8)
# b, h, w = features.size()
# features = features.view(b, h * w)
# features = nn.functional.normalize(features, p=2, dim=1)
# acts = features.view(b, h, w)
# all_acts = []
# for i in range(b):
# act = acts[i].numpy()
# act = cv2.resize(act, (sz[1], sz[0]))
# act = 255 * (act - act.max()) / (act.max() - act.min() + 1e-12)
# act = np.uint8(np.floor(act))
# all_acts.append(act)
# return all_acts