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Commit 55baf374 authored by Yuxin Wu's avatar Yuxin Wu
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[MaskRCNN] improve visualization palette

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.github/ISSUE_TEMPLATE/unexpected-problems---bugs.md
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......@@ -18,14 +18,16 @@ feel free to delete everything in this template.
(3) **If not using examples, tell us what you did:**
Note that we may not be able to investigate it if there is no reproducible code.
It's always better to paste what you did instead of describing them.
It's always better to copy-paste what you did than to describe them.
Please try to provide enough information to let other __reproduce__ your issues.
Without reproducing the issue, we may not be able to investigate it.
### 2. What you observed:
(1) **Include the ENTIRE logs here:**
It's always better to paste what you observed instead of describing them.
It's always better to copy-paste what you observed instead of describing them.
It's always better to paste **as much as possible**, although sometimes a partial log is OK.
......@@ -42,11 +44,11 @@ If you expect higher speed, please read
http://tensorpack.readthedocs.io/tutorial/performance-tuning.html
before posting.
If you expect higher accuracy, only in one of the two conditions can we help with it:
If you expect certain accuracy, only in one of the two conditions can we help with it:
(1) You're unable to match the accuracy documented in tensorpack examples.
(2) It appears to be a tensorpack bug.
Otherwise, how to get high accuracy is a machine learning question and is
Otherwise, how to train a model to certain accuracy is a machine learning question and is
not our responsibility to figure out.
### 4. Your environment:
......
.github/ISSUE_TEMPLATE/using-understanding-tensorpack.md
View file @ 55baf374
......@@ -4,7 +4,7 @@ about: More general questions about Tensorpack.
---
+ If you did something with tensorpack and it failed, please use the "Unexpected Problems /
+ If you did something with tensorpack and it failed, PLEASE STOP HERE and use the "Unexpected Problems /
Bugs" category.
+ Your question is probably answered in [tutorials](http://tensorpack.readthedocs.io/tutorial/index.html#user-tutorials). Read it first.
......@@ -23,6 +23,7 @@ Some typical questions that we DO NOT answer:
We have no plans to do so. We don't consider feature
requests for examples or implement a paper for you.
If you don't know how to do something yourself, you may ask a usage question.
+ "The examples do not perform as expected after I change the models/dataset/parameters/etc.":
Tensorpack maintainers make sure the examples perform well without modifications.
......
examples/FasterRCNN/README.md
View file @ 55baf374
......@@ -63,7 +63,7 @@ Some reasonable configurations are listed in the table below.
To predict on an image (needs DISPLAY to show the outputs):
```
./train.py --predict input.jpg --load /path/to/Trained-Model-Checkpoint --config SAME-AS-TRAINING
./train.py --predict input1.jpg input2.jpg --load /path/to/Trained-Model-Checkpoint --config SAME-AS-TRAINING
```
To evaluate the performance of a model on COCO:
......
examples/FasterRCNN/config.py
View file @ 55baf374
......@@ -201,7 +201,7 @@ _C.TEST.FRCNN_NMS_THRESH = 0.5
# Smaller threshold value gives significantly better mAP. But we use 0.05 for consistency with Detectron.
# mAP with 1e-4 threshold can be found at https://github.com/tensorpack/tensorpack/commit/26321ae58120af2568bdbf2269f32aa708d425a8#diff-61085c48abee915b584027e1085e1043 # noqa
_C.TEST.RESULT_SCORE_THRESH = 0.05
_C.TEST.RESULT_SCORE_THRESH_VIS = 0.3 # only visualize confident results
_C.TEST.RESULT_SCORE_THRESH_VIS = 0.5 # only visualize confident results
_C.TEST.RESULTS_PER_IM = 100
_C.freeze() # avoid typo / wrong config keys
......
examples/FasterRCNN/train.py
View file @ 55baf374
......@@ -393,7 +393,7 @@ def do_predict(pred_func, input_file):
final = draw_final_outputs(img, results)
viz = np.concatenate((img, final), axis=1)
cv2.imwrite("output.png", viz)
logger.info("Inference output written to output.png")
logger.info("Inference output for {} written to output.png".format(input_file))
tpviz.interactive_imshow(viz)
......@@ -405,7 +405,7 @@ if __name__ == '__main__':
parser.add_argument('--evaluate', help="Run evaluation. "
"This argument is the path to the output json evaluation file")
parser.add_argument('--predict', help="Run prediction on a given image. "
"This argument is the path to the input image file")
"This argument is the path to the input image file", nargs='+')
parser.add_argument('--config', help="A list of KEY=VALUE to overwrite those defined in config.py",
nargs='+')
......@@ -440,7 +440,9 @@ if __name__ == '__main__':
input_names=MODEL.get_inference_tensor_names()[0],
output_names=MODEL.get_inference_tensor_names()[1])
if args.predict:
do_predict(OfflinePredictor(predcfg), args.predict)
predictor = OfflinePredictor(predcfg)
for image_file in args.predict:
do_predict(predictor, image_file)
elif args.evaluate:
assert args.evaluate.endswith('.json'), args.evaluate
do_evaluate(predcfg, args.evaluate)
......
examples/FasterRCNN/viz.py
View file @ 55baf374
......@@ -9,6 +9,7 @@ from tensorpack.utils.palette import PALETTE_RGB
from config import config as cfg
from utils.np_box_ops import iou as np_iou
from utils.np_box_ops import area as np_area
def draw_annotation(img, boxes, klass, is_crowd=None):
......@@ -70,14 +71,19 @@ def draw_final_outputs(img, results):
if len(results) == 0:
return img
# Display in largest to smallest order to reduce occlusion
boxes = np.asarray([r.box for r in results])
areas = np_area(boxes)
sorted_inds = np.argsort(-areas)
tags = []
for r in results:
tags.append(
"{},{:.2f}".format(cfg.DATA.CLASS_NAMES[r.class_id], r.score))
boxes = np.asarray([r.box for r in results])
ret = viz.draw_boxes(img, boxes, tags)
for r in results:
for result_id in sorted_inds:
r = results[result_id]
if r.mask is not None:
ret = draw_mask(ret, r.mask)
return ret
......
tensorpack/utils/palette.py
View file @ 55baf374
......@@ -5,7 +5,7 @@ import numpy as np
__all__ = ['PALETTE_RGB']
# copied from https://stackoverflow.com/questions/2328339/how-to-generate-n-different-colors-for-any-natural-number-n
# Copied from https://stackoverflow.com/questions/2328339/how-to-generate-n-different-colors-for-any-natural-number-n
PALETTE_HEX = [
"#000000", "#FFFF00", "#1CE6FF", "#FF34FF", "#FF4A46", "#008941", "#006FA6", "#A30059",
"#FFDBE5", "#7A4900", "#0000A6", "#63FFAC", "#B79762", "#004D43", "#8FB0FF", "#997D87",
......@@ -26,6 +26,92 @@ PALETTE_HEX = [
"#7ED379", "#012C58"]
# Copied from https://github.com/facebookresearch/Detectron/blob/master/detectron/utils/colormap.py
DETECTRON_PALETTE = np.array(
[
0.000, 0.447, 0.741,
0.850, 0.325, 0.098,
0.929, 0.694, 0.125,
0.494, 0.184, 0.556,
0.466, 0.674, 0.188,
0.301, 0.745, 0.933,
0.635, 0.078, 0.184,
0.300, 0.300, 0.300,
0.600, 0.600, 0.600,
1.000, 0.000, 0.000,
1.000, 0.500, 0.000,
0.749, 0.749, 0.000,
0.000, 1.000, 0.000,
0.000, 0.000, 1.000,
0.667, 0.000, 1.000,
0.333, 0.333, 0.000,
0.333, 0.667, 0.000,
0.333, 1.000, 0.000,
0.667, 0.333, 0.000,
0.667, 0.667, 0.000,
0.667, 1.000, 0.000,
1.000, 0.333, 0.000,
1.000, 0.667, 0.000,
1.000, 1.000, 0.000,
0.000, 0.333, 0.500,
0.000, 0.667, 0.500,
0.000, 1.000, 0.500,
0.333, 0.000, 0.500,
0.333, 0.333, 0.500,
0.333, 0.667, 0.500,
0.333, 1.000, 0.500,
0.667, 0.000, 0.500,
0.667, 0.333, 0.500,
0.667, 0.667, 0.500,
0.667, 1.000, 0.500,
1.000, 0.000, 0.500,
1.000, 0.333, 0.500,
1.000, 0.667, 0.500,
1.000, 1.000, 0.500,
0.000, 0.333, 1.000,
0.000, 0.667, 1.000,
0.000, 1.000, 1.000,
0.333, 0.000, 1.000,
0.333, 0.333, 1.000,
0.333, 0.667, 1.000,
0.333, 1.000, 1.000,
0.667, 0.000, 1.000,
0.667, 0.333, 1.000,
0.667, 0.667, 1.000,
0.667, 1.000, 1.000,
1.000, 0.000, 1.000,
1.000, 0.333, 1.000,
1.000, 0.667, 1.000,
0.167, 0.000, 0.000,
0.333, 0.000, 0.000,
0.500, 0.000, 0.000,
0.667, 0.000, 0.000,
0.833, 0.000, 0.000,
1.000, 0.000, 0.000,
0.000, 0.167, 0.000,
0.000, 0.333, 0.000,
0.000, 0.500, 0.000,
0.000, 0.667, 0.000,
0.000, 0.833, 0.000,
0.000, 1.000, 0.000,
0.000, 0.000, 0.167,
0.000, 0.000, 0.333,
0.000, 0.000, 0.500,
0.000, 0.000, 0.667,
0.000, 0.000, 0.833,
0.000, 0.000, 1.000,
0.000, 0.000, 0.000,
0.143, 0.143, 0.143,
0.286, 0.286, 0.286,
0.429, 0.429, 0.429,
0.571, 0.571, 0.571,
0.714, 0.714, 0.714,
0.857, 0.857, 0.857,
1.000, 1.000, 1.000
]
).astype(np.float32).reshape(-1, 3) * 255
def _parse_hex_color(s):
r = int(s[1:3], 16)
g = int(s[3:5], 16)
......@@ -33,6 +119,9 @@ def _parse_hex_color(s):
return (r, g, b)
PALETTE_RGB = np.asarray(
list(map(_parse_hex_color, PALETTE_HEX)),
dtype='int32')
# PALETTE_RGB = np.asarray(
# list(map(_parse_hex_color, PALETTE_HEX)),
# dtype='int32')
# This seems more beautiful
PALETTE_RGB = DETECTRON_PALETTE
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