Note
Go to the end to download the full example code.
Model Inference
This example demonstrates how to detect structures with a CNN using discopat.
Imports
from tqdm import tqdm
from discopat.core import Movie
from discopat.display import plot_frame
from discopat.repositories import repository_factory
from discopat.nn_models import FasterRCNNModel
Definitions
movie_name = "blob_i/density"
model_name = "faster_rcnn_241113_131447"
computing_device = "cpu"
data_source = "osf"
framework = "torch"
Load the images
movie_repository = repository_factory(data_source, "input_movies")
movie = movie_repository.read(movie_name)
for frame in movie.frames:
plot_frame(frame)
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Figure(640x480)
Figure(640x480)
Figure(640x480)
Load the model
model_repository = repository_factory(data_source, "models")
raw_model = model_repository.read(model_name)
model = FasterRCNNModel.from_dict(raw_model)
model.set_device(computing_device)
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3%|▎ | 4.28M/166M [00:04<02:37, 1.02Mbytes/s]
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4%|▎ | 5.88M/166M [00:06<02:36, 1.02Mbytes/s]
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4%|▎ | 6.11M/166M [00:06<02:46, 958kbytes/s]
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4%|▍ | 6.32M/166M [00:06<02:42, 982kbytes/s]
4%|▍ | 6.44M/166M [00:06<02:39, 998kbytes/s]
4%|▍ | 6.55M/166M [00:06<02:37, 1.01Mbytes/s]
4%|▍ | 6.67M/166M [00:07<02:35, 1.02Mbytes/s]
4%|▍ | 6.78M/166M [00:07<02:34, 1.03Mbytes/s]
4%|▍ | 6.90M/166M [00:07<02:33, 1.03Mbytes/s]
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4%|▍ | 7.36M/166M [00:07<02:32, 1.04Mbytes/s]
5%|▍ | 7.47M/166M [00:07<02:32, 1.03Mbytes/s]
5%|▍ | 7.59M/166M [00:07<02:33, 1.03Mbytes/s]
5%|▍ | 7.70M/166M [00:08<02:45, 954kbytes/s]
5%|▍ | 7.82M/166M [00:08<02:41, 978kbytes/s]
5%|▍ | 7.93M/166M [00:08<02:37, 999kbytes/s]
5%|▍ | 8.04M/166M [00:08<02:36, 1.01Mbytes/s]
5%|▍ | 8.16M/166M [00:08<02:34, 1.02Mbytes/s]
5%|▍ | 8.27M/166M [00:08<02:33, 1.03Mbytes/s]
5%|▌ | 8.39M/166M [00:08<02:30, 1.04Mbytes/s]
5%|▌ | 8.50M/166M [00:08<02:30, 1.04Mbytes/s]
5%|▌ | 8.62M/166M [00:09<02:30, 1.04Mbytes/s]
5%|▌ | 8.73M/166M [00:09<02:45, 946kbytes/s]
5%|▌ | 8.85M/166M [00:09<02:41, 971kbytes/s]
5%|▌ | 8.96M/166M [00:09<02:37, 994kbytes/s]
5%|▌ | 9.08M/166M [00:09<02:33, 1.02Mbytes/s]
6%|▌ | 9.19M/166M [00:09<02:33, 1.02Mbytes/s]
6%|▌ | 9.31M/166M [00:09<02:32, 1.03Mbytes/s]
6%|▌ | 9.42M/166M [00:09<02:31, 1.03Mbytes/s]
6%|▌ | 9.54M/166M [00:10<03:07, 832kbytes/s]
6%|▌ | 9.63M/166M [00:10<03:04, 844kbytes/s]
6%|▌ | 9.73M/166M [00:10<02:59, 868kbytes/s]
6%|▌ | 9.83M/166M [00:10<02:53, 896kbytes/s]
6%|▌ | 9.93M/166M [00:10<02:51, 906kbytes/s]
6%|▌ | 10.0M/166M [00:10<02:49, 918kbytes/s]
6%|▌ | 10.1M/166M [00:10<02:43, 949kbytes/s]
6%|▌ | 10.3M/166M [00:10<02:37, 988kbytes/s]
6%|▋ | 10.4M/166M [00:10<02:34, 1.00Mbytes/s]
6%|▋ | 10.5M/166M [00:10<02:30, 1.03Mbytes/s]
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6%|▋ | 10.7M/166M [00:11<02:27, 1.05Mbytes/s]
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7%|▋ | 10.9M/166M [00:11<02:27, 1.05Mbytes/s]
7%|▋ | 11.1M/166M [00:11<02:30, 1.03Mbytes/s]
7%|▋ | 11.2M/166M [00:11<02:27, 1.05Mbytes/s]
7%|▋ | 11.3M/166M [00:11<02:25, 1.06Mbytes/s]
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8%|▊ | 12.9M/166M [00:13<02:51, 889kbytes/s]
8%|▊ | 13.0M/166M [00:13<02:39, 960kbytes/s]
8%|▊ | 13.1M/166M [00:13<02:34, 987kbytes/s]
8%|▊ | 13.3M/166M [00:13<02:30, 1.01Mbytes/s]
8%|▊ | 13.4M/166M [00:13<02:28, 1.03Mbytes/s]
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16%|█▋ | 26.9M/166M [00:27<02:36, 887kbytes/s]
16%|█▋ | 27.0M/166M [00:27<02:29, 930kbytes/s]
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17%|█▋ | 28.0M/166M [00:28<02:16, 1.01Mbytes/s]
17%|█▋ | 28.1M/166M [00:28<02:46, 826kbytes/s]
17%|█▋ | 28.2M/166M [00:28<02:35, 885kbytes/s]
17%|█▋ | 28.3M/166M [00:28<02:31, 906kbytes/s]
17%|█▋ | 28.4M/166M [00:28<02:29, 919kbytes/s]
17%|█▋ | 28.5M/166M [00:28<02:28, 924kbytes/s]
17%|█▋ | 28.6M/166M [00:28<02:21, 971kbytes/s]
17%|█▋ | 28.7M/166M [00:28<02:18, 990kbytes/s]
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18%|█▊ | 29.2M/166M [00:29<02:11, 1.04Mbytes/s]
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18%|█▊ | 29.4M/166M [00:29<02:12, 1.03Mbytes/s]
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18%|█▊ | 30.2M/166M [00:30<02:08, 1.06Mbytes/s]
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19%|█▉ | 31.2M/166M [00:31<02:07, 1.05Mbytes/s]
19%|█▉ | 31.4M/166M [00:31<02:29, 899kbytes/s]
19%|█▉ | 31.5M/166M [00:31<02:18, 972kbytes/s]
19%|█▉ | 31.6M/166M [00:31<02:15, 991kbytes/s]
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19%|█▉ | 32.3M/166M [00:32<02:07, 1.05Mbytes/s]
20%|█▉ | 32.4M/166M [00:32<02:07, 1.05Mbytes/s]
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Downloading: "https://download.pytorch.org/models/resnet50-0676ba61.pth" to /home/docs/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth
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Compute predictions
analysed_frames = [model.predict(frame) for frame in tqdm(movie.frames)]
analysed_movie = Movie(name=movie.name, frames=analysed_frames, tracks=[])
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Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:836.)
self.score = float(score)
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Display predictions
for frame in analysed_movie.frames:
plot_frame(frame)
Figure(640x480)
Figure(640x480)
Figure(640x480)
Total running time of the script: (3 minutes 41.766 seconds)