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113 lines
3.5 KiB
Markdown
113 lines
3.5 KiB
Markdown
# Progressbar
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Big circuits can take a long time to execute, and waiting for execution to finish without having any indication of its progress can be frustrating. For this reason, progressbar feature is introduced:
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```python
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import time
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import matplotlib.pyplot as plt
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import numpy as np
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import randimage
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from concrete import fhe
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configuration = fhe.Configuration(
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enable_unsafe_features=True,
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use_insecure_key_cache=True,
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insecure_key_cache_location=".keys",
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# To enable displaying progressbar
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show_progress=True,
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# To enable showing tags in the progressbar (does not work in notebooks)
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progress_tag=True,
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# To give a title to the progressbar
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progress_title="Evaluation:",
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)
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@fhe.compiler({"image": "encrypted"})
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def to_grayscale(image):
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with fhe.tag("scaling.r"):
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r = image[:, :, 0]
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r = (r * 0.30).astype(np.int64)
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with fhe.tag("scaling.g"):
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g = image[:, :, 1]
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g = (g * 0.59).astype(np.int64)
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with fhe.tag("scaling.b"):
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b = image[:, :, 2]
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b = (b * 0.11).astype(np.int64)
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with fhe.tag("combining.rgb"):
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gray = r + g + b
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with fhe.tag("creating.result"):
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gray = np.expand_dims(gray, axis=2)
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result = np.concatenate((gray, gray, gray), axis=2)
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return result
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image_size = (16, 16)
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image_data = (randimage.get_random_image(image_size) * 255).round().astype(np.int64)
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print()
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print(f"Compilation started @ {time.strftime('%H:%M:%S', time.localtime())}")
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start = time.time()
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inputset = [np.random.randint(0, 256, size=image_data.shape) for _ in range(100)]
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circuit = to_grayscale.compile(inputset, configuration)
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end = time.time()
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print(f"(took {end - start:.3f} seconds)")
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print()
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print(f"Key generation started @ {time.strftime('%H:%M:%S', time.localtime())}")
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start = time.time()
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circuit.keygen()
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end = time.time()
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print(f"(took {end - start:.3f} seconds)")
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print()
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print(f"Evaluation started @ {time.strftime('%H:%M:%S', time.localtime())}")
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start = time.time()
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grayscale_image_data = circuit.encrypt_run_decrypt(image_data)
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end = time.time()
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print(f"(took {end - start:.3f} seconds)")
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fig, axs = plt.subplots(1, 2)
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axs = axs.flatten()
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axs[0].set_title("Original")
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axs[0].imshow(image_data)
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axs[0].axis("off")
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axs[1].set_title("Grayscale")
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axs[1].imshow(grayscale_image_data)
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axs[1].axis("off")
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plt.show()
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```
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When you run this code, you will see a progressbar like:
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```
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Evaluation: 10% |█████.............................................| 10% (scaling.r)
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^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^
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Title Progressbar Tag
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```
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And as the circuit progresses, this progressbar would fill:
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```
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Evaluation: 30% |███████████████...................................| 30% (scaling.g)
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```
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```
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Evaluation: 50% |█████████████████████████.........................| 50% (scaling.b)
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```
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{% hint style="info" %}
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It is not a uniform progressbar. For example, when the progressbar shows 50%, this does not mean that half of the execution is performed in terms of seconds. Instead, it means that half of the nodes in the graph have been calculated. Since different node types can take a different amount of time, this should not be used to get an ETA.
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{% endhint %}
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Once the progressbar fills and execution completes, you will see the following figure:
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