51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
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#from Data import *
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from sets.Casia import *
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'''
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Because the size of samples per class had the biggest impact
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on training outcome, I decided to check the maximum amount of data
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I can get from a class.
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The highest I can get is
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Rank | Identity ID |Count
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-----------------------------------
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1 | 3782 | 35
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2 | 2820 | 35
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3 | 3227 | 35
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4 | 3745 | 34
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5 | 3699 | 34
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6 | 8968 | 32
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7 | 9152 | 32
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8 | 9256 | 32
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9 | 2114 | 31
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... | ... | ...
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17 | 4126 | 31
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18 | 3185 | 30
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... | ... | ...
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50 | 3186 | 30
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as can be seen, 3 classes have 35, 2 have 34, 3 have 32 and the rest have 30.
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'''
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def print_top_identity_stats(dataset, top_n=50):
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# we get data
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ids, counts = get_ids_and_counts(dataset)
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# sort in descending order
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sorted_counts, sorted_indices = torch.sort(counts, descending=True)
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# coresponding sorted ids
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sorted_ids = ids[sorted_indices]
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# 4. Slice the first 'top_n' and print
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print(f"{'Rank':<8} | {'Identity ID':<12} | {'Image Count':<12}")
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print("-" * 35)
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for i in range(top_n):
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identity_id = sorted_ids[i].item()
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count = sorted_counts[i].item()
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print(f"{i+1:<8} | {identity_id:<12} | {count:<12}")
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# Usage:
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dataset = get_set()
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print_top_identity_stats(dataset, 50)
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