24 lines
737 B
Python
24 lines
737 B
Python
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def calculate_conv_dimensions(W_in, H_in, K, S, P):
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"""
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Calculate the output dimensions of a convolutional layer.
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Parameters:
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W_in (int): Width of the input image
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H_in (int): Height of the input image
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K (int): Size of the filter (assumed to be square)
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S (int): Stride
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P (int): Padding
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Returns:
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(int, int): Width and height of the output activation map
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"""
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W_out = (W_in - K + (2 * P)) / S + 1
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H_out = (H_in - K + (2 * P)) / S + 1
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return W_out, H_out
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print(f"w, h = {calculate_conv_dimensions(W_in=2048, H_in=32, K=11, S=4, P=2)=}")
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# w, h = calculate_conv_dimensions(W_in=2048, H_in=32, K=11, S=4, P=2)
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# print(f"{calculate_conv_dimensions(W_in=w, H_in=h, K=11, S=4, P=2)=}")
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