Fix a block_size issue when using upconv_7 model
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9103d393fe
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6be1479710
1 changed files with 3 additions and 3 deletions
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@ -9,8 +9,8 @@ local function reconstruct_nn(model, x, inner_scale, offset, block_size, batch_s
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end
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end
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local ch = x:size(1)
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local ch = x:size(1)
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local new_x = torch.Tensor(x:size(1), x:size(2) * inner_scale, x:size(3) * inner_scale):zero()
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local new_x = torch.Tensor(x:size(1), x:size(2) * inner_scale, x:size(3) * inner_scale):zero()
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local input_block_size = block_size / inner_scale
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local input_block_size = block_size
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local output_block_size = block_size
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local output_block_size = block_size * inner_scale
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local output_size = output_block_size - offset * 2
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local output_size = output_block_size - offset * 2
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local output_size_in_input = input_block_size - math.ceil(offset / inner_scale) * 2
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local output_size_in_input = input_block_size - math.ceil(offset / inner_scale) * 2
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local input_indexes = {}
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local input_indexes = {}
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@ -81,7 +81,7 @@ local function padding_params(x, model, block_size)
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p.x_h = x:size(2)
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p.x_h = x:size(2)
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p.inner_scale = reconstruct.inner_scale(model)
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p.inner_scale = reconstruct.inner_scale(model)
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local input_offset = math.ceil(offset / p.inner_scale)
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local input_offset = math.ceil(offset / p.inner_scale)
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local input_block_size = block_size / p.inner_scale
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local input_block_size = block_size
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local process_size = input_block_size - input_offset * 2
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local process_size = input_block_size - input_offset * 2
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local h_blocks = math.floor(p.x_h / process_size) +
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local h_blocks = math.floor(p.x_h / process_size) +
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((p.x_h % process_size == 0 and 0) or 1)
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((p.x_h % process_size == 0 and 0) or 1)
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