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165 lines
6.5 KiB
Python
165 lines
6.5 KiB
Python
"""
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/* Copyright (c) 2023 Amazon
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Written by Jan Buethe */
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/*
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions
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are met:
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- Redistributions of source code must retain the above copyright
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notice, this list of conditions and the following disclaimer.
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- Redistributions in binary form must reproduce the above copyright
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notice, this list of conditions and the following disclaimer in the
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documentation and/or other materials provided with the distribution.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
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OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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"""
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import os
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import argparse
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import torch
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import numpy as np
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from models import model_dict
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from utils import endoscopy
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parser = argparse.ArgumentParser()
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parser.add_argument('checkpoint_path', type=str, help='path to folder containing checkpoints "lace_checkpoint.pth" and nolace_checkpoint.pth"')
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parser.add_argument('output_folder', type=str, help='output folder for testvectors')
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parser.add_argument('--debug', action='store_true', help='add debug output to output folder')
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def create_adaconv_testvector(prefix, adaconv, num_frames, debug=False):
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feature_dim = adaconv.feature_dim
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in_channels = adaconv.in_channels
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out_channels = adaconv.out_channels
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frame_size = adaconv.frame_size
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features = torch.randn((1, num_frames, feature_dim))
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x_in = torch.randn((1, in_channels, num_frames * frame_size))
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x_out = adaconv(x_in, features, debug=debug)
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features = features[0].detach().numpy()
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x_in = x_in[0].reshape(in_channels, num_frames, frame_size).permute(1, 0, 2).detach().numpy()
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x_out = x_out[0].reshape(out_channels, num_frames, frame_size).permute(1, 0, 2).detach().numpy()
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features.tofile(prefix + '_features.f32')
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x_in.tofile(prefix + '_x_in.f32')
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x_out.tofile(prefix + '_x_out.f32')
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def create_adacomb_testvector(prefix, adacomb, num_frames, debug=False):
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feature_dim = adacomb.feature_dim
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in_channels = 1
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frame_size = adacomb.frame_size
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features = torch.randn((1, num_frames, feature_dim))
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x_in = torch.randn((1, in_channels, num_frames * frame_size))
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p_in = torch.randint(adacomb.kernel_size, 250, (1, num_frames))
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x_out = adacomb(x_in, features, p_in, debug=debug)
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features = features[0].detach().numpy()
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x_in = x_in[0].permute(1, 0).detach().numpy()
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p_in = p_in[0].detach().numpy().astype(np.int32)
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x_out = x_out[0].permute(1, 0).detach().numpy()
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features.tofile(prefix + '_features.f32')
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x_in.tofile(prefix + '_x_in.f32')
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p_in.tofile(prefix + '_p_in.s32')
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x_out.tofile(prefix + '_x_out.f32')
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def create_adashape_testvector(prefix, adashape, num_frames):
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feature_dim = adashape.feature_dim
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frame_size = adashape.frame_size
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features = torch.randn((1, num_frames, feature_dim))
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x_in = torch.randn((1, 1, num_frames * frame_size))
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x_out = adashape(x_in, features)
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features = features[0].detach().numpy()
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x_in = x_in.flatten().detach().numpy()
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x_out = x_out.flatten().detach().numpy()
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features.tofile(prefix + '_features.f32')
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x_in.tofile(prefix + '_x_in.f32')
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x_out.tofile(prefix + '_x_out.f32')
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def create_feature_net_testvector(prefix, model, num_frames):
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num_features = model.num_features
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num_subframes = 4 * num_frames
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input_features = torch.randn((1, num_subframes, num_features))
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periods = torch.randint(32, 300, (1, num_subframes))
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numbits = model.numbits_range[0] + torch.rand((1, num_frames, 2)) * (model.numbits_range[1] - model.numbits_range[0])
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pembed = model.pitch_embedding(periods)
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nembed = torch.repeat_interleave(model.numbits_embedding(numbits).flatten(2), 4, dim=1)
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full_features = torch.cat((input_features, pembed, nembed), dim=-1)
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cf = model.feature_net(full_features)
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input_features.float().numpy().tofile(prefix + "_in_features.f32")
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periods.numpy().astype(np.int32).tofile(prefix + "_periods.s32")
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numbits.float().numpy().tofile(prefix + "_numbits.f32")
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full_features.detach().numpy().tofile(prefix + "_full_features.f32")
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cf.detach().numpy().tofile(prefix + "_out_features.f32")
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if __name__ == "__main__":
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args = parser.parse_args()
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os.makedirs(args.output_folder, exist_ok=True)
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lace_checkpoint = torch.load(os.path.join(args.checkpoint_path, "lace_checkpoint.pth"), map_location='cpu')
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nolace_checkpoint = torch.load(os.path.join(args.checkpoint_path, "nolace_checkpoint.pth"), map_location='cpu')
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lace = model_dict['lace'](**lace_checkpoint['setup']['model']['kwargs'])
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nolace = model_dict['nolace'](**nolace_checkpoint['setup']['model']['kwargs'])
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lace.load_state_dict(lace_checkpoint['state_dict'])
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nolace.load_state_dict(nolace_checkpoint['state_dict'])
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if args.debug:
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endoscopy.init(args.output_folder)
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# lace af1, 1 input channel, 1 output channel
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create_adaconv_testvector(os.path.join(args.output_folder, "lace_af1"), lace.af1, 5, debug=args.debug)
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# nolace af1, 1 input channel, 2 output channels
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create_adaconv_testvector(os.path.join(args.output_folder, "nolace_af1"), nolace.af1, 5, debug=args.debug)
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# nolace af4, 2 input channel, 1 output channels
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create_adaconv_testvector(os.path.join(args.output_folder, "nolace_af4"), nolace.af4, 5, debug=args.debug)
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# nolace af2, 2 input channel, 2 output channels
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create_adaconv_testvector(os.path.join(args.output_folder, "nolace_af2"), nolace.af2, 5, debug=args.debug)
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# lace cf1
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create_adacomb_testvector(os.path.join(args.output_folder, "lace_cf1"), lace.cf1, 5, debug=args.debug)
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# nolace tdshape1
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create_adashape_testvector(os.path.join(args.output_folder, "nolace_tdshape1"), nolace.tdshape1, 5)
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# lace feature net
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create_feature_net_testvector(os.path.join(args.output_folder, 'lace'), lace, 5)
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if args.debug:
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endoscopy.close()
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