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