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This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -1,3 +1,7 @@ import torch input = torch.randn(1, 2, 1025); input ##### ENCODER # layer-1 downsample_1a = torch.nn.Conv1d(2, 20, 5 , stride=1, padding=0) -
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Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,127 @@ ##### ENCODER # layer-1 downsample_1a = torch.nn.Conv1d(2, 20, 5 , stride=1, padding=0) downsample_1b = torch.nn.Conv1d(2, 20, 50 , stride=1, padding=0) downsample_1c = torch.nn.Conv1d(2, 20, 256 , stride=1, padding=0) downsample_1d = torch.nn.Conv1d(2, 20, 512 , stride=1, padding=0) downsample_1e = torch.nn.Conv1d(2, 20, 1025 , stride=1, padding=0) out_1a = downsample_1a(input); print(out_1a.shape) # [1,20,1021] out_1b = downsample_1b(input); print(out_1b.shape) # [1,20,976] out_1c = downsample_1c(input); print(out_1c.shape) out_1d = downsample_1d(input); print(out_1d.shape) out_1e = downsample_1e(input); print(out_1e.shape) temp_1a = torch.zeros(1,20,1025); # print(temp_1a) temp_1b = torch.zeros(1,20,1025); # print(temp_1b) temp_1c = torch.zeros(1,20,1025); # print(temp_1c) temp_1d = torch.zeros(1,20,1025); # print(temp_1c) temp_1e = torch.zeros(1,20,1025); # print(temp_1c) temp_1a[:,:,0:out_1a.shape[2]] = out_1a temp_1b[:,:,0:out_1b.shape[2]] = out_1b temp_1c[:,:,0:out_1c.shape[2]] = out_1c temp_1d[:,:,0:out_1d.shape[2]] = out_1d temp_1e[:,:,0:out_1e.shape[2]] = out_1e # print(temp_1a.shape) # print(temp_1b[:,:,0:out_1b.shape[2]].shape) out_1x = torch.cat((temp_1a, temp_1b, temp_1c, temp_1d, temp_1e), dim=1) print(f'encoder_lyr1-------{out_1x.shape}') # layer-2 downsample_2a = torch.nn.Conv1d(100, 50, 5 , stride=1, padding=0) downsample_2b = torch.nn.Conv1d(100, 25, 50 , stride=1, padding=0) downsample_2c = torch.nn.Conv1d(100, 20, 256 , stride=1, padding=0) downsample_2d = torch.nn.Conv1d(100, 20, 512 , stride=1, padding=0) downsample_2e = torch.nn.Conv1d(100, 20, 1025 , stride=1, padding=0) out_2a = downsample_2a(out_1x); print(out_2a.shape) out_2b = downsample_2b(out_1x); print(out_2b.shape) out_2c = downsample_2c(out_1x); print(out_2c.shape) out_2d = downsample_2d(out_1x); print(out_2d.shape) out_2e = downsample_2e(out_1x); print(out_2e.shape) temp_2a = torch.zeros(1,50,1025); # print(temp_1a) temp_2b = torch.zeros(1,25,1025); # print(temp_1b) temp_2c = torch.zeros(1,20,1025); # print(temp_1c) temp_2d = torch.zeros(1,20,1025); # print(temp_1c) temp_2e = torch.zeros(1,20,1025); # print(temp_1c) temp_2a[:,:,0:out_2a.shape[2]] = out_2a temp_2b[:,:,0:out_2b.shape[2]] = out_2b temp_2c[:,:,0:out_2c.shape[2]] = out_2c temp_2d[:,:,0:out_2d.shape[2]] = out_2d temp_2e[:,:,0:out_2e.shape[2]] = out_2e out_2x = torch.cat((temp_2a, temp_2b, temp_2c, temp_2d, temp_2e), dim=1) print(f'encoder_lyr2-------{out_2x.shape}') # DECODER upsample_1a = torch.nn.ConvTranspose1d(135, 50, 5, stride=1, padding=0) upsample_1b = torch.nn.ConvTranspose1d(135, 25, 50, stride=1, padding=0) upsample_1c = torch.nn.ConvTranspose1d(135, 20, 256, stride=1, padding=0) upsample_1d = torch.nn.ConvTranspose1d(135, 20, 512, stride=1, padding=0) upsample_1e = torch.nn.ConvTranspose1d(135, 20, 1025, stride=1, padding=0) out_3a = upsample_1a(out_2x); print(out_3a.shape) out_3b = upsample_1b(out_2x); print(out_3b.shape) out_3c = upsample_1c(out_2x); print(out_3c.shape) out_3d = upsample_1d(out_2x); print(out_3d.shape) out_3e = upsample_1e(out_2x); print(out_3e.shape) temp_3a = torch.zeros(1,50,2049); # print(temp_1a) temp_3b = torch.zeros(1,25,2049); # print(temp_1b) temp_3c = torch.zeros(1,20,2049); # print(temp_1c) temp_3d = torch.zeros(1,20,2049); # print(temp_1c) temp_3e = torch.zeros(1,20,2049); # print(temp_1c) temp_3a[:,:,0:out_3a.shape[2]] = out_3a temp_3b[:,:,0:out_3b.shape[2]] = out_3b temp_3c[:,:,0:out_3c.shape[2]] = out_3c temp_3d[:,:,0:out_3d.shape[2]] = out_3d temp_3e[:,:,0:out_3e.shape[2]] = out_3e out_3x = torch.cat((temp_3a, temp_3b, temp_3c, temp_3d, temp_3e), dim=1) print(f'encoder_lyr3-------{out_3x.shape}') upsample_2a = torch.nn.ConvTranspose1d(135, 20, 5, stride=1, padding=0) upsample_2b = torch.nn.ConvTranspose1d(135, 20, 50, stride=1, padding=0) upsample_2c = torch.nn.ConvTranspose1d(135, 20, 256, stride=1, padding=0) upsample_2d = torch.nn.ConvTranspose1d(135, 20, 512, stride=1, padding=0) upsample_2e = torch.nn.ConvTranspose1d(135, 20, 1025, stride=1, padding=0) out_4a = upsample_2a(out_3x); print(out_4a.shape) out_4b = upsample_2b(out_3x); print(out_4b.shape) out_4c = upsample_2c(out_3x); print(out_4c.shape) out_4d = upsample_2d(out_3x); print(out_4d.shape) out_4e = upsample_2e(out_3x); print(out_4e.shape) m = out_4e.shape[2] temp_4a = torch.zeros(1,20,m); # print(temp_1a) temp_4b = torch.zeros(1,20,m); # print(temp_1b) temp_4c = torch.zeros(1,20,m); # print(temp_1c) temp_4d = torch.zeros(1,20,m); # print(temp_1c) temp_4e = torch.zeros(1,20,m); # print(temp_1c) temp_4a[:,:,0:out_4a.shape[2]] = out_4a temp_4b[:,:,0:out_4b.shape[2]] = out_4b temp_4c[:,:,0:out_4c.shape[2]] = out_4c temp_4d[:,:,0:out_4d.shape[2]] = out_4d temp_4e[:,:,0:out_4e.shape[2]] = out_4e out_4x = torch.cat((temp_4a, temp_4b, temp_4c, temp_4d, temp_4e), dim=1) print(f'encoder_lyr4-------{out_4x.shape}') #### OUTPUT layer output_layer = torch.nn.ConvTranspose1d(100, 2, 1025, stride=1, padding=0) out_5x = output_layer(out_4x) print(f'encoder_lyr5-------{out_5x.shape}') # import numpy as np # print(out_3e[:,:,1026:-1])