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model2.py
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model2.py
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import config
from ext import pickle_save, pickle_load
from data import hann, ihann
from model import FF, FFS, FFT, make_model, prop_Flayer
from model import sequence_loss
from torch import tensor, Tensor, cat, stack
from torch import zeros, ones, eye, randn
from torch import sin, cos, acos, arange
from torch import conv1d, conv_transpose1d, transpose
from torch import sigmoid, tanh, relu, softmax
from torch import pow, log, exp, sqrt, norm, mean, abs
from torch import float32, no_grad
from torch.nn.init import xavier_normal_
from numpy import pi
##
def prop_model(model, io):
for layer in model:
io = prop_Flayer(layer,io)
# dropout(out, inplace=True)
return io
def make_model_higher():
w_conv = randn(config.frame_out,config.frame_len, requires_grad=config.conv_deconv_grad)
if config.init_fourier:
with no_grad():
for f in range(config.frame_out):
w_conv[f,...] = cos(2*pi * (f+1)/config.frame_len * arange(0,config.frame_len,1))
convolver = FF(w_conv)
else:
if config.init_xavier:
xavier_normal_(w_conv, gain=5/3)
convolver = FFT(w_conv)
if config.conv_deconv_same:
deconvolver = convolver
else:
if config.init_fourier:
w_deconv = w_conv.detach()
w_deconv.requires_grad = config.conv_deconv_grad
deconvolver = FF(w_deconv)
else:
w_deconv = randn(config.frame_out, config.frame_len, requires_grad=config.conv_deconv_grad)
if config.init_xavier:
xavier_normal_(w_deconv, gain=5/3)
deconvolver = FFT(w_deconv)
convolver = [convolver]
deconvolver = [deconvolver]
# body = config.creation_info[1:-1]
enc = make_model(config.attention1_info)
dec = make_model(config.attention2_info)
return [convolver, enc,dec, deconvolver]
##
def respond_to(model, sequences, training_run=True, extra_steps=0):
responses = [[] for _ in range(len(sequences))]
loss = 0
convolver, enc,dec, deconvolver = model
hann_w = hann() if not config.use_gpu else hann().cuda()
ihann_w = ihann() if not config.use_gpu else ihann().cuda()
# with no_grad():
# #print(convolver[0].w.size(), hann().size())
# convolver[0].w *= hann_w
# # deconvolver[0].w *= ihann(deconvolver[0].w)
for i,sequence in enumerate(sequences):
#print(f'seq{i}/{len(sequences)}')
#print('in size:',sequence.size(),'conv_w size:',convolver[0].w.unsqueeze(1).size())
sequence = conv1d(sequence, (convolver[0].w * hann_w).unsqueeze(1), stride=config.frame_stride)
sequence = transpose(sequence,1,2)
sequence /=config.frame_len
#print('conved size:',sequence.size())
# make key,query from all here.. => the transformer stuff
for t in range(sequence.size(1)-1):
#curr_inp = sequence[:,t:t+1,:]
prev_inps = sequence[:,:t+1,:]
lbl = sequence[:,t+1:t+2,:]
positions = Tensor([[t+1/config.max_T,i/config.max_T] for i in range(t+1)]).view(1,-1,2)
if config.use_gpu: positions = positions.cuda()
#print(f'{t}/{sequence.size(1)}')
# print('t:',t,',prev inps size:',prev_inps.size(),'curr inp size:',curr_inp.size())
#todo: hmmmm..
#inp = cat([prev_inps,curr_inp.repeat(1,t+1,1)], -1)
inp = cat([prev_inps,positions], -1)
# if config.seq_force_ratio != 1 and t>=2:
# seq_force_ratio = config.seq_force_ratio**t
# inp *= seq_force_ratio
# inp +=
#print('inp size:',inp.size())
enced = prop_model(enc,inp)
# print('enced size:', enced.size())
attn_inp = (softmax(enced,1) * prev_inps).sum(1)
# print('attnded size:', attn_inp.size())
deced = prop_model(dec,attn_inp)
loss += sequence_loss(lbl,deced)
responses[-1].append(deced)
# input("halt here")
#input('halt here..')
if training_run:
loss.backward()
return float(loss)
else:
#print("seq size", sequence.size(1), 'hm resps', len(responses[-1]))
if len(sequences)==1:
for t_extra in range(extra_steps):
t = sequence.size(1)+t_extra-1
#print(f't extra:{t}')
curr_inp = responses[-1][t-1]
# print(sequence[:,:,:].size(), stack(responses[-1][sequence.size(1)-1-1:],1).size())
prev_inps = cat([sequence[:,:-1,:], stack(responses[-1][sequence.size(1)-1-1:],1)],1)
inp = cat([prev_inps,curr_inp.repeat(1,t+1,1)], -1)
#print(inp.size())
enced = prop_model(enc, inp)
# print('enced size:', enced.size())
attn_inp = (softmax(enced,1) * prev_inps).sum(1)
# print('attnded size:', attn_inp.size())
deced = prop_model(dec, attn_inp)
responses[-1].append(deced)
responses = responses[-1]
responses = [(deconvolver[0].w * resp).sum(1) for resp in responses]
responses = [resp*ihann_w for resp in responses]
hm_windows = (len(sequence) - config.frame_len//config.frame_stride) +1
responses = [ ] # todo: stitch together responses here..
responses = Tensor(responses).view(1,1,-1)
return float(loss), responses