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train_arc.py
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train_arc.py
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import random
import time
import numpy as np
import matplotlib.pyplot as plt
import torch
from torch import nn, optim
import torch.nn.functional as F
from torch.utils.data import DataLoader
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print('using : ', device)
from torchvision import datasets, transforms, utils
from tqdm import tqdm
import dataset_arc
from utils import *
use_wandb = False
if use_wandb: import wandb
if use_wandb: wandb.init(project="earc")
from model_rn import RelationNetworks as Ereason
def sample_data(loader, one_batch= False, loop= True, max_loops = 1000000):
loader_iter = iter(loader)
loop_count = 0
memorize_one_batch = next(loader_iter)
while one_batch and loop_count<max_loops: #if one_batch is True, keep yielding the exact same batch, for model checking.
loop_count += 1
yield (memorize_one_batch)
while loop and loop_count<max_loops:
try:
loop_count += 1
yield next(loader_iter)
except StopIteration:
loader_iter = iter(loader)
yield next(loader_iter)
most_recent_time = time.process_time()
no_of_demos = 5
batch_size = 16
one_batch_debug = True
conv_channels_dim = 64
w_dim = 64
debug=True
torch.autograd.set_detect_anomaly(debug) # this slows down computations but allows better traces for backward functions.
# it also checks for NaN during the backward computation.
#%%
def train(model, loader, alpha=0.1, step_size=0.1, sample_step=10, device=device, most_recent_time=most_recent_time):
# tracemalloc.start(5)
# snapshots = []
parameters = model.parameters()
optimizer = optim.Adam(parameters, lr=1e-4, betas=(0.0, 0.999))
w_init = torch.randn([512, 4, w_dim], device=device)
for t, batch in loader: # [i/o, demos, batch, c, w, h]
# for each batch update the copy of the model, used for backward prop through the gradient
for from_param, to_param in zip(model.parameters(), model_copy.parameters()):
to_param.data.copy_(from_param)
requires_grad(model_copy.parameters(), False)
# w = nn.Parameter(torch.randn([len(batch[0]), 4, w_dim], device=device))
w = nn.Parameter(w_init[:len(batch[0])])
# w = torch.randn([len(batch[0]), 4, w_dim], device=device)
w.requires_grad = True
model.train()
batch[0] = batch[0].to(device) # send input x0 to cuda
batch[1] = batch[1].to(device) # send outputs x1 to cuda
'''############# Getting W across all demos ################'''
for k in tqdm(range(sample_step)):
for i in range(1,no_of_demos): # leave the first pair for the training of the meta-loop
pos_inp = (batch[0][:,i], batch[1][:,i])
noise = torch.randn_like(w, device=device)
noise.normal_(0, 0.005)
w= w + (noise.data)
w_out = model(pos_inp, w)
w_grad = torch.autograd.grad(w_out.sum(), w,
create_graph=True)[0] #Create_graph ensures that differentiation is added to the gradient graph for later derivatives calculation
w = w + (-0.1 * step_size * w_grad) # ! make lr smaller
# if debug: print('k*i: {} \t norm of w: {:.4e} \t of grad w: {:.4e}'.format(k*i, torch.norm(w), torch.norm(w_grad[0])))
'''############# Solving for X1_ using W ####################'''
if use_wandb: wandb.log( {'w': wandb.Histogram( w.detach().to('cpu').numpy() ),
'E_out': wandb.Histogram( w_out.detach().to('cpu').numpy() ),
}, commit=False)
grad_viz_container = []; grad_norms = []
total_loss = 0.
for i in tqdm(range(1)): # ! look only at the first pair
# the order here flips (for task for k, because each x1_ is propagated serparately
#for each input-output in training set)
x0, x1 = (batch[0][:,i], batch[1][:,i])
# x1_ = x0.detach().clone()
x1_ = nn.Parameter(torch.rand_like(x0.detach()))
# x1_ = torch.rand_like(x0.detach())
x1_.requires_grad = True
demo_loss = 0.
for k in range(sample_step):
noise = torch.randn_like(x1, device=device)
noise.normal_(0, 0.005)
x1_ = x1_ + (noise.data)
x1_out = model((x0, x1_), w)
x1_grad = torch.autograd.grad(
outputs= x1_out.sum(),
inputs = x1_,
only_inputs=True,
create_graph=True,)[0] #grad returns a tuple of 1 :/
# retain_graph=True )
x1_grad = torch.sign(x1_grad) # TODO taking the sign
# if debug:
# grad_norms.append(torch.norm((x1_grad)))
# print('grad_norm for step {}: {} '.format(k, grad_norms[k]))
# grad_viz_container.append(x1_grad)
# x1_= x1_ + (- 1000 * step_size* x1_grad) # ! multiply by 100
x1_= x1_ + (- step_size* x1_grad) # ! multiply by 100
# TODO Step size according to RL reward!!! That might be it! save the pixel rewards and nudge each backprop step based on how it changes pixel reward?
# accumlate loss every step
# demo_loss_local = torch.norm(x1_.detach().cpu()- x1)/ (torch.norm(x1) + torch.norm(x1_.detach().cpu()))
# demo_loss += demo_loss_local
if debug:
plt.subplot(1,3,1)
im_log(x1); plt.title('x1')
plt.subplot(1,3,2)
im_log(x1_); plt.title('x1_')
plt.subplot(1,3,3)
im_log((x1_grad)); plt.title('gard')
plt.savefig('samples/x1_out_and_grad.png')
stats(x1_, 'x1_')
stats((x1_grad), 'signed x1_grad')
# ! for each demo:
kl_loss = model_copy([x0, x1_], w.detach()) # Grad Not: model, w Grad x1_ and the Langevin dynamics.
# requires_grad(parameters, True)
pos_out = model([x0, x1], w)
#force gradient to go through w, so that params os model change to give this particular one more energy.
neg_out = model([x0, x1_.detach()], w)
# norm_loss = alpha * (pos_out ** 2 + neg_out ** 2)
# loss = ( (pos_out - neg_out) ) + (kl_loss ) # + norm_loss
loss = F.softplus( (pos_out - neg_out + 1) ) #+ F.softplus(kl_loss ) # + norm_loss
print('pos_out: {}\t neg_out {} \t kl_loss {}\t loss: {}'.format(pos_out[0].item(), neg_out[0].item(), kl_loss[0].item(), loss[0].item()))
total_loss += loss.mean()
#! for all demos
# with torch.autograd.profiler.record_function("Outer optim"): # label the block
# import graphviz
# import torchviz
# dot = torchviz.make_dot(loss)
# ff = dot.render('round-table2.gv', view=True)
optimizer.zero_grad()
total_loss.backward()
# clip_grad(parameters, optimizer)
optimizer.step()
x1_.detach_()
w.detach_()
total_loss.detach_()
# buffer.push(w, neg_id)
loader.set_description(f'loss: {total_loss.item():.5f}')
# if i == 0:# log only the first input deomo into wandb
if use_wandb: wandb.log({
'model_loss': total_loss,
# "latents": wandb.Histogram(w[0].detach().to('cpu').numpy()),
})
# wandb.run.summary.update({"gradients": wandb.Histogram(np_histogram=np.histogram(data))})
# log occasionally
# if t % 50 == 0:
# if use_wandb: wandb.log({ "examples":[wandb.Image( im_log(x0 ), caption='question'),
# wandb.Image( im_log(x1 ), caption='answer'),
# wandb.Image( im_log(x1_), caption='prediction')],
# })
total_loss = 0.
if t % 50 == 0:
try:
# utils.save_image( #this is nice because it can display a whole batch of photos
# w.detach().to('cpu'),
# f'samples/{str(i).zfill(5)}.png',
# nrow=16,
# normalize=True,
# range=(0, 1),
# )
plt.figure(dpi=200, figsize=[10, 10])
plt.subplot(2, 2, 1)
plt.imshow(x0[0].detach().to('cpu').squeeze().numpy()[0])
plt.title('question')
plt.subplot(2, 2, 2)
plt.imshow(x1[0].detach().to('cpu').squeeze().numpy()[0])
plt.title('answer')
plt.subplot(2, 2, 4)
plt.imshow(x1_[0].detach().to('cpu').squeeze().numpy()[0])
plt.title('prediction')
plt.savefig(f'samples/{str(t).zfill(5)}.png')
if use_wandb: wandb.log ( {'plots':wandb.Image(plt)}, commit=False)
plt.close()
except:
print('the shape of the image: {} \n info: {}'.format(w.shape, w))
# snapshot = tracemalloc.take_snapshot()
# snapshots.append(snapshot)
# display_top(snapshots[0], limit=20)
#%%
import tracemalloc
if __name__ == '__main__':
model = Ereason(channels_out = conv_channels_dim,use_wandb=use_wandb).to(device)
model_copy = Ereason(channels_out = conv_channels_dim).to(device)
if use_wandb: wandb.watch(model)
dataset = dataset_arc.ARCDataset('./ARC/data/', 'both', transform= dataset_arc.all_transforms, no_of_demos=no_of_demos) #, transform=transforms.Normalize(0., 10.)
loader = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=0) # Error: Couldn't open shared event, when workers is 4, hmmm but also 1
loader = tqdm(enumerate(sample_data(loader, max_loops = 1000, one_batch=one_batch_debug)))
print('loaded data {}'.format(time.process_time()))
most_recent_time = time.process_time()
else:
model = Ereason(channels_out = conv_channels_dim, use_wandb=use_wandb).to(device)
model_copy = Ereason(channels_out = conv_channels_dim).to(device)
# with torch.autograd.profiler.profile(use_cuda = torch.cuda.is_available(), enabled=debug,) as prof: # record_shapes=True
# with torch.backends.cudnn.flags(enabled=False):
most_recent_time = time.process_time()
train(model, loader)
# print(prof.key_averages().table(sort_by="self_cpu_time_total"))