More ExtensibleTrainer work
This commit is contained in:
James Betker
parent
f40545f235
commit
e59e712e39
|
|
@ -1,18 +1,16 @@
|
|||
import logging
|
||||
import os
|
||||
import random
|
||||
from collections import OrderedDict
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import torchvision.utils as utils
|
||||
from apex import amp
|
||||
from torch.nn.parallel import DataParallel, DistributedDataParallel
|
||||
import torch.nn as nn
|
||||
|
||||
import models.lr_scheduler as lr_scheduler
|
||||
import models.networks as networks
|
||||
from models.base_model import BaseModel
|
||||
from models.steps.steps import ConfigurableStep
|
||||
import torchvision.utils as utils
|
||||
|
||||
logger = logging.getLogger('base')
|
||||
|
||||
|
|
@ -34,7 +32,9 @@ class ExtensibleTrainer(BaseModel):
|
|||
|
||||
self.netsG = {}
|
||||
self.netsD = {}
|
||||
self.netF = networks.define_F().to(self.device) # Used to compute feature loss.
|
||||
self.networks = []
|
||||
self.visuals = {}
|
||||
for name, net in opt['networks'].items():
|
||||
if net['type'] == 'generator':
|
||||
new_net = networks.define_G(net, None, opt['scale']).to(self.device)
|
||||
|
|
@ -105,10 +105,10 @@ class ExtensibleTrainer(BaseModel):
|
|||
self.updated = True
|
||||
|
||||
def feed_data(self, data):
|
||||
self.var_L = torch.chunk(corrupted_L, chunks=self.mega_batch_factor, dim=0)
|
||||
self.var_H = [t.to(self.device) for t in torch.chunk(data['GT'], chunks=self.mega_batch_factor, dim=0)]
|
||||
self.lq = torch.chunk(corrupted_L, chunks=self.mega_batch_factor, dim=0)
|
||||
self.hq = [t.to(self.device) for t in torch.chunk(data['GT'], chunks=self.mega_batch_factor, dim=0)]
|
||||
input_ref = data['ref'] if 'ref' in data else data['GT']
|
||||
self.var_ref = [t.to(self.device) for t in torch.chunk(input_ref, chunks=self.mega_batch_factor, dim=0)]
|
||||
self.ref = [t.to(self.device) for t in torch.chunk(input_ref, chunks=self.mega_batch_factor, dim=0)]
|
||||
|
||||
def optimize_parameters(self, step):
|
||||
# Some models need to make parametric adjustments per-step. Do that here.
|
||||
|
|
@ -117,7 +117,8 @@ class ExtensibleTrainer(BaseModel):
|
|||
net.update_for_step(step, os.path.join(self.opt['path']['models'], ".."))
|
||||
|
||||
# Iterate through the steps, performing them one at a time.
|
||||
state = {'lq': self.var_L, 'hq': self.var_H, 'ref': self.var_ref}
|
||||
self.visuals = {}
|
||||
state = {'lq': self.lq, 'hq': self.hq, 'ref': self.ref}
|
||||
for step_num, s in enumerate(self.steps):
|
||||
# Only set requires_grad=True for the network being trained.
|
||||
nets_to_train = s.get_networks_trained()
|
||||
|
|
@ -148,529 +149,75 @@ class ExtensibleTrainer(BaseModel):
|
|||
# And finally perform optimization.
|
||||
s.do_step()
|
||||
|
||||
# Record visual outputs for usage in debugging and testing.
|
||||
if 'visuals' in self.opt['train'].keys():
|
||||
sample_save_path = os.path.join(self.opt['path']['models'], "..", "visual_dbg")
|
||||
for v in self.opt['train']['visuals']:
|
||||
self.visuals[v] = state[v].detach().cpu()
|
||||
if step % self.opt['train']['visual_debug_rate'] == 0:
|
||||
for i, dbgv in enumerate(self.visuals[v]):
|
||||
os.makedirs(os.path.join(sample_save_path, v), exist_ok=True)
|
||||
utils.save_image(dbgv, os.path.join(sample_save_path, v, "%05i_%02i.png" % (step, i)))
|
||||
|
||||
|
||||
# G
|
||||
for p in self.netsD.parameters():
|
||||
p.requires_grad = False
|
||||
if self.spsr_enabled:
|
||||
for p in self.netD_grad.parameters():
|
||||
p.requires_grad = False
|
||||
|
||||
self.swapout_D(step)
|
||||
self.swapout_G(step)
|
||||
|
||||
# Turning off G-grad is required to enable mega-batching and D_update_ratio to work together for some reason.
|
||||
if step % self.D_update_ratio == 0 and step >= self.D_init_iters:
|
||||
if self.spsr_enabled and self.branch_pretrain and step < self.branch_init_iters:
|
||||
for k, v in self.netsG.named_parameters():
|
||||
if v.dtype != torch.int64 and v.dtype != torch.bool:
|
||||
v.requires_grad = '_branch_pretrain' in k
|
||||
else:
|
||||
for p in self.netsG.parameters():
|
||||
if p.dtype != torch.int64 and p.dtype != torch.bool:
|
||||
p.requires_grad = True
|
||||
else:
|
||||
for p in self.netsG.parameters():
|
||||
p.requires_grad = False
|
||||
|
||||
# Calculate a standard deviation for the gaussian noise to be applied to the discriminator, termed noise-theta.
|
||||
if self.D_noise_final == 0:
|
||||
noise_theta = 0
|
||||
else:
|
||||
noise_theta = (self.D_noise_theta - self.D_noise_theta_floor) * (self.D_noise_final - min(step, self.D_noise_final)) / self.D_noise_final + self.D_noise_theta_floor
|
||||
|
||||
if _profile:
|
||||
print("Misc setup %f" % (time() - _t,))
|
||||
_t = time()
|
||||
|
||||
if step >= self.D_init_iters:
|
||||
self.optimizer_G.zero_grad()
|
||||
self.fake_GenOut = []
|
||||
self.fea_GenOut = []
|
||||
self.fake_H = []
|
||||
self.spsr_grad_GenOut = []
|
||||
var_ref_skips = []
|
||||
for var_L, var_LGAN, var_H, var_ref, pix in zip(self.var_L, self.gan_img, self.var_H, self.var_ref, self.pix):
|
||||
if self.spsr_enabled:
|
||||
using_gan_img = False
|
||||
# SPSR models have outputs from three different branches.
|
||||
fake_H_branch, fake_GenOut, grad_LR = self.netsG(var_L)
|
||||
fea_GenOut = fake_GenOut
|
||||
self.spsr_grad_GenOut.append(fake_H_branch)
|
||||
# Get image gradients for later use.
|
||||
fake_H_grad = self.get_grad_nopadding(fake_GenOut)
|
||||
else:
|
||||
if random.random() > self.gan_lq_img_use_prob:
|
||||
fea_GenOut, fake_GenOut = self.netsG(var_L)
|
||||
using_gan_img = False
|
||||
else:
|
||||
fea_GenOut, fake_GenOut = self.netsG(var_LGAN)
|
||||
using_gan_img = True
|
||||
|
||||
if _profile:
|
||||
print("Gen forward %f" % (time() - _t,))
|
||||
_t = time()
|
||||
|
||||
self.fake_GenOut.append(fake_GenOut.detach())
|
||||
self.fea_GenOut.append(fea_GenOut.detach())
|
||||
|
||||
l_g_total = 0
|
||||
if step % self.D_update_ratio == 0 and step >= self.D_init_iters:
|
||||
fea_w = self.l_fea_sched.get_weight_for_step(step)
|
||||
l_g_pix_log = None
|
||||
l_g_fea_log = None
|
||||
l_g_fdpl = None
|
||||
l_g_fea_log = None
|
||||
if self.cri_pix and not using_gan_img: # pixel loss
|
||||
l_g_pix = self.l_pix_w * self.cri_pix(fea_GenOut, pix)
|
||||
l_g_pix_log = l_g_pix / self.l_pix_w
|
||||
l_g_total += l_g_pix
|
||||
if self.spsr_enabled and self.cri_pix_grad: # gradient pixel loss
|
||||
if self.disjoint_data:
|
||||
grad_truth = self.get_grad_nopadding(var_L)
|
||||
grad_pred = F.interpolate(fake_H_grad, size=grad_truth.shape[2:], mode="nearest")
|
||||
else:
|
||||
grad_truth = self.get_grad_nopadding(var_H)
|
||||
grad_pred = fake_H_grad
|
||||
l_g_pix_grad = self.l_pix_grad_w * self.cri_pix_grad(grad_pred, grad_truth)
|
||||
l_g_total += l_g_pix_grad
|
||||
if self.spsr_enabled and self.cri_pix_branch: # branch pixel loss
|
||||
if self.disjoint_data:
|
||||
grad_truth = self.get_grad_nopadding(var_L)
|
||||
grad_pred = F.interpolate(fake_H_branch, size=grad_truth.shape[2:], mode="nearest")
|
||||
else:
|
||||
grad_truth = self.get_grad_nopadding(var_H)
|
||||
grad_pred = fake_H_branch
|
||||
l_g_pix_grad_branch = self.l_pix_branch_w * self.cri_pix_branch(grad_pred, grad_truth)
|
||||
l_g_total += l_g_pix_grad_branch
|
||||
if self.fdpl_enabled and not using_gan_img:
|
||||
l_g_fdpl = self.cri_fdpl(fea_GenOut, pix)
|
||||
l_g_total += l_g_fdpl * self.fdpl_weight
|
||||
if self.cri_fea and not using_gan_img and fea_w > 0: # feature loss
|
||||
if self.lr_netF is not None:
|
||||
real_fea = self.lr_netF(var_L, interpolate_factor=self.opt['scale'])
|
||||
else:
|
||||
real_fea = self.netF(pix).detach()
|
||||
fake_fea = self.netF(fea_GenOut)
|
||||
l_g_fea = fea_w * self.cri_fea(fake_fea, real_fea)
|
||||
l_g_fea_log = l_g_fea / fea_w
|
||||
l_g_total += l_g_fea
|
||||
|
||||
if _profile:
|
||||
print("Fea forward %f" % (time() - _t,))
|
||||
_t = time()
|
||||
|
||||
# Note to future self: The BCELoss(0, 1) and BCELoss(0, 0) = .6931
|
||||
# Effectively this means that the generator has only completely "won" when l_d_real and l_d_fake is
|
||||
# equal to this value. If I ever come up with an algorithm that tunes fea/gan weights automatically,
|
||||
# it should target this
|
||||
|
||||
l_g_fix_disc = torch.zeros(1, requires_grad=False, device=self.device).squeeze()
|
||||
for fixed_disc in self.fixed_disc_nets:
|
||||
weight = fixed_disc.module.fdisc_weight
|
||||
real_fea = fixed_disc(pix).detach()
|
||||
fake_fea = fixed_disc(fea_GenOut)
|
||||
l_g_fix_disc = l_g_fix_disc + weight * self.cri_fea(fake_fea, real_fea)
|
||||
l_g_total += l_g_fix_disc
|
||||
|
||||
|
||||
if self.l_gan_w > 0:
|
||||
if self.opt['train']['gan_type'] in ['gan', 'pixgan', 'pixgan_fea', 'crossgan']:
|
||||
if self.opt['train']['gan_type'] == 'crossgan':
|
||||
pred_g_fake = self.netsD(fake_GenOut, var_L)
|
||||
else:
|
||||
pred_g_fake = self.netsD(fake_GenOut)
|
||||
l_g_gan = self.l_gan_w * self.cri_gan(pred_g_fake, True)
|
||||
elif self.opt['train']['gan_type'] == 'ragan':
|
||||
pred_d_real = self.netsD(var_ref).detach()
|
||||
pred_g_fake = self.netsD(fake_GenOut)
|
||||
l_g_gan = self.l_gan_w * (
|
||||
self.cri_gan(pred_d_real - torch.mean(pred_g_fake), False) +
|
||||
self.cri_gan(pred_g_fake - torch.mean(pred_d_real), True)) / 2
|
||||
l_g_gan_log = l_g_gan / self.l_gan_w
|
||||
l_g_total += l_g_gan
|
||||
|
||||
if self.spsr_enabled and self.cri_grad_gan:
|
||||
if self.opt['train']['gan_type'] == 'crossgan':
|
||||
pred_g_fake_grad = self.netsD(fake_H_grad, var_L)
|
||||
else:
|
||||
pred_g_fake_grad = self.netsD(fake_H_grad)
|
||||
pred_g_fake_grad_branch = self.netD_grad(fake_H_branch)
|
||||
if self.opt['train']['gan_type'] in ['gan', 'pixgan', 'pixgan_fea', 'crossgan']:
|
||||
l_g_gan_grad = self.l_gan_grad_w * self.cri_grad_gan(pred_g_fake_grad, True)
|
||||
l_g_gan_grad_branch = self.l_gan_grad_w * self.cri_grad_gan(pred_g_fake_grad_branch, True)
|
||||
elif self.opt['train']['gan_type'] == 'ragan':
|
||||
pred_g_real_grad = self.netD_grad(self.get_grad_nopadding(var_ref)).detach()
|
||||
l_g_gan_grad = self.l_gan_w * (
|
||||
self.cri_gan(pred_g_real_grad - torch.mean(pred_g_fake_grad), False) +
|
||||
self.cri_gan(pred_g_fake_grad - torch.mean(pred_g_real_grad), True)) / 2
|
||||
l_g_gan_grad_branch = self.l_gan_w * (
|
||||
self.cri_gan(pred_g_real_grad - torch.mean(pred_g_fake_grad_branch), False) +
|
||||
self.cri_gan(pred_g_fake_grad_branch - torch.mean(pred_g_real_grad), True)) / 2
|
||||
l_g_total += l_g_gan_grad + l_g_gan_grad_branch
|
||||
|
||||
# Scale the loss down by the batch factor.
|
||||
l_g_total_log = l_g_total
|
||||
l_g_total = l_g_total / self.mega_batch_factor
|
||||
|
||||
with amp.scale_loss(l_g_total, self.optimizer_G, loss_id=0) as l_g_total_scaled:
|
||||
l_g_total_scaled.backward()
|
||||
|
||||
if _profile:
|
||||
print("Gen backward %f" % (time() - _t,))
|
||||
_t = time()
|
||||
|
||||
self.optimizer_G.step()
|
||||
|
||||
if _profile:
|
||||
print("Gen step %f" % (time() - _t,))
|
||||
_t = time()
|
||||
|
||||
# D
|
||||
if self.l_gan_w > 0 and step >= self.G_warmup:
|
||||
for p in self.netsD.parameters():
|
||||
if p.dtype != torch.int64 and p.dtype != torch.bool:
|
||||
p.requires_grad = True
|
||||
|
||||
noise = torch.randn_like(var_ref) * noise_theta
|
||||
noise.to(self.device)
|
||||
real_disc_images = []
|
||||
fake_disc_images = []
|
||||
for fake_GenOut, var_LGAN, var_L, var_H, var_ref, pix in zip(self.fake_GenOut, self.gan_img, self.var_L, self.var_H, self.var_ref, self.pix):
|
||||
if random.random() > self.gan_lq_img_use_prob:
|
||||
fake_H = fake_GenOut.clone().detach().requires_grad_(False)
|
||||
else:
|
||||
# Re-compute generator outputs with the GAN inputs.
|
||||
with torch.no_grad():
|
||||
if self.spsr_enabled:
|
||||
_, fake_H, _ = self.netsG(var_LGAN)
|
||||
else:
|
||||
_, fake_H = self.netsG(var_LGAN)
|
||||
fake_H = fake_H.detach()
|
||||
|
||||
if _profile:
|
||||
print("Gen forward for disc %f" % (time() - _t,))
|
||||
_t = time()
|
||||
|
||||
# Apply noise to the inputs to slow discriminator convergence.
|
||||
var_ref = var_ref + noise
|
||||
fake_H = fake_H + noise
|
||||
l_d_fea_real = 0
|
||||
l_d_fea_fake = 0
|
||||
self.optimizer_D.zero_grad()
|
||||
if self.opt['train']['gan_type'] == 'pixgan_fea':
|
||||
# Compute a feature loss which is added to the GAN loss computed later to guide the discriminator better.
|
||||
disc_fea_scale = .1
|
||||
_, fea_real = self.netsD(var_ref, output_feature_vector=True)
|
||||
actual_fea = self.netF(var_ref)
|
||||
l_d_fea_real = self.cri_fea(fea_real, actual_fea) * disc_fea_scale / self.mega_batch_factor
|
||||
_, fea_fake = self.netsD(fake_H, output_feature_vector=True)
|
||||
actual_fea = self.netF(fake_H)
|
||||
l_d_fea_fake = self.cri_fea(fea_fake, actual_fea) * disc_fea_scale / self.mega_batch_factor
|
||||
if self.opt['train']['gan_type'] == 'crossgan':
|
||||
# need to forward and backward separately, since batch norm statistics differ
|
||||
# real
|
||||
pred_d_real = self.netsD(var_ref, var_L)
|
||||
l_d_real = self.cri_gan(pred_d_real, True)
|
||||
l_d_real_log = l_d_real
|
||||
# fake
|
||||
pred_d_fake = self.netsD(fake_H, var_L)
|
||||
l_d_fake = self.cri_gan(pred_d_fake, False)
|
||||
l_d_fake_log = l_d_fake
|
||||
# mismatched
|
||||
mismatched_L = torch.roll(var_L, shifts=1, dims=0)
|
||||
pred_d_real_mismatched = self.netsD(var_ref, mismatched_L)
|
||||
pred_d_fake_mismatched = self.netsD(fake_H, mismatched_L)
|
||||
l_d_mismatched = (self.cri_gan(pred_d_real_mismatched, False) + self.cri_gan(pred_d_fake_mismatched, False)) / 2
|
||||
|
||||
l_d_total = (l_d_real + l_d_fake + l_d_mismatched) / 3
|
||||
l_d_total = l_d_total / self.mega_batch_factor
|
||||
with amp.scale_loss(l_d_total, self.optimizer_D, loss_id=1) as l_d_total_scaled:
|
||||
l_d_total_scaled.backward()
|
||||
elif self.opt['train']['gan_type'] == 'gan':
|
||||
# real
|
||||
pred_d_real = self.netsD(var_ref)
|
||||
l_d_real = self.cri_gan(pred_d_real, True) / self.mega_batch_factor
|
||||
l_d_real_log = l_d_real * self.mega_batch_factor
|
||||
# fake
|
||||
pred_d_fake = self.netsD(fake_H)
|
||||
l_d_fake = self.cri_gan(pred_d_fake, False) / self.mega_batch_factor
|
||||
l_d_fake_log = l_d_fake * self.mega_batch_factor
|
||||
|
||||
l_d_total = (l_d_real + l_d_fake) / 2
|
||||
with amp.scale_loss(l_d_total, self.optimizer_D, loss_id=1) as l_d_total_scaled:
|
||||
l_d_total_scaled.backward()
|
||||
elif 'pixgan' in self.opt['train']['gan_type']:
|
||||
pixdisc_channels, pixdisc_output_reduction = self.netsD.module.pixgan_parameters()
|
||||
disc_output_shape = (var_ref.shape[0], pixdisc_channels, var_ref.shape[2] // pixdisc_output_reduction, var_ref.shape[3] // pixdisc_output_reduction)
|
||||
b, _, w, h = var_ref.shape
|
||||
real = torch.ones((b, pixdisc_channels, w, h), device=var_ref.device)
|
||||
fake = torch.zeros((b, pixdisc_channels, w, h), device=var_ref.device)
|
||||
if not self.disjoint_data:
|
||||
# randomly determine portions of the image to swap to keep the discriminator honest.
|
||||
SWAP_MAX_DIM = w // 4
|
||||
SWAP_MIN_DIM = 16
|
||||
assert SWAP_MAX_DIM > 0
|
||||
if random.random() > .5: # Make this only happen half the time. Earlier experiments had it happen
|
||||
# more often and the model was "cheating" by using the presence of
|
||||
# easily discriminated fake swaps to count the entire generated image
|
||||
# as fake.
|
||||
random_swap_count = random.randint(0, 4)
|
||||
for i in range(random_swap_count):
|
||||
# Make the swap across fake_H and var_ref
|
||||
swap_x, swap_y = random.randint(0, w - SWAP_MIN_DIM), random.randint(0, h - SWAP_MIN_DIM)
|
||||
swap_w, swap_h = random.randint(SWAP_MIN_DIM, SWAP_MAX_DIM), random.randint(SWAP_MIN_DIM, SWAP_MAX_DIM)
|
||||
if swap_x + swap_w > w:
|
||||
swap_w = w - swap_x
|
||||
if swap_y + swap_h > h:
|
||||
swap_h = h - swap_y
|
||||
t = fake_H[:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)].clone()
|
||||
fake_H[:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)] = var_ref[:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)]
|
||||
var_ref[:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)] = t
|
||||
real[:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)] = 0.0
|
||||
fake[:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)] = 1.0
|
||||
|
||||
# Interpolate down to the dimensionality that the discriminator uses.
|
||||
real = F.interpolate(real, size=disc_output_shape[2:], mode="bilinear", align_corners=False)
|
||||
fake = F.interpolate(fake, size=disc_output_shape[2:], mode="bilinear", align_corners=False)
|
||||
|
||||
# We're also assuming that this is exactly how the flattened discriminator output is generated.
|
||||
real = real.view(-1, 1)
|
||||
fake = fake.view(-1, 1)
|
||||
|
||||
# real
|
||||
pred_d_real = self.netsD(var_ref)
|
||||
l_d_real = self.cri_gan(pred_d_real, real) / self.mega_batch_factor
|
||||
l_d_real_log = l_d_real * self.mega_batch_factor
|
||||
l_d_real += l_d_fea_real
|
||||
# fake
|
||||
pred_d_fake = self.netsD(fake_H)
|
||||
l_d_fake = self.cri_gan(pred_d_fake, fake) / self.mega_batch_factor
|
||||
l_d_fake_log = l_d_fake * self.mega_batch_factor
|
||||
l_d_fake += l_d_fea_fake
|
||||
|
||||
l_d_total = (l_d_real + l_d_fake) / 2
|
||||
with amp.scale_loss(l_d_total, self.optimizer_D, loss_id=1) as l_d_total_scaled:
|
||||
l_d_total_scaled.backward()
|
||||
|
||||
pdr = pred_d_real.detach() + torch.abs(torch.min(pred_d_real))
|
||||
pdr = pdr / torch.max(pdr)
|
||||
real_disc_images.append(pdr.view(disc_output_shape))
|
||||
pdf = pred_d_fake.detach() + torch.abs(torch.min(pred_d_fake))
|
||||
pdf = pdf / torch.max(pdf)
|
||||
fake_disc_images.append(pdf.view(disc_output_shape))
|
||||
elif self.opt['train']['gan_type'] == 'ragan':
|
||||
pred_d_fake = self.netsD(fake_H)
|
||||
pred_d_real = self.netsD(var_ref)
|
||||
l_d_real = self.cri_gan(pred_d_real - torch.mean(pred_d_fake), True)
|
||||
l_d_real_log = l_d_real
|
||||
l_d_fake = self.cri_gan(pred_d_fake - torch.mean(pred_d_real), False)
|
||||
l_d_fake_log = l_d_fake
|
||||
l_d_total = (l_d_real + l_d_fake) / 2
|
||||
l_d_total /= self.mega_batch_factor
|
||||
with amp.scale_loss(l_d_total, self.optimizer_D, loss_id=1) as l_d_total_scaled:
|
||||
l_d_total_scaled.backward()
|
||||
var_ref_skips.append(var_ref.detach())
|
||||
self.fake_H.append(fake_H.detach())
|
||||
self.optimizer_D.step()
|
||||
|
||||
if _profile:
|
||||
print("Disc step %f" % (time() - _t,))
|
||||
_t = time()
|
||||
|
||||
# D_grad.
|
||||
if self.spsr_enabled and self.cri_grad_gan and step >= self.G_warmup:
|
||||
for p in self.netD_grad.parameters():
|
||||
p.requires_grad = True
|
||||
self.optimizer_D_grad.zero_grad()
|
||||
for var_ref, fake_H, fake_H_grad_branch in zip(var_ref_skips, self.fake_H, self.spsr_grad_GenOut):
|
||||
fake_H_grad = self.get_grad_nopadding(fake_H).detach()
|
||||
var_ref_grad = self.get_grad_nopadding(var_ref)
|
||||
pred_d_real_grad = self.netD_grad(var_ref_grad)
|
||||
pred_d_fake_grad = self.netD_grad(fake_H_grad) # Tensor already detached above.
|
||||
# var_ref and fake_H already has noise added to it. We **must** add noise to fake_H_grad_branch too.
|
||||
fake_H_grad_branch = fake_H_grad_branch.detach() + noise
|
||||
pred_d_fake_grad_branch = self.netD_grad(fake_H_grad_branch)
|
||||
if self.opt['train']['gan_type'] == 'gan':
|
||||
l_d_real_grad = self.cri_gan(pred_d_real_grad, True)
|
||||
l_d_fake_grad = (self.cri_gan(pred_d_fake_grad, False) + self.cri_gan(pred_d_fake_grad_branch, False)) / 2
|
||||
elif self.opt['train']['gan_type'] == 'crossgan':
|
||||
assert False
|
||||
elif self.opt['train']['gan_type'] == 'pixgan':
|
||||
real = torch.ones_like(pred_d_real_grad)
|
||||
fake = torch.zeros_like(pred_d_fake_grad)
|
||||
l_d_real_grad = self.cri_grad_gan(pred_d_real_grad, real)
|
||||
l_d_fake_grad = (self.cri_grad_gan(pred_d_fake_grad, fake) + \
|
||||
self.cri_grad_gan(pred_d_fake_grad_branch, fake)) / 2
|
||||
elif self.opt['train']['gan_type'] == 'ragan':
|
||||
l_d_real_grad = self.cri_grad_gan(pred_d_real_grad - torch.mean(pred_d_fake_grad), True)
|
||||
l_d_fake_grad = (self.cri_grad_gan(pred_d_fake_grad - torch.mean(pred_d_real_grad), False) + \
|
||||
self.cri_grad_gan(pred_d_fake_grad_branch - torch.mean(pred_d_real_grad), False)) / 2
|
||||
|
||||
l_d_total_grad = (l_d_real_grad + l_d_fake_grad) / 2
|
||||
l_d_total_grad /= self.mega_batch_factor
|
||||
with amp.scale_loss(l_d_total_grad, self.optimizer_D_grad, loss_id=2) as l_d_total_grad_scaled:
|
||||
l_d_total_grad_scaled.backward()
|
||||
self.optimizer_D_grad.step()
|
||||
|
||||
|
||||
# Log sample images from first microbatch.
|
||||
if step % self.img_debug_steps == 0:
|
||||
sample_save_path = os.path.join(self.opt['path']['models'], "..", "temp")
|
||||
os.makedirs(os.path.join(sample_save_path, "hr"), exist_ok=True)
|
||||
os.makedirs(os.path.join(sample_save_path, "lr"), exist_ok=True)
|
||||
os.makedirs(os.path.join(sample_save_path, "gen_fea"), exist_ok=True)
|
||||
os.makedirs(os.path.join(sample_save_path, "gen"), exist_ok=True)
|
||||
os.makedirs(os.path.join(sample_save_path, "disc_fake"), exist_ok=True)
|
||||
os.makedirs(os.path.join(sample_save_path, "pix"), exist_ok=True)
|
||||
os.makedirs(os.path.join(sample_save_path, "disc"), exist_ok=True)
|
||||
os.makedirs(os.path.join(sample_save_path, "ref"), exist_ok=True)
|
||||
if self.spsr_enabled:
|
||||
os.makedirs(os.path.join(sample_save_path, "gen_grad"), exist_ok=True)
|
||||
|
||||
for i in range(self.mega_batch_factor):
|
||||
utils.save_image(self.var_H[i].cpu(), os.path.join(sample_save_path, "hr", "%05i_%02i.png" % (step, i)))
|
||||
utils.save_image(self.var_L[i].cpu(), os.path.join(sample_save_path, "lr", "%05i_%02i.png" % (step, i)))
|
||||
utils.save_image(self.pix[i].cpu(), os.path.join(sample_save_path, "pix", "%05i_%02i.png" % (step, i)))
|
||||
utils.save_image(self.fake_GenOut[i].cpu(), os.path.join(sample_save_path, "gen", "%05i_%02i.png" % (step, i)))
|
||||
utils.save_image(self.fea_GenOut[i].cpu(), os.path.join(sample_save_path, "gen_fea", "%05i_%02i.png" % (step, i)))
|
||||
if self.spsr_enabled:
|
||||
utils.save_image(self.spsr_grad_GenOut[i].cpu(), os.path.join(sample_save_path, "gen_grad", "%05i_%02i.png" % (step, i)))
|
||||
if self.l_gan_w > 0 and step >= self.G_warmup and 'pixgan' in self.opt['train']['gan_type']:
|
||||
utils.save_image(var_ref_skips[i].cpu(), os.path.join(sample_save_path, "ref", "%05i_%02i.png" % (step, i)))
|
||||
utils.save_image(self.fake_H[i], os.path.join(sample_save_path, "disc_fake", "fake%05i_%02i.png" % (step, i)))
|
||||
utils.save_image(F.interpolate(fake_disc_images[i], scale_factor=4), os.path.join(sample_save_path, "disc", "fake%05i_%02i.png" % (step, i)))
|
||||
utils.save_image(F.interpolate(real_disc_images[i], scale_factor=4), os.path.join(sample_save_path, "disc", "real%05i_%02i.png" % (step, i)))
|
||||
|
||||
# Log metrics
|
||||
if step % self.D_update_ratio == 0 and step >= self.D_init_iters:
|
||||
if self.cri_pix and l_g_pix_log is not None:
|
||||
self.add_log_entry('l_g_pix', l_g_pix_log.detach().item())
|
||||
if self.fdpl_enabled and l_g_fdpl is not None:
|
||||
self.add_log_entry('l_g_fdpl', l_g_fdpl.detach().item())
|
||||
if self.cri_fea and l_g_fea_log is not None:
|
||||
self.add_log_entry('feature_weight', fea_w)
|
||||
self.add_log_entry('l_g_fea', l_g_fea_log.detach().item())
|
||||
self.add_log_entry('l_g_fix_disc', l_g_fix_disc.detach().item())
|
||||
if self.l_gan_w > 0:
|
||||
self.add_log_entry('l_g_gan', l_g_gan_log.detach().item())
|
||||
self.add_log_entry('l_g_total', l_g_total_log.detach().item())
|
||||
if self.opt['train']['gan_type'] == 'pixgan_fea':
|
||||
self.add_log_entry('l_d_fea_fake', l_d_fea_fake.detach().item() * self.mega_batch_factor)
|
||||
self.add_log_entry('l_d_fea_real', l_d_fea_real.detach().item() * self.mega_batch_factor)
|
||||
self.add_log_entry('l_d_fake_total', l_d_fake.detach().item() * self.mega_batch_factor)
|
||||
self.add_log_entry('l_d_real_total', l_d_real.detach().item() * self.mega_batch_factor)
|
||||
if self.opt['train']['gan_type'] == 'crossgan':
|
||||
self.add_log_entry('l_d_mismatched', l_d_mismatched.detach().item())
|
||||
if self.spsr_enabled:
|
||||
if self.cri_pix_grad:
|
||||
self.add_log_entry('l_g_pix_grad_branch', l_g_pix_grad.detach().item())
|
||||
if self.cri_pix_branch:
|
||||
self.add_log_entry('l_g_pix_grad_branch', l_g_pix_grad_branch.detach().item())
|
||||
if self.cri_grad_gan:
|
||||
self.add_log_entry('l_g_gan_grad', l_g_gan_grad.detach().item())
|
||||
self.add_log_entry('l_g_gan_grad_branch', l_g_gan_grad_branch.detach().item())
|
||||
if self.l_gan_w > 0 and step >= self.G_warmup:
|
||||
self.add_log_entry('l_d_real', l_d_real_log.detach().item())
|
||||
self.add_log_entry('l_d_fake', l_d_fake_log.detach().item())
|
||||
self.add_log_entry('D_fake', torch.mean(pred_d_fake.detach()))
|
||||
self.add_log_entry('D_diff', torch.mean(pred_d_fake.detach()) - torch.mean(pred_d_real.detach()))
|
||||
if self.spsr_enabled:
|
||||
self.add_log_entry('l_d_real_grad', l_d_real_grad.detach().item())
|
||||
self.add_log_entry('l_d_fake_grad', l_d_fake_grad.detach().item())
|
||||
self.add_log_entry('D_fake_grad', torch.mean(pred_d_fake_grad.detach()))
|
||||
self.add_log_entry('D_diff_grad', torch.mean(pred_d_fake_grad.detach()) - torch.mean(pred_d_real_grad.detach()))
|
||||
|
||||
# Log learning rates.
|
||||
for i, pg in enumerate(self.optimizer_G.param_groups):
|
||||
self.add_log_entry('gen_lr_%i' % (i,), pg['lr'])
|
||||
for i, pg in enumerate(self.optimizer_D.param_groups):
|
||||
self.add_log_entry('disc_lr_%i' % (i,), pg['lr'])
|
||||
|
||||
if step % self.corruptor_swapout_steps == 0 and step > 0:
|
||||
self.load_random_corruptor()
|
||||
|
||||
# Allows the log to serve as an easy-to-use rotating buffer.
|
||||
def add_log_entry(self, key, value):
|
||||
key_it = "%s_it" % (key,)
|
||||
log_rotating_buffer_size = 50
|
||||
if key not in self.log_dict.keys():
|
||||
self.log_dict[key] = []
|
||||
self.log_dict[key_it] = 0
|
||||
if len(self.log_dict[key]) < log_rotating_buffer_size:
|
||||
self.log_dict[key].append(value)
|
||||
else:
|
||||
self.log_dict[key][self.log_dict[key_it] % log_rotating_buffer_size] = value
|
||||
self.log_dict[key_it] += 1
|
||||
# TODO: Do logging and image dumps
|
||||
|
||||
def compute_fea_loss(self, real, fake):
|
||||
with torch.no_grad():
|
||||
real = real.unsqueeze(dim=0).to(self.device)
|
||||
fake = fake.unsqueeze(dim=0).to(self.device)
|
||||
real_fea = self.netF(real).detach()
|
||||
fake_fea = self.netF(fake)
|
||||
return self.cri_fea(fake_fea, real_fea).item()
|
||||
logits_real = self.netF(real)
|
||||
logits_fake = self.netF(fake)
|
||||
return nn.L1Loss().to(self.device)(logits_fake, logits_real)
|
||||
|
||||
def test(self):
|
||||
self.netsG.eval()
|
||||
for net in self.netsG.values():
|
||||
net.eval()
|
||||
|
||||
with torch.no_grad():
|
||||
if self.spsr_enabled:
|
||||
self.fake_H_branch = []
|
||||
self.fake_GenOut = []
|
||||
self.grad_LR = []
|
||||
fake_H_branch, fake_GenOut, grad_LR = self.netsG(self.var_L[0])
|
||||
self.fake_H_branch.append(fake_H_branch)
|
||||
self.fake_GenOut.append(fake_GenOut)
|
||||
self.grad_LR.append(grad_LR)
|
||||
else:
|
||||
self.fake_GenOut = [self.netsG(self.var_L[0])]
|
||||
self.netsG.train()
|
||||
# Iterate through the steps, performing them one at a time.
|
||||
self.visuals = {}
|
||||
state = {'lq': self.lq, 'hq': self.hq, 'ref': self.ref}
|
||||
for step_num, s in enumerate(self.steps):
|
||||
ns = s.do_forward_backward(state, 0, step_num, backward=False)
|
||||
for k, v in ns.items():
|
||||
state[k] = [v.detach()]
|
||||
|
||||
self.eval_state = state
|
||||
|
||||
for net in self.netsG.values():
|
||||
net.train()
|
||||
|
||||
# Fetches a summary of the log.
|
||||
def get_current_log(self, step):
|
||||
return_log = {}
|
||||
for k in self.log_dict.keys():
|
||||
if not isinstance(self.log_dict[k], list):
|
||||
continue
|
||||
return_log[k] = sum(self.log_dict[k]) / len(self.log_dict[k])
|
||||
log = {}
|
||||
for s in self.steps:
|
||||
log.update(s.get_metrics())
|
||||
|
||||
# Some generators can do their own metric logging.
|
||||
if hasattr(self.netsG.module, "get_debug_values"):
|
||||
return_log.update(self.netsG.module.get_debug_values(step))
|
||||
if hasattr(self.netsD.module, "get_debug_values"):
|
||||
return_log.update(self.netsD.module.get_debug_values(step))
|
||||
|
||||
return return_log
|
||||
for net in self.networks:
|
||||
if hasattr(net.module, "get_debug_values"):
|
||||
log.update(net.module.get_debug_values(step))
|
||||
return log
|
||||
|
||||
def get_current_visuals(self, need_GT=True):
|
||||
out_dict = OrderedDict()
|
||||
out_dict['LQ'] = self.var_L[0].detach().float().cpu()
|
||||
gen_batch = self.fake_GenOut[0]
|
||||
if isinstance(gen_batch, tuple):
|
||||
gen_batch = gen_batch[0]
|
||||
out_dict['rlt'] = gen_batch.detach().float().cpu()
|
||||
if need_GT:
|
||||
out_dict['GT'] = self.var_H[0].detach().float().cpu()
|
||||
if self.spsr_enabled:
|
||||
out_dict['SR_branch'] = self.fake_H_branch[0].float().cpu()
|
||||
out_dict['LR_grad'] = self.grad_LR[0].float().cpu()
|
||||
return out_dict
|
||||
# Conforms to an archaic format from MMSR.
|
||||
return {'LQ': self.eval_state['lq'][0].float().cpu(),
|
||||
'GT': self.eval_state['hq'][0].float().cpu(),
|
||||
'rlt': self.eval_state[self.opt['eval']['output_state']][0].float().cpu()}
|
||||
|
||||
def print_network(self):
|
||||
for name, net in self.networks.items():
|
||||
for net in self.networks:
|
||||
s, n = self.get_network_description(net)
|
||||
net_struc_str = '{}'.format(net.__class__.__name__)
|
||||
if self.rank <= 0:
|
||||
logger.info('Network ' + name + ' structure: {}, with parameters: {:,d}'.format(net_struc_str, n))
|
||||
logger.info('Network structure: {}, with parameters: {:,d}'.format(net_struc_str, n))
|
||||
logger.info(s)
|
||||
|
||||
def load(self):
|
||||
for name, net in self.networks.items():
|
||||
load_path = opt['path'][name]
|
||||
if load_path is not None:
|
||||
logger.info('Loading model for %s: [%s]' % (name, load_path))
|
||||
self.load_network(load_path, net)
|
||||
for netdict in [self.netsG, self.netsD]:
|
||||
for name, net in netdict.items():
|
||||
load_path = self.opt['path'][name]
|
||||
if load_path is not None:
|
||||
logger.info('Loading model for [%s]' % (load_path))
|
||||
self.load_network(load_path, net)
|
||||
|
||||
def save(self, iter_step):
|
||||
for name, net in self.networks.items():
|
||||
|
|
|
|||
|
|
@ -63,7 +63,7 @@ class SPSRModel(BaseModel):
|
|||
logger.info('Remove feature loss.')
|
||||
self.cri_fea = None
|
||||
if self.cri_fea: # load VGG perceptual loss
|
||||
self.netF = networks.define_F(opt, use_bn=False).to(self.device)
|
||||
self.netF = networks.define_F(use_bn=False).to(self.device)
|
||||
|
||||
# GD gan loss
|
||||
self.cri_gan = GANLoss(train_opt['gan_type'], 1.0, 0.0).to(self.device)
|
||||
|
|
|
|||
|
|
@ -132,10 +132,10 @@ class SRGANModel(BaseModel):
|
|||
logger.info('Remove feature loss.')
|
||||
self.cri_fea = None
|
||||
if self.cri_fea: # load VGG perceptual loss
|
||||
self.netF = networks.define_F(opt, use_bn=False).to(self.device)
|
||||
self.netF = networks.define_F(use_bn=False).to(self.device)
|
||||
self.lr_netF = None
|
||||
if 'lr_fea_path' in train_opt.keys():
|
||||
self.lr_netF = networks.define_F(opt, use_bn=False, load_path=train_opt['lr_fea_path']).to(self.device)
|
||||
self.lr_netF = networks.define_F(use_bn=False, load_path=train_opt['lr_fea_path']).to(self.device)
|
||||
self.disjoint_data = True
|
||||
|
||||
if opt['dist']:
|
||||
|
|
|
|||
|
|
@ -20,8 +20,8 @@ class FeatureModel(BaseModel):
|
|||
self.rank = -1 # non dist training
|
||||
train_opt = opt['train']
|
||||
|
||||
self.fea_train = networks.define_F(opt, for_training=True).to(self.device)
|
||||
self.net_ref = networks.define_F(opt).to(self.device)
|
||||
self.fea_train = networks.define_F(for_training=True).to(self.device)
|
||||
self.net_ref = networks.define_F().to(self.device)
|
||||
|
||||
self.load()
|
||||
|
||||
|
|
|
|||
|
|
@ -194,10 +194,9 @@ def define_fixed_D(opt):
|
|||
|
||||
|
||||
# Define network used for perceptual loss
|
||||
def define_F(opt, use_bn=False, for_training=False, load_path=None):
|
||||
gpu_ids = opt['gpu_ids']
|
||||
def define_F(which_model='vgg', use_bn=False, for_training=False, load_path=None):
|
||||
device = torch.device('cuda' if gpu_ids else 'cpu')
|
||||
if 'which_model_F' not in opt['train'].keys() or opt['train']['which_model_F'] == 'vgg':
|
||||
if which_model == 'vgg':
|
||||
# PyTorch pretrained VGG19-54, before ReLU.
|
||||
if use_bn:
|
||||
feature_layer = 49
|
||||
|
|
@ -205,12 +204,14 @@ def define_F(opt, use_bn=False, for_training=False, load_path=None):
|
|||
feature_layer = 34
|
||||
if for_training:
|
||||
netF = feature_arch.TrainableVGGFeatureExtractor(feature_layer=feature_layer, use_bn=use_bn,
|
||||
use_input_norm=True, device=device)
|
||||
use_input_norm=True)
|
||||
else:
|
||||
netF = feature_arch.VGGFeatureExtractor(feature_layer=feature_layer, use_bn=use_bn,
|
||||
use_input_norm=True, device=device)
|
||||
elif opt['train']['which_model_F'] == 'wide_resnet':
|
||||
netF = feature_arch.WideResnetFeatureExtractor(use_input_norm=True, device=device)
|
||||
use_input_norm=True)
|
||||
elif which_model == 'wide_resnet':
|
||||
netF = feature_arch.WideResnetFeatureExtractor(use_input_norm=True)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
if load_path:
|
||||
# Load the model parameters:
|
||||
|
|
|
|||
|
|
@ -6,13 +6,17 @@ def create_injector(opt_inject, env):
|
|||
type = opt_inject['type']
|
||||
if type == 'img_grad':
|
||||
return ImageGradientInjector(opt_inject, env)
|
||||
elif type == 'add_noise':
|
||||
return AddNoiseInjector(opt_inject, env)
|
||||
elif type == 'greyscale':
|
||||
return GreyInjector(opt_inject, env)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
class Injector(torch.nn.Module):
|
||||
def __init__(self, opt, env):
|
||||
super(self, Injector).__init__()
|
||||
super(Injector, self).__init__()
|
||||
self.opt = opt
|
||||
self.env = env
|
||||
self.input = opt['in']
|
||||
|
|
@ -23,10 +27,33 @@ class Injector(torch.nn.Module):
|
|||
raise NotImplementedError
|
||||
|
||||
|
||||
# Creates an image gradient from [in] and injects it into [out]
|
||||
class ImageGradientInjector(Injector):
|
||||
def __init__(self, opt, env):
|
||||
super(self, ImageGradientInjector).__init__(opt, env)
|
||||
super(ImageGradientInjector, self).__init__(opt, env)
|
||||
self.img_grad_fn = ImageGradientNoPadding()
|
||||
|
||||
def forward(self, state):
|
||||
return {self.opt['out']: self.img_grad_fn(state[self.opt['in']])}
|
||||
return {self.opt['out']: self.img_grad_fn(state[self.opt['in']])}
|
||||
|
||||
|
||||
# Adds gaussian noise to [in], scales it to [0,[scale]] and injects into [out]
|
||||
class AddNoiseInjector(Injector):
|
||||
def __init__(self, opt, env):
|
||||
super(AddNoiseInjector, self).__init__(opt, env)
|
||||
|
||||
def forward(self, state):
|
||||
noise = torch.randn_like(state[self.opt['in']]) * self.opt['scale']
|
||||
return {self.opt['out']: state[self.opt['in']] + noise}
|
||||
|
||||
|
||||
# Averages the channel dimension (1) of [in] and saves to [out]. Dimensions are
|
||||
# kept the same, the average is simply repeated.
|
||||
class GreyInjector(Injector):
|
||||
def __init__(self, opt, env):
|
||||
super(GreyInjector, self).__init__(opt, env)
|
||||
|
||||
def forward(self, state):
|
||||
mean = torch.mean(state[self.opt['in']], dim=1, keepdim=True)
|
||||
mean = torch.repeat(mean, (-1, 3, -1, -1))
|
||||
return {self.opt['out']: mean}
|
||||
|
|
@ -20,7 +20,7 @@ def create_generator_loss(opt_loss, env):
|
|||
|
||||
class ConfigurableLoss(nn.Module):
|
||||
def __init__(self, opt, env):
|
||||
super(self, ConfigurableLoss).__init__()
|
||||
super(ConfigurableLoss, self).__init__()
|
||||
self.opt = opt
|
||||
self.env = env
|
||||
|
||||
|
|
@ -30,16 +30,16 @@ class ConfigurableLoss(nn.Module):
|
|||
|
||||
def get_basic_criterion_for_name(name, device):
|
||||
if name == 'l1':
|
||||
return nn.L1Loss(device=device)
|
||||
return nn.L1Loss().to(device)
|
||||
elif name == 'l2':
|
||||
return nn.MSELoss(device=device)
|
||||
return nn.MSELoss().to(device)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
class PixLoss(ConfigurableLoss):
|
||||
def __init__(self, opt, env):
|
||||
super(self, PixLoss).__init__(opt, env)
|
||||
super(PixLoss, self).__init__(opt, env)
|
||||
self.opt = opt
|
||||
self.criterion = get_basic_criterion_for_name(opt['criterion'], env['device'])
|
||||
|
||||
|
|
@ -49,21 +49,21 @@ class PixLoss(ConfigurableLoss):
|
|||
|
||||
class FeatureLoss(ConfigurableLoss):
|
||||
def __init__(self, opt, env):
|
||||
super(self, FeatureLoss).__init__(opt, env)
|
||||
super(FeatureLoss, self).__init__(opt, env)
|
||||
self.opt = opt
|
||||
self.criterion = get_basic_criterion_for_name(opt['criterion'], env['device'])
|
||||
self.netF = define_F(opt).to(self.env['device'])
|
||||
self.netF = define_F(which_model=opt['which_model_F']).to(self.env['device'])
|
||||
|
||||
def forward(self, net, state):
|
||||
with torch.no_grad():
|
||||
logits_real = self.netF(state[self.opt['real']])
|
||||
logits_fake = self.netF(state[self.opt['fake']])
|
||||
logits_fake = self.netF(state[self.opt['fake']])
|
||||
return self.criterion(logits_fake, logits_real)
|
||||
|
||||
|
||||
class GeneratorGanLoss(ConfigurableLoss):
|
||||
def __init__(self, opt, env):
|
||||
super(self, GeneratorGanLoss).__init__(opt, env)
|
||||
super(GeneratorGanLoss, self).__init__(opt, env)
|
||||
self.opt = opt
|
||||
self.criterion = GANLoss(opt['gan_type'], 1.0, 0.0).to(env['device'])
|
||||
self.netD = env['discriminators'][opt['discriminator']]
|
||||
|
|
@ -86,7 +86,7 @@ class GeneratorGanLoss(ConfigurableLoss):
|
|||
|
||||
class DiscriminatorGanLoss(ConfigurableLoss):
|
||||
def __init__(self, opt, env):
|
||||
super(self, DiscriminatorGanLoss).__init__(opt, env)
|
||||
super(DiscriminatorGanLoss, self).__init__(opt, env)
|
||||
self.opt = opt
|
||||
self.criterion = GANLoss(opt['gan_type'], 1.0, 0.0).to(env['device'])
|
||||
|
||||
|
|
|
|||
|
|
@ -72,7 +72,7 @@ class ConfigurableStep(Module):
|
|||
# Performs all forward and backward passes for this step given an input state. All input states are lists of
|
||||
# chunked tensors. Use grad_accum_step to dereference these steps. Should return a dict of tensors that later
|
||||
# steps might use. These tensors are automatically detached and accumulated into chunks.
|
||||
def do_forward_backward(self, state, grad_accum_step, amp_loss_id):
|
||||
def do_forward_backward(self, state, grad_accum_step, amp_loss_id, backward=True):
|
||||
# First, do a forward pass with the generator.
|
||||
results = self.gen(state[self.step_opt['generator_input']][grad_accum_step])
|
||||
# Extract the resultants into a "new_state" dict per the configuration.
|
||||
|
|
@ -92,17 +92,18 @@ class ConfigurableStep(Module):
|
|||
local_state.update(injected)
|
||||
new_state.update(injected)
|
||||
|
||||
# Finally, compute the losses.
|
||||
total_loss = 0
|
||||
for loss_name, loss in self.losses.items():
|
||||
l = loss(self.training_net, local_state)
|
||||
self.loss_accumulator.add_loss(loss_name, l)
|
||||
total_loss += l * self.weights[loss_name]
|
||||
self.loss_accumulator.add_loss("total", total_loss)
|
||||
if backward:
|
||||
# Finally, compute the losses.
|
||||
total_loss = 0
|
||||
for loss_name, loss in self.losses.items():
|
||||
l = loss(self.training_net, local_state)
|
||||
self.loss_accumulator.add_loss(loss_name, l)
|
||||
total_loss += l * self.weights[loss_name]
|
||||
self.loss_accumulator.add_loss("total", total_loss)
|
||||
|
||||
# Get dem grads!
|
||||
with amp.scale_loss(total_loss, self.optimizers, amp_loss_id) as scaled_loss:
|
||||
scaled_loss.backward()
|
||||
# Get dem grads!
|
||||
with amp.scale_loss(total_loss, self.optimizers, amp_loss_id) as scaled_loss:
|
||||
scaled_loss.backward()
|
||||
|
||||
return new_state
|
||||
|
||||
|
|
@ -114,4 +115,4 @@ class ConfigurableStep(Module):
|
|||
opt.step()
|
||||
|
||||
def get_metrics(self):
|
||||
return self.loss_accumulator.as_dict()
|
||||
return self.loss_accumulator.as_dict()
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user