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train_x3d_charades_loc.py
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train_x3d_charades_loc.py
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import os
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
import torchvision
from torchvision import datasets, transforms
from torchsummary import summary
import numpy as np
from barbar import Bar
import pkbar
from apmeter import APMeter
import x3d as resnet_x3d
from charades import Charades
from charades import custom_collate_fn as collate_fn
from transforms.spatial_transforms import Compose, Normalize, RandomHorizontalFlip, MultiScaleRandomCrop, MultiScaleRandomCropMultigrid, ToTensor, CenterCrop, CenterCropScaled
from transforms.temporal_transforms import TemporalRandomCrop
from transforms.target_transforms import ClassLabel
import warnings
warnings.filterwarnings("ignore")
parser = argparse.ArgumentParser()
parser.add_argument('-gpu', default='0', type=str)
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"]=args.gpu
BS = 16
BS_UPSCALE = 2
INIT_LR = 0.02 * BS_UPSCALE
GPUS = 2
X3D_VERSION = 'M'
CHARADES_ROOT = '/nfs/bigneuron/add_disk0/kumarak/Charades_v1_rgb'
CHARADES_ANNO = 'data/charades.json'
CHARADES_DATASET_SIZE = {'train':7900, 'val':1850}
CHARADES_MEAN = [0.413, 0.368, 0.338]
CHARADES_STD = [0.131, 0.125, 0.132] # CALCULATED ON CHARADES TRAINING SET FOR FRAME-WISE MEANS
# ON VAL SET MEAN:[0.415 0.384 0.366], STD:[0.146 0.140 0.137]
# warmup_steps=0
def run(init_lr=INIT_LR, max_epochs=100, root=CHARADES_ROOT, anno=CHARADES_ANNO, batch_size=BS*BS_UPSCALE):
frames=80 # DOUBLED INSIDE DATASET, AS LONGER CLIPS
crop_size = {'S':160, 'M':224, 'XL':312}[X3D_VERSION]
resize_size = {'S':[180.,225.], 'M':[256.,256.], 'XL':[360.,450.]}[X3D_VERSION] # 'M':[256.,320.] FOR LONGER SCHEDULE
gamma_tau = {'S':6, 'M':5, 'XL':5}[X3D_VERSION] # DOUBLED INSIDE DATASET, AS LONGER CLIPS
st_steps = 0 #0 # FOR LR WARM-UP
load_steps = 0 #0 # FOR LOADING AND PRINT SCHEDULE
steps = 0 #0
epochs = 0 #0
num_steps_per_update = 1 # ACCUMULATE GRADIENT IF NEEDED
cur_iterations = steps * num_steps_per_update
iterations_per_epoch = CHARADES_DATASET_SIZE['train']//batch_size
val_iterations_per_epoch = CHARADES_DATASET_SIZE['val']//(batch_size//2)
max_steps = iterations_per_epoch * max_epochs
train_spatial_transforms = Compose([MultiScaleRandomCropMultigrid([crop_size/i for i in resize_size], crop_size),
RandomHorizontalFlip(),
ToTensor(255),
Normalize(CHARADES_MEAN, CHARADES_STD)])
val_spatial_transforms = Compose([CenterCropScaled(crop_size),
ToTensor(255),
Normalize(CHARADES_MEAN, CHARADES_STD)])
dataset = Charades(anno, 'training', root, train_spatial_transforms,
task='loc', frames=80, gamma_tau=gamma_tau, crops=1)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True,
num_workers=8, pin_memory=True, collate_fn=collate_fn)
val_dataset = Charades(anno, 'testing', root, val_spatial_transforms,
task='loc', frames=80, gamma_tau=gamma_tau, crops=10)
val_dataloader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size//2, shuffle=False,
num_workers=8, pin_memory=True, collate_fn=collate_fn)
dataloaders = {'train': dataloader, 'val': val_dataloader}
datasets = {'train': dataset, 'val': val_dataset}
print('train',len(datasets['train']),'val',len(datasets['val']))
print('Total iterations:', max_steps, 'Total epochs:', max_epochs)
print('datasets created')
x3d = resnet_x3d.generate_model(x3d_version=X3D_VERSION, n_classes=400, n_input_channels=3, task='loc', dropout=0.5, base_bn_splits=1)
load_ckpt = torch.load('models/x3d_multigrid_kinetics_fb_pretrained.pt')
x3d.load_state_dict(load_ckpt['model_state_dict'])
save_model = 'models/x3d_charades_loc_rgb_sgd_'
x3d.replace_logits(157)
if steps>0:
load_ckpt = torch.load('models/x3d_charades_loc_rgb_sgd_'+str(load_steps).zfill(6)+'.pt')
x3d.load_state_dict(load_ckpt['model_state_dict'])
x3d.cuda()
x3d = nn.DataParallel(x3d)
print('model loaded')
lr = init_lr
print ('INIT LR: %f'%lr)
optimizer = optim.SGD(x3d.parameters(), lr=lr, momentum=0.9, weight_decay=1e-5)
lr_sched = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', patience=2, factor=0.1, verbose=True)
if steps>0:
optimizer.load_state_dict(load_ckpt['optimizer_state_dict'])
lr_sched.load_state_dict(load_ckpt['scheduler_state_dict'])
criterion = nn.BCEWithLogitsLoss()
val_apm = APMeter()
tr_apm = APMeter()
while epochs < max_epochs:
print ('Step {} Epoch {}'.format(steps, epochs))
print ('-' * 10)
# Each epoch has a training and validation phase
for phase in 2*['train']+['val']:
bar_st = iterations_per_epoch if phase == 'train' else val_iterations_per_epoch
bar = pkbar.Pbar(name='update: ', target=bar_st)
if phase == 'train':
x3d.train(True)
epochs += 1
torch.autograd.set_grad_enabled(True)
else:
x3d.train(False) # Set model to evaluate mode
_ = x3d.module.aggregate_sub_bn_stats() # FOR EVAL AGGREGATE BN STATS
torch.autograd.set_grad_enabled(False)
tot_loss = 0.0
tot_loc_loss = 0.0
tot_cls_loss = 0.0
num_iter = 0
optimizer.zero_grad()
# Iterate over data.
print(phase)
for i,data in enumerate(dataloaders[phase]):
num_iter += 1
bar.update(i)
if phase == 'train':
inputs, labels, masks = data
else:
inputs, labels, masks = data
inputs = inputs.cuda() # B 3 T W H
tl = labels.size(2)
#labels = torch.max(labels, dim=2)[0] # B C T --> B C
labels = labels.cuda() # B C TL
masks = masks.cuda() # B TL
valid_t = torch.sum(masks, dim=1).int()
per_frame_logits = x3d(inputs) # B C T
per_frame_logits = F.interpolate(per_frame_logits, tl, mode='linear')
probs = F.sigmoid(per_frame_logits) * masks.unsqueeze(1)
cls_loss = criterion(torch.max(per_frame_logits, dim=2)[0], torch.max(labels, dim=2)[0])
tot_cls_loss += cls_loss.item()
loc_loss = criterion(per_frame_logits, labels)
tot_loc_loss += loc_loss.item()
if phase == 'train':
for b in range(labels.shape[0]):
tr_apm.add(probs[b][:,:valid_t[b].item()].transpose(0,1).detach().cpu().numpy(),
labels[b][:,:valid_t[b].item()].transpose(0,1).cpu().numpy())
else:
for b in range(labels.shape[0]):
val_apm.add(probs[b][:,:valid_t[b].item()].transpose(0,1).detach().cpu().numpy(),
labels[b][:,:valid_t[b].item()].transpose(0,1).cpu().numpy())
loss = (cls_loss + loc_loss)/(2 * num_steps_per_update)
tot_loss += loss.item()
if phase == 'train':
loss.backward()
if num_iter == num_steps_per_update and phase == 'train':
#lr_warmup(lr, steps-st_steps, warmup_steps, optimizer)
steps += 1
num_iter = 0
optimizer.step()
optimizer.zero_grad()
#lr_sched.step()
s_times = iterations_per_epoch//2
if (steps-load_steps) % s_times == 0:
tr_map = tr_apm.value().mean()
tr_apm.reset()
print (' Epoch:{} {} steps: {} Loc Loss: {:.4f} Cls Loss: {:.4f} Tot Loss: {:.4f} mAP: {:.4f}'.format(epochs, phase,
steps, tot_loc_loss/(s_times*num_steps_per_update), tot_cls_loss/(s_times*num_steps_per_update), tot_loss/s_times, tr_map))#, tot_acc/(s_times*num_steps_per_update)))
tot_loss = tot_loc_loss = tot_cls_loss = 0.
if steps % (1000) == 0:
#tr_apm.reset()
ckpt = {'model_state_dict': x3d.module.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'scheduler_state_dict': lr_sched.state_dict()}
torch.save(ckpt, save_model+str(steps).zfill(6)+'.pt')
if phase == 'val':
val_map = val_apm.value().mean()
lr_sched.step(tot_loss)
val_apm.reset()
print (' Epoch:{} {} Loc Loss: {:.4f} Cls Loss: {:.4f} Tot Loss: {:.4f} mAP: {:.4f}'.format(epochs, phase,
tot_loc_loss/num_iter, tot_cls_loss/num_iter, (tot_loss*num_steps_per_update)/num_iter, val_map))#, tot_acc/num_iter))
tot_loss = tot_loc_loss = tot_cls_loss = 0.
def lr_warmup(init_lr, cur_steps, warmup_steps, opt):
start_after = 1
if cur_steps < warmup_steps and cur_steps > start_after:
lr_scale = min(1., float(cur_steps + 1) / warmup_steps)
for pg in opt.param_groups:
pg['lr'] = lr_scale * init_lr
def print_stats(long_ind, batch_size, stats, gamma_tau, bn_splits, lr):
bs = batch_size * LONG_CYCLE[long_ind]
if long_ind in [0,1]:
bs = [bs*j for j in [2,1]]
print(' ***** LR {} Frames {}/{} BS ({},{}) W/H ({},{}) BN_splits {} long_ind {} *****'.format(lr, stats[0][0], gamma_tau, bs[0], bs[1], stats[2][0], stats[3][0], bn_splits, long_ind))
else:
bs = [bs*j for j in [4,2,1]]
print(' ***** LR {} Frames {}/{} BS ({},{},{}) W/H ({},{},{}) BN_splits {} long_ind {} *****'.format(lr, stats[0][0], gamma_tau, bs[0], bs[1], bs[2], stats[1][0], stats[2][0], stats[3][0], bn_splits, long_ind))
if __name__ == '__main__':
run()