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import argparse
import yaml
import math
import numpy as np
# torch
import torch
import torch.nn as nn
import torch.optim as optim
# torchlight
import torchlight.torchlight as torchlight
from torchlight.torchlight import str2bool
from torchlight.torchlight import DictAction
from torchlight.torchlight import import_class
from .processor import Processor
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv1d') != -1 or classname.find('Conv2d') != -1 or classname.find('Linear') != -1:
m.weight.data.normal_(0.0, 0.02)
if m.bias is not None:
m.bias.data.fill_(0)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
class LE_Processor(Processor):
"""
Processor for Linear Evaluation.
"""
def load_model(self):
self.model = self.io.load_model(self.arg.model,
**(self.arg.model_args))
self.model.apply(weights_init)
for name, param in self.model.encoder_q.named_parameters():
if name not in ['fc.weight', 'fc.bias']:
param.requires_grad = False
self.num_grad_layers = 2
if hasattr(self.model, 'encoder_q_motion'):
for name, param in self.model.encoder_q_motion.named_parameters():
if name not in ['fc.weight', 'fc.bias']:
param.requires_grad = False
self.num_grad_layers += 2
if hasattr(self.model, 'encoder_q_bone'):
for name, param in self.model.encoder_q_bone.named_parameters():
if name not in ['fc.weight', 'fc.bias']:
param.requires_grad = False
self.num_grad_layers += 2
self.loss = nn.CrossEntropyLoss()
def load_optimizer(self):
parameters = list(filter(lambda p: p.requires_grad, self.model.parameters()))
assert len(parameters) == self.num_grad_layers
if self.arg.optimizer == 'SGD':
self.optimizer = optim.SGD(
parameters,
lr=self.arg.base_lr,
momentum=0.9,
nesterov=self.arg.nesterov,
weight_decay=self.arg.weight_decay)
elif self.arg.optimizer == 'Adam':
self.optimizer = optim.Adam(
parameters,
lr=self.arg.base_lr,
weight_decay=self.arg.weight_decay)
else:
raise ValueError()
def adjust_lr(self):
if self.arg.optimizer == 'SGD' and self.arg.step:
lr = self.arg.base_lr * (
0.1**np.sum(self.meta_info['epoch'] > np.array(self.arg.step)))
for param_group in self.optimizer.param_groups:
param_group['lr'] = lr
self.lr = lr
else:
self.lr = self.arg.base_lr
def show_topk(self, k):
rank = self.result.argsort()
hit_top_k = [l in rank[i, -k:] for i, l in enumerate(self.label)]
accuracy = sum(hit_top_k) * 1.0 / len(hit_top_k)
self.io.print_log('\tTop{}: {:.2f}%'.format(k, 100 * accuracy))
def show_best(self, k):
rank = self.result.argsort()
hit_top_k = [l in rank[i, -k:] for i, l in enumerate(self.label)]
accuracy = 100 * sum(hit_top_k) * 1.0 / len(hit_top_k)
accuracy = round(accuracy, 5)
self.current_result = accuracy
if self.best_result <= accuracy:
self.best_result = accuracy
self.io.print_log('\tBest Top{}: {:.2f}%'.format(k, self.best_result))
def train(self, epoch):
self.model.eval()
self.adjust_lr()
loader = self.data_loader['train']
loss_value = []
for data, label in loader:
self.global_step += 1
# get data
data = data.float().to(self.dev, non_blocking=True)
label = label.long().to(self.dev, non_blocking=True)
if self.arg.stream == 'joint':
pass
elif self.arg.stream == 'motion':
motion = torch.zeros_like(data)
motion[:, :, :-1, :, :] = data[:, :, 1:, :, :] - data[:, :, :-1, :, :]
data = motion
elif self.arg.stream == 'bone':
Bone = [(1, 2), (2, 21), (3, 21), (4, 3), (5, 21), (6, 5), (7, 6), (8, 7), (9, 21),
(10, 9), (11, 10), (12, 11), (13, 1), (14, 13), (15, 14), (16, 15), (17, 1),
(18, 17), (19, 18), (20, 19), (21, 21), (22, 23), (23, 8), (24, 25), (25, 12)]
bone = torch.zeros_like(data)
for v1, v2 in Bone:
bone[:, :, :, v1 - 1, :] = data[:, :, :, v1 - 1, :] - data[:, :, :, v2 - 1, :]
data = bone
else:
raise ValueError
# forward
output = self.model(None, data)
#print(output)
#continue
loss = self.loss(output, label)
# backward
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# statistics
self.iter_info['loss'] = loss.data.item()
self.iter_info['lr'] = '{:.6f}'.format(self.lr)
loss_value.append(self.iter_info['loss'])
self.show_iter_info()
self.meta_info['iter'] += 1
self.train_log_writer(epoch)
self.epoch_info['train_mean_loss']= np.mean(loss_value)
self.train_writer.add_scalar('loss', self.epoch_info['train_mean_loss'], epoch)
self.show_epoch_info()
def test(self, epoch):
self.model.eval()
loader = self.data_loader['test']
loss_value = []
result_frag = []
label_frag = []
for data, label in loader:
# get data
data = data.float().to(self.dev, non_blocking=True)
label = label.long().to(self.dev, non_blocking=True)
if self.arg.stream == 'joint':
pass
elif self.arg.stream == 'motion':
motion = torch.zeros_like(data)
motion[:, :, :-1, :, :] = data[:, :, 1:, :, :] - data[:, :, :-1, :, :]
data = motion
elif self.arg.stream == 'bone':
Bone = [(1, 2), (2, 21), (3, 21), (4, 3), (5, 21), (6, 5), (7, 6), (8, 7), (9, 21),
(10, 9), (11, 10), (12, 11), (13, 1), (14, 13), (15, 14), (16, 15), (17, 1),
(18, 17), (19, 18), (20, 19), (21, 21), (22, 23), (23, 8), (24, 25), (25, 12)]
bone = torch.zeros_like(data)
for v1, v2 in Bone:
bone[:, :, :, v1 - 1, :] = data[:, :, :, v1 - 1, :] - data[:, :, :, v2 - 1, :]
data = bone
else:
raise ValueError
# inference
with torch.no_grad():
output = self.model(None, data)
result_frag.append(output.data.cpu().numpy())
# get loss
loss = self.loss(output, label)
loss_value.append(loss.item())
label_frag.append(label.data.cpu().numpy())
self.result = np.concatenate(result_frag)
self.label = np.concatenate(label_frag)
self.eval_info['eval_mean_loss']= np.mean(loss_value)
self.show_eval_info()
# show top-k accuracy
for k in self.arg.show_topk:
self.show_topk(k)
self.show_best(1)
self.eval_log_writer(epoch)
@staticmethod
def get_parser(add_help=False):
# parameter priority: command line > config > default
parent_parser = Processor.get_parser(add_help=False)
parser = argparse.ArgumentParser(
add_help=add_help,
parents=[parent_parser],
description='Spatial Temporal Graph Convolution Network')
# evaluation
parser.add_argument('--show_topk', type=int, default=[1, 5], nargs='+', help='which Top K accuracy will be shown')
# optim
parser.add_argument('--base_lr', type=float, default=0.01, help='initial learning rate')
parser.add_argument('--step', type=int, default=[], nargs='+', help='the epoch where optimizer reduce the learning rate')
parser.add_argument('--optimizer', default='SGD', help='type of optimizer')
parser.add_argument('--nesterov', type=str2bool, default=True, help='use nesterov or not')
parser.add_argument('--weight_decay', type=float, default=0.0001, help='weight decay for optimizer')
parser.add_argument('--stream', type=str, default='joint', help='the stream of input')
parser.add_argument('--mining_epoch', type=int, default=1e6,
help='the starting epoch of nearest neighbor mining')
parser.add_argument('--topk', type=int, default=1, help='topk samples in nearest neighbor mining')
return parser
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