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selected_dropout3.py
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selected_dropout3.py
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import os
import numpy as np
from sklearn.metrics import mutual_info_score
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import toml
if __name__ == '__main__':
import sys
import time
import signal
import importlib
import torch
import torch.nn as nn
from utils import *
from callbacks import (PlotLearning, AverageMeter)
from models.multi_column import MultiColumn
import torchvision
from transforms_video import *
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str)
parser.add_argument('--drate', type=float)
parser.add_argument('--mode', type=str)
args = parser.parse_args()
config_name = args.config
drate = args.drate
config = load_json_config(f'configs/config_{config_name}.json')
# set column model
file_name = config['conv_model']
cnn_def = importlib.import_module(f'{file_name}_select')
# setup device - CPU or GPU
device, device_ids = 'cuda', [0]
print(" > Using device: {}".format(device))
print(" > Active GPU ids: {}".format(device_ids))
best_loss = float('Inf')
if config["input_mode"] == "av":
from data_loader_av import VideoFolder
elif config["input_mode"] == "skvideo":
from data_loader_skvideo import VideoFolder
elif config["input_mode"] == "uiuc":
from data_loader_uiuc import VideoFolder
else:
raise ValueError("Please provide a valid input mode")
# set run output folder
model_name = config["model_name"]
output_dir = config["output_dir"]
save_dir = os.path.join(output_dir, model_name)
# assign Ctrl+C signal handler
signal.signal(signal.SIGINT, ExperimentalRunCleaner(save_dir))
with open(f'entropy_{model_name[:-8]}.toml', 'r') as f:
entropy_dict = toml.load(f)
score = [float(value) for value in entropy_dict.values()]
score = np.array(score)
d_rate = drate
mode = args.mode
# create model
print(" > Creating model ... !")
model = cnn_def.Model(config['num_classes'], score, d_rate, mode).to(device)
# optionally resume from a checkpoint
checkpoint_path = os.path.join(config['output_dir'],
config['model_name'],
'model_best.pth.tar')
if os.path.isfile(checkpoint_path):
checkpoint = torch.load(checkpoint_path)
start_epoch = checkpoint['epoch']
best_loss = checkpoint['best_loss']
model.load_state_dict(checkpoint['state_dict'], strict=False)
print(" > Loaded checkpoint '{}' (epoch {})"
.format(checkpoint_path, checkpoint['epoch']))
else:
print(" !#! No checkpoint found at '{}'".format(
checkpoint_path))
# define augmentation pipeline
upscale_size_eval = int(config['input_spatial_size'] * config["upscale_factor_eval"])
# Center crop videos during evaluation
transform_eval_pre = ComposeMix([
[Scale(upscale_size_eval), "img"],
[torchvision.transforms.ToPILImage(), "img"],
[torchvision.transforms.CenterCrop(config['input_spatial_size']), "img"],
])
# Transforms common to train and eval sets and applied after "pre" transforms
transform_post = ComposeMix([
[torchvision.transforms.ToTensor(), "img"],
[torchvision.transforms.Normalize(
mean=[0.485, 0.456, 0.406], # default values for imagenet
std=[0.229, 0.224, 0.225]), "img"]
])
val_data = VideoFolder(root=config['data_folder'],
json_file_input=config['json_data_val'],
json_file_labels=config['json_file_labels'],
clip_size=config['clip_size'],
nclips=config['nclips_val'],
step_size=config['step_size_val'],
is_val=True,
transform_pre=transform_eval_pre,
transform_post=transform_post,
get_item_id=True,
)
val_loader = torch.utils.data.DataLoader(
val_data,
batch_size=config['batch_size'], shuffle=False,
num_workers=config['num_workers'], pin_memory=True,
drop_last=False)
criterion = nn.CrossEntropyLoss().to(device)
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model.eval()
logits_matrix = []
features_matrix = []
targets_list = []
item_id_list = []
end = time.time()
with torch.no_grad():
vector = []
label = []
for i, (input, target, item_id) in enumerate(val_loader):
if config['nclips_val'] > 1:
input_var = list(input.split(config['clip_size'], 2))
for idx, inp in enumerate(input_var):
input_var[idx] = inp.to(device)
else:
input_var = [input.to(device)]
target1 = target // 8
target2 = target % 8
target1 = target1.to(device)
target2 = target2.to(device)
# compute output and loss
output = model(input_var)
loss_list = []
for j in range(50):
loss_list.append(criterion(output[j], target1))
for j in range(50):
loss_list.append(criterion(output[50 + j], target2))
loss = torch.stack(loss_list).sum()
# measure accuracy and record loss
prec1_1, prec5_1 = accuracy(output[49].detach().cpu(), target1.detach().cpu(), topk=(1, 5))
prec1_2, prec5_2 = accuracy(output[-1].detach().cpu(), target2.detach().cpu(), topk=(1, 5))
prec1, prec5 = ((prec1_1 + prec1_2) / 2.0, (prec5_1 + prec5_2) / 2.0)
losses.update(loss.item(), input.size(0))
top1.update(prec1.item(), input.size(0))
top5.update(prec5.item(), input.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % config["print_freq"] == 0:
print('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
i, len(val_loader), batch_time=batch_time, loss=losses,
top1=top1, top5=top5))
print(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'
.format(top1=top1, top5=top5))
acc_filename = 'entropy_selected_dropout_acc.toml'
with open(acc_filename, 'r') as f:
accs = toml.load(f)
accs[f'{config_name}_{args.mode}_{str(d_rate)}'] = top1.avg
with open(acc_filename,'w') as f:
toml.dump(accs, f)