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evaluate.py
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evaluate.py
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"""Evaluates the model"""
import argparse
import logging
import os
import random
from tqdm import tqdm
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.profiler import profile, record_function, ProfilerActivity
import utils
import model.net as net
import model.dataset as dataset
parser = argparse.ArgumentParser()
parser.add_argument('--target_dir', default=None,
help="Directory containing the val target set")
parser.add_argument('--lr_dir', default=None,
help="Directory containing the val lr set")
parser.add_argument('--key_dir', default=None,
help="Directory containing the val key set")
parser.add_argument('--file_fmt', default='frame%d.png',
help="Dataset file fmt")
parser.add_argument('--model_dir', default='experiments/keyvsrc_attn',
help="Directory containing params.json")
parser.add_argument('--num_steps', default=None, type=int,
help="Number of batches to evaluate on. Full dataset when set to None.")
parser.add_argument('--eval_batch_size', default=1,
help="Batch size for evaluation.")
parser.add_argument('--restore_file', default=None,
help="Optional, name of the file in --model_dir containing weights to reload before \
training") # 'best' or 'train'
parser.add_argument('--output_dir', default=None,
help="Directory containing the val lr set")
parser.add_argument('--profile', default=0, type=int,
help="Log profiling information.")
parser.add_argument('--use_cpu', default=0, type=int,
help="Use CPU even when GPUs are available.")
parser.add_argument('--gpu', default=0, type=int,
help="Use CPU even when GPUs are available.")
parser.add_argument('--data_parallel', default=0, type=int,
help="Use data parallel for multi-gpu inference")
def evaluate(model, loss_fn, dataloader, params, metrics, num_steps=None, output_dir=None, file_fmt="frmae%d.png",
profiling=0):
"""Evaluate the model on `num_steps` batches.
Args:
model: (torch.nn.Module) the neural network
loss_fn: a function that takes batch_output and batch_labels and computes the loss for the batch
dataloader: (DataLoader) a torch.utils.data.DataLoader object that fetches data
metrics: (dict) a dictionary of functions that compute a metric using the output and labels of each batch
params: (Params) hyperparameters
num_steps: (int) number of batches to train on, each of size params.batch_size
"""
if output_dir:
logging.info("Writing results to %s..." % output_dir)
# set model to evaluation mode
model.eval()
# summary for current eval loop
summ = {k: [] for k in metrics.metrics + ['loss', 'runtime_per_batch', 'sample_ids']}
with torch.no_grad():
# compute metrics over the dataset
for i, (train_batch, target, sample_ids) in enumerate(tqdm(dataloader)):
if (num_steps is not None) and (i == num_steps):
break
train_batch = net.batch_to_device(train_batch, params.device)
target = net.batch_to_device(target, params.device)
# compute model output and runtime
with profile(activities=[ProfilerActivity.CPU], record_shapes=True) as prof:
with record_function("model_inference"):
output_batch = model(train_batch)
if profiling:
logging.info(prof.key_averages().table(sort_by="cpu_time_total", row_limit=20))
summ['runtime_per_batch'].append(prof.profiler.self_cpu_time_total/1000)
# Compute loss
loss = loss_fn(output_batch, target)
train_batch = {k: train_batch[k].data.cpu() for k in train_batch}
output_batch = {k: output_batch[k].data.cpu() for k in output_batch}
target = {k: target[k].data.cpu() for k in target}
# compute all metrics on this batch
metrics_batch = metrics(output_batch, target)
for metric in metrics_batch:
summ[metric] += metrics_batch[metric]
summ['loss'].append(loss.item())
summ['sample_ids'] += sample_ids
if output_dir:
net.write_outputs(train_batch, output_batch, target, sample_ids,
output_dir, params, file_fmt)
# compute mean of all metrics in summary
for i, sample in enumerate(summ['sample_ids']):
sample_metrics = {metric: summ[metric][i]
for metric in metrics.metrics + ['runtime_per_batch']}
sample_metrics_string = " ; ".join("{}: {:06.4f}".format(k, v)
for k, v in sample_metrics.items())
logging.info("%s : " % sample + sample_metrics_string)
# Remove first batch's runtime before computing mean, to account for warmup
summ['runtime_per_batch'] = summ['runtime_per_batch'][1:]
mean_metrics = {metric: np.mean(summ[metric])
for metric in metrics.metrics + ['loss']}
mean_metrics_string = " ; ".join("{}: {:06.4f}".format(k, v)
for k, v in mean_metrics.items())
logging.info("- Mean metrics : " + mean_metrics_string)
return mean_metrics, summ
if __name__ == '__main__':
"""
Evaluate the model on the test set.
"""
# Load the parameters
args = parser.parse_args()
json_path = os.path.join(args.model_dir, 'params.json')
assert os.path.isfile(
json_path), "No json configuration file found at {}".format(json_path)
params = utils.Params(json_path)
# use GPU if available
params.device = torch.device(
"cuda:%d" % args.gpu if (torch.cuda.is_available() and args.use_cpu==0) else "cpu")
params.data_parallel = True if args.data_parallel != 0 else False
# Set the random seed for reproducible experiments
torch.manual_seed(230)
random.seed(230)
np.random.seed(230)
if params.device.type == 'cuda':
torch.cuda.manual_seed(230)
# Get the logger
utils.set_logger(os.path.join(args.model_dir, 'evaluate.log'))
# Create the input data pipeline
logging.info("Creating the dataset...")
# fetch dataloaders
params.eval_batch_size = args.eval_batch_size
test_dl = dataset.get_dataloader(args.target_dir, args.lr_dir, args.key_dir,
params, frame_fmt=args.file_fmt, train=False)
logging.info("- done.")
# Define the model
model = net.Net(params)
if params.data_parallel:
logging.info('Using data parallel model')
model = nn.DataParallel(model)
model.to(params.device)
logging.info("Evaluating %s" % params.net)
# Metric
metrics = net.Metrics(params)
# fetch loss function and metrics
loss_fn = net.loss_fn(params)
logging.info("Starting evaluation")
# Reload weights from the saved file
if args.restore_file:
utils.load_checkpoint(os.path.join(
args.model_dir, args.restore_file + '.pth.tar'), model, data_parallel=params.data_parallel)
# Evaluate
test_metrics, _ = evaluate(model, loss_fn, test_dl, params, metrics, args.num_steps,
args.output_dir, args.file_fmt, args.profile)
save_path = os.path.join(
args.model_dir, "metrics_test_{}.json".format(args.restore_file))
utils.save_dict_to_json(test_metrics, save_path)