def log_epoch_stats(self,
cur_epoch,
writer,
params=0,
flops=0,
is_master=False):
stats = self.get_epoch_stats(cur_epoch)
lu.log_json_stats(stats,
cur_epoch,
writer,
is_epoch=False,
params=params,
flops=flops,
is_master=is_master)
def compute_precise_time(model, loss_fun):
"""Computes precise time."""
# Generate a dummy mini-batch
im_size = cfg.TRAIN.IM_SIZE
inputs = torch.rand(cfg.PREC_TIME.BATCH_SIZE, 3, im_size, im_size)
labels = torch.zeros(cfg.PREC_TIME.BATCH_SIZE, dtype=torch.int64)
# Copy the data to the GPU
inputs = inputs.cuda(non_blocking=False)
labels = labels.cuda(non_blocking=False)
# Compute precise time
fw_test_time = compute_fw_test_time(model, inputs)
fw_time, bw_time = compute_fw_bw_time(model, loss_fun, inputs, labels)
# Log precise time
lu.log_json_stats({
"prec_test_fw_time": fw_test_time,
"prec_train_fw_time": fw_time,
"prec_train_bw_time": bw_time,
"prec_train_fw_bw_time": fw_time + bw_time,
})
def log_epoch_stats(self, cur_epoch):
stats = self.get_epoch_stats(cur_epoch)
lu.log_json_stats(stats)
def log_iter_stats(self, cur_epoch, cur_iter):
if (cur_iter + 1) % cfg.LOG_PERIOD != 0:
return
stats = self.get_iter_stats(cur_epoch, cur_iter)
lu.log_json_stats(stats)