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gaotingquan 2023-03-09 17:37:14 +00:00 committed by Wei Shengyu
parent 9beb154bc3
commit 392b75b1ac
3 changed files with 12 additions and 3 deletions
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1
ppcls/arch/backbone/legendary_models/mobilenet_v1.py
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@ -165,7 +165,6 @@ class MobileNet(TheseusLayer):
return_stages=return_stages) return_stages=return_stages)
@AMP_forward_decorator @AMP_forward_decorator
@clas_forward_decorator
def forward(self, x): def forward(self, x):
x = self.conv(x) x = self.conv(x)
x = self.blocks(x) x = self.blocks(x)

2
ppcls/engine/evaluation/classification.py
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@ -67,7 +67,7 @@ class ClassEval(object):
if not self.config["Global"].get("use_multilabel", False): if not self.config["Global"].get("use_multilabel", False):
batch[1] = batch[1].reshape([-1, 1]).astype("int64") batch[1] = batch[1].reshape([-1, 1]).astype("int64")
out = self.model(batch) out = self.model(batch[0])
# just for DistributedBatchSampler issue: repeat sampling # just for DistributedBatchSampler issue: repeat sampling
current_samples = batch_size * paddle.distributed.get_world_size() current_samples = batch_size * paddle.distributed.get_world_size()

12
ppcls/engine/train/classification.py
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@ -41,6 +41,12 @@ class ClassTrainer(object):
# gradient accumulation # gradient accumulation
self.update_freq = self.config["Global"].get("update_freq", 1) self.update_freq = self.config["Global"].get("update_freq", 1)
if "Head" in self.config["Arch"] or self.config["Arch"].get("is_rec",
False):
self.is_rec = True
else:
self.is_rec = False
# TODO(gaotingquan): mv to build_model # TODO(gaotingquan): mv to build_model
# build EMA model # build EMA model
self.model_ema = self._build_ema_model() self.model_ema = self._build_ema_model()
@ -197,7 +203,11 @@ class ClassTrainer(object):
batch[1] = batch[1].reshape([batch_size, -1]) batch[1] = batch[1].reshape([batch_size, -1])
self.global_step += 1 self.global_step += 1
out = self.model(batch) if self.is_rec:
out = self.model(batch)
else:
out = self.model(batch[0])
loss_dict = self.loss_func(out, batch[1]) loss_dict = self.loss_func(out, batch[1])
# TODO(gaotingquan): mv update_freq to loss and optimizer # TODO(gaotingquan): mv update_freq to loss and optimizer
loss = loss_dict["loss"] / self.update_freq loss = loss_dict["loss"] / self.update_freq