replace cross_entropy except in python/paddle/fluid/tests/unittests/*.py && unittests/*/*.py (#48922)

Author: kangguangli

python/paddle/fluid/tests/unittests/collective/fleet/auto_parallel_parallelizer.py

@@ -22,7 +22,6 @@
 import paddle.utils as utils
 from paddle.distributed import fleet
 from paddle.distributed.fleet import auto
-from paddle.fluid import layers
 
 paddle.enable_static()
 _global_parallel_strategy = None
@@ -93,7 +92,9 @@ def mlp_pretrain_forward(train_program, start_program):
 
  predict = mlp(input)
 
- cost = layers.cross_entropy(input=predict, label=label)
+ cost = paddle.nn.functional.cross_entropy(
+ input=predict, label=label, reduction='none', use_softmax=False
+ )
  avg_cost = paddle.mean(x=cost)
 
  return avg_cost, train_program, start_program

python/paddle/fluid/tests/unittests/collective/fleet/dist_mnist_gradient_merge.py

@@ -34,7 +34,9 @@ def get_model(self, batch_size=2):
 
  # Train program
  predict = cnn_model(images)
- cost = fluid.layers.cross_entropy(input=predict, label=label)
+ cost = paddle.nn.functional.cross_entropy(
+ input=predict, label=label, reduction='none', use_softmax=False
+ )
  avg_cost = paddle.mean(x=cost)
 
  # Evaluator

python/paddle/fluid/tests/unittests/collective/fleet/parallel_dygraph_se_resnext.py

@@ -350,7 +350,9 @@ def run_one_loop(self, model, opt, data):
 
  out = model(img)
  softmax_out = paddle.nn.functional.softmax(out, use_cudnn=False)
- loss = fluid.layers.cross_entropy(input=softmax_out, label=label)
+ loss = paddle.nn.functional.cross_entropy(
+ input=softmax_out, label=label, reduction='none', use_softmax=False
+ )
  avg_loss = paddle.mean(x=loss)
  return avg_loss
 

python/paddle/fluid/tests/unittests/collective/fleet/pipeline_mnist.py

@@ -100,7 +100,9 @@ def get_model(self, batch_size=2, use_dgc=False, dist_strategy=None):
  # Train program
  predict = cnn_model(images)
  with fluid.device_guard("gpu:1"):
- cost = fluid.layers.cross_entropy(input=predict, label=label)
+ cost = paddle.nn.functional.cross_entropy(
+ input=predict, label=label, reduction='none', use_softmax=False
+ )
  avg_cost = paddle.mean(x=cost)
 
  # Evaluator

python/paddle/fluid/tests/unittests/collective/fleet/pipeline_mnist_multi_device.py

@@ -100,7 +100,9 @@ def get_model(self, batch_size=2, use_dgc=False, dist_strategy=None):
  # Train program
  predict = cnn_model(images)
  with fluid.device_guard("gpu:1"):
- cost = fluid.layers.cross_entropy(input=predict, label=label)
+ cost = paddle.nn.functional.cross_entropy(
+ input=predict, label=label, reduction='none', use_softmax=False
+ )
  avg_cost = paddle.mean(x=cost)
 
  # Evaluator

python/paddle/fluid/tests/unittests/collective/fleet/pipeline_mnist_one_device.py

@@ -92,7 +92,9 @@ def get_model(self, batch_size=2, use_dgc=False, dist_strategy=None):
  # Train program
  predict = cnn_model(images)
  with fluid.device_guard("gpu:0"):
- cost = fluid.layers.cross_entropy(input=predict, label=label)
+ cost = paddle.nn.functional.cross_entropy(
+ input=predict, label=label, reduction='none', use_softmax=False
+ )
  avg_cost = paddle.mean(x=cost)
 
  # Evaluator

python/paddle/fluid/tests/unittests/collective/fleet/test_fleet_checkpoint.py

@@ -41,7 +41,9 @@ def _test_checkpoint(self, fs, dir_path):
  feed_list=[image, label], place=fluid.CPUPlace()
  )
  predict = fluid.layers.fc(input=image, size=10, act='softmax')
- loss = fluid.layers.cross_entropy(input=predict, label=label)
+ loss = paddle.nn.functional.cross_entropy(
+ input=predict, label=label, reduction='none', use_softmax=False
+ )
  avg_loss = paddle.mean(loss)
  optimizer = fluid.optimizer.AdamOptimizer(learning_rate=0.001)
 

python/paddle/fluid/tests/unittests/collective/fleet/test_imperative_auto_mixed_precision.py

@@ -1308,7 +1308,9 @@ def train_resnet(self, enable_amp=True, level='O1'):
  ):
  out = resnet(img)
 
- loss = fluid.layers.cross_entropy(input=out, label=label)
+ loss = paddle.nn.functional.cross_entropy(
+ input=out, label=label, reduction='none', use_softmax=False
+ )
  avg_loss = paddle.mean(x=loss)
 
  dy_out = avg_loss.numpy()

python/paddle/fluid/tests/unittests/collective/fleet/test_imperative_auto_mixed_precision_for_eager.py

@@ -1298,7 +1298,9 @@ def train_resnet(self, enable_amp=True, level='O1'):
  ):
  out = resnet(img)
 
- loss = fluid.layers.cross_entropy(input=out, label=label)
+ loss = paddle.nn.functional.cross_entropy(
+ input=out, label=label, reduction='none', use_softmax=False
+ )
  avg_loss = paddle.mean(x=loss)
 
  dy_out = avg_loss.numpy()

python/paddle/fluid/tests/unittests/ir/inference/test_trt_conv_quant_dequant_pass.py

@@ -53,7 +53,12 @@ def network():
  elif self.conv_groups == 4:
  cout = paddle.reshape(conv_out, shape=[1, 1, 10816])
  result = fluid.layers.relu(cout)
- loss = fluid.layers.cross_entropy(input=result, label=label_shape)
+ loss = paddle.nn.functional.cross_entropy(
+ input=result,
+ label=label_shape,
+ reduction='none',
+ use_softmax=False,
+ )
  avg_loss = paddle.mean(loss)
  return avg_loss, result
 
@@ -156,7 +161,12 @@ def network():
  )
  cout = paddle.reshape(conv_out, shape=[1, 1, 10816])
  result = fluid.layers.relu(cout)
- loss = fluid.layers.cross_entropy(input=result, label=label_shape)
+ loss = paddle.nn.functional.cross_entropy(
+ input=result,
+ label=label_shape,
+ reduction='none',
+ use_softmax=False,
+ )
  avg_loss = paddle.mean(loss)
  return avg_loss, result
 
@@ -257,7 +267,12 @@ def network():
  elif self.conv_groups == 4:
  cout = paddle.reshape(conv_out, shape=[1, 1, 10816])
  result = fluid.layers.relu(cout)
- loss = fluid.layers.cross_entropy(input=result, label=label_shape)
+ loss = paddle.nn.functional.cross_entropy(
+ input=result,
+ label=label_shape,
+ reduction='none',
+ use_softmax=False,
+ )
  avg_loss = paddle.mean(loss)
  return avg_loss, result