fix flops api (#31081)

* remove PrettyTable dependence from paddle.flops

* fix bug in python2.7

* fix flops

* fix flops

* fix bug

* fix bug

python/paddle/hapi/dynamic_flops.py

@@ -121,7 +121,7 @@ def count_convNd(m, x, y):
     bias_ops = 1 if m.bias is not None else 0
     total_ops = int(y.numel()) * (
         x.shape[1] / m._groups * kernel_ops + bias_ops)
-    m.total_ops += total_ops
+    m.total_ops += abs(int(total_ops))
 
 
 def count_leaky_relu(m, x, y):
@@ -135,15 +135,14 @@ def count_bn(m, x, y):
     nelements = x.numel()
     if not m.training:
         total_ops = 2 * nelements
-
-    m.total_ops += int(total_ops)
+    m.total_ops += abs(int(total_ops))
 
 
 def count_linear(m, x, y):
     total_mul = m.weight.shape[0]
     num_elements = y.numel()
     total_ops = total_mul * num_elements
-    m.total_ops += int(total_ops)
+    m.total_ops += abs(int(total_ops))
 
 
 def count_avgpool(m, x, y):
@@ -161,8 +160,7 @@ def count_adap_avgpool(m, x, y):
     kernel_ops = total_add + total_div
     num_elements = y.numel()
     total_ops = kernel_ops * num_elements
-
-    m.total_ops += int(total_ops)
+    m.total_ops += abs(int(total_ops))
 
 
 def count_zero_ops(m, x, y):
@@ -173,7 +171,7 @@ def count_parameters(m, x, y):
     total_params = 0
     for p in m.parameters():
         total_params += p.numel()
-    m.total_params[0] = int(total_params)
+    m.total_params[0] = abs(int(total_params))
 
 
 def count_io_info(m, x, y):

python/paddle/hapi/static_flops.py

@@ -127,6 +127,7 @@ def count_convNd(op):
     bias_ops = 1 if len(op.inputs("Bias")) > 0 else 0
     output_numel = np.product(op.outputs("Output")[0].shape()[1:])
     total_ops = output_numel * (filter_ops + bias_ops)
+    total_ops = abs(total_ops)
     return total_ops
 
 
@@ -138,6 +139,7 @@ def count_leaky_relu(op):
 def count_bn(op):
     output_numel = np.product(op.outputs("Y")[0].shape()[1:])
     total_ops = 2 * output_numel
+    total_ops = abs(total_ops)
     return total_ops
 
 
@@ -145,6 +147,7 @@ def count_linear(op):
     total_mul = op.inputs("Y")[0].shape()[0]
     numel = np.product(op.outputs("Out")[0].shape()[1:])
     total_ops = total_mul * numel
+    total_ops = abs(total_ops)
     return total_ops
 
 
@@ -157,12 +160,14 @@ def count_pool2d(op):
     kernel_ops = total_add + total_div
     num_elements = np.product(output_shape[1:])
     total_ops = kernel_ops * num_elements
+    total_ops = abs(total_ops)
     return total_ops
 
 
 def count_element_op(op):
     input_shape = op.inputs("X")[0].shape()
     total_ops = np.product(input_shape[1:])
+    total_ops = abs(total_ops)
     return total_ops