def dygraph2program(layer,
inputs,
feed_prefix='feed_',
fetch_prefix='fetch_',
tmp_prefix='t_',
extract_inputs_fn=None,
extract_outputs_fn=None,
dtypes=None):
print(type(layer))
assert isinstance(layer, Layer)
extract_inputs_fn = extract_inputs_fn if extract_inputs_fn is not None else extract_vars
extract_outputs_fn = extract_outputs_fn if extract_outputs_fn is not None else extract_vars
if os.environ.get("FLAGS_enable_eager_mode") == "1":
return _dy2prog(layer, inputs, feed_prefix, fetch_prefix, tmp_prefix,
extract_inputs_fn, extract_outputs_fn, dtypes)
tracer = _dygraph_tracer()._get_program_desc_tracer()
with program_desc_tracing_guard(True):
if _is_shape(inputs):
shapes = [inputs]
inputs = _create_tensors(shapes, dtypes=dtypes)
input_var_list = inputs
elif _is_shapes(inputs):
inputs = _create_tensors(inputs, dtypes=dtypes)
input_var_list = inputs
else:
inputs = to_variables(inputs)
input_var_list = extract_inputs_fn(inputs)
original_outputs = layer(*inputs)
# 'original_outputs' may be dict, so we should convert it to list of varibles.
# And should not create new varibles in 'extract_vars'.
out_var_list = extract_outputs_fn(original_outputs)
program_desc, feed_names, fetch_names, parameters = tracer.create_program_desc(
input_var_list, feed_prefix, out_var_list, fetch_prefix,
tmp_prefix)
tracer.reset()
with _dygraph_guard(None):
program = Program()
program.desc = program_desc
program.blocks = [Block(program, 0)]
program._sync_with_cpp()
return program
def create_program_from_desc(program_desc):
program = Program()
program.desc = program_desc
program.blocks = [Block(program, 0)]
program._sync_with_cpp()
return program