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# coding=utf-8
# Copyright 2022 The IDEA Authors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from detrex.layers import FFN
from utils import MLP
def test_ffn_output():
embed_dim = 256
feedforward_dim = 256
output_dim = 4
num_fcs = 3
mlp_layer = MLP(
input_dim=embed_dim, hidden_dim=feedforward_dim, output_dim=output_dim, num_layers=num_fcs
)
ffn_layer = FFN(
embed_dim=embed_dim,
feedforward_dim=feedforward_dim,
output_dim=output_dim,
num_fcs=num_fcs,
add_identity=False,
)
# transfer weight
ffn_layer.layers[0][0].weight = mlp_layer.layers[0].weight
ffn_layer.layers[0][0].bias = mlp_layer.layers[0].bias
ffn_layer.layers[1][0].weight = mlp_layer.layers[1].weight
ffn_layer.layers[1][0].bias = mlp_layer.layers[1].bias
ffn_layer.layers[2].weight = mlp_layer.layers[2].weight
ffn_layer.layers[2].bias = mlp_layer.layers[2].bias
# test output
x = torch.randn(16, 256)
mlp_output = mlp_layer(x)
ffn_output = ffn_layer(x)
torch.allclose(mlp_output.sum(), ffn_output.sum())
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