paddlers/tests/rs_models/test_model.py

# Copyright (c) 2022 PaddlePaddle 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 paddle
import numpy as np

from test_utils import CommonTest


class TestModel(CommonTest):
    MODEL_CLASS = None
    DEFAULT_HW = (256, 256)
    DEFAULT_BATCH_SIZE = 2

    def setUp(self):
        self.set_specs()
        self.set_inputs()
        self.set_targets()
        self.set_models()

    def test_forward(self):
        for i, (input, model, target
                ) in enumerate(zip(self.inputs, self.models, self.targets)):
            with self.subTest(i=i):
                output = model(input)
                self.check_output(output, target)

    def check_output(self, output, target):
        pass

    def set_specs(self):
        self.specs = []

    def set_models(self):
        self.models = (self.build_model(spec) for spec in self.specs)

    def set_inputs(self):
        self.inputs = []

    def set_targets(self):
        self.targets = []

    def build_model(self, spec):
        if '_phase' in spec:
            phase = spec.pop('_phase')
        else:
            phase = 'train'
        if '_stop_grad' in spec:
            stop_grad = spec.pop('_stop_grad')
        else:
            stop_grad = False

        model = self.MODEL_CLASS(**spec)

        if phase == 'train':
            model.train()
        elif phase == 'eval':
            model.eval()
            if stop_grad:
                for p in model.parameters():
                    p.stop_gradient = True

        return model

    def get_shape(self, c, b=None, h=None, w=None):
        if h is None or w is None:
            h, w = self.DEFAULT_HW
        if b is None:
            b = self.DEFAULT_BATCH_SIZE
        return (b, c, h, w)

    def get_zeros_array(self, c, b=None, h=None, w=None):
        shape = self.get_shape(c, b, h, w)
        return np.zeros(shape)

    def get_randn_tensor(self, c, b=None, h=None, w=None):
        shape = self.get_shape(c, b, h, w)
        return paddle.randn(shape)