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120 lines
3.8 KiB
120 lines
3.8 KiB
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. |
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# |
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# Licensed under the Apache License, Version 2.0 (the "License"); |
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# you may not use this file except in compliance with the License. |
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# You may obtain a copy of the License at |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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import inspect |
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import paddle |
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import numpy as np |
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from paddle.static import InputSpec |
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from testing_utils import CommonTest |
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class _TestModelNamespace: |
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class TestModel(CommonTest): |
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MODEL_CLASS = None |
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DEFAULT_HW = (256, 256) |
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DEFAULT_BATCH_SIZE = 2 |
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def setUp(self): |
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self.set_specs() |
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self.set_inputs() |
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self.set_targets() |
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self.set_models() |
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def test_forward(self): |
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for i, ( |
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input, model, target |
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) in enumerate(zip(self.inputs, self.models, self.targets)): |
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with self.subTest(i=i): |
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if isinstance(input, list): |
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output = model(*input) |
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else: |
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output = model(input) |
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self.check_output(output, target) |
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def test_to_static(self): |
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for i, ( |
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input, model, target |
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) in enumerate(zip(self.inputs, self.models, self.targets)): |
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with self.subTest(i=i): |
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static_model = paddle.jit.to_static( |
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model, input_spec=self.get_input_spec(model, input)) |
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def check_output(self, output, target): |
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pass |
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def set_specs(self): |
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self.specs = [] |
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def set_models(self): |
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self.models = (self.build_model(spec) for spec in self.specs) |
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def set_inputs(self): |
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self.inputs = [] |
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def set_targets(self): |
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self.targets = [] |
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def build_model(self, spec): |
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if '_phase' in spec: |
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phase = spec.pop('_phase') |
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else: |
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phase = 'train' |
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if '_stop_grad' in spec: |
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stop_grad = spec.pop('_stop_grad') |
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else: |
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stop_grad = False |
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model = self.MODEL_CLASS(**spec) |
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if phase == 'train': |
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model.train() |
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elif phase == 'eval': |
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model.eval() |
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if stop_grad: |
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for p in model.parameters(): |
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p.stop_gradient = True |
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return model |
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def get_shape(self, c, b=None, h=None, w=None): |
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if h is None or w is None: |
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h, w = self.DEFAULT_HW |
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if b is None: |
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b = self.DEFAULT_BATCH_SIZE |
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return (b, c, h, w) |
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def get_zeros_array(self, c, b=None, h=None, w=None): |
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shape = self.get_shape(c, b, h, w) |
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return np.zeros(shape) |
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def get_randn_tensor(self, c, b=None, h=None, w=None): |
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shape = self.get_shape(c, b, h, w) |
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return paddle.randn(shape) |
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def get_input_spec(self, model, input): |
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if not isinstance(input, list): |
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input = [input] |
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input_spec = [] |
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for param_name, tensor in zip( |
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inspect.signature(model.forward).parameters, input): |
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# XXX: Hard-code dtype |
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input_spec.append( |
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InputSpec( |
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shape=tensor.shape, name=param_name, dtype='float32')) |
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return input_spec |
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TestModel = _TestModelNamespace.TestModel
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