mirror of
https://github.com/storytold/storyteller-ml.git
synced 2026-10-17 00:23:14 +00:00
Brandon Thomas
115 lines
3.3 KiB
Python
115 lines
3.3 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (c) 2020, NVIDIA CORPORATION. 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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from typing import Optional
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from torch import nn
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from nemo.core.classes import NeuralModule, typecheck
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from nemo.core.config import Config
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from nemo.core.neural_types import AxisKind, AxisType, ImageValue, LogprobsType, NeuralType
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class LeNet5(NeuralModule):
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"""
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Classical LeNet-5 model for MNIST image classification.
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"""
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def __init__(self, cfg: Config = Config()):
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"""
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Creates the LeNet-5 model.
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Args:
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cfg: Default NeMo config containing name.
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"""
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# Call the base class constructor.
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super().__init__() # name=name)
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# Create the LeNet-5 model.
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self.model = nn.Sequential(
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nn.Conv2d(1, 6, kernel_size=(5, 5)),
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nn.ReLU(),
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nn.MaxPool2d(kernel_size=(2, 2), stride=2),
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nn.Conv2d(6, 16, kernel_size=(5, 5)),
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nn.ReLU(),
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nn.MaxPool2d(kernel_size=(2, 2), stride=2),
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nn.Conv2d(16, 120, kernel_size=(5, 5)),
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nn.ReLU(),
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# reshape to [-1, 120]
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nn.Flatten(),
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nn.Linear(120, 84),
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nn.ReLU(),
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nn.Linear(84, 10),
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nn.LogSoftmax(dim=1),
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)
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@property
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def input_types(self):
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""" Returns definitions of module input ports. """
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return {
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"images": NeuralType(
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axes=(
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AxisType(kind=AxisKind.Batch),
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AxisType(kind=AxisKind.Channel, size=1),
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AxisType(kind=AxisKind.Height, size=32),
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AxisType(kind=AxisKind.Width, size=32),
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),
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elements_type=ImageValue(),
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)
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}
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@property
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def output_types(self):
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""" Returns definitions of module output ports. """
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return {
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"predictions": NeuralType(
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axes=(AxisType(kind=AxisKind.Batch), AxisType(kind=AxisKind.Dimension)), elements_type=LogprobsType()
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)
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}
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@typecheck()
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def forward(self, images):
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"""
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Performs the forward step of the LeNet-5 model.
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Args:
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images: Batch of images to be classified.
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Returns:
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Batch of predictions.
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"""
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predictions = self.model(images)
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return predictions
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def save_to(self, save_path: str):
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"""Not implemented yet.
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Serialize model.
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Args:
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save_path (str): path to save serialization.
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"""
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pass
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@classmethod
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def restore_from(cls, restore_path: str):
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""" Not implemented yet.
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Restore module from serialization.
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Args:
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restore_path (str): path to serialization
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"""
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pass
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