File size: 1,884 Bytes
ba44f26 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | import torch
from transformers import PreTrainedModel
from typing import OrderedDict
from .configuration_lenet import LeNetConfig
class LeNet(torch.nn.Module):
def __init__(self):
super().__init__()
self.model = torch.nn.Sequential(
OrderedDict(
[
("conv1", torch.nn.Conv2d(1, 6, 5, padding=2)),
("pooling1", torch.nn.AvgPool2d(2, stride=2)),
("sigmoid1", torch.nn.Sigmoid()),
("conv2", torch.nn.Conv2d(6, 16, 5)),
("pooling2", torch.nn.AvgPool2d(2, stride=2)),
("sigmoid1", torch.nn.Sigmoid()),
("flatten", torch.nn.Flatten()),
("dense1", torch.nn.Linear(400, 120)),
("sigmoid3", torch.nn.Sigmoid()),
("dense2", torch.nn.Linear(120, 84)),
("sigmoid4", torch.nn.Sigmoid()),
("dense3", torch.nn.Linear(84, 10)),
]
)
)
def forward(self, pixel_values) -> torch.Tensor:
return self.model(pixel_values)
class LeNetModel(PreTrainedModel):
config_class = LeNetConfig
def __init__(self, config):
super().__init__(config)
self.model = LeNet()
def forward(self, pixel_values):
return self.model.forward_features(pixel_values)
class LeNetModelForImageClassification(PreTrainedModel):
config_class = LeNetConfig
def __init__(self, config):
super().__init__(config)
self.model = LeNet()
def forward(self, pixel_values, labels=None):
logits = self.model(pixel_values)
if labels is not None:
loss = torch.nn.functional.cross_entropy(logits, labels)
return {"loss": loss, "logits": logits}
return {"logits": logits}
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