| 546 | def test_repr_basic(): |
| 547 | # test whether __repr__ can output correct information |
| 548 | class ConvModel(Module): |
| 549 | def __init__(self): |
| 550 | super().__init__() |
| 551 | self.conv1 = Conv2d(3, 128, 3, padding=1, bias=False) |
| 552 | self.conv2 = Conv2d(3, 128, 3, dilation=2, bias=False) |
| 553 | self.bn1 = BatchNorm1d(128) |
| 554 | self.bn2 = BatchNorm2d(128) |
| 555 | self.pooling = MaxPool2d(kernel_size=2, padding=0) |
| 556 | modules = OrderedDict() |
| 557 | modules["depthwise"] = Conv2d(256, 256, 3, 1, 0, groups=256, bias=False,) |
| 558 | modules["pointwise"] = Conv2d( |
| 559 | 256, 256, kernel_size=1, stride=1, padding=0, bias=True, |
| 560 | ) |
| 561 | self.submodule1 = Sequential(modules) |
| 562 | self.list1 = [Dropout(drop_prob=0.1), [Softmax(axis=100)]] |
| 563 | self.tuple1 = ( |
| 564 | Dropout(drop_prob=0.1), |
| 565 | (Softmax(axis=100), Dropout(drop_prob=0.2)), |
| 566 | ) |
| 567 | self.dict1 = {"Dropout": Dropout(drop_prob=0.1)} |
| 568 | self.fc1 = Linear(512, 1024) |
| 569 | |
| 570 | def forward(self, inputs): |
| 571 | pass |
| 572 | |
| 573 | ground_truth = ( |
| 574 | "ConvModel(\n" |
no outgoing calls