(pretrained, x, function_name="forward_features")
| 61 | |
| 62 | |
| 63 | def forward_default(pretrained, x, function_name="forward_features"): |
| 64 | exec(f"pretrained.model.{function_name}(x)") |
| 65 | |
| 66 | layer_1 = pretrained.activations["1"] |
| 67 | layer_2 = pretrained.activations["2"] |
| 68 | layer_3 = pretrained.activations["3"] |
| 69 | layer_4 = pretrained.activations["4"] |
| 70 | |
| 71 | if hasattr(pretrained, "act_postprocess1"): |
| 72 | layer_1 = pretrained.act_postprocess1(layer_1) |
| 73 | if hasattr(pretrained, "act_postprocess2"): |
| 74 | layer_2 = pretrained.act_postprocess2(layer_2) |
| 75 | if hasattr(pretrained, "act_postprocess3"): |
| 76 | layer_3 = pretrained.act_postprocess3(layer_3) |
| 77 | if hasattr(pretrained, "act_postprocess4"): |
| 78 | layer_4 = pretrained.act_postprocess4(layer_4) |
| 79 | |
| 80 | return layer_1, layer_2, layer_3, layer_4 |
| 81 | |
| 82 | |
| 83 | def forward_adapted_unflatten(pretrained, x, function_name="forward_features"): |
no outgoing calls
no test coverage detected