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import torch |
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import torch.nn as nn |
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class Slice(nn.Module): |
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def __init__(self, start_index=1): |
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super(Slice, self).__init__() |
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self.start_index = start_index |
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def forward(self, x): |
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return x[:, self.start_index:] |
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class AddReadout(nn.Module): |
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def __init__(self, start_index=1): |
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super(AddReadout, self).__init__() |
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self.start_index = start_index |
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def forward(self, x): |
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if self.start_index == 2: |
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readout = (x[:, 0] + x[:, 1]) / 2 |
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else: |
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readout = x[:, 0] |
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return x[:, self.start_index:] + readout.unsqueeze(1) |
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class ProjectReadout(nn.Module): |
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def __init__(self, in_features, start_index=1): |
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super(ProjectReadout, self).__init__() |
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self.start_index = start_index |
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self.project = nn.Sequential(nn.Linear(2 * in_features, in_features), nn.GELU()) |
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def forward(self, x): |
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readout = x[:, 0].unsqueeze(1).expand_as(x[:, self.start_index:]) |
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features = torch.cat((x[:, self.start_index:], readout), -1) |
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return self.project(features) |
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class Transpose(nn.Module): |
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def __init__(self, dim0, dim1): |
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super(Transpose, self).__init__() |
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self.dim0 = dim0 |
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self.dim1 = dim1 |
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def forward(self, x): |
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x = x.transpose(self.dim0, self.dim1) |
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return x |
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activations = {} |
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def get_activation(name): |
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def hook(model, input, output): |
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activations[name] = output |
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return hook |
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def forward_default(pretrained, x, function_name="forward_features"): |
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exec(f"pretrained.model.{function_name}(x)") |
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layer_1 = pretrained.activations["1"] |
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layer_2 = pretrained.activations["2"] |
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layer_3 = pretrained.activations["3"] |
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layer_4 = pretrained.activations["4"] |
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if hasattr(pretrained, "act_postprocess1"): |
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layer_1 = pretrained.act_postprocess1(layer_1) |
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if hasattr(pretrained, "act_postprocess2"): |
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layer_2 = pretrained.act_postprocess2(layer_2) |
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if hasattr(pretrained, "act_postprocess3"): |
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layer_3 = pretrained.act_postprocess3(layer_3) |
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if hasattr(pretrained, "act_postprocess4"): |
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layer_4 = pretrained.act_postprocess4(layer_4) |
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return layer_1, layer_2, layer_3, layer_4 |
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def forward_adapted_unflatten(pretrained, x, function_name="forward_features"): |
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b, c, h, w = x.shape |
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exec(f"glob = pretrained.model.{function_name}(x)") |
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layer_1 = pretrained.activations["1"] |
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layer_2 = pretrained.activations["2"] |
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layer_3 = pretrained.activations["3"] |
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layer_4 = pretrained.activations["4"] |
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layer_1 = pretrained.act_postprocess1[0:2](layer_1) |
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layer_2 = pretrained.act_postprocess2[0:2](layer_2) |
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layer_3 = pretrained.act_postprocess3[0:2](layer_3) |
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layer_4 = pretrained.act_postprocess4[0:2](layer_4) |
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unflatten = nn.Sequential( |
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nn.Unflatten( |
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2, |
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torch.Size( |
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[ |
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h // pretrained.model.patch_size[1], |
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w // pretrained.model.patch_size[0], |
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] |
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), |
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) |
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) |
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if layer_1.ndim == 3: |
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layer_1 = unflatten(layer_1) |
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if layer_2.ndim == 3: |
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layer_2 = unflatten(layer_2) |
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if layer_3.ndim == 3: |
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layer_3 = unflatten(layer_3) |
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if layer_4.ndim == 3: |
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layer_4 = unflatten(layer_4) |
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layer_1 = pretrained.act_postprocess1[3: len(pretrained.act_postprocess1)](layer_1) |
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layer_2 = pretrained.act_postprocess2[3: len(pretrained.act_postprocess2)](layer_2) |
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layer_3 = pretrained.act_postprocess3[3: len(pretrained.act_postprocess3)](layer_3) |
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layer_4 = pretrained.act_postprocess4[3: len(pretrained.act_postprocess4)](layer_4) |
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return layer_1, layer_2, layer_3, layer_4 |
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def get_readout_oper(vit_features, features, use_readout, start_index=1): |
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if use_readout == "ignore": |
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readout_oper = [Slice(start_index)] * len(features) |
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elif use_readout == "add": |
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readout_oper = [AddReadout(start_index)] * len(features) |
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elif use_readout == "project": |
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readout_oper = [ |
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ProjectReadout(vit_features, start_index) for out_feat in features |
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] |
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else: |
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assert ( |
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False |
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), "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'" |
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return readout_oper |
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def make_backbone_default( |
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model, |
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features=[96, 192, 384, 768], |
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size=[384, 384], |
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hooks=[2, 5, 8, 11], |
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vit_features=768, |
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use_readout="ignore", |
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start_index=1, |
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start_index_readout=1, |
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): |
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pretrained = nn.Module() |
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pretrained.model = model |
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pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1")) |
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pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2")) |
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pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3")) |
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pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4")) |
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pretrained.activations = activations |
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readout_oper = get_readout_oper(vit_features, features, use_readout, start_index_readout) |
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pretrained.act_postprocess1 = nn.Sequential( |
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readout_oper[0], |
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Transpose(1, 2), |
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nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), |
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nn.Conv2d( |
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in_channels=vit_features, |
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out_channels=features[0], |
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kernel_size=1, |
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stride=1, |
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padding=0, |
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), |
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nn.ConvTranspose2d( |
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in_channels=features[0], |
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out_channels=features[0], |
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kernel_size=4, |
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stride=4, |
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padding=0, |
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bias=True, |
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dilation=1, |
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groups=1, |
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), |
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) |
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pretrained.act_postprocess2 = nn.Sequential( |
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readout_oper[1], |
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Transpose(1, 2), |
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nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), |
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nn.Conv2d( |
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in_channels=vit_features, |
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out_channels=features[1], |
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kernel_size=1, |
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stride=1, |
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padding=0, |
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), |
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nn.ConvTranspose2d( |
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in_channels=features[1], |
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out_channels=features[1], |
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kernel_size=2, |
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stride=2, |
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padding=0, |
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bias=True, |
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dilation=1, |
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groups=1, |
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), |
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) |
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pretrained.act_postprocess3 = nn.Sequential( |
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readout_oper[2], |
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Transpose(1, 2), |
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nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), |
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nn.Conv2d( |
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in_channels=vit_features, |
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out_channels=features[2], |
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kernel_size=1, |
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stride=1, |
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padding=0, |
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), |
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) |
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pretrained.act_postprocess4 = nn.Sequential( |
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readout_oper[3], |
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Transpose(1, 2), |
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nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), |
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nn.Conv2d( |
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in_channels=vit_features, |
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out_channels=features[3], |
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kernel_size=1, |
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stride=1, |
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padding=0, |
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), |
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nn.Conv2d( |
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in_channels=features[3], |
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out_channels=features[3], |
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kernel_size=3, |
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stride=2, |
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padding=1, |
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), |
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) |
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pretrained.model.start_index = start_index |
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pretrained.model.patch_size = [16, 16] |
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return pretrained |
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