class Pooling(nn.Module): def __init__(self, pool_block): super(Pooling, self).__init__() self.pool_block = pool_block def forward(self, cls_token, x, padding_mask=None): """Perform Pooling module. Args: cls_token: A `float` tensor of shape `[batch_size, 1, channels]` x:...
features.shape[0] 46 changes: 46 additions & 0 deletions 46 autoencoder/model.py Original file line numberDiff line numberDiff line change @@ -0,0 +1,46 @@ import torch import torch.nn as nn class Autoencoder(nn.Module): def __init__(self, encoder_hidden_dims, decoder_hidden_...
public final class CosmosContainerProactiveInitConfigBuilder A builder to build CosmosContainerProactiveInitConfig Constructor Summary 展開表格 ConstructorDescription CosmosContainerProactiveInitConfigBuilder(List<CosmosContainerIdentity> cosmosContainerIdentities) Instantiates CosmosContainerProactiveInitConfigBuilder ...
I tired to create the tflite model from the code you provided but i ran into this error and no tflite model was created. ValueError Traceback (most recent call last) Cell In[1], line 9 5 output_shape = [1, 21] 7 tf_input = keras.Input(input_shape[1:], batch_size=input_shape...
class FAST_LIO_SAM_QN_CLASS { private: /// basic params string m_map_frame; /// shared data - odom and pcd mutex m_realtime_pose_mutex, m_keyframes_mutex; mutex m_graph_mutex, m_vis_mutex; bool m_init=false; int m_current_keyframe_idx = 0; pose_pcd m_current_frame; vector<...
pilot_class.h player.h player_external.h player_external_struct.h postrender.cpp postrender.h powerup.h procedurals.cpp procedurals.h program.cpp program.h render.cpp render.h renderobject.cpp renderobject.h resource.h robot.h robotfire.cpp robotfire.h robotfirestruct.h robotfirestr...
return model, _transform(model.visual.input_resolution)# patch the device names device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[]) device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)]...
from baselinemodel.algorithm import Algorithm class Singleton(object): _instance_lock = threading.Lock() def __init__(self, cls): self._cls = cls self._instance = {} def __call__(self): if self._cls not in self._instance: with Singleton._instance_lock: if self._cls not in self...
mask vit: your config + mmseg pth:RuntimeError: Expected all tensors to be on the same device... mask vit: your config + my pth:RuntimeError: Expected all tensors to be on the same device... My env: mmseg: 0.24.1 MMDeploy: 0.6.0 MMCV: 1.6.0 PyTorch: 1.12.0a0+2c916ef.nv...
"MultiClassNonMaxSuppression" "/Gather/Gather_2:0"), "Resized_shape": ("Preprocessor/map/while" "/ResizeToRange/stack_1:0"), } for key, tensor_name in key_tensor_map.items(): if tensor_name in all_tensor_names: tensor_dict[tensor_name] = ( tf.compat.v1.get_default_graph() ....