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if self.disable_custom_kernels:
# PyTorch implementation
output = multi_scale_deformable_attention(
value, spatial_shapes_list, sampling_locations, attention_weights
)
else:
try:
# custom kernel
output = MultiScaleDe... | 3,389 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
class OmDetTurboConvNormLayer(nn.Module):
def __init__(self, config, in_channels, out_channels, kernel_size, stride, padding=None, activation=None):
super().__init__()
self.conv = nn.Conv2d(
in_channels,
out_channels,
kernel_size,
stride,
p... | 3,390 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
class OmDetTurboRepVggBlock(nn.Module):
"""
RepVGG architecture block introduced by the work "RepVGG: Making VGG-style ConvNets Great Again".
"""
def __init__(self, config: OmDetTurboConfig):
super().__init__()
activation = config.csp_activation
hidden_channels = int(config.enc... | 3,391 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
class OmDetTurboCSPRepLayer(nn.Module):
"""
Cross Stage Partial (CSP) network layer with RepVGG blocks.
"""
def __init__(self, config: OmDetTurboConfig):
super().__init__()
in_channels = config.encoder_hidden_dim * 2
out_channels = config.encoder_hidden_dim
num_blocks =... | 3,392 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
def forward(self, hidden_state):
device = hidden_state.device
hidden_state_1 = self.conv1(hidden_state)
hidden_state_1 = self.bottlenecks(hidden_state_1).to(device)
hidden_state_2 = self.conv2(hidden_state).to(device)
return self.conv3(hidden_state_1 + hidden_state_2) | 3,392 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
class OmDetTurboMultiheadAttention(nn.Module):
"""Equivalent implementation of nn.MultiheadAttention with `batch_first=True`."""
def __init__(self, config, hidden_size, num_attention_heads, dropout):
super().__init__()
if hidden_size % num_attention_heads != 0:
raise ValueError(
... | 3,393 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
queries: torch.Tensor,
keys: torch.Tensor,
... | 3,393 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
if attention_mask is not None:
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
#... | 3,393 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
class OmDetTurboEncoderLayer(nn.Module):
def __init__(self, config: OmDetTurboConfig):
super().__init__()
self.self_attn = OmDetTurboMultiheadAttention(
config,
hidden_size=config.encoder_hidden_dim,
num_attention_heads=config.num_attention_heads,
drop... | 3,394 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
@staticmethod
def with_pos_embed(tensor, pos_embed):
return tensor if pos_embed is None else tensor + pos_embed | 3,394 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
position_embeddings: torch.Tensor = None,
output_attentions: bool = False,
):
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len,... | 3,394 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
query = key = self.with_pos_embed(hidden_states, position_embeddings) | 3,394 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
hidden_states = self.self_attn(
queries=query,
keys=key,
values=hidden_states,
attention_mask=attention_mask,
output_attentions=output_attentions,
)
hidden_states, attentions = hidden_states if output_attentions else (hidden_states[0], None)
... | 3,394 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) | 3,394 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
if output_attentions:
return hidden_states, attentions
return (hidden_states,) | 3,394 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
class OmDetTurboEncoder(nn.Module):
def __init__(self, config: OmDetTurboConfig):
super().__init__()
self.layers = nn.ModuleList([OmDetTurboEncoderLayer(config) for _ in range(config.encoder_layers)])
def forward(
self, src, src_mask=None, pos_embed=None, output_attentions: bool = Fals... | 3,395 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
class OmDetTurboHybridEncoder(nn.Module):
"""
Encoder consisting of channel projection layers, a set of `OmDetTurboEncoder`, a top-down Feature Pyramid Network
(FPN) and a bottom-up Path Aggregation Network (PAN). More details on the paper: https://arxiv.org/abs/2304.08069
Args:
config: OmDetTu... | 3,396 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
self.channel_projection_layers = nn.ModuleList()
for in_channel in self.in_channels:
self.channel_projection_layers.append(
nn.Sequential(
nn.Conv2d(in_channel, self.encoder_hidden_dim, kernel_size=(1, 1), bias=False),
nn.BatchNorm2d(self.encod... | 3,396 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
# encoder transformer
self.encoder = nn.ModuleList([OmDetTurboEncoder(config) for _ in range(len(self.encoder_projection_indices))])
# top-down fpn
self.lateral_convs = nn.ModuleList()
self.fpn_blocks = nn.ModuleList()
for _ in range(len(self.in_channels) - 1, 0, -1):
... | 3,396 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
# bottom-up pan
self.downsample_convs = nn.ModuleList()
self.pan_blocks = nn.ModuleList()
for _ in range(len(self.in_channels) - 1):
self.downsample_convs.append(
OmDetTurboConvNormLayer(
config,
in_channels=self.encoder_hidden_... | 3,396 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
@staticmethod
def build_2d_sincos_position_embedding(
width, height, embed_dim=256, temperature=10000.0, device="cpu", dtype=torch.float32
):
grid_w = torch.arange(int(width), dtype=dtype, device=device)
grid_h = torch.arange(int(height), dtype=dtype, device=device)
grid_w, grid_... | 3,396 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
def forward(
self,
inputs_embeddings=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
Args:
inputs_embeddings (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Flattened f... | 3,396 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None els... | 3,396 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
hidden_states = inputs_embeddings | 3,396 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
# get projection features
projected_features = [self.channel_projection_layers[i](feature) for i, feature in enumerate(hidden_states)]
# encoder
for encoder_layer_index, featu... | 3,396 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
self.encoder_hidden_dim,
self.positional_encoding_temperature,
device=src_flatten.device,
dtype=src_flatten.dtype,
).to(src_flatten.device, src_flatten.dtype)
else:
pos_embed = None
layer_outputs = self.e... | 3,396 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (projected_features[feature_to_project_index],)
# Feature Pyramid Network (FPN)
fpn_feature_maps = [projected_features[-1]]
... | 3,396 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
# Path Aggregation Network (PAN)
fpn_states = [fpn_feature_maps[0]]
for idx in range(len(self.in_channels) - 1):
feat_low = fpn_states[-1]
feat_high = fpn_feature_maps[idx + 1]
downsample_feat = self.downsample_convs[idx](feat_low)
hidden_states = self.pan... | 3,396 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
class OmDetTurboMLPWithDropout(nn.Module):
def __init__(self, config):
super().__init__()
self.linear1 = nn.Linear(config.class_embed_dim, config.task_encoder_hidden_dim)
self.activation = ACT2FN[config.decoder_activation]
self.dropout = nn.Dropout(config.decoder_dropout)
sel... | 3,397 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
class OmDetTurboMLP(nn.Module):
"""Very simple multi-layer perceptron (also called FFN)"""
def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
super().__init__()
self.num_layers = num_layers
hidden_layers_dims = [hidden_dim] * (num_layers - 1)
layers_dims = [input... | 3,398 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
class OmDetTurboResidualLayer(nn.Module):
"""
A residual connection followed by a layer norm.
"""
def __init__(self, config):
super().__init__()
self.norm1 = nn.LayerNorm(config.class_embed_dim, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.decoder_dropout)
de... | 3,399 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
class OmDetTurboTaskEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.mlp = OmDetTurboMLPWithDropout(config)
self.res1 = OmDetTurboResidualLayer(config)
def forward(self, x):
mlp_out = self.mlp(x)
x = self.res1(x, mlp_out)
return x | 3,400 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
class OmDetTurboDeformableTransformerDecoderLayer(nn.Module):
"""
A single layer of the Deformable Transformer Decoder.
"""
def __init__(self, config):
super().__init__()
# self attention
self.self_attn = OmDetTurboMultiheadAttention(
config,
hidden_size=... | 3,401 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
# feed forward network
self.linear1 = nn.Linear(config.decoder_hidden_dim, config.decoder_dim_feedforward)
self.act = ACT2FN[config.decoder_activation]
self.dropout3 = nn.Dropout(config.decoder_dropout)
self.linear2 = nn.Linear(config.decoder_dim_feedforward, config.decoder_hidden_dim)
... | 3,401 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
def forward(
self,
decoder_embeddings,
task_features,
reference_points,
vision_features,
vision_shapes,
vision_shapes_list,
level_start_index=None,
attention_mask=None,
padding_mask=None,
query_position=None,
output_attentio... | 3,401 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
# self attention
query = key = self.with_pos_embed(decoder_embeddings, query_position)
# combine task_features with query, key, value
task_features = task_features.transpose(0, 1)
query = torch.cat((query, task_features), dim=1)
key = torch.cat((key, task_features), dim=1)
... | 3,401 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
# cross attention
hidden_states = self.with_pos_embed(decoder_embeddings, query_position)
reference_points = reference_points.unsqueeze(2)
outputs, cross_attention = self.cross_attn(
hidden_states=hidden_states,
attention_mask=padding_mask,
encoder_hidden_stat... | 3,401 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
return (
decoder_embeddings,
task_features,
self_attention if output_attentions else None,
cross_attention if output_attentions else None,
) | 3,401 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
class OmDetTurboPreTrainedModel(PreTrainedModel):
config_class = OmDetTurboConfig
base_model_prefix = "model"
main_input_name = "pixel_values"
def _init_weights(self, module):
def linear_init_(module_to_init):
bound = 1 / math.sqrt(module_to_init.weight.shape[0])
nn.init... | 3,402 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
if isinstance(module, OmDetTurboEncoderLayer):
linear_init_(module.fc1)
linear_init_(module.fc2)
elif isinstance(module, OmDetTurboDecoder):
nn.init.constant_(module.encoder_bbox_head.layers[-1].weight, 0.0)
nn.init.constant_(module.encoder_bbox_head.layers[-1].bi... | 3,402 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
nn.init.xavier_uniform_(layer[0].weight)
elif isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)):
module.weight.data.normal_(mean=0.0, std=self.config.init_std)
if module.bias is not None:
module.bias.data.zero_() | 3,402 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, OmDetTurboDecoder):
module.gradient_checkpointing = value
@staticmethod
def _get_cache_key_at_index(input_ids, attention_mask, index):
input_ids = input_ids[index]
input_mask = attention_mask[i... | 3,402 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
def get_cached_class_embeddings(self, classes_input_ids, classes_attention_mask):
not_cached_index = []
not_cached_classes = []
total_embeddings = []
for idx, _ in enumerate(classes_input_ids):
cache_key = self._get_cache_key_at_index(classes_input_ids, classes_attention_mask... | 3,402 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
if not_cached_classes:
not_cached_classes_ids = torch.stack([classes_input_ids[idx] for idx in not_cached_index])
embeddings = self.language_backbone(not_cached_classes_ids, encode_type="class")
for idx, emb in enumerate(embeddings):
idx_to_put = not_cached_index[idx]... | 3,402 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
def get_cached_task_embeddings(self, tasks_input_ids, tasks_attention_mask):
not_cached_index = []
not_cached_tasks = []
total_task_features = []
total_task_masks = []
for idx, _ in enumerate(tasks_input_ids):
cache_key = self._get_cache_key_at_index(tasks_input_ids, ... | 3,402 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
if not_cached_tasks:
not_cached_index_ids = torch.stack([tasks_input_ids[idx] for idx in not_cached_index])
not_cached_mask = torch.stack([tasks_attention_mask[idx] for idx in not_cached_index])
embeddings, masks = self.language_backbone(not_cached_index_ids, mask=not_cached_mask, en... | 3,402 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
# pad before concat if needed
max_len = max([task.shape[0] for task in total_task_features])
for idx, task in enumerate(total_task_features):
if task.shape[0] < max_len:
pad_size = max_len - task.shape[0]
total_task_features[idx] = F.pad(task, (0, 0, 0, 0, 0, ... | 3,402 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
def get_language_embedding(
self,
classes_input_ids,
classes_attention_mask,
tasks_input_ids,
tasks_attention_mask,
classes_structure,
):
batched_classes_embeddings = self.get_cached_class_embeddings(classes_input_ids, classes_attention_mask)
# regroup... | 3,402 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
class OmDetTurboDecoder(OmDetTurboPreTrainedModel):
def __init__(self, config: OmDetTurboConfig):
self.config = config
super().__init__(config)
self.gradient_checkpointing = False
hidden_dim = config.decoder_hidden_dim
self.num_queries = config.num_queries
self.class... | 3,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
# Transformer module
self.layers = nn.ModuleList(
[OmDetTurboDeformableTransformerDecoderLayer(config) for _ in range(config.decoder_num_layers)]
)
self.decoder_num_layers = config.decoder_num_layers
# decoder embedding
if self.learn_initial_query:
self.tg... | 3,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
# decoder head
self.decoder_class_head = nn.ModuleList(
[nn.Linear(config.class_embed_dim, hidden_dim) for _ in range(config.decoder_num_layers)]
)
self.decoder_bbox_head = nn.ModuleList(
[OmDetTurboMLP(hidden_dim, hidden_dim, 4, num_layers=3) for _ in range(config.decode... | 3,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
anchors = []
for level, (height, width) in enumerate(spatial_shapes):
grid_y, grid_x = torch.meshgrid(
torch.arange(end=height, dtype=dtype, device=device),
torch.arange(end=width, dtype=dtype, device=device),
indexing="ij",
)
g... | 3,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
return anchors, valid_mask
def _get_encoder_input(self, vision_features):
# get projection features
vision_features = [self.channel_projection_layers[i](feat) for i, feat in enumerate(vision_features)]
# get encoder inputs
new_vision_features = []
new_vision_shapes_list = []... | 3,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
return new_vision_features, new_vision_shapes, new_vision_shapes_list, level_start_index
def _get_decoder_input(
self, vision_features, vision_shapes, class_features, denoise_embeddings=None, denoise_bboxes=None
):
batch_size = len(vision_features)
# prepare input for decoder
an... | 3,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
# dynamic anchors + static content
# (batch_size, height*width, 4)
encoder_outputs_bboxes = self.encoder_bbox_head(predicted_class_features) + anchors
# query selection
# (batch_size, num_queries)
topk_ind = torch.topk(encoder_class_similarity.max(-1).values, self.num_queries, d... | 3,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
reference_points = encoder_outputs_bboxes[batch_ind, topk_ind].view(batch_size, self.num_queries, -1)
encoder_bboxes = reference_points.sigmoid()
if denoise_bboxes is not None:
reference_points = torch.cat([denoise_bboxes, reference_points], 1)
if self.training:
reference... | 3,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
def forward(
self,
vision_features,
class_features,
task_features,
task_mask,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
"""
Args:
vision_features (`torch.FloatTensor`): The sequence of vision fe... | 3,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
layers. See `attentions` under returned tensors for more detail.
output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See
`hidden_states` under returned tensors for more detail.
return_dict (`bool`, *optional*): Whether or not to re... | 3,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
vision_features, vision_shapes, vision_shapes_list, level_start_index = self._get_encoder_input(
vision_features
)
# todo add denoising for training
denoise_embeddings, denoise_bboxes, key_padding_mask = None, None, None
batch_size = task_mask.shape[0] | 3,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
# compose attn_mask for vision_emb and task_emb fusion
task_features = self.task_encoder(task_features)
if self.task_project is not None:
task_features = self.task_project(task_features)
src_key_mask = (task_mask == 0).detach()
attn_mask_len = self.num_queries
fusion_... | 3,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
all_hidden_states = () if output_hidden_states else None
all_attns = () if output_attentions else None
all_self_attns = () if output_attentions else None
all_cross_attns = () if output_attentions else None
predicted_class_features = decoder_embeddings | 3,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
if output_hidden_states:
all_hidden_states = all_hidden_states + (predicted_class_features,)
decoder_bboxes = []
decoder_classes = []
last_refined_bbox = None
reference_points = reference_points.sigmoid()
for i, layer in enumerate(self.layers):
if self.gra... | 3,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
)
else:
predicted_class_features, task_features, self_attention, cross_attention = layer(
predicted_class_features,
... | 3,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
if output_hidden_states:
all_hidden_states = all_hidden_states + (predicted_class_features,) | 3,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
refined_bbox = torch.sigmoid(
self.decoder_bbox_head[i](predicted_class_features) + _inverse_sigmoid(reference_points)
)
original_class_projected = self.decoder_class_head[i](class_features).permute(1, 2, 0)
if self.training:
decoder_classes.append(
... | 3,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
decoder_classes.append(
get_class_similarity(self.class_distance_type, predicted_class_features, original_class_projected)
)
decoder_bboxes.append(refined_bbox)
break
last_refined_bbox = refined_bbox
reference_points = refined_b... | 3,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
last_hidden_state = predicted_class_features
decoder_bboxes = torch.stack(decoder_bboxes)
decoder_classes = torch.stack(decoder_classes)
if not return_dict:
return (
last_hidden_state,
all_hidden_states,
all_attns,
deco... | 3,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
return OmDetTurboDecoderOutput(
last_hidden_state=last_hidden_state,
hidden_states=all_hidden_states,
attentions=all_attns,
decoder_coords=decoder_bboxes,
decoder_classes=decoder_classes,
encoder_coord_logits=encoder_bboxes,
encoder_cla... | 3,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
class OmDetTurboForObjectDetection(OmDetTurboPreTrainedModel):
def __init__(self, config: OmDetTurboConfig):
super().__init__(config)
self.vision_backbone = OmDetTurboVisionBackbone(config)
self.language_backbone = OmDetTurboLanguageBackbone(config)
self.encoder = OmDetTurboHybridEnc... | 3,404 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None) -> nn.Embedding:
model_embeds = self.language_backbone.model.resize_token_embeddings(
new_num_tokens=new_num_tokens, pad_to_multiple_of=pad_to_multiple_of
)
self.config.text_config.vocab_... | 3,404 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
@add_start_docstrings_to_model_forward(OMDET_TURBO_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=OmDetTurboObjectDetectionOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: torch.FloatTensor,
classes_input_ids: torch.LongTensor,
classes_attention_ma... | 3,404 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
>>> processor = AutoProcessor.from_pretrained("omlab/omdet-turbo-swin-tiny-hf")
>>> model = OmDetTurboForObjectDetection.from_pretrained("omlab/omdet-turbo-swin-tiny-hf")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw... | 3,404 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
>>> # convert outputs (bounding boxes and class logits)
>>> results = processor.post_process_grounded_object_detection(
... outputs,
... classes=classes,
... target_sizes=[image.size[::-1]],
... score_threshold=0.3,
... nms_threshold=0.3,
>>> )... | 3,404 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
if labels is not None:
raise NotImplementedError("Training is not implemented yet") | 3,404 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None els... | 3,404 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
loss = None
image_features = self.vision_backbone(pixel_values)
encoder_outputs = self.encoder(
image_features,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
class_features, task_... | 3,404 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
if not return_dict:
return tuple(
output
for output in [
loss,
decoder_outputs[3][-1],
decoder_outputs[4][-1],
decoder_outputs[7],
decoder_outputs[8],
decod... | 3,404 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
return OmDetTurboObjectDetectionOutput(
loss=loss,
decoder_coord_logits=decoder_outputs.decoder_coords[-1],
decoder_class_logits=decoder_outputs.decoder_classes[-1],
init_reference_points=decoder_outputs.init_reference_points,
intermediate_reference_points=dec... | 3,404 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py |
class OmDetTurboConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`OmDetTurboForObjectDetection`].
It is used to instantiate a OmDet-Turbo model according to the specified arguments, defining the model architecture
Instantiating a configuration with the defa... | 3,405 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/configuration_omdet_turbo.py |
Args:
text_config (`PretrainedConfig`, *optional*):
The configuration of the text backbone.
backbone_config (`PretrainedConfig`, *optional*):
The configuration of the vision backbone.
use_timm_backbone (`bool`, *optional*, defaults to `True`):
Whether to use t... | 3,405 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/configuration_omdet_turbo.py |
Whether to apply layer normalization on the feature maps of the vision backbone output.
image_size (`int`, *optional*, defaults to 640):
The size (resolution) of each image.
disable_custom_kernels (`bool`, *optional*, defaults to `False`):
Whether to disable custom kernels.
... | 3,405 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/configuration_omdet_turbo.py |
task_encoder_hidden_dim (`int`, *optional*, defaults to 1024):
The feedforward dimension for the task encoder.
class_embed_dim (`int`, *optional*, defaults to 512):
The dimension of the classes embeddings.
class_distance_type (`str`, *optional*, defaults to `"cosine"`):
... | 3,405 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/configuration_omdet_turbo.py |
The activation function for the feedforward network of the encoder.
encoder_feedforward_dropout (`float`, *optional*, defaults to 0.0):
The dropout rate following the activation of the encoder feedforward network.
encoder_dropout (`float`, *optional*, defaults to 0.0):
The dropou... | 3,405 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/configuration_omdet_turbo.py |
The indices of the input features projected by each layers.
encoder_attention_heads (`int`, *optional*, defaults to 8):
The number of attention heads for the encoder.
encoder_dim_feedforward (`int`, *optional*, defaults to 2048):
The feedforward dimension for the encoder.
... | 3,405 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/configuration_omdet_turbo.py |
decoder_num_layers (`int`, *optional*, defaults to 6):
The number of layers for the decoder.
decoder_activation (`str`, *optional*, defaults to `"relu"`):
The activation function for the decoder.
decoder_dim_feedforward (`int`, *optional*, defaults to 2048):
The feedf... | 3,405 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/configuration_omdet_turbo.py |
cache_size (`int`, *optional*, defaults to 100):
The cache size for the classes and prompts caches.
is_encoder_decoder (`bool`, *optional*, defaults to `True`):
Whether the model is used as an encoder-decoder model or not.
kwargs (`Dict[str, Any]`, *optional*):
Additi... | 3,405 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/configuration_omdet_turbo.py |
Examples:
```python
>>> from transformers import OmDetTurboConfig, OmDetTurboForObjectDetection
>>> # Initializing a OmDet-Turbo omlab/omdet-turbo-swin-tiny-hf style configuration
>>> configuration = OmDetTurboConfig()
>>> # Initializing a model (with random weights) from the omlab/omdet-turbo-sw... | 3,405 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/configuration_omdet_turbo.py |
def __init__(
self,
text_config=None,
backbone_config=None,
use_timm_backbone=True,
backbone="swin_tiny_patch4_window7_224",
backbone_kwargs=None,
use_pretrained_backbone=False,
apply_layernorm_after_vision_backbone=True,
image_size=640,
di... | 3,405 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/configuration_omdet_turbo.py |
encoder_attention_heads=8,
encoder_dim_feedforward=2048,
encoder_layers=1,
positional_encoding_temperature=10000,
num_feature_levels=3,
decoder_hidden_dim=256,
decoder_num_heads=8,
decoder_num_layers=6,
decoder_activation="relu",
decoder_dim_feedfo... | 3,405 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/configuration_omdet_turbo.py |
window_size=7,
image_size=image_size,
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
out_indices=[2, 3, 4],
)
elif isinstance(backbone_config, dict):
backbone_model_type = backbone_config.get("m... | 3,405 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/configuration_omdet_turbo.py |
verify_backbone_config_arguments(
use_timm_backbone=use_timm_backbone,
use_pretrained_backbone=use_pretrained_backbone,
backbone=backbone,
backbone_config=backbone_config,
backbone_kwargs=backbone_kwargs,
)
if text_config is None:
... | 3,405 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/configuration_omdet_turbo.py |
self.text_config = text_config
self.backbone_config = backbone_config
self.use_timm_backbone = use_timm_backbone
self.backbone = backbone
self.backbone_kwargs = backbone_kwargs
self.use_pretrained_backbone = use_pretrained_backbone
self.apply_layernorm_after_vision_backbo... | 3,405 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/configuration_omdet_turbo.py |
self.encoder_feedforward_activation = encoder_feedforward_activation
self.encoder_feedforward_dropout = encoder_feedforward_dropout
self.encoder_dropout = encoder_dropout
self.hidden_expansion = hidden_expansion
self.vision_features_channels = vision_features_channels
self.encode... | 3,405 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/configuration_omdet_turbo.py |
self.decoder_dim_feedforward = decoder_dim_feedforward
self.decoder_num_points = decoder_num_points
self.decoder_dropout = decoder_dropout
self.eval_size = eval_size
self.learn_initial_query = learn_initial_query
self.cache_size = cache_size
self.is_encoder_decoder = is_e... | 3,405 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/configuration_omdet_turbo.py |
super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs) | 3,405 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/configuration_omdet_turbo.py |
class EnglishNormalizer:
def __init__(self):
# List of (regular expression, replacement) pairs for abbreviations:
self._abbreviations = [
(re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
for x in [
("mrs", "misess"),
("mr", "mister"),
... | 3,406 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/number_normalizer.py |
self.ones = ["", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"]
self.teens = [
"ten",
"eleven",
"twelve",
"thirteen",
"fourteen",
"fifteen",
"sixteen",
"seventeen",
"eighteen",
... | 3,406 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/number_normalizer.py |
Please note that it only supports upto - "'nine hundred ninety-nine quadrillion, nine hundred ninety-nine
trillion, nine hundred ninety-nine billion, nine hundred ninety-nine million, nine hundred ninety-nine
thousand, nine hundred ninety-nine'" or `number_to_words(999_999_999_999_999_999)`.
"""... | 3,406 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/number_normalizer.py |
+ (", " + self.number_to_words(num % 1000) if num % 1000 != 0 else "")
)
elif num < 1_000_000_000:
return (
self.number_to_words(num // 1_000_000)
+ " million"
+ (", " + self.number_to_words(num % 1_000_000) if num % 1_000_000 != 0 else "")... | 3,406 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/number_normalizer.py |
self.number_to_words(num // 1_000_000_000_000_000)
+ " quadrillion"
+ (
", " + self.number_to_words(num % 1_000_000_000_000_000)
if num % 1_000_000_000_000_000 != 0
else ""
)
)
else:
... | 3,406 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/number_normalizer.py |
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