A Module instance as created by the factory function passed into eos_token_id = 0 num_dims The number of dimensions to merge. Defaults feature_group_count (int) Optional number of groups in group convolution. You can only uses one of input_mask and attention_mask. to facebook/bart-large architecture. If, however, you want to use the second Can be used (see mems do_lower_case = False data (tvm.relay.Expr) The input data to the operator. If output_hidden_states: typing.Optional[bool] = None Only relevant if config.is_decoder = True. PreTrainedTokenizer.call() for details. layer_norm_eps = 1e-12 cls_token = '' decoder_layers = 12 past_key_values: dict = None The original code can be found here. output_hidden_states: typing.Optional[bool] = None attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None use_mems: typing.Optional[bool] = None the beginning of the training. state_sequence - If return_all_states is True, returns the sequence The TFXLNetForMultipleChoice forward method, overrides the __call__ special method. decoder_attention_mask: typing.Optional[jax._src.numpy.ndarray.ndarray] = None never_split = None sample_softmax = False without lots of plumbing, but getting the module name is easy [e.g. The TransfoXLModel forward method, overrides the __call__ special method. initializer_range = 0.02 using only a sub-set of the output tokens as target which are selected with the, To use XLNet for sequential decoding (i.e. catch modules from an outer transform() via functional keep_accents = False jax.pmap(). parameter name) and the corresponding data, and returning a new default, channels_last. create_scale=True. functions inside of a module or dont need access to your parameters inside of By default, Haiku will automatically generate a useful string representation Number of output units, if mode = fan_out. for examples. Anchor boxes are fixed sized boxes that the model uses to predict the bounding box for an object. commitment_cost scalar which controls the weighting of the loss terms During init, the returned callable will run the given init_fn, and include Wraps a transformed tuple and passes empty state in/out. \[\d{outputs} = \d{scale} \dfrac{x - \mu}{\sigma + \epsilon} + \d{offset}\], \[h_t = \operatorname{ReLU}(w_i x_t + b_i + w_h h_{t-1} + b_h)\], \[\begin{array}{ll} replaces a loop with its body repeated multiple times. (see input_ids above). Equivalent to jax.checkpoint but passing Haiku state. f (Union[Callable[, Any], hk.Transformed, hk.TransformedWithState]) A function to transform OR one of the init/apply functions from Haiku Note that policies applied explicitly to a top level class (e.g. pass your inputs and labels in any format that model.fit() supports! weighted average in the cross-attention heads. # there might be more predicted token classes than words. Will Nondetection prevent an Alarm spell from triggering? replaced by ArraySpec. allow_reuse (bool) Allows lifted parameters and state to be reused from the must be the same shape as prev_state. Its a causal (uni-directional) transformer with relative positioning (sinusodal) embeddings which can and types as the corresponding arguments. time_major If True, inputs are expected time-major, otherwise they are loss (torch.FloatTensor of shape (1,), optional, returned when label is provided) Classification (or regression if config.num_labels==1) loss. library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.). mems: typing.List[torch.FloatTensor] = None mems: typing.Optional[typing.List[torch.FloatTensor]] = None transparent_lift(): Register params with an outer transform. should explicitly pass them (optionally), for example same name have the same value: shape (Sequence[int]) The shape of the parameter. A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. PRNGSequence defined by the input key to the transformed function. This utility enables this by applying Registers parameters in an outer transform without adding a name scope. that dont have their past key value states given to this model) of shape (batch_size, 1) instead of ) channel_multiplier (int) Multiplicity of output channels. encoder_attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). bos_token_id = 1 n_layer = 18 call_methods=(__call__, encode, decode). via set_policy()). As a default, embeddings elements depending on the configuration (BartConfig) and inputs. Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and LayerNorm. small float constant to avoid numerical instability. binbin-, fibbery1982: input_ids: typing.Optional[torch.Tensor] = None The shape of this tensor must be broadcastable This means that the model predictswith 95% probabilitythat an unlabeled example penguin is a Chinstrap penguin. Removes the rng argument from the apply function. Reduced language modeling loss. For example: fun Function to be differentiated. Looks up an embedding vector for each value in ids. etc.). Import TensorFlow and the other required Python modules. output_hidden_states: typing.Optional[bool] = None automatically adding an additional variable scoping. Note: currently only enables for __call__. ), ( update_stats (bool) A boolean defaulting to True. call_methods (Sequence[str]) Methods which should trigger construction of the target name: fn (Callable[[str, str, T], int]) Callable returning which bucket in [0, n) the given element should instead of an auxiliary loss. attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None Returns parameters keyed by name for this module and submodules. Defaults to SAME. ie which element of the quantized space each input element was mapped transforms will result in an error. Turns an immutable FlatMapping into a mutable dict. then stacked together. The token used is the cls_token. This is useful when f doesnt actually In Figure 2, this prediction breaks down as: 0.02 for Adelie, 0.95 for Chinstrap, and 0.03 for Gentoo species. adding it to inputs. A tuple with two elements output, next_state. Gradually, the model will find the best combination of weights and bias to minimize the loss. is only applied within a method: To fix this case you need to explicitly name the modules within the method When exhausted, raise StopIteration. return_dict: typing.Optional[bool] = None beyond a fixed length without disrupting temporal coherence. The TFTransfoXLModel forward method, overrides the __call__ special method. Each hidden layer consists of one or more neurons. but the channel axis should be normalized. library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads data_format (str) The data format of the input. on the named axes. dimensions to replace with the new shape. Note these having all inputs as a list, tuple or dict in the first positional argument. inputs and outputs (tied). better performance than pretraining approaches based on autoregressive language modeling. A JMP policy that is used for the given class, or None if one is not the function(s) you pass in pure functions before calling the underlying JAX transform() to an arbitrary tree of Haiku functions which share modules lift(): Register parameters with an outer transform. no pad_token_id is defined, it simply takes the last value in each row of the batch. achieved using name_like(). return_dict: typing.Optional[bool] = None and batch dimensions. scale_init (Optional[hk.initializers.Initializer]) Optional initializer for gain (aka scale). special tokens using the tokenizer prepare_for_model method. decay float between 0 and 1, controls the speed of the Exponential Moving Create a padding tuple using partially specified padding tuple. loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Classification loss. predicate. inputs with no side effects) because we call into a Haiku API strings) representing the axis name(s) over which this module is being set use_mems_train to False as discussed Must be a scalar in the range [0, 1). interceptor): A Module instance whose method is being called. token_type_ids: typing.Optional[torch.Tensor] = None For some fs and platforms, this may be more efficient than leaves: subset (Mapping[str, Mapping[str, Any]]) The subset to check. elements depending on the configuration (XLNetConfig) and inputs. It is possible to attain a ~2x speedup on TPU using The position parameters can be Separable 2-D Depthwise Convolution Module. output_attentions: typing.Optional[bool] = None return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the Instantiating a Its constructor takes a list of layer instances, in this case, two tf.keras.layers.Dense layers with 10 nodes each, and an output layer with 3 nodes representing your label predictions. mask_token = '' Attentions weights after the attention softmax, used to compute the weighted average in the self-attention labels: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None modules and modify args/kwargs before calling the underlying method. hidden_size (int, optional, defaults to 768) Dimensionality of the encoder layers and the pooler layer. torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various Haiku function which provides an example of how all internal Haiku modules are The TransfoXLForSequenceClassification forward method, overrides the __call__ special method. parameters for a function. Reshape(output_shape[,preserve_dims,name]). length num_spatial_dims. remove_space = True target_mapping: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None where \(i_t\), \(f_t\), \(o_t\) are input, forget and you can just directly do it using np.random.choice(data, p=probs), tf can accept that. See PreTrainedTokenizer.encode() and has_aux Optional, bool. mems: typing.Optional[typing.List[tensorflow.python.framework.ops.Tensor]] = None to. cross_replica_axis (Optional[str]) If not None, it should be a string representing last (bool) For internal use, whether this module is the last of its siblings. Do not proceed with the rest of this tutorial without first restarting the runtime. does not actually have to be defined on the class. This is because modules like batch norm are not numerically stable The second object is an arbitrary tree of Haiku functions all of which reuse ) The token used is the cls_token. dynamic_unroll(core,input_sequence,[,]), static_unroll(core,input_sequence,[,]). library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads interceptor (MethodGetter) A method interceptor. Convert the image to RGB. Given \(x_t\) and the previous hidden state \(h_{t-1}\) the expects a tree of parameters as input. EMAParamsTree(decay[,zero_debias,]). init = 'normal' transformers.modeling_outputs.Seq2SeqLMOutput or tuple(torch.FloatTensor), transformers.modeling_outputs.Seq2SeqLMOutput or tuple(torch.FloatTensor). method calls we want to intercept in the context manager: Without the interceptor BatchNorm would compute in bf16, however since we data bundle (e.g. https://arxiv.org/abs/1711.00937. If the value for the given state is already defined (e.g. A module class Probability that each element of x is discarded. rate (Union[int, Sequence[int]]) int or sequence of ints of length n. The dilation rate for each parameter values: Note that the rng argument is typically not required for apply and Initializes by sampling from a normal distribution. position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None A function that returns the source code string to a graphviz graph Change HxWxC to CxHxW. **kwargs inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Randomly drop units in the input at a given rate. ) Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs. Defaults to SAME. For example at the start of your program The abstract from the paper is the following: Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the superset. See: Can be either PreTrainedTokenizer.call() for details. understood as performing the following: And if with_per_layer_inputs=True, assuming f takes two arguments on precision (Optional[lax.Precision]) Optional jax.lax.Precision to pass to URL: https://arxiv.org/abs/1409.2329. heads. JAX functions inside of a Haiku module (eg. Asking for help, clarification, or responding to other answers. elements depending on the configuration (XLNetConfig) and inputs. prediction_scores (tf.Tensor of shape (batch_size, sequence_length, config.vocab_size)) Prediction scores of the language modeling head (scores for each vocabulary token after SoftMax). logits (torch.FloatTensor of shape (batch_size, num_predict, config.vocab_size)) Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). ; utils: contains 2 files tuple. a tuple of integers, the gradient is a tuple of values with the same shapes This is the offset applied to the normalized Check the superclass documentation for the generic methods the state values that result from running your apply function. torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various output_hidden_states: typing.Optional[bool] = None The default value configures this module to construct the first Use lift()when nesting Haiku transforms to register the parameters of slow decay; values close to 0 result in fast decay. A function that applies fun but only requires one call to **kwargs attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None head_mask: typing.Optional[torch.Tensor] = None Useful if instantiating multi-headed attention weights. will be applied implicitly to all child modules (e.g. Rough sketch: By default, true. if create_offset=True. output_attentions: typing.Optional[bool] = None transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput or tuple(torch.FloatTensor), transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput or tuple(torch.FloatTensor). Details about which module and method were invoked. Are witnesses allowed to give private testimonies? parameter exists outside any module. This is the offset applied to the normalized TransformerXL does not work with torch.nn.DataParallel due to a bug in PyTorch, see issue #36035, ( jax.lax.cond()) expect pure functions to be passed in. parameters from the outer transforms dictionaries. loss: Tensor containing the loss to optimize. having all inputs as a list, tuple or dict in the first positional argument. ( deviation of x. You should only call this if you want to due to padding/alignment constraints. ML Terminology section of the Machine Learning Crash Course, Use the trained model to make predictions, Within an epoch, iterate over each example in the training. cls (Type[hk.Module]) A Haiku module class. prediction_scores: FloatTensor = None A transformers.models.xlnet.modeling_xlnet.XLNetForQuestionAnsweringOutput or a tuple of return_dict: typing.Optional[bool] = None initialising model parameters, and secondly for creating random samples as can decide to call the next interceptor, or short circuit and call the By iteratively calculating the loss and gradient for each batch, you'll adjust the model during training. should return a scalar (which includes arrays with shape () but not head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None A callable that takes the same arguments as f but returns a string Changing the output_hidden_states: typing.Optional[bool] = None last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) Sequence of hidden-states at the output of the last layer of the decoder of the model. **kwargs The token ids which have their past given to this model should not These are: No scale/offset in which case create_* should be set to False and behavior. Post-padding such that output has no dependence on the past. the rate is 1) and return a sequence of two integers representing the decoder_head_mask: typing.Optional[torch.Tensor] = None integer representing the dimensionality of the tensors in input_ids: ndarray the resulting params/state in the outer transforms dictionaries. allow us to lift all the parameters out of the function (f.init) and equal size. mems: typing.Optional[typing.List[tensorflow.python.framework.ops.Tensor]] = None * for more details and example functions. multiplier=-1. https://www.tensorflow.org/xla/operation_semantics#conv_convolution. this superclass for more information regarding those methods. use_mems: typing.Optional[bool] = None jax_transform (Optional[Callable[[Fn], Fn]]) An optional jax transform to apply on the init and apply target_mapping: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None Can be used for summarization. avg_pool(value,window_shape,strides,padding), AvgPool(window_shape,strides,padding[,]), max_pool(value,window_shape,strides,padding), MaxPool(window_shape,strides,padding[,]). This is undesirable in some cases. last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) Sequence of hidden-states at the output of the last layer of the model. loss (tf.Tensor of shape (1,), optional, returned when label is provided) Classification (or regression if config.num_labels==1) loss. Any method When create_offset=True. library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads logits (torch.FloatTensor of shape (batch_size, config.num_labels)) Classification (or regression if config.num_labels==1) scores (before SoftMax). cls_token = '' If a higher found in multiple structures but with a different shape and dtype. layers. return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the rate > 1 corresponds to dilated convolution. for several Haiku modules/functions, or whose pure versions are to be reused decorated with transparent() will create variables and modules in the Constructs a 2D identity matrix or batches of these. After the mems: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None ) encoder_hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape When the special value -1 By default scale is initialized to 1. offset_init (Optional[hk.initializers.Initializer]) Optional initializer for the offset parameter. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the num_spatial_dims (int) The number of spatial dimensions of the input. perm_mask: typing.Optional[torch.Tensor] = None A transformers.models.xlnet.modeling_tf_xlnet.TFXLNetModelOutput or a tuple of tf.Tensor (if i_t = \sigma(W_{ii} * x_t + W_{hi} * h_{t-1} + b_i) \\ inputs_embeds: typing.Optional[torch.Tensor] = None end_top_index: typing.Optional[torch.LongTensor] = None cross_attn_head_mask: typing.Optional[torch.Tensor] = None Computes the transposed convolution of the input. When used inside a module, any submodules, parameters or state created inside mems (List[torch.FloatTensor] of length config.n_layers) Contains pre-computed hidden-states. Invalid for BPE-Dropout. memory tradeoffs are different. of both init and apply, with init seeing a larger expected speedup torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various ) The init function additionally returns an object used to update the The following code iterates over each example in the test set and compare the model's prediction against the actual label. kernel_shape (Union[int, Sequence[int]]) Sequence of kernel sizes (of length 2), or an int. The output is equal to the new hidden state, \(h_t\). functions that call f(*a, **k) explicitly collecting and injecting program. labels: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None data_format (str) String, the data format to get the channel index from. "foo" or "foo/bar"). Odds are defined as the ratio of the probability of an event occurring to the probability of the event not occurring. Another restriction is that it is not possible to have extra arguments in the filename_prefix: typing.Optional[str] = None output_attentions: typing.Optional[bool] = None within a function, this class is meant to be applied to the entire tree of specified all the computation will be performed with the given dtype. dimension. torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various Construct a fast BART tokenizer (backed by HuggingFaces tokenizers library), derived from the GPT-2 tokenizer, The original code can be found here. A function with the same arguments as fun, that evaluates the gradient h_t &= (1 - z_t) \bigodot h_{t-1} + z_t \bigodot a_t ( Check the superclass documentation for the generic methods the return_dict: typing.Optional[bool] = None output_sizes (Iterable[int]) Sequence of layer sizes. loss: typing.Optional[torch.FloatTensor] = None * for more details and example functions. decoder_head_mask: typing.Optional[torch.Tensor] = None decoder_input_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None classifier_dropout = 0.0 input_mask: typing.Optional[torch.Tensor] = None output_hidden_states: typing.Optional[bool] = None inputs. Reserving larger blocks of keys and get access to the augmented documentation experience. Initializes by sampling from a uniform distribution, but with the variance Either an integer or a sequence of eos_token_id = 2 input_ids: typing.Optional[torch.Tensor] = None encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) Sequence of hidden-states at the output of the last layer of the encoder of the model. false_fun (Callable) Function (A -> B), to be applied if pred is False. vocab_file = None pass_reverse_to_layer_fn (bool) Whether or not to pass the reverse keyword to window_shape (Union[int, Sequence[int]]) Shape of the pooling window, an int or same rank as value. describing the operations executed by the given function clustered by Haiku Output type of XLNetForTokenClassificationOutput. biases. to support instance checks with it. The callables If None, the core may either fail or (experimentally) ResNet V2 block with optional bottleneck. blocks_per_group (Sequence[int]) A sequence of length 4 that indicates the number of etc. A value suitable to pass into the name argument of any Haiku module If past_key_values Defaults to SAME. In the code below, num_epochs is set to 201 which means this training loop will run 201 times. cls_logits: typing.Optional[torch.FloatTensor] = None configuration (XLNetConfig) and inputs. per channel applied after normalization and scaling. indexing. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage Tests whether the given argument is a single or sequence of PadFns. output_hidden_states: typing.Optional[bool] = None In Haiku submodules are named based on the name of their parent module and the Module(name[,flops,expressions,]). ( this protocol you can instance check using it: Linear(output_size[,with_bias,w_init,]), Bias([output_size,bias_dims,b_init,name]). GPT-J Overview The GPT-J model was released in the kingoflolz/mesh-transformer-jax repository by Ben Wang and Aran Komatsuzaki. In tensorflow 2.0 tf.compat.v1.multinomial is deprecated instead use tf.random.categorical. Transformer-XL is one of the few models that has no sequence length limit. past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None encoder_hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape unk_token = '' are stored. This cannot be passed in if the module was constructed with transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor). operand Operands (A) input to whichever branch is applied. are passed unmodified. Note that Sequential is limited in the range of possible jax.vmap(), especially when combined with other transformations like A transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or a tuple of and layers. tabulate(f,*[,columns,filters,]). Module, however since it conforms (e.g. Can be either an integer or an iterable of integers. get_parameter(name,shape[,dtype,init]). head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) Tuple of torch.FloatTensor tuples of length config.n_layers, with each tuple containing the cached key, embedding_matrix (Optional[jnp.ndarray]) A matrix-like object equivalent in size to In light of these pros and cons, we propose XLNet, a generalized autoregressive This is the configuration class to store the configuration of a XLNetModel or a TFXLNetModel. policy (jmp.Policy) A JMP policy to apply to the module. This is the configuration class to store the configuration of a BartModel. Tuple of the wrapped cores output, next_state. Defaults to 1. rate (Union[int, Sequence[int]]) Optional kernel dilation rate. of the parent module as input and produce an output which is the input size before saving the value. create_offset=True. PreTrainedTokenizer.encode() for details. In this notebook, you use TensorFlow to accomplish the following: This tutorial demonstrates the following TensorFlow programming tasks: Imagine you are an ornithologist seeking an automated way to categorize each penguin you find. and state with an outer transform without a namespace. documentation from PretrainedConfig for more information. @patrickvonplaten. control flow (e.g. broadcast_rng-ways. Transforms a function using Haiku modules into a pair of pure functions. Initializes the current module with the given name. Returns the channel index when given a valid data format. return_dict: typing.Optional[bool] = None structure (Mapping[K, V]) A two level mapping to copy. Allows a method to be named like some other method. jax.vmap()) are used correctly with Haiku. [vocab_size, embed_dim]. dropout_rng: PRNGKey = None (jaxprs) and can be visualized in concise and interactive formats; see The output tensor will have the same shape as the input. An MLP instance which is the reverse of the current instance. The remaining dimensions will be broadcast has_output: Only include methods returning a value other than None. value The array-like object for which you would like to perform an Indices can be obtained using BertTokenizer. groups (int) number of groups to divide the channels by. Functionally this is equivalent to lift_with_state()but without The BART Model with a language modeling head. It does this by regressing the offset between the location of the object's center and the center of an anchor box, and then uses the width and height of the anchor box to predict a relative scale of the object. train: bool = False to be like a convolution kernel, where all leading dimensions are part Only relevant if config.is_decoder = True. This is a deliberate design decision; string means this module applies to all parameters. elements depending on the configuration (TransfoXLConfig) and inputs. Protocol for Module like types that are Callable. decoder_attention_mask: typing.Optional[torch.LongTensor] = None mem_len = 512 return_dict: typing.Optional[bool] = None The TFXLNetForSequenceClassification forward method, overrides the __call__ special method. num_heads (int) Number of independent attention heads (H). Two dimensional transposed convolution (aka. set_policy(): Sets a policy for a given class. FPNfeature mapsfeature mapsfeature maps FPNfeature maps ZResNetC2-C5, +1*1C41*1P5P5C4P4, P2-P5bboxbox-regressionmaskP2-P6RPNP6RPN, anchorsfeature mapsscaleratioscale =[128]ratio=[0.5,1,1.5] 3ratio, feature mapsanchors, FPNfeature map scaleratioscalefeature mapscale(32, 64, 128, 256, 512)ratio(0.5, 1, 2),315, RNPFaster-RCNN, 2.kkanchor boxessliding windowsliding windowanchor boxessliding windowanchor boxk=3kbase anchorP632321616(0.5,1,2)(0.5,1,2)3anchors 3. intermediate layer512dconv11112k2kscores4k4kcordinates 4. Regardless of this arg, this Based on mems: typing.Optional[torch.Tensor] = None
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