ports
ApiKey
#
Bases: PydanticBase
API key for authentication.
Source code in focoos/ports.py
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|
ArtifactName
#
Bases: str
, Enum
Model artifact type.
Source code in focoos/ports.py
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|
DatasetLayout
#
Bases: str
, Enum
Supported dataset formats in Focoos.
Values
- ROBOFLOW_COCO: (Detection,Instance Segmentation)
- ROBOFLOW_SEG: (Semantic Segmentation)
Example:
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|
Source code in focoos/ports.py
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|
DatasetMetadata
dataclass
#
Dataclass for storing dataset metadata.
Source code in focoos/ports.py
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|
dump_json(path)
#
Dump DatasetMetadata to a json file.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path
|
str
|
Path to json file. |
required |
Source code in focoos/ports.py
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|
from_dict(metadata)
classmethod
#
Create DatasetMetadata from a dictionary.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
metadata
|
dict
|
Dictionary containing metadata. |
required |
Returns:
Name | Type | Description |
---|---|---|
DatasetMetadata |
Instance of DatasetMetadata. |
Source code in focoos/ports.py
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|
from_json(path)
classmethod
#
Create DatasetMetadata from a json file.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path
|
str
|
Path to json file. |
required |
Returns:
Name | Type | Description |
---|---|---|
DatasetMetadata |
Instance of DatasetMetadata. |
Source code in focoos/ports.py
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|
DatasetPreview
#
Bases: PydanticBase
Preview information for a Focoos dataset.
This class provides metadata about a dataset in the Focoos platform, including its identification, task type, and layout format.
Attributes:
Name | Type | Description |
---|---|---|
ref |
str
|
Unique reference ID for the dataset. |
name |
str
|
Human-readable name of the dataset. |
task |
FocoosTask
|
The computer vision task this dataset is designed for. |
layout |
DatasetLayout
|
The structural format of the dataset (e.g., ROBOFLOW_COCO, ROBOFLOW_SEG). |
description |
Optional[str]
|
Optional description of the dataset's purpose or contents. |
spec |
Optional[DatasetSpec]
|
Detailed specifications about the dataset's composition and size. |
Source code in focoos/ports.py
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|
DatasetSpec
#
Bases: PydanticBase
Specification details for a dataset in the Focoos platform.
This class provides information about the dataset's size and composition, including the number of samples in training and validation sets and the total size.
Attributes:
Name | Type | Description |
---|---|---|
train_length |
int
|
Number of samples in the training set. |
valid_length |
int
|
Number of samples in the validation set. |
size_mb |
float
|
Total size of the dataset in megabytes. |
Source code in focoos/ports.py
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|
DictClass
#
Bases: OrderedDict
Source code in focoos/ports.py
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|
__post_init__()
#
Check the BasicContainer dataclass.
Only occurs if @dataclass decorator has been used.
Source code in focoos/ports.py
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|
to_tuple()
#
Convert self to a tuple containing all the attributes/keys that are not None
.
Source code in focoos/ports.py
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|
DynamicAxes
dataclass
#
Dynamic axes for model export.
Source code in focoos/ports.py
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|
ExportCfg
dataclass
#
Configuration for model export.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
out_dir
|
str
|
Output directory for exported model |
required |
onnx_opset
|
int
|
ONNX opset version to use |
17
|
onnx_dynamic
|
bool
|
Whether to use dynamic axes in ONNX export |
True
|
onnx_simplify
|
bool
|
Whether to simplify ONNX model |
True
|
model_fuse
|
bool
|
Whether to fuse model layers |
True
|
format
|
Literal['onnx', 'torchscript']
|
Export format ("onnx" or "torchscript") |
'onnx'
|
device
|
Optional[str]
|
Device to use for export |
'cuda'
|
Source code in focoos/ports.py
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|
ExportFormat
#
Bases: str
, Enum
Available export formats for model inference.
Values
- ONNX: ONNX format
- TORCHSCRIPT: TorchScript format
Source code in focoos/ports.py
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|
FocoosDet
dataclass
#
Represents a single result from a Focoos model.
This dataclass encapsulates all relevant information for a detected object, including: - Bounding box coordinates (bbox) in [x1, y1, x2, y2] format, where (x1, y1) is the top-left and (x2, y2) is the bottom-right. - Confidence score (conf) between 0 and 1. - Class ID (cls_id) corresponding to the model's class list. - Human-readable label (label) for the detected class. - Optional segmentation mask (mask) as a base64-encoded PNG, cropped to the bbox region. - Optional keypoints for pose estimation or similar tasks.
Notes
- The mask field is only present for instance or semantic segmentation models.
- The mask is a base64-encoded PNG string, with its origin at the top-left of the bbox and dimensions matching the bbox.
- Keypoints, if present, are a list of (x, y, visibility) tuples.
Attributes:
Name | Type | Description |
---|---|---|
bbox |
Optional[list[int]]
|
Bounding box [x1, y1, x2, y2]. |
conf |
Optional[float]
|
Detection confidence score. |
cls_id |
Optional[int]
|
Class index. |
label |
Optional[str]
|
Class label. |
mask |
Optional[str]
|
Base64-encoded PNG mask (cropped to bbox). |
keypoints |
Optional[list[tuple[int, int, float]]]
|
Optional keypoints. |
Source code in focoos/ports.py
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|
FocoosDetections
dataclass
#
Represents a collection of detection or segmentation results from a Focoos model.
This dataclass holds a list of FocoosDet objects, and optionally: - The image (as a base64 string or numpy array) associated with the detections. - Latency information for the inference process, such as time spent in preprocessing, inference, postprocessing, and annotation.
Attributes:
Name | Type | Description |
---|---|---|
detections |
list[FocoosDet]
|
List of detection results. |
image |
Optional[Union[str, ndarray]]
|
The image associated with the detections, either as a base64-encoded string or a numpy array. If present, the string is typically a base64-encoded annotated image. |
latency |
Optional[dict]
|
Dictionary with timing information for each inference step. Keys may include 'inference', 'preprocess', 'postprocess', and 'annotate', with values in seconds. |
Source code in focoos/ports.py
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|
infer_print()
#
Print a formatted summary of the detections and timing information.
Source code in focoos/ports.py
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|
GPUDevice
#
Bases: PydanticBase
Information about a GPU device.
Source code in focoos/ports.py
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|
GPUInfo
#
Bases: PydanticBase
Information about a GPU driver.
Source code in focoos/ports.py
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|
InferLatency
dataclass
#
Represents the latency data for a Focoos model.
Source code in focoos/ports.py
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|
LatencyMetrics
dataclass
#
Performance metrics for model inference.
This class provides performance metrics for model inference, including frames per second (FPS), engine used, minimum latency, maximum latency, mean latency, standard deviation of latency, input image size, and device type.
Attributes:
Name | Type | Description |
---|---|---|
fps |
int
|
Frames per second (FPS) of the inference process. |
engine |
str
|
The inference engine used (e.g., "onnx", "torchscript"). |
min |
float
|
Minimum latency in milliseconds. |
max |
float
|
Maximum latency in milliseconds. |
mean |
float
|
Mean latency in milliseconds. |
std |
float
|
Standard deviation of latency in milliseconds. |
im_size |
int
|
Input image size. |
device |
str
|
Device type. |
Source code in focoos/ports.py
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|
Metrics
dataclass
#
Collection of training and inference metrics.
Source code in focoos/ports.py
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|
ModelExtension
#
Bases: str
, Enum
Supported model extension.
Values
- ONNX: ONNX format
- TORCHSCRIPT: TorchScript format
- WEIGHTS: Weights format
Source code in focoos/ports.py
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|
ModelFamily
#
Bases: str
, Enum
Enumerazione delle famiglie di modelli disponibili
Source code in focoos/ports.py
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|
ModelInfo
dataclass
#
Bases: DictClass
Comprehensive metadata for a Focoos model.
This dataclass encapsulates all relevant information required to identify, configure, and evaluate a model within the Focoos platform. It is used for serialization, deserialization, and programmatic access to model properties.
Attributes:
Name | Type | Description |
---|---|---|
name |
str
|
Human-readable name or unique identifier for the model. |
model_family |
ModelFamily
|
The model's architecture family (e.g., RTDETR, M2F). |
classes |
list[str]
|
List of class names that the model can detect or segment. |
im_size |
int
|
Input image size (usually square, e.g., 640). |
task |
Task
|
Computer vision task performed by the model (e.g., detection, segmentation). |
config |
dict
|
Model-specific configuration parameters. |
ref |
Optional[str]
|
Optional unique reference string for the model. |
focoos_model |
Optional[str]
|
Optional Focoos base model identifier. |
status |
Optional[ModelStatus]
|
Current status of the model (e.g., training, ready). |
description |
Optional[str]
|
Optional human-readable description of the model. |
train_args |
Optional[TrainerArgs]
|
Optional training arguments used to train the model. |
weights_uri |
Optional[str]
|
Optional URI or path to the model weights. |
val_dataset |
Optional[str]
|
Optional name or reference of the validation dataset. |
val_metrics |
Optional[dict]
|
Optional dictionary of validation metrics (e.g., mAP, accuracy). |
focoos_version |
Optional[str]
|
Optional Focoos version string. |
latency |
Optional[list[LatencyMetrics]]
|
Optional list of latency measurements for different runtimes. |
updated_at |
Optional[str]
|
Optional ISO timestamp of the last update. |
Source code in focoos/ports.py
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|
dump_json(path)
#
Serialize ModelInfo to a JSON file.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path
|
str
|
Path where the JSON file will be saved. |
required |
Source code in focoos/ports.py
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|
from_json(data)
classmethod
#
Load ModelInfo from a JSON file.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path
|
Optional[str]
|
Path to the JSON file containing model metadata. |
required |
data
|
Optional[dict]
|
Dictionary containing model metadata. |
required |
Returns:
Name | Type | Description |
---|---|---|
ModelInfo |
An instance of ModelInfo populated with data from the file. |
Source code in focoos/ports.py
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|
pprint()
#
Pretty-print the main model information using the Focoos logger.
Source code in focoos/ports.py
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|
ModelNotFound
#
Bases: Exception
Exception raised when a requested model is not found.
Source code in focoos/ports.py
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|
ModelOutput
dataclass
#
Bases: DictClass
Model output base container.
Source code in focoos/ports.py
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|
ModelPreview
#
Bases: PydanticBase
Preview information for a Focoos model.
This class provides a lightweight preview of model information in the Focoos platform, containing essential details like reference ID, name, task type, and status.
Attributes:
Name | Type | Description |
---|---|---|
ref |
str
|
Unique reference ID for the model. |
name |
str
|
Human-readable name of the model. |
task |
FocoosTask
|
The computer vision task this model is designed for. |
description |
Optional[str]
|
Optional description of the model's purpose or capabilities. |
status |
ModelStatus
|
Current status of the model (e.g., training, ready, failed). |
focoos_model |
str
|
The base model architecture identifier. |
Source code in focoos/ports.py
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|
ModelStatus
#
Bases: str
, Enum
Status of a Focoos model during its lifecycle.
Values
- CREATED: Model has been created
- TRAINING_STARTING: Training is about to start
- TRAINING_RUNNING: Training is in progress
- TRAINING_ERROR: Training encountered an error
- TRAINING_COMPLETED: Training finished successfully
- TRAINING_STOPPED: Training was stopped
- DEPLOYED: Model is deployed
- DEPLOY_ERROR: Deployment encountered an error
Example
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|
Source code in focoos/ports.py
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|
OnnxRuntimeOpts
dataclass
#
ONNX runtime configuration options.
This class provides configuration options for the ONNX runtime used for model inference.
Attributes:
Name | Type | Description |
---|---|---|
fp16 |
Optional[bool]
|
Enable FP16 precision. Default is False. |
cuda |
Optional[bool]
|
Enable CUDA acceleration for GPU inference. Default is False. |
vino |
Optional[bool]
|
Enable OpenVINO acceleration for Intel hardware. Default is False. |
verbose |
Optional[bool]
|
Enable verbose logging during inference. Default is False. |
trt |
Optional[bool]
|
Enable TensorRT acceleration for NVIDIA GPUs. Default is False. |
coreml |
Optional[bool]
|
Enable CoreML acceleration for Apple hardware. Default is False. |
warmup_iter |
int
|
Number of warmup iterations to run before benchmarking. Default is 0. |
Source code in focoos/ports.py
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|
Quotas
#
Bases: PydanticBase
Usage quotas and limits for a user account.
Attributes:
Name | Type | Description |
---|---|---|
total_inferences |
int
|
Total number of inferences allowed. |
max_inferences |
int
|
Maximum number of inferences allowed. |
used_storage_gb |
float
|
Used storage in gigabytes. |
max_storage_gb |
float
|
Maximum storage in gigabytes. |
active_training_jobs |
list[str]
|
List of active training job IDs. |
max_active_training_jobs |
int
|
Maximum number of active training jobs allowed. |
Source code in focoos/ports.py
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|
RemoteModelInfo
#
Bases: PydanticBase
Complete metadata for a Focoos model.
This class contains comprehensive information about a model in the Focoos platform, including its identification, configuration, performance metrics, and training details.
Attributes:
Name | Type | Description |
---|---|---|
ref |
str
|
Unique reference ID for the model. |
name |
str
|
Human-readable name of the model. |
description |
Optional[str]
|
Optional description of the model's purpose or capabilities. |
owner_ref |
str
|
Reference ID of the model owner. |
focoos_model |
str
|
The base model architecture used. |
task |
FocoosTask
|
The task type the model is designed for (e.g., DETECTION, SEMSEG). |
created_at |
datetime
|
Timestamp when the model was created. |
updated_at |
datetime
|
Timestamp when the model was last updated. |
status |
ModelStatus
|
Current status of the model (e.g., TRAINING, DEPLOYED). |
metrics |
Optional[dict]
|
Performance metrics of the model (e.g., mAP, accuracy). |
latencies |
Optional[list[dict]]
|
Inference latency measurements across different configurations. |
classes |
Optional[list[str]]
|
List of class names the model can detect or segment. |
im_size |
Optional[int]
|
Input image size the model expects. |
training_info |
Optional[TrainingInfo]
|
Information about the training process. |
location |
Optional[str]
|
Storage location of the model. |
dataset |
Optional[DatasetPreview]
|
Information about the dataset used for training. |
Source code in focoos/ports.py
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|
RuntimeType
#
Bases: str
, Enum
Available runtime configurations for model inference.
Values
- ONNX_CUDA32: ONNX with CUDA FP32
- ONNX_TRT32: ONNX with TensorRT FP32
- ONNX_TRT16: ONNX with TensorRT FP16
- ONNX_CPU: ONNX on CPU
- ONNX_COREML: ONNX with CoreML
- TORCHSCRIPT_32: TorchScript FP32
Source code in focoos/ports.py
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|
SystemInfo
#
Bases: PydanticBase
System information including hardware and software details.
Source code in focoos/ports.py
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|
pprint(level='DEBUG')
#
Pretty print the system info.
Source code in focoos/ports.py
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|
Task
#
Bases: str
, Enum
Types of computer vision tasks supported by Focoos.
Values
- DETECTION: Object detection
- SEMSEG: Semantic segmentation
- INSTANCE_SEGMENTATION: Instance segmentation
- CLASSIFICATION: Image classification
- KEYPOINT: Keypoint detection
Source code in focoos/ports.py
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|
TorchscriptRuntimeOpts
dataclass
#
TorchScript runtime configuration options.
This class provides configuration options for the TorchScript runtime used for model inference.
Attributes:
Name | Type | Description |
---|---|---|
warmup_iter |
int
|
Number of warmup iterations to run before benchmarking. Default is 0. |
optimize_for_inference |
bool
|
Enable inference optimizations. Default is True. |
set_fusion_strategy |
bool
|
Enable operator fusion. Default is True. |
Source code in focoos/ports.py
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|
TrainerArgs
dataclass
#
Configuration class for unified model training.
Attributes:
Name | Type | Description |
---|---|---|
run_name |
str
|
Name of the training run |
output_dir |
str
|
Directory to save outputs |
ckpt_dir |
Optional[str]
|
Directory for checkpoints |
init_checkpoint |
Optional[str]
|
Initial checkpoint to load |
resume |
bool
|
Whether to resume from checkpoint |
num_gpus |
int
|
Number of GPUs to use |
device |
str
|
Device to use (cuda/cpu) |
workers |
int
|
Number of data loading workers |
amp_enabled |
bool
|
Whether to use automatic mixed precision |
ddp_broadcast_buffers |
bool
|
Whether to broadcast buffers in DDP |
ddp_find_unused |
bool
|
Whether to find unused parameters in DDP |
checkpointer_period |
int
|
How often to save checkpoints |
checkpointer_max_to_keep |
int
|
Maximum checkpoints to keep |
eval_period |
int
|
How often to evaluate |
log_period |
int
|
How often to log |
vis_period |
int
|
How often to visualize |
samples |
int
|
Number of samples for visualization |
seed |
int
|
Random seed |
early_stop |
bool
|
Whether to use early stopping |
patience |
int
|
Early stopping patience |
ema_enabled |
bool
|
Whether to use EMA |
ema_decay |
float
|
EMA decay rate |
ema_warmup |
int
|
EMA warmup period |
learning_rate |
float
|
Base learning rate |
weight_decay |
float
|
Weight decay |
max_iters |
int
|
Maximum training iterations |
batch_size |
int
|
Batch size |
scheduler |
str
|
Learning rate scheduler type |
scheduler_extra |
Optional[dict]
|
Extra scheduler parameters |
optimizer |
str
|
Optimizer type |
optimizer_extra |
Optional[dict]
|
Extra optimizer parameters |
weight_decay_norm |
float
|
Weight decay for normalization layers |
weight_decay_embed |
float
|
Weight decay for embeddings |
backbone_multiplier |
float
|
Learning rate multiplier for backbone |
decoder_multiplier |
float
|
Learning rate multiplier for decoder |
head_multiplier |
float
|
Learning rate multiplier for head |
freeze_bn |
bool
|
Whether to freeze batch norm |
clip_gradients |
float
|
Gradient clipping value |
size_divisibility |
int
|
Input size divisibility requirement |
gather_metric_period |
int
|
How often to gather metrics |
zero_grad_before_forward |
bool
|
Whether to zero gradients before forward pass |
Source code in focoos/ports.py
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|
TrainingInfo
dataclass
#
Information about a model's training process.
This class contains details about the training job configuration, status, and timing.
Attributes:
Name | Type | Description |
---|---|---|
algorithm_name |
Optional[str]
|
The name of the training algorithm used. |
instance_type |
Optional[str]
|
The compute instance type used for training. |
volume_size |
Optional[int]
|
The storage volume size in GB allocated for the training job. |
max_runtime_in_seconds |
Optional[int]
|
Maximum allowed runtime for the training job in seconds. |
main_status |
Optional[str]
|
The primary status of the training job (e.g., "InProgress", "Completed"). |
secondary_status |
Optional[str]
|
Additional status information about the training job. |
failure_reason |
Optional[str]
|
Description of why the training job failed, if applicable. |
elapsed_time |
Optional[str]
|
Time elapsed since the start of the training job in seconds. |
status_transitions |
Optional[list[dict]]
|
List of status change events during the training process. |
start_time |
Optional[str]
|
Timestamp when the training job started. |
end_time |
Optional[str]
|
Timestamp when the training job completed or failed. |
artifact_location |
Optional[str]
|
Storage location of the training artifacts and model outputs. |
Source code in focoos/ports.py
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|
User
#
Bases: PydanticBase
User account information.
This class represents a user account in the Focoos platform, containing personal information, API key, and usage quotas.
Attributes:
Name | Type | Description |
---|---|---|
email |
str
|
The user's email address. |
created_at |
datetime
|
When the user account was created. |
updated_at |
datetime
|
When the user account was last updated. |
company |
Optional[str]
|
The user's company name, if provided. |
api_key |
ApiKey
|
The API key associated with the user account. |
quotas |
Quotas
|
Usage quotas and limits for the user account. |
Source code in focoos/ports.py
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|