clean-up pipelines, updatetyping and descriptions

src/deepsparse/clip/README.md

@@ -110,7 +110,7 @@ kwargs = {
 pipeline = BasePipeline.create(task="clip_caption", **kwargs)
 
 pipeline_input = CLIPCaptionInput(image=CLIPVisualInput(images="thailand.jpg"))
-output = pipeline(pipeline_input)
+output = pipeline(pipeline_input).caption
 print(output[0])
 ```
 Running the code above, we get the following caption:

src/deepsparse/clip/captioning_pipeline.py

@@ -53,6 +53,25 @@ class CLIPCaptionOutput(BaseModel):
 
 @BasePipeline.register(task="clip_caption", default_model_path=None)
 class CLIPCaptionPipeline(BasePipeline):
+    """
+    Pipelines designed to generate a caption for a given image. The CLIPCaptionPipeline
+    relies on 3 other pipelines: CLIPVisualPipeline, CLIPTextPipeline, and the
+    CLIPDecoder Pipeline. The pipeline takes in a single image and then uses the
+    pipelines along with Beam Search to generate a caption.
+
+    :param visual_model_path: either a local path or sparsezoo stub for the CLIP visual
+    branch onnx model
+    :param text_model_path: either a local path or sparsezoo stub for the CLIP text
+    branch onnx model
+    :param decoder_model_path: either a local path or sparsezoo stub for the CLIP
+    decoder branch onnx model
+    :param num_beams: number of beams to use in Beam Search
+    :param num_beam_groups: number of beam groups to use in Beam Search
+    :param min_seq_len: the minimum length of the caption sequence
+    :param max_seq_len: the maxmium length of the caption sequence
+
+    """
+
     def __init__(
         self,
         visual_model_path: str,
@@ -61,17 +80,13 @@ def __init__(
         num_beams: int = 10,
         num_beam_groups: int = 5,
         min_seq_len: int = 5,
-        seq_len: int = 20,
-        fixed_output_length: bool = False,
+        max_seq_len: int = 20,
         **kwargs,
     ):
         self.num_beams = num_beams
         self.num_beam_groups = num_beam_groups
-        self.seq_len = seq_len
+        self.max_seq_len = max_seq_len
         self.min_seq_len = min_seq_len
-        self.fixed_output_length = fixed_output_length
-
-        super().__init__(**kwargs)
 
         self.visual = Pipeline.create(
             task="clip_visual",
@@ -86,8 +101,9 @@ def __init__(
             **{"model_path": decoder_model_path},
         )
 
-    # TODO: have to verify all input types
-    def _encode_and_decode(self, text, image_embs):
+        super().__init__(**kwargs)
+
+    def _encode_and_decode(self, text: torch.Tensor, image_embs: torch.Tensor):
         original_size = text.shape[-1]
         padded_tokens = F.pad(text, (15 - original_size, 0))
         text_embeddings = self.text(
@@ -104,16 +120,15 @@ def _encode_and_decode(self, text, image_embs):
         }
 
     # Adapted from open_clip
-    def _generate(self, pipeline_inputs):
-        # Make these input values?
+    def _generate(self, pipeline_inputs: CLIPCaptionInput):
         sot_token_id = 49406
         eos_token_id = 49407
         pad_token_id = 0
         batch_size = 1
         repetition_penalty = 1.0
         device = "cpu"
 
-        stopping_criteria = [MaxLengthCriteria(max_length=self.seq_len)]
+        stopping_criteria = [MaxLengthCriteria(max_length=self.max_seq_len)]
         stopping_criteria = StoppingCriteriaList(stopping_criteria)
 
         logits_processor = LogitsProcessorList(

src/deepsparse/clip/decoder_pipeline.py

@@ -29,8 +29,12 @@ class CLIPDecoderInput(BaseModel):
     Input for the CLIP Decoder Branch
     """
 
-    text_embeddings: Any = Field(description="Text emebddings from the text branch")
-    image_embeddings: Any = Field(description="Image embeddings from the visual branch")
+    text_embeddings: Any = Field(
+        description="np.array of text emebddings from the " "text branch"
+    )
+    image_embeddings: Any = Field(
+        description="np.array of image embeddings from the " "visual branch"
+    )
 
 
 class CLIPDecoderOutput(BaseModel):
@@ -39,7 +43,7 @@ class CLIPDecoderOutput(BaseModel):
     """
 
     logits: List[Any] = Field(
-        description="Logits produced from the text and image emebeddings."
+        description="np.array of logits produced from the decoder."
     )
 
 

src/deepsparse/clip/text_pipeline.py

@@ -31,7 +31,7 @@ class CLIPTextInput(BaseModel):
     """
 
     text: Union[str, List[str], Any, List[Any]] = Field(
-        description="Either raw text or text embeddings"
+        description="Either raw strings or an np.array with tokenized text"
     )
 
 
@@ -41,8 +41,10 @@ class CLIPTextOutput(BaseModel):
     """
 
     text_embeddings: List[Any] = Field(
-        description="Text embeddings for the single text or list of embeddings for "
-        "multiple."
+        description="np.array of text embeddings. For the caption "
+        "pipeline, a list of two embeddings is produced. For zero-shot "
+        "classifcation, one array is produced with the embeddings stacked along "
+        "batch axis."
     )
 
 

src/deepsparse/clip/visual_pipeline.py

@@ -44,8 +44,10 @@ class CLIPVisualOutput(BaseModel):
     """
 
     image_embeddings: List[Any] = Field(
-        description="Image embeddings for the single image or list of embeddings for "
-        "multiple images"
+        description="np.arrays consisting of image embeddings. For the caption "
+        "pipeline, a list of two image embeddings is produced. For zero-shot "
+        "classifcation, one array is produced with the embeddings stacked along "
+        "batch axis."
     )
 
 

src/deepsparse/clip/zeroshot_pipeline.py

@@ -32,18 +32,23 @@ class CLIPZeroShotInput(BaseModel):
     """
 
     image: CLIPVisualInput = Field(
-        description="Path to image to run zero-shot prediction on."
+        description="Image(s) to run zero-shot prediction. See CLIPVisualPipeline "
+        "for details."
+    )
+    text: CLIPTextInput = Field(
+        description="Text/classes to run zero-shot prediction "
+        "see CLIPTextPipeline for details."
     )
-    text: CLIPTextInput = Field(description="List of text to process")
 
 
 class CLIPZeroShotOutput(BaseModel):
     """
     Output for the CLIP Zero Shot Model
     """
 
-    # TODO: Maybe change this to a dictionary where keys are text inputs
-    text_scores: List[Any] = Field(description="Probability of each text class")
+    text_scores: List[Any] = Field(
+        description="np.array consisting of probabilities " " each class provided."
+    )
 
 
 @BasePipeline.register(task="clip_zeroshot", default_model_path=None)