test

optimum_benchmark/generators/task_generator.py

@@ -2,13 +2,17 @@
 import random
 import string
 from abc import ABC
-from typing import Tuple
+from typing import List, Tuple
 
 # TODO: drop torch dependency and use numpy instead
 import torch
 
 LOGGER = logging.getLogger("generators")
 
+DEFAULT_NUM_LABELS = 2
+DEFAULT_VOCAB_SIZE = 2
+DEFAULT_TYPE_VOCAB_SIZE = 2
+
 
 class TaskGenerator(ABC):
     def __init__(self, shapes, with_labels: bool):
@@ -17,61 +21,21 @@ def __init__(self, shapes, with_labels: bool):
 
     @staticmethod
     def generate_random_integers(min_value: int, max_value: int, shape: Tuple[int]):
-        if min_value is None:
-            LOGGER.warning("min_value is None, setting it to 0")
-            min_value = 0
-
-        if max_value is None:
-            LOGGER.warning("max_value is None, setting it to 2")
-            max_value = 2
-
-        if None in shape:
-            LOGGER.warning("shape contains None, setting it to (1, 1)")
-            shape = (1, 1)
-
         return torch.randint(min_value, max_value, shape)
 
     @staticmethod
     def generate_random_floats(min_value: float, max_value: float, shape: Tuple[int]):
-        if min_value is None:
-            LOGGER.warning("min_value is None, setting it to 0")
-            min_value = 0
-
-        if max_value is None:
-            LOGGER.warning("max_value is None, setting it to 1")
-            max_value = 1
-
-        if None in shape:
-            LOGGER.warning("shape contains None, setting it to (1, 1)")
-            shape = (1, 1)
-
         return torch.rand(shape) * (max_value - min_value) + min_value
 
     @staticmethod
     def generate_ranges(start: int, stop: int, shape: Tuple[int]):
-        if start is None:
-            LOGGER.warning("start is None, setting it to 0")
-            start = 0
-
-        if stop is None:
-            LOGGER.warning("stop is None, setting it to 1")
-            stop = 1
-
-        if None in shape:
-            LOGGER.warning("shape contains None, setting it to (1, 1)")
-            shape = (1, 1)
-
         return torch.arange(start, stop).repeat(shape[0], 1)
 
     @staticmethod
-    def generate_random_strings(shape: Tuple[int]):
-        if None in shape:
-            LOGGER.warning("shape contains None, setting it to (1, 1)")
-            shape = (1, 1)
-
+    def generate_random_strings(num_seq: int) -> List[str]:
         return [
-            "".join(random.choice(string.ascii_letters + string.digits) for _ in range(shape[1]))
-            for _ in range(shape[0])
+            "".join(random.choice(string.ascii_letters + string.digits) for _ in range(random.randint(10, 100)))
+            for _ in range(num_seq)
         ]
 
     def __call__(self):
@@ -82,7 +46,7 @@ class TextGenerator(TaskGenerator):
     def input_ids(self):
         return self.generate_random_integers(
             min_value=0,
-            max_value=self.shapes["vocab_size"],
+            max_value=self.shapes["vocab_size"] or DEFAULT_VOCAB_SIZE,
             shape=(self.shapes["batch_size"], self.shapes["sequence_length"]),
         )
 
@@ -96,7 +60,7 @@ def attention_mask(self):
     def token_type_ids(self):
         return self.generate_random_integers(
             min_value=0,
-            max_value=self.shapes["type_vocab_size"],
+            max_value=self.shapes["type_vocab_siza"] or DEFAULT_TYPE_VOCAB_SIZE,
             shape=(self.shapes["batch_size"], self.shapes["sequence_length"]),
         )
 
@@ -140,7 +104,7 @@ def input_features(self):
 class TextClassificationGenerator(TextGenerator):
     def labels(self):
         return self.generate_random_integers(
-            min_value=0, max_value=self.shapes["num_labels"], shape=(self.shapes["batch_size"],)
+            min_value=0, max_value=self.shapes["num_labels"] or DEFAULT_NUM_LABELS, shape=(self.shapes["batch_size"],)
         )
 
     def __call__(self):
@@ -165,7 +129,7 @@ class TokenClassificationGenerator(TextGenerator):
     def labels(self):
         return self.generate_random_integers(
             min_value=0,
-            max_value=self.shapes["num_labels"],
+            max_value=self.shapes["num_labels"] or DEFAULT_NUM_LABELS,
             shape=(self.shapes["batch_size"], self.shapes["sequence_length"]),
         )
 
@@ -292,7 +256,7 @@ def __call__(self):
 class ImageClassificationGenerator(ImageGenerator):
     def labels(self):
         return self.generate_random_integers(
-            min_value=0, max_value=self.shapes["num_labels"], shape=(self.shapes["batch_size"],)
+            min_value=0, max_value=self.shapes["num_labels"] or DEFAULT_NUM_LABELS, shape=(self.shapes["batch_size"],)
         )
 
     def __call__(self):
@@ -310,7 +274,9 @@ def labels(self):
         return [
             {
                 "class_labels": self.generate_random_integers(
-                    min_value=0, max_value=self.shapes["num_labels"], shape=(self.shapes["num_queries"],)
+                    min_value=0,
+                    max_value=self.shapes["num_labels"] or DEFAULT_NUM_LABELS,
+                    shape=(self.shapes["num_queries"],),
                 ),
                 "boxes": self.generate_random_floats(min_value=-1, max_value=1, shape=(self.shapes["num_queries"], 4)),
             }
@@ -331,7 +297,7 @@ class SemanticSegmentationGenerator(ImageGenerator):
     def labels(self):
         return self.generate_random_integers(
             min_value=0,
-            max_value=self.shapes["num_labels"],
+            max_value=self.shapes["num_labels"] or DEFAULT_NUM_LABELS,
             shape=(self.shapes["batch_size"], self.shapes["height"], self.shapes["width"]),
         )
 
@@ -348,7 +314,7 @@ def __call__(self):
 class AudioClassificationGenerator(AudioGenerator):
     def labels(self):
         return self.generate_random_integers(
-            min_value=0, max_value=self.shapes["num_labels"], shape=(self.shapes["batch_size"],)
+            min_value=0, max_value=self.shapes["num_labels"] or DEFAULT_NUM_LABELS, shape=(self.shapes["batch_size"],)
         )
 
     def __call__(self):
@@ -365,7 +331,7 @@ class AutomaticSpeechRecognitionGenerator(AudioGenerator):
     def labels(self):
         return self.generate_random_integers(
             min_value=0,
-            max_value=self.shapes["vocab_size"],
+            max_value=self.shapes["vocab_size"] or DEFAULT_TYPE_VOCAB_SIZE,
             shape=(self.shapes["batch_size"], self.shapes["sequence_length"]),
         )
 
@@ -381,7 +347,7 @@ def __call__(self):
 
 class PromptGenerator(TaskGenerator):
     def prompt(self):
-        return self.generate_random_strings(shape=(self.shapes["batch_size"], 10))
+        return self.generate_random_strings(num_seq=self.shapes["batch_size"])
 
     def __call__(self):
         dummy = {}
@@ -394,12 +360,7 @@ class FeatureExtractionGenerator(TextGenerator, ImageGenerator):
     def __call__(self):
         dummy = {}
 
-        if (
-            "num_channels" in self.shapes
-            and self.shapes["num_channels"] is not None
-            and "height" in self.shapes
-            and self.shapes["height"] is not None
-        ):
+        if self.shapes.get("num_channels", None) is not None and self.shapes.get("height", None) is not None:
             dummy["pixel_values"] = self.pixel_values()
         else:
             dummy["input_ids"] = self.input_ids()