support float32 and double64 of ndarray

visualdl/component/base_component.py

@@ -60,7 +60,7 @@ def imgarray2bytes(np_array):
     """Convert image ndarray to bytes.
 
     Args:
-        np_array (numpy.ndarray): Array to converte.
+        np_array (np.ndarray): Array to converte.
 
     Returns:
         Binary bytes of np_array.
@@ -106,7 +106,7 @@ def convert_to_HWC(tensor, input_format):
     """Convert `NCHW`, `HWC`, `HW` to `HWC`
 
     Args:
-        tensor (numpy.ndarray): Value of image
+        tensor (np.ndarray): Value of image
         input_format (string): Format of image
 
     Return:
@@ -138,12 +138,26 @@ def convert_to_HWC(tensor, input_format):
         return tensor
 
 
+def denormalization(image_array):
+    """Renormalise ndarray matrix.
+
+    Args:
+        image_array(np.ndarray): Value of image
+
+    Return:
+        Matrix after renormalising.
+    """
+    if image_array.max() <= 1 and image_array.min() >= 0:
+        image_array *= 255
+    return image_array.astype(np.uint8)
+
+
 def image(tag, image_array, step, walltime=None, dataformats="HWC"):
     """Package data to one image.
 
     Args:
         tag (string): Data identifier
-        image_array (numpy.ndarray): Value of iamge
+        image_array (np.ndarray): Value of image
         step (int): Step of image
         walltime (int): Wall time of image
         dataformats (string): Format of image
@@ -151,6 +165,7 @@ def image(tag, image_array, step, walltime=None, dataformats="HWC"):
     Return:
         Package with format of record_pb2.Record
     """
+    image_array = denormalization(image_array)
     image_array = convert_to_HWC(image_array, dataformats)
     image_bytes = imgarray2bytes(image_array)
     image = Record.Image(encoded_image_string=image_bytes)
@@ -165,7 +180,7 @@ def embedding(tag, labels, hot_vectors, step, labels_meta=None, walltime=None):
     Args:
         tag (string): Data identifier
         labels (list): A list of labels.
-        hot_vectors (numpy.array or list): A matrix which each row is
+        hot_vectors (np.array or list): A matrix which each row is
             feature of labels.
         step (int): Step of embeddings.
         walltime (int): Wall time of embeddings.
@@ -199,7 +214,7 @@ def audio(tag, audio_array, sample_rate, step, walltime):
 
     Args:
         tag (string): Data identifier
-        audio_array (numpy.ndarray or list): audio represented by a numpy.array
+        audio_array (np.ndarray or list): audio represented by a np.array
         sample_rate (int): Sample rate of audio
         step (int): Step of audio
         walltime (int): Wall time of audio
@@ -246,8 +261,8 @@ def histogram(tag, hist, bin_edges, step, walltime):
 
     Args:
         tag (string): Data identifier
-        hist (numpy.ndarray or list): The values of the histogram
-        bin_edges (numpy.ndarray or list): The bin edges
+        hist (np.ndarray or list): The values of the histogram
+        bin_edges (np.ndarray or list): The bin edges
         step (int): Step of histogram
         walltime (int): Wall time of histogram
 
@@ -265,8 +280,8 @@ def compute_curve(labels, predictions, num_thresholds=None, weights=None):
     """ Compute precision-recall curve data by labels and predictions.
 
     Args:
-        labels (numpy.ndarray or list): Binary labels for each element.
-        predictions (numpy.ndarray or list): The probability that an element be
+        labels (np.ndarray or list): Binary labels for each element.
+        predictions (np.ndarray or list): The probability that an element be
             classified as true.
         num_thresholds (int): Number of thresholds used to draw the curve.
         weights (float): Multiple of data to display on the curve.
@@ -318,8 +333,8 @@ def pr_curve(tag, labels, predictions, step, walltime, num_thresholds=127,
 
     Args:
         tag (string): Data identifier
-        labels (numpy.ndarray or list): Binary labels for each element.
-        predictions (numpy.ndarray or list): The probability that an element be
+        labels (np.ndarray or list): Binary labels for each element.
+        predictions (np.ndarray or list): The probability that an element be
             classified as true.
         step (int): Step of pr_curve
         walltime (int): Wall time of pr_curve