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import gradio as gr
import keras
import numpy as np
# All reshaping layers and their args, descriptions
layers = {
"Reshape":{
"args":["target_shape"],
"descriptions":["""target_shape: Target shape. Tuple of integers, does not include the
samples dimension (batch size)."""]
},
"Flatten":{
"args":["data_format"],
"descriptions":["""data_format: A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs.
channels_last corresponds to inputs with shape (batch, ..., channels) while channels_first corresponds to inputs with shape (batch, channels, ...).
It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json.
If you never set it, then it will be "channels_last"."""]
},
"RepeatVector":{
"args":["n"],
"descriptions":["n: Integer, repetition factor."]
},
"Permute":{
"args":["dims"],
"descriptions":["""dims: Tuple of integers.
Permutation pattern does not include the samples dimension. Indexing starts at 1.
For instance, (2, 1) permutes the first and second dimensions of the input."""]
},
"Cropping1D":{
"args":["cropping"],
"descriptions":["""cropping: Int or tuple of int (length 2)
How many units should be trimmed off at the beginning and end of the cropping dimension (axis 1).
If a single int is provided, the same value will be used for both."""]
},
"Cropping2D":{
"args":["cropping", "data_format"],
"descriptions":["""cropping: Int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
If int: the same symmetric cropping is applied to height and width.
If tuple of 2 ints: interpreted as two different symmetric cropping values for height and width: (symmetric_height_crop, symmetric_width_crop).
If tuple of 2 tuples of 2 ints: interpreted as ((top_crop, bottom_crop), (left_crop, right_crop))""",
"""data_format: A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs.
channels_last corresponds to inputs with shape (batch_size, height, width, channels) while channels_first corresponds to inputs with shape
(batch_size, channels, height, width). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json.
If you never set it, then it will be "channels_last"."""],
},
"Cropping3D":{
"args":["cropping", "data_format"],
"descriptions":["""cropping: Int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints.
If int: the same symmetric cropping is applied to depth, height, and width.
If tuple of 3 ints: interpreted as two different symmetric cropping values for depth, height, and width: (symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop).
If tuple of 3 tuples of 2 ints: interpreted as ((left_dim1_crop, right_dim1_crop), (left_dim2_crop, right_dim2_crop), (left_dim3_crop, right_dim3_crop))""",
"""data_format: A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape
(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) while channels_first corresponds to inputs with shape
(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json.
If you never set it, then it will be "channels_last"."""]
},
"UpSampling1D":{
"args":["size"],
"descriptions":["size: Integer. UpSampling factor."]
},
"UpSampling2D":{
"args":["size", "data_format", "interpolation"],
"descriptions":["size: Int, or tuple of 2 integers. The UpSampling factors for rows and columns.",
"""data_format: A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs.
channels_last corresponds to inputs with shape (batch_size, height, width, channels) while channels_first corresponds to inputs with
shape (batch_size, channels, height, width). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json.
If you never set it, then it will be "channels_last".""",
"""interpolation: A string, one of "area", "bicubic", "bilinear", "gaussian", "lanczos3", "lanczos5", "mitchellcubic", "nearest"."""]
},
"UpSampling3D":{
"args":["size","data_format"],
"descriptions":["size: Int, or tuple of 3 integers. The UpSampling factors for dim1, dim2 and dim3.",
"""data_format: A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs.
channels_last corresponds to inputs with shape (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) while
channels_first corresponds to inputs with shape (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3).
It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it,
then it will be "channels_last"."""]
},
"ZeroPadding1D":{
"args":["padding"],
"descriptions":["""padding: Int, or tuple of int (length 2), or dictionary. - If int:
How many zeros to add at the beginning and end of the padding dimension (axis 1). -
If tuple of int (length 2): How many zeros to add at the beginning and the end of the padding dimension ((left_pad, right_pad))."""]
},
"ZeroPadding2D":{
"args":["padding", "data_format"],
"descriptions":["""padding: Int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
If int: the same symmetric padding is applied to height and width.
If tuple of 2 ints: interpreted as two different symmetric padding values for height and width: (symmetric_height_pad, symmetric_width_pad).
If tuple of 2 tuples of 2 ints: interpreted as ((top_pad, bottom_pad), (left_pad, right_pad))""",
"""data_format: A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs.
channels_last corresponds to inputs with shape (batch_size, height, width, channels) while channels_first corresponds to inputs with shape
(batch_size, channels, height, width). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json.
If you never set it, then it will be "channels_last"."""]
},
"ZeroPadding3D":{
"args":["padding", "data_format"],
"descriptions":["""padding: Int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints.
If int: the same symmetric padding is applied to height and width.
If tuple of 3 ints: interpreted as two different symmetric padding values for height and width: (symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad).
If tuple of 3 tuples of 2 ints: interpreted as ((left_dim1_pad, right_dim1_pad), (left_dim2_pad, right_dim2_pad), (left_dim3_pad, right_dim3_pad))""",
"""data_format: A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs
with shape (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels) while channels_first corresponds to inputs with shape
(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). It defaults to the image_data_format value found in your Keras config file
at ~/.keras/keras.json. If you never set it, then it will be "channels_last"."""]
}
}
with gr.Blocks() as demo:
gr.Markdown(f'')
gr.Markdown("# Reshaping Layers")
gr.Markdown("""This app allows you to play with various Keras Reshaping layers, and is meant to be a
supplement to the documentation. You are free to change the layer, tensor/array shape, and arguments associated
with that layer. Execution will show you the command used as well as your resulting array/tensor.
Keras documentation can be found [here](https://keras.io/api/layers/reshaping_layers/).<br>
App built by [Brenden Connors](https://github.com/brendenconnors).<br>
Built using keras==2.9.0.
<br>""")
with gr.Row():
with gr.Column(variant='panel'):
layers_dropdown = gr.Dropdown(choices=list(layers.keys()), value="Reshape", label="Keras Layer")
with gr.Box():
gr.Markdown("**Please enter desired shape.**")
desired_shape2d = gr.Dataframe(value = [[2,2]],
headers = ["Rows", "Columns"],
row_count=(1, 'fixed'),
col_count=(2, "fixed"),
datatype="number",
type = "numpy",
interactive=True,
visible = False
)
desired_shape3d = gr.Dataframe(value = [[2,2,2]],
headers = ["Rows", "Columns", "Depth/Channels"],
row_count=(1, 'fixed'),
col_count=(3, "fixed"),
datatype="number",
type = "numpy",
interactive=True,
visible = True
)
desired_shape4d = gr.Dataframe(value = [[2,2,2,2]],
headers = ["Rows", "Columns", "Depth", "Channels"],
row_count=(1, 'fixed'),
col_count=(4, "fixed"),
datatype="number",
type = "numpy",
interactive=True,
visible = False
)
button = gr.Button("Generate Tensor")
input_arr = gr.Textbox(label = "Input Tensor",
interactive = False,
value = np.array([[1,2],[3,4]]))
with gr.Box():
gr.Markdown("**Layer Args**")
with gr.Row():
arg1 = gr.Textbox(label='target_shape')
arg2 = gr.Textbox(label='arg2',visible=False)
arg3 = gr.Textbox(label='arg3',visible=False)
with gr.Row():
desc1 = gr.Textbox(label= '', value = layers["Reshape"]["descriptions"][0])
desc2 = gr.Textbox(label = '', visible=False)
desc3 = gr.Textbox(label = '', visible=False)
result_button = gr.Button("Execute", variant="primary")
with gr.Column(variant='panel'):
output = gr.Textbox(label = 'Command Used')
output2 = gr.Textbox(label = 'Result')
def generate_arr(layer, data1, data2, data3):
"""
Create Input tensor
"""
if '1D' in layer:
data = data1[0]
elif '2D' in layer:
data = data2[0]
elif '3D' in layer:
data = data3[0]
elif layer=="RepeatVector":
data = data1[0]
else:
data = data2[0]
shape = tuple([int(x) for x in data if int(x)!=0])
elements = [x+1 for x in range(np.prod(shape))]
return np.array(elements).reshape(shape)
def add_dim(layer):
"""
Adjust dimensions component dependent on layer type
"""
if '1D' in layer:
return gr.DataFrame.update(visible=True), gr.DataFrame.update(visible=False), gr.DataFrame.update(visible=False)
elif '2D' in layer:
return gr.DataFrame.update(visible=False), gr.DataFrame.update(visible=True), gr.DataFrame.update(visible=False)
elif '3D' in layer:
return gr.DataFrame.update(visible=False), gr.DataFrame.update(visible=False), gr.DataFrame.update(visible=True)
elif layer=="RepeatVector":
return gr.DataFrame.update(visible=True), gr.DataFrame.update(visible=False), gr.DataFrame.update(visible=False)
return gr.DataFrame.update(visible=False), gr.DataFrame.update(visible=True), gr.DataFrame.update(visible=False)
def change_args(layer):
"""
Change layer args dependent on layer name
"""
n_args = len(layers[layer]["args"])
args = layers[layer]["args"]
descriptions = layers[layer]["descriptions"]
descriptions = descriptions + ['None']*3
args = args + ['None']*3
visible_bool = [True if i<=n_args else False for i in range(1,4)]
return gr.Textbox.update(label=args[0], visible=visible_bool[0]),\
gr.Textbox.update(label=args[1], visible=visible_bool[1]),\
gr.Textbox.update(label=args[2], visible=visible_bool[2]),\
gr.Textbox.update(value = descriptions[0], visible = visible_bool[0]),\
gr.Textbox.update(value = descriptions[1], visible = visible_bool[1]),\
gr.Textbox.update(value = descriptions[2], visible = visible_bool[2])
def create_layer(layer_name, arg1, arg2, arg3):
"""
Create layer given layer name and args
"""
args = [arg1, arg2, arg3]
real_args = [x for x in args if x != '']
arg_str = ','.join(real_args)
return f"keras.layers.{layer_name}({arg_str})"
def execute(layer_name, arg1, arg2, arg3, shape1, shape2, shape3):
"""
Execute keras reshaping layer given input tensor
"""
args = [arg1, arg2, arg3]
real_args = [x for x in args if x != '']
arg_str = ','.join(real_args)
try:
layer = eval(f"keras.layers.{layer_name}({arg_str})")
except Exception as e:
return f"Error: {e}"
def arr(data, layer_name):
if layer_name == "RepeatVector":
shape = tuple([int(x) for x in data[0] if int(x)!=0])
else:
shape = tuple([1] + [int(x) for x in data[0] if int(x)!=0])
elements = [x+1 for x in range(np.prod(shape))]
return np.array(elements).reshape(shape)
if '1D' in layer_name:
inp = arr(shape1, layer_name)
elif '2D' in layer_name:
inp = arr(shape2, layer_name)
elif '3D' in layer_name:
inp = arr(shape3, layer_name)
elif layer_name=="RepeatVector":
inp = arr(shape1, layer_name)
else:
inp = arr(shape2, layer_name)
try:
return layer(inp)
except Exception as e:
return e
# Generate tensor
button.click(generate_arr, [layers_dropdown, desired_shape2d, desired_shape3d, desired_shape4d], input_arr)
# All changes dependent on layer selected
layers_dropdown.change(add_dim, layers_dropdown, [desired_shape2d, desired_shape3d, desired_shape4d])
layers_dropdown.change(change_args, layers_dropdown, [arg1, arg2, arg3, desc1, desc2, desc3])
layers_dropdown.change(generate_arr, [layers_dropdown, desired_shape2d, desired_shape3d, desired_shape4d], input_arr)
# Show command used and execute it
result_button.click(create_layer, [layers_dropdown, arg1, arg2, arg3], output)
result_button.click(execute, [layers_dropdown, arg1, arg2, arg3, desired_shape2d, desired_shape3d, desired_shape4d], output2)
demo.launch()
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