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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() | |