Spaces:
Runtime error
Runtime error
| from matplotlib import cm | |
| import matplotlib.pyplot as plt | |
| from mpl_toolkits.axes_grid1 import make_axes_locatable | |
| import numpy as np | |
| # import onnx | |
| import onnxruntime as ort | |
| # from onnx import helper | |
| # from optimum.onnxruntime import ORTModel | |
| # import pandas as pd | |
| from PIL import Image | |
| from scipy import special | |
| # import torch | |
| # import torch.utils.data | |
| import gradio as gr | |
| # from transformers import pipeline | |
| # model_path = 'chlab/planet_detection_models/' | |
| model_path = './models/' | |
| # plotting a prameters | |
| labels = 20 | |
| ticks = 14 | |
| legends = 14 | |
| text = 14 | |
| titles = 22 | |
| lw = 3 | |
| ps = 200 | |
| cmap = 'magma' | |
| def normalize_array(x: list): | |
| '''Makes array between 0 and 1''' | |
| x = np.array(x) | |
| return (x - np.min(x)) / np.max(x - np.min(x)) | |
| def load_model(model: str, activation: bool=True): | |
| if activation: | |
| model += '_w_activation' | |
| options = ort.SessionOptions() | |
| options.intra_op_num_threads = 1 | |
| options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL | |
| provider = "CPUExecutionProvider" | |
| ort_session = ort.InferenceSession(model_path + '%s.onnx' % (model), options, providers=[provider]) | |
| # ort_session = ORTModel.load_model(model_path + '%s.onnx' % (model)) | |
| return ort_session | |
| def get_activations(intermediate_model, image: list, | |
| layer=None, vmax=2.5, sub_mean=True): | |
| '''Gets activations for a given input image''' | |
| input_name = intermediate_model.get_inputs()[0].name | |
| outputs = intermediate_model.run(None, {input_name: image}) | |
| # outputs = intermediate_model(image) | |
| output_1 = outputs[1] | |
| output_2 = outputs[2] | |
| output = outputs[0][0] | |
| output = special.softmax(output) | |
| # origin = 'lower' | |
| # plt.rcParams['xtick.labelsize'] = ticks | |
| # plt.rcParams['ytick.labelsize'] = ticks | |
| # fig, axs = plt.subplots(nrows=1, ncols=3, figsize=(28, 8)) | |
| # ax1, ax2, ax3 = axs[0], axs[1], axs[2] | |
| in_image = np.sum(image[0, :, :, :], axis=0) | |
| in_image = normalize_array(in_image) | |
| # im1 = ax1.imshow(in_image, cmap=cmap, vmin=0, vmax=vmax, origin=origin) | |
| if layer is None: | |
| activation_1 = np.sum(output_1[0, :, :, :], axis=0) | |
| activation_2 = np.sum(output_2[0, :, :, :], axis=0) | |
| else: | |
| activation_1 = output_1[0, layer, :, :] | |
| activation_2 = output_2[0, layer, :, :] | |
| if sub_mean: | |
| activation_1 -= np.mean(activation_1) | |
| activation_1 = np.abs(activation_1) | |
| activation_2 -= np.mean(activation_2) | |
| activation_2 = np.abs(activation_2) | |
| # im2 = ax2.imshow(activation_1, cmap=cmap, #vmin=0, vmax=1, | |
| # origin=origin) | |
| # im3 = ax3.imshow(activation_2, cmap=cmap, #vmin=0, vmax=1, | |
| # origin=origin) | |
| # ims = [im1, im2, im3] | |
| # for (i, ax) in enumerate(axs): | |
| # divider = make_axes_locatable(ax) | |
| # cax = divider.append_axes('right', size='5%', pad=0.05) | |
| # fig.colorbar(ims[i], cax=cax, orientation='vertical') | |
| # ax1.set_title('Input', fontsize=titles) | |
| # plt.show() | |
| return output, in_image, activation_1, activation_2 | |
| def predict_and_analyze(model_name, num_channels, dim, image): | |
| '''Loads a model with activations, passes through image and shows activations | |
| The image must be a numpy array of shape (C, W, W) or (1, C, W, W) | |
| ''' | |
| num_channels = int(num_channels) | |
| W = int(dim) | |
| # image = image.read() | |
| # with open(image, 'rb') as f: | |
| # im = f.readlines() | |
| # image = np.frombuffer(image) | |
| print("Loading data") | |
| image = np.load(image.name, allow_pickle=True) | |
| # image = image.reshape((num_channels, W, W)) | |
| # W = int(np.sqrt(image.shape[1])) | |
| # image = image.reshape((num_channels, W, W)) | |
| if len(image.shape) != 4: | |
| image = image[np.newaxis, :, :, :] | |
| assert image.shape == (1, num_channels, W, W), "Data is the wrong shape" | |
| model_name += '_%i' % (num_channels) | |
| print("Loading model") | |
| model = load_model(model_name, activation=True) | |
| print("Model loaded") | |
| print("Looking at activations") | |
| output, input_image, activation_1, activation_2 = get_activations(model, image, sub_mean=True) | |
| print("Activations and predictions finished") | |
| if output[0] < output[1]: | |
| output = 'Planet predicted with %f percent confidence' % (100*output[1]) | |
| else: | |
| output = 'No planet predicted with %f percent confidence' % (100 - 100*output[0]) | |
| input_image = normalize_array(input_image) | |
| activation_1 = normalize_array(activation_1) | |
| activation_2 = normalize_array(activation_2) | |
| # convert input image to RGB | |
| input_image = Image.fromarray(np.uint8(cm.magma(input_image)*255)) | |
| print("Plotting") | |
| origin = 'lower' | |
| plt.rcParams['xtick.labelsize'] = ticks | |
| plt.rcParams['ytick.labelsize'] = ticks | |
| fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(19, 8)) | |
| ax1, ax2 = axs[0], axs[1] | |
| im1 = ax1.imshow(activation_1, cmap=cmap, #vmin=0, vmax=1, | |
| origin=origin) | |
| im2 = ax2.imshow(activation_2, cmap=cmap, #vmin=0, vmax=1, | |
| origin=origin) | |
| ims = [im1, im2] | |
| for (i, ax) in enumerate(axs): | |
| divider = make_axes_locatable(ax) | |
| cax = divider.append_axes('right', size='5%', pad=0.05) | |
| fig.colorbar(ims[i], cax=cax, orientation='vertical') | |
| ax1.set_title('First Activation', fontsize=titles) | |
| ax2.set_title('Second Activation', fontsize=titles) | |
| print("Sending to Hugging Face") | |
| return output, input_image, fig | |
| if __name__ == "__main__": | |
| demo = gr.Interface( | |
| fn=predict_and_analyze, | |
| inputs=[gr.Dropdown(["regnet", "efficientnet"], | |
| value="efficientnet", | |
| label="Model Selection", | |
| show_label=True), | |
| gr.Dropdown(["45", "61", "75"], | |
| value="61", | |
| label="Number of Velocity Channels", | |
| show_label=True), | |
| gr.Dropdown(["600"], | |
| value="600", | |
| label="Image Dimensions", | |
| show_label=True), | |
| gr.File(label="Input Data", show_label=True)], | |
| outputs=[gr.Textbox(lines=1, label="Prediction", show_label=True), | |
| gr.Image(label="Input Image", show_label=True), | |
| # gr.Image(label="Activation 1", show_label=True), | |
| # gr.Image(label="Actication 2", show_label=True)], | |
| gr.Plot(label="Activations", show_label=True) | |
| # gr.Plot(label="Actication 2", show_label=True)], | |
| ], | |
| title="Kinematic Planet Detector" | |
| ) | |
| demo.launch() | |