From de2ea4140842879de4aff989c2c2de13893d09c2 Mon Sep 17 00:00:00 2001 From: Eduardo Hirata-Miyasaki Date: Tue, 10 Sep 2024 14:52:27 -0700 Subject: [PATCH 1/4] initial demo --- examples/gradio/demo_gradio.py | 124 +++++++++++++++++++++++++++++++++ examples/gradio/hek.png | Bin 0 -> 98142 bytes 2 files changed, 124 insertions(+) create mode 100644 examples/gradio/demo_gradio.py create mode 100644 examples/gradio/hek.png diff --git a/examples/gradio/demo_gradio.py b/examples/gradio/demo_gradio.py new file mode 100644 index 00000000..23fda36d --- /dev/null +++ b/examples/gradio/demo_gradio.py @@ -0,0 +1,124 @@ +# %% +from PIL import Image +from torchvision import transforms + +from viscy.data.hcs import HCSDataModule +from viscy.light.engine import FcmaeUNet +from viscy.light.predict_writer import HCSPredictionWriter +from viscy.light.trainer import VSTrainer +from viscy.light.engine import VSUNet +from viscy.transforms import NormalizeSampled +import torch +import gradio as gr +import numpy as np +from numpy.typing import ArrayLike +from skimage import io, transform, exposure + + +class VSGradio: + def __init__(self, model_config, model_ckpt_path): + self.model_config = model_config + self.model_ckpt_path = model_ckpt_path + self.model = None + self.load_model() + + def load_model(self): + # Load the model checkpoint + self.model = VSUNet.load_from_checkpoint( + self.model_ckpt_path, + architecture="UNeXt2_2D", + model_config=self.model_config, + accelerator="gpu", + ) + self.model.eval() + + def normalize_fov(self, input: ArrayLike): + "Normalizing the fov with zero mean and unit variance" + mean = np.mean(input) + std = np.std(input) + return (input - mean) / std + + def resize_to_divisible_by_32(self, img): + # Load the image using skimage + + # Get the current height and width of the image + height, width = img.shape[-2:] + + # Calculate the new width and height to be divisible by 32 + new_width = (width // 32) * 32 + new_height = (height // 32) * 32 + + # Resize the image if needed + if width != new_width or height != new_height: + img_resized = transform.resize( + img, (new_height, new_width), anti_aliasing=True + ) + else: + img_resized = img + + return img_resized + + def predict(self, inp): + # normalize the input + inp = self.resize_to_divisible_by_32(inp) + inp = self.normalize_fov(inp) + inp = torch.from_numpy(np.array(inp).astype(np.float32)) + # ensure inp is tensor has to be a (B,C,D,H,W) tensor + if len(inp.shape) == 2: + inp = inp.unsqueeze(0) + inp = inp.unsqueeze(0) + inp = inp.unsqueeze(0) + + elif len(inp.shape) == 3: + inp = inp.unsqueeze(0) + inp = inp.unsqueeze(0) + + elif len(inp.shape) == 4: + inp = inp.unsqueeze(0) + + elif len(inp.shape) == 5: + pass + + with torch.inference_mode(): + inp = inp.to(self.model.device) + pred = self.model(inp).cpu().numpy() + + # Return a 2D image + nuc_pred = pred[0, 0, 0] + mem_pred = pred[0, 1, 0] + # pred = np.clip(pred, 0, 1) + nuc_pred = exposure.rescale_intensity(nuc_pred, out_range=(0, 1)) + mem_pred = exposure.rescale_intensity(mem_pred, out_range=(0, 1)) + return nuc_pred, mem_pred + + +# %% +if __name__ == "__main__": + input_data_path = "" + model_ckpt_path = "/hpc/projects/comp.micro/virtual_staining/datasets/public/VS_models/VSCyto2D/VSCyto2D/epoch=399-step=23200.ckpt" + + model_config = { + "in_channels": 1, + "out_channels": 2, + "encoder_blocks": [3, 3, 9, 3], + "dims": [96, 192, 384, 768], + "decoder_conv_blocks": 2, + "stem_kernel_size": [1, 2, 2], + "in_stack_depth": 1, + "pretraining": False, + } + + vsgradio = VSGradio(model_config, model_ckpt_path) + + gr.Interface( + fn=vsgradio.predict, + inputs=gr.Image(type="numpy", image_mode="L", format="png"), + outputs=[ + gr.Image(type="numpy", format="png"), + gr.Image(type="numpy", format="png"), + ], + examples=[ + "/home/eduardo.hirata/repos/viscy/examples/gradio/hek.png", + "/home/eduardo.hirata/repos/viscy/examples/gradio/dog.jpeg", + ], + ).launch() diff --git a/examples/gradio/hek.png 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model_ckpt_path = "/hpc/projects/comp.micro/virtual_staining/datasets/public/VS_models/VSCyto2D/VSCyto2D/epoch=399-step=23200.ckpt" + model_ckpt_path = "./VS_models/VSCyto2D/VSCyto2D/epoch=399-step=23200.ckpt" model_config = { "in_channels": 1, "out_channels": 2, @@ -76,7 +76,7 @@ def predict(self, inp): gr.Image(type="numpy", format="png"), ], examples=[ - "/home/eduardo.hirata/repos/viscy/examples/gradio/hek.png", - "/home/eduardo.hirata/repos/viscy/examples/gradio/a549.png", + "./hek.png", + "./a549.png", ], - ).launch() + ).launch(share=False) From 0a8f42132bba9e0d9c6731d2ba213cef88a48817 Mon Sep 17 00:00:00 2001 From: Eduardo Hirata-Miyasaki Date: Wed, 11 Sep 2024 14:16:37 -0700 Subject: [PATCH 4/4] updating gradio as the one on Huggingface --- examples/gradio/a549.png | Bin 107189 -> 0 bytes examples/gradio/demo_gradio.py | 122 +++++++++++++++++++++++++-------- examples/gradio/hek.png | Bin 98142 -> 0 bytes 3 files changed, 92 insertions(+), 30 deletions(-) delete mode 100644 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zaBP9bJsI5hYR!-MJ*~*=%a<=_KD?sx%?4cT!(4I0gERKohOHQ#YvQ8EhaAJLp2UDR zw%Oz#XSz1?HrC%SzaE~>$Ftbc`NzY?{NGQ9J3CDL>g9tzT`{%+yII2ktN8Fjo^QJ3 z<5|vNHSW9xW3$3DZt02zUt%}s4?4ODm}gI%^wp%;_+)cGp7_|o9scplh6W!_=s$b* zZ2kpYavm7SsUy9{2VLs|W-*HchH&!X!2nm{CCh$3!>g{1vtJ&?Ne(Xd#lwy<`Qy)= zc;s1UhaP!NtvHt#{>ge}*Sf|{xpXcraImage Translation (Virtual Staining) of cellular landmark organelles" + ) + # Improved description block with better formatting + gr.HTML( + """ + + """ + ) + + vsgradio = VSGradio(model_config, model_ckpt_path) + + # Layout for input and output images + with gr.Row(): + input_image = gr.Image(type="numpy", image_mode="L", label="Upload Image") + with gr.Column(): + output_nucleus = gr.Image(type="numpy", label="VS Nucleus") + output_membrane = gr.Image(type="numpy", label="VS Membrane") + + # Button to trigger prediction + submit_button = gr.Button("Submit") + + # Define what happens when the button is clicked + submit_button.click( + vsgradio.predict, + inputs=input_image, + outputs=[output_nucleus, output_membrane], + ) + + # Example images and article + gr.Examples( + examples=["examples/a549.png", "examples/hek.png"], inputs=input_image + ) + + # Article or footer information + gr.HTML( + """ +
+

Model trained primarily on HEK293T, BJ5, and A549 cells. For best results, use quantitative phase images (QPI) or Zernike phase contrast.

+

For training, inference and evaluation of the model refer to the GitHub repository.

+
+ """ + ) + + # Launch the Gradio app + demo.launch() diff --git a/examples/gradio/hek.png b/examples/gradio/hek.png deleted file mode 100644 index 9192f3219cadab42c25583035cdd3ff68539ad6e..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 98142 zcmZU(2UJtdw>L}=HS`{uAgEwy5_%9(kcSd|Pyy*pR01T_0HF(^R}};V5fzmVigXAe zK%|KX1VKnbFJb}+K_Yzo?|Z-d-t~QFtyyQE+55Nm%${@R%H*78VxTUz6n3(u1 zuV4L_iHTYLwD5CsoYn{jUD-Z;aDQNCcEi%lO#Vht;JpWMA10=Ax&Ao~RxBP8{bj0V zd5KaNq@+ME3BBM{smU|!tPPJIwjXtjULMPCPrv`ZJo&!h!v{Q0e|R1aD*R!+#3^;= zWrK0h`PO-u>c>A&*HStDUq?GTOfHR|BSu-}0}bvxRG_#yy6i@|ec_Sww)VQ=1L0T6 z=X!|!6xQ|KSgAaG(bN8LFSNJ0)Vzmi7=4UrSV zQvqrQX>^DB@ugtw-Ta}<(&bx^=}%kAd?tBm+Q`d(yP1&DRmjXVA0^UJcf#tG*)7FF zoRQGp-|J?kocXsjKd+rUyWypr#-i8k;law$h`4WH4YU9nAVJC!$^!Ct1Vo~)O?7L- zBa(zPb~!@Col3)4oVQtV{1*fFMAl|m!S9_vek=2ST{PfY_HU7zMO>-MbhHd>hsEX2 zwYFbpr)$c_k1a^w8Eh0M@0WS3DixB1?^$;6C#z{GY+F`pKx)565W znwQK3IK2yX?c3L^9u6u2?)L)7-FN=v3II!>cK6C 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