Spaces:
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fix app
Browse files- app.py +35 -63
- config.yaml +0 -11
app.py
CHANGED
@@ -1,26 +1,29 @@
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from typing import Any
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import pytorch_lightning as pl
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from torchvision.models import
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import torch
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from torch import nn
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from torchvision import transforms
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from torch.nn import functional as F
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import yaml
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from yaml.loader import SafeLoader
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from PIL import Image
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import gradio as gr
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import os
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class WeedModel(pl.LightningModule):
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def __init__(self, params):
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super().__init__()
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self.params = params
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model = self.params["model"]
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if
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if
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num_ftrs = self.base_model.classifier[-1].in_features
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self.base_model.classifier[-1] = nn.Linear(num_ftrs, self.params["n_class"])
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@@ -31,41 +34,15 @@ class WeedModel(pl.LightningModule):
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embedding = self.base_model(x)
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return embedding
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def
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elif(self.params["optimizer"] == "SGD"):
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optimizer = torch.optim.SGD(self.parameters(), lr=self.params["Lr"])
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return optimizer
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def training_step(self, train_batch, batch_idx):
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x = train_batch["image"]
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y = train_batch["label"]
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y_hat = self(x)
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loss = F.cross_entropy(y_hat, y)
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self.log('metrics/batch/train_loss', loss, prog_bar=False)
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preds = F.softmax(y_hat, dim=-1)
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return loss
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def validation_step(self, val_batch, batch_idx):
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x = val_batch["image"]
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y = val_batch["label"]
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y_hat = self(x)
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loss = F.cross_entropy(y_hat, y)
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self.log('metrics/batch/val_loss', loss)
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def predict_step(self, batch: Any, batch_idx: int=0, dataloader_idx: int = 0) -> Any:
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y_hat = self(batch)
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preds = torch.softmax(y_hat, dim=-1).tolist()
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# preds = torch.argmax(preds, dim=-1)
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return preds
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def predict(image):
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@@ -80,45 +57,40 @@ title = " AISeed AI Application Demo "
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description = "# A Demo of Deep Learning for Weed Classification"
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example_list = [["examples/" + example] for example in os.listdir("examples")]
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with open("class_names.txt", "r", encoding=
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class_names = f.read().splitlines()
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with gr.Blocks() as demo:
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demo.title = title
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gr.Markdown(description)
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with gr.Tabs():
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with gr.TabItem("
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with gr.Row():
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with gr.Column():
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im = gr.Image(type="pil", label="input image")
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with gr.Column():
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label_conv = gr.Label(label="Predictions", num_top_classes=4)
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btn = gr.Button(value="predict")
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btn.click(predict, inputs=im, outputs=[label_conv])
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gr.Examples(examples=example_list, inputs=[im], outputs=[label_conv])
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# capture = gr.Image(type="pil", label="output image")
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with gr.Column():
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label = gr.Label(label="Predictions", num_top_classes=4)
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webcam.change(predict, inputs=webcam, outputs=[label])
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if __name__ == '__main__':
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with open('config.yaml') as f:
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PARAMS = yaml.load(f, Loader=SafeLoader)
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print(PARAMS)
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model = WeedModel.load_from_checkpoint(
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model.eval()
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transform = transforms.Compose(
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demo.launch()
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from typing import Any
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import pytorch_lightning as pl
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from torchvision.models import efficientnet_v2_s, EfficientNet_V2_S_Weights
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import torch
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from torch import nn
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from torchvision import transforms
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import yaml
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from yaml.loader import SafeLoader
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import gradio as gr
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import os
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class WeedModel(pl.LightningModule):
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def __init__(self, params):
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super().__init__()
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self.params = params
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model = self.params["model"]
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if model.lower() == "efficientnet":
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if self.params["pretrained"]:
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self.base_model = efficientnet_v2_s(
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weights=EfficientNet_V2_S_Weights.IMAGENET1K_V1
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)
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else:
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self.base_model = efficientnet_v2_s(weights=None)
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num_ftrs = self.base_model.classifier[-1].in_features
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self.base_model.classifier[-1] = nn.Linear(num_ftrs, self.params["n_class"])
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embedding = self.base_model(x)
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return embedding
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def predict_step(
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self, batch: Any, batch_idx: int = 0, dataloader_idx: int = 0
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) -> Any:
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y_hat = self(batch)
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preds = torch.softmax(y_hat, dim=-1).tolist()
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# preds = torch.argmax(preds, dim=-1)
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return preds
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def predict(image):
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description = "# A Demo of Deep Learning for Weed Classification"
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example_list = [["examples/" + example] for example in os.listdir("examples")]
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with open("class_names.txt", "r", encoding="utf-8") as f:
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class_names = f.read().splitlines()
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with gr.Blocks() as demo:
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demo.title = title
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gr.Markdown(description)
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with gr.Tabs():
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with gr.TabItem("Images"):
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with gr.Row():
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with gr.Column():
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im = gr.Image(type="pil", label="input image", sources=["upload", "webcam"])
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with gr.Column():
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label_conv = gr.Label(label="Predictions", num_top_classes=4)
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btn = gr.Button(value="predict")
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btn.click(predict, inputs=im, outputs=[label_conv])
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gr.Examples(examples=example_list, inputs=[im], outputs=[label_conv])
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if __name__ == "__main__":
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with open("config.yaml") as f:
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PARAMS = yaml.load(f, Loader=SafeLoader)
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print(PARAMS)
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model = WeedModel.load_from_checkpoint(
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"model/epoch=08.ckpt", params=PARAMS, map_location=torch.device("cpu")
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)
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model.eval()
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transform = transforms.Compose(
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[
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transforms.Resize(256),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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# transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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]
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)
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demo.launch()
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config.yaml
CHANGED
@@ -1,19 +1,8 @@
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{
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#Dataset
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"train_path": "./train.txt",
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"test_path": "./test.txt",
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"val_path": "./val.txt",
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"n_data": 1,
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#Model
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"model": "EfficientNet", # [Alexnet, VGG, GoogleNet, ResNet, DenseNet, MobileNet, SqueezeNet, ShuffleNet, EfficientNet, SE-ResNet (not available)]
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"pretrained": True,
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"n_class": 40,
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#Training
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"B_sz": 4,
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"Lr": 0.001,
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"Epoch": 5,
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"optimizer": "Adam" #[Adam, SGD]
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}
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{
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#Model
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"model": "EfficientNet", # [Alexnet, VGG, GoogleNet, ResNet, DenseNet, MobileNet, SqueezeNet, ShuffleNet, EfficientNet, SE-ResNet (not available)]
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"pretrained": True,
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"n_class": 40,
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}
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