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Upload 5 files
Browse files- app.py +170 -4
- mini_resnet.py +89 -0
- model_weights/weights.pt +3 -0
app.py
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import gradio as gr
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def greet(name):
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return "Hello " + name + "!!"
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import os
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from io import BytesIO
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from pathlib import Path
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from random import shuffle
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import cv2
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import gradio as gr
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import matplotlib.pyplot as plt
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import numpy as np
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import torch
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from mini_resnet import CustomResNet
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from PIL import Image
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from pytorch_grad_cam import GradCAM
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from pytorch_grad_cam.utils.image import show_cam_on_image
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from torchvision import transforms as T
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mean = (0.49139968, 0.48215841, 0.44653091)
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std = (0.24703223, 0.24348513, 0.26158784)
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transforms = T.Compose([T.ToTensor(), T.Normalize(mean=mean, std=std)])
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classes = ("plane", "car", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck")
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softmax = torch.nn.Softmax(dim=0)
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model = CustomResNet()
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model.load_state_dict(torch.load("model_weights/weights.pt", map_location=torch.device("cpu")))
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model.eval()
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misclf_path = "images/miss_classified"
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mis_classified_imgs = list(Path(misclf_path).glob("*"))
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def get_traget_layer(block: str, layer: int):
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layer_num = 0 if layer == 0 else -1
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if block == "block1":
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return model.layer1[layer_num]
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if block == "block2":
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return model.layer2[layer_num]
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if block == "block3":
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return model.layer3[layer_num]
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default_cam = GradCAM(model=model, target_layers=[get_traget_layer("block3", -1)])
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def make_image(p: Path | str, pred: str, label: str):
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im = cv2.imread(str(p))
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im = cv2.resize(im, (64, 64))
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plt.imshow(im)
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plt.title(f"{pred} / {label}")
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plt.axis("off")
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buffer = BytesIO()
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plt.savefig(buffer, format="png")
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buffer.seek(0)
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img_array = np.frombuffer(buffer.getvalue(), dtype=np.uint8)
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buffer.close()
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# Decode the image array using OpenCV
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im = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
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return im
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@torch.inference_mode()
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def predict_img(img: np.ndarray, top_k: int = 10):
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preds = model(img)
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preds = softmax(preds.flatten())
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preds = {classes[i]: float(preds[i]) for i in range(10)}
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preds = {
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k: v for k, v in sorted(preds.items(), key=lambda item: item[1], reverse=True)[:top_k]
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}
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return preds
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def display_cam(cam: GradCAM, org_img: np.ndarray, img: torch.Tensor, transparency: float):
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grayscale_cam = cam(input_tensor=img, targets=None)
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grayscale_cam = grayscale_cam[0, :]
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visualization = show_cam_on_image(
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org_img / 255, grayscale_cam, use_rgb=True, image_weight=transparency
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)
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return visualization
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def inference(
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org_img: np.ndarray,
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top_k: int,
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show_cam: str,
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num_cam_imgs: int,
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cam_block: str,
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target_layer_num: int,
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transparency: float,
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show_misclf: str,
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num_misclf: int,
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):
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input_img = transforms(org_img)
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input_img = input_img.unsqueeze(0)
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preds = predict_img(input_img, top_k)
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org_img = display_cam(default_cam, org_img, input_img, transparency)
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shuffle(mis_classified_imgs)
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cam_outputs = []
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if show_cam:
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img_list = []
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target_layers = [get_traget_layer(cam_block, target_layer_num)]
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cam = GradCAM(model=model, target_layers=target_layers, use_cuda=False)
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for p in mis_classified_imgs[:num_cam_imgs]:
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im = cv2.imread(str(p))
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inp_im = transforms(im)
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inp_im = inp_im.unsqueeze(0)
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grayscale_cam = cam(input_tensor=inp_im, targets=None)
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grayscale_cam = grayscale_cam[0, :]
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visualization = show_cam_on_image(
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im / 255, grayscale_cam, use_rgb=True, image_weight=transparency
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)
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cam_outputs.append(visualization)
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del cam, img_list
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misclf_images_output = []
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if show_misclf:
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img_list = []
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gt = []
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for p in mis_classified_imgs[:num_misclf]:
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img_list.append(transforms(Image.open(p).convert("RGB")))
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gt.append(p.name.split("_")[0])
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misclf_out = softmax(model(torch.stack(img_list))).argmax(dim=1).tolist()
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del img_list
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for imp, pred, label in zip(mis_classified_imgs[:num_misclf], misclf_out, gt):
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pred = classes[pred]
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misclf_images_output.append(make_image(imp, pred, label))
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return org_img, preds, cam_outputs, misclf_images_output
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title = "CIFAR10 trained on Custom Model inspired by ResNet with GradCAM"
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description = "A simple Gradio interface to infer on ResNet model, and get GradCAM results"
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# examples = [["cat.jpg", 0.5, -1], ["dog.jpg", 0.5, -1]]
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demo = gr.Interface(
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inference,
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inputs=[
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gr.Image(shape=(32, 32), label="Input Image"),
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gr.Slider(1, 10, value=3, step=1, label="Top K predictions"),
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gr.Checkbox(label="Show Grad Cam"),
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gr.Slider(1, 20, value=5, step=1, label="Number of images"),
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gr.Radio(label="Which Block?", choices=["block1", "block2", "block3"]),
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gr.Slider(0, 1, value=1, step=1, label="Which Layer?"),
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gr.Slider(0, 1, value=0.5, label="Opacity of GradCAM"),
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gr.Checkbox(label="Show Misclassified Images"),
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gr.Slider(1, 20, value=5, step=5, label="Number of Misclassification Images"),
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],
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outputs=[
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gr.Image(shape=(32, 32), label="Output", width=128, height=128),
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"label",
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gr.Gallery(label="GradCAM Output"),
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gr.Gallery(
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label="Misclassified Images Pred/G.T.",
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columns=[2],
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rows=[2],
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object_fit="contain",
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height="auto",
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),
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],
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title=title,
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description=description,
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# examples=examples,
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)
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demo.launch(share=True)
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mini_resnet.py
ADDED
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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# from common import BaseNet
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class ResBlock(nn.Module):
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def __init__(self, in_planes: int, out_planes: int, stride: int = 1, drop: float = 0) -> None:
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super().__init__()
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self.dropout = nn.Dropout2d(drop)
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self.conv1 = nn.Conv2d(
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in_planes,
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out_planes,
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kernel_size=3,
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stride=stride,
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padding=1,
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bias=False,
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)
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self.bn1 = nn.BatchNorm2d(out_planes)
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self.conv2 = nn.Conv2d(
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out_planes,
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out_planes,
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kernel_size=3,
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stride=stride,
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padding=1,
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bias=False,
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)
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self.bn2 = nn.BatchNorm2d(out_planes)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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out = F.relu(self.bn1(self.conv1(x)))
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out = self.dropout(out)
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out = self.bn2(self.conv2(out))
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out += x
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out = F.relu(out)
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out = self.dropout(out)
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return out
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class CustomResNet(nn.Module):
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def __init__(self, drop: float = 0, num_classes: int = 10) -> None:
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super().__init__()
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# perp layer
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self.perlayer = nn.Sequential(
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nn.Conv2d(3, 64, 3, padding=1, bias=False),
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nn.BatchNorm2d(64),
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nn.ReLU(),
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nn.Dropout2d(drop),
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)
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self.layer1 = nn.Sequential(
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nn.Conv2d(64, 128, 3, padding=1, bias=False),
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nn.MaxPool2d(2, 2),
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nn.BatchNorm2d(128),
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nn.ReLU(),
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nn.Dropout2d(drop),
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ResBlock(128, 128, drop=drop),
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)
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self.layer2 = nn.Sequential(
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nn.Conv2d(128, 256, 3, padding=1, bias=False),
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nn.MaxPool2d(2, 2),
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nn.BatchNorm2d(256),
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nn.ReLU(),
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nn.Dropout2d(drop),
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)
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self.layer3 = nn.Sequential(
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nn.Conv2d(256, 512, 3, padding=1, bias=False),
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nn.MaxPool2d(2, 2),
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nn.BatchNorm2d(512),
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nn.ReLU(),
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nn.Dropout2d(drop),
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ResBlock(512, 512, drop=drop),
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)
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self.pool = nn.MaxPool2d(4)
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self.out = nn.Conv2d(512, num_classes, 1, bias=False)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = self.perlayer(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.pool(x)
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x = self.out(x)
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return x.view(-1, 10)
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model_weights/weights.pt
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:7bfb94e78ae17040a9dba004bdf9e3ba9633cf4bb730184cf6e487458747e3a2
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size 26325330
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