ResNet-18

A real, torchvision-provided architecture (not a toy model) - resnet18 has 8 residual blocks across 4 stages, with a projection shortcut (an extra Conv2d+BatchNorm2d in the skip path) at the first block of each of the last 3 stages, where the spatial size is downsampled and the channel count changes. Each residual skip connection is correctly routed above the diagram.

Conv2d is orange, BatchNorm2d is green, and ReLU is sky blue.

from collections import defaultdict

import matplotlib.pyplot as plt
import visualtorch
from torch import nn
from torchvision.models import resnet18

model = resnet18(weights=None, num_classes=10)

input_shape = (1, 3, 64, 64)

color_map: dict = defaultdict(dict)
color_map[nn.Conv2d]["fill"] = "#E69F00"
color_map[nn.BatchNorm2d]["fill"] = "#009E73"
color_map[nn.ReLU]["fill"] = "#56B4E9"

img = visualtorch.render(model, input_shape, style="graph", show_neurons=False, color_map=color_map, layer_spacing=60)

dpi = 150  # rendered at 2x this in the final doc build (savefig.dpi=300 in conf.py)
plt.figure(figsize=(img.width / dpi, img.height / dpi), dpi=dpi)
plt.imshow(img)
plt.axis("off")
plt.tight_layout()
plt.show()