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geetu040/
DepthPro_SR_4x_256p

Name: DepthPro_SR_4x_256p
Availability: InStock

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DepthPro: Human Segmentation

This work is a part of the DepthPro: Beyond Depth Estimation repository, which further explores this model's capabilities on:
- Image Segmentation - Human Segmentation
- Image Super Resolution - 384px to 1536px (4x Upscaling)
- Image Super Resolution - 256px to 1024px (4x Upscaling)

Usage

Install the required libraries:

bash

pip install -q numpy pillow torch torchvision
pip install -q git+https://github.com/geetu040/transformers.git@depth-pro-projects#egg=transformers

Import the required libraries:

import requests
from PIL import Image
import torch
from huggingface_hub import hf_hub_download
import matplotlib.pyplot as plt

# custom installation from this PR: https://github.com/huggingface/transformers/pull/34583
# !pip install git+https://github.com/geetu040/transformers.git@depth-pro-projects#egg=transformers
from transformers import DepthProConfig, DepthProImageProcessorFast, DepthProForDepthEstimation

Load DepthProForDepthEstimation model

# load DepthPro model, used as backbone
config = DepthProConfig(
    patch_size=32,
    patch_embeddings_size=4,
    num_hidden_layers=12,
    intermediate_hook_ids=[11, 8, 7, 5],
    intermediate_feature_dims=[256, 256, 256, 256],
    scaled_images_ratios=[0.5, 1.0],
    scaled_images_overlap_ratios=[0.5, 0.25],
    scaled_images_feature_dims=[1024, 512],
    use_fov_model=False,
)
depthpro_for_depth_estimation = DepthProForDepthEstimation(config)

Create DepthProForSuperResolution model

# create DepthPro for super resolution
class DepthProForSuperResolution(torch.nn.Module):
    def __init__(self, depthpro_for_depth_estimation):
        super().__init__()

        self.depthpro_for_depth_estimation = depthpro_for_depth_estimation
        hidden_size = self.depthpro_for_depth_estimation.config.fusion_hidden_size

        self.image_head = torch.nn.Sequential(
            torch.nn.ConvTranspose2d(
                in_channels=config.num_channels,
                out_channels=hidden_size,
                kernel_size=4, stride=2, padding=1
            ),
            torch.nn.ReLU(),
        )

        self.head = torch.nn.Sequential(
            torch.nn.Conv2d(
                in_channels=hidden_size,
                out_channels=hidden_size,
                kernel_size=3, stride=1, padding=1
            ),
            torch.nn.ReLU(),
            torch.nn.ConvTranspose2d(
                in_channels=hidden_size,
                out_channels=hidden_size,
                kernel_size=4, stride=2, padding=1
            ),
            torch.nn.ReLU(),
            torch.nn.Conv2d(
                in_channels=hidden_size,
                out_channels=self.depthpro_for_depth_estimation.config.num_channels,
                kernel_size=3, stride=1, padding=1
            ),
        )

    def forward(self, pixel_values):
        # x is the low resolution image
        x = pixel_values
        encoder_features = self.depthpro_for_depth_estimation.depth_pro(x).features
        fused_hidden_state = self.depthpro_for_depth_estimation.fusion_stage(encoder_features)[-1]
        x = self.image_head(x)
        x = torch.nn.functional.interpolate(x, size=fused_hidden_state.shape[2:])
        x = x + fused_hidden_state
        x = self.head(x)
        return x

Load the model and image processor:

# initialize the model
model = DepthProForSuperResolution(depthpro_for_depth_estimation)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)

# load weights
weights_path = hf_hub_download(repo_id="geetu040/DepthPro_SR_4x_256p", filename="model_weights.pth")
model.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu')))

# load image processor
image_processor = DepthProImageProcessorFast(
    do_resize=False,
    do_rescale=True,
    do_normalize=True
)

Inference:

# inference

url = "https://huggingface.co/spaces/geetu040/DepthPro_SR_4x_256p/resolve/main/assets/examples/man_with_arms_open.jpeg"

image = Image.open(requests.get(url, stream=True).raw)
image.thumbnail((256, 256)) # resizes the image object to fit within a 256x256 pixel box

# prepare image for the model
inputs = image_processor(images=image, return_tensors="pt")
inputs = {k: v.to(device) for k, v in inputs.items()}

with torch.no_grad():
    outputs = model(**inputs)

# convert tensors to PIL.Image
output = outputs[0]                        # extract the first and only batch
output = output.cpu()                      # unload from cuda if used
output = torch.permute(output, (1, 2, 0))  # (C, H, W) -> (H, W, C)
output = output * 0.5 + 0.5                # undo normalization
output = output * 255.                     # undo scaling
output = output.clip(0, 255.)              # fix out of range
output = output.numpy()                    # convert to numpy
output = output.astype('uint8')            # convert to PIL.Image compatible format
output = Image.fromarray(output)           # create PIL.Image object

# visualize the prediction
fig, axes = plt.subplots(1, 2, figsize=(20, 20))
axes[0].imshow(image)
axes[0].set_title(f'Low-Resolution (LR) {image.size}')
axes[0].axis('off')
axes[1].imshow(output)
axes[1].set_title(f'Super-Resolution (SR) {output.size}')
axes[1].axis('off')
plt.subplots_adjust(wspace=0, hspace=0)
plt.show()