f3c77b1750
- Implemented the WiFi DensePose model in PyTorch, including CSI phase processing, modality translation, and DensePose prediction heads. - Added a comprehensive training utility for the model, including loss functions and training steps. - Created a CSV file to document hardware specifications, architecture details, training parameters, performance metrics, and advantages of the model.
261 lines
9.5 KiB
Python
261 lines
9.5 KiB
Python
# DensePose-RCNN Architecture for WiFi-based Human Pose Estimation
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# Based on the DensePose paper and WiFi-DensePose implementation
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import numpy as np
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from typing import Dict, List, Tuple, Optional
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from collections import OrderedDict
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class ResNetFPN:
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"""
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Simulated ResNet-FPN backbone for feature extraction
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"""
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def __init__(self, input_channels=3, output_channels=256):
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self.input_channels = input_channels
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self.output_channels = output_channels
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print(f"Initialized ResNet-FPN backbone:")
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print(f" Input channels: {input_channels}")
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print(f" Output channels: {output_channels}")
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def extract_features(self, input_tensor):
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"""
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Simulates feature extraction through ResNet-FPN
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Returns a dict of feature maps at different levels (P2-P5)
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"""
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input_shape = input_tensor.shape
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print(f"Extracting features from input shape: {input_shape}")
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# Simulate FPN feature maps at different scales
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P2 = np.random.rand(input_shape[0], self.output_channels, input_shape[1]//4, input_shape[2]//4)
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P3 = np.random.rand(input_shape[0], self.output_channels, input_shape[1]//8, input_shape[2]//8)
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P4 = np.random.rand(input_shape[0], self.output_channels, input_shape[1]//16, input_shape[2]//16)
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P5 = np.random.rand(input_shape[0], self.output_channels, input_shape[1]//32, input_shape[2]//32)
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return {
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'P2': P2,
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'P3': P3,
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'P4': P4,
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'P5': P5
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}
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class RegionProposalNetwork:
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"""
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Simulated Region Proposal Network (RPN)
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"""
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def __init__(self, feature_channels=256, anchor_scales=[8, 16, 32], anchor_ratios=[0.5, 1, 2]):
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self.feature_channels = feature_channels
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self.anchor_scales = anchor_scales
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self.anchor_ratios = anchor_ratios
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print(f"Initialized Region Proposal Network:")
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print(f" Feature channels: {feature_channels}")
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print(f" Anchor scales: {anchor_scales}")
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print(f" Anchor ratios: {anchor_ratios}")
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def propose_regions(self, feature_maps, num_proposals=100):
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"""
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Simulates proposing regions of interest from feature maps
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"""
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proposals = []
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# Generate proposals with varying confidence
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for i in range(num_proposals):
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# Create random bounding box
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x = np.random.uniform(0, 1)
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y = np.random.uniform(0, 1)
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w = np.random.uniform(0.05, 0.3)
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h = np.random.uniform(0.1, 0.5)
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# Add confidence score
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confidence = np.random.beta(5, 2) # Biased toward higher confidence
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proposals.append({
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'bbox': [x, y, w, h],
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'confidence': confidence
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})
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# Sort by confidence
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proposals.sort(key=lambda x: x['confidence'], reverse=True)
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return proposals
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class ROIAlign:
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"""
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Simulated ROI Align operation
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"""
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def __init__(self, output_size=(7, 7)):
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self.output_size = output_size
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print(f"Initialized ROI Align with output size: {output_size}")
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def extract_features(self, feature_maps, proposals):
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"""
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Simulates ROI Align to extract fixed-size features for each proposal
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"""
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roi_features = []
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for proposal in proposals:
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# Create a random feature map for each proposal
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features = np.random.rand(feature_maps['P2'].shape[1], self.output_size[0], self.output_size[1])
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roi_features.append(features)
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return np.array(roi_features)
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class DensePoseHead:
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"""
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DensePose prediction head for estimating UV coordinates
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"""
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def __init__(self, input_channels=256, num_parts=24, output_size=(112, 112)):
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self.input_channels = input_channels
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self.num_parts = num_parts
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self.output_size = output_size
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print(f"Initialized DensePose Head:")
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print(f" Input channels: {input_channels}")
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print(f" Body parts: {num_parts}")
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print(f" Output size: {output_size}")
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def predict(self, roi_features):
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"""
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Predict body part labels and UV coordinates
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"""
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batch_size = roi_features.shape[0]
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# Predict part classification (24 parts + background)
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part_pred = np.random.rand(batch_size, self.num_parts + 1, self.output_size[0], self.output_size[1])
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part_pred = np.exp(part_pred) / np.sum(np.exp(part_pred), axis=1, keepdims=True) # Apply softmax
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# Predict UV coordinates for each part
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u_pred = np.random.rand(batch_size, self.num_parts, self.output_size[0], self.output_size[1])
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v_pred = np.random.rand(batch_size, self.num_parts, self.output_size[0], self.output_size[1])
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return {
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'part_pred': part_pred,
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'u_pred': u_pred,
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'v_pred': v_pred
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}
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class KeypointHead:
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"""
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Keypoint prediction head for estimating body keypoints
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"""
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def __init__(self, input_channels=256, num_keypoints=17, output_size=(56, 56)):
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self.input_channels = input_channels
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self.num_keypoints = num_keypoints
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self.output_size = output_size
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print(f"Initialized Keypoint Head:")
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print(f" Input channels: {input_channels}")
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print(f" Keypoints: {num_keypoints}")
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print(f" Output size: {output_size}")
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def predict(self, roi_features):
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"""
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Predict keypoint heatmaps
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"""
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batch_size = roi_features.shape[0]
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# Predict keypoint heatmaps
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keypoint_heatmaps = np.random.rand(batch_size, self.num_keypoints, self.output_size[0], self.output_size[1])
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# Apply softmax to get probability distributions
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keypoint_heatmaps = np.exp(keypoint_heatmaps) / np.sum(np.exp(keypoint_heatmaps), axis=(2, 3), keepdims=True)
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return keypoint_heatmaps
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class DensePoseRCNN:
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"""
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Complete DensePose-RCNN architecture
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"""
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def __init__(self):
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self.backbone = ResNetFPN(input_channels=3, output_channels=256)
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self.rpn = RegionProposalNetwork()
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self.roi_align = ROIAlign(output_size=(7, 7))
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self.densepose_head = DensePoseHead()
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self.keypoint_head = KeypointHead()
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print("Initialized DensePose-RCNN architecture")
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def forward(self, input_tensor):
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"""
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Forward pass through the DensePose-RCNN network
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"""
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# Extract features from backbone
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feature_maps = self.backbone.extract_features(input_tensor)
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# Generate region proposals
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proposals = self.rpn.propose_regions(feature_maps)
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# Keep only top proposals
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top_proposals = proposals[:10]
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# Extract ROI features
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roi_features = self.roi_align.extract_features(feature_maps, top_proposals)
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# Predict DensePose outputs
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densepose_outputs = self.densepose_head.predict(roi_features)
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# Predict keypoints
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keypoint_heatmaps = self.keypoint_head.predict(roi_features)
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# Process results into a structured format
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results = []
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for i, proposal in enumerate(top_proposals):
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# Get most likely part label for each pixel
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part_probs = densepose_outputs['part_pred'][i]
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part_labels = np.argmax(part_probs, axis=0)
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# Extract UV coordinates for the predicted parts
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u_coords = densepose_outputs['u_pred'][i]
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v_coords = densepose_outputs['v_pred'][i]
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# Extract keypoint coordinates from heatmaps
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keypoints = []
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for k in range(self.keypoint_head.num_keypoints):
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heatmap = keypoint_heatmaps[i, k]
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max_idx = np.argmax(heatmap)
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y, x = np.unravel_index(max_idx, heatmap.shape)
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confidence = np.max(heatmap)
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keypoints.append([x, y, confidence])
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results.append({
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'bbox': proposal['bbox'],
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'confidence': proposal['confidence'],
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'part_labels': part_labels,
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'u_coords': u_coords,
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'v_coords': v_coords,
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'keypoints': keypoints
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})
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return results
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# Demonstrate the DensePose-RCNN architecture
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print("="*60)
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print("DENSEPOSE-RCNN ARCHITECTURE DEMONSTRATION")
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print("="*60)
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# Create model
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model = DensePoseRCNN()
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# Create a dummy input tensor
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input_tensor = np.random.rand(1, 3, 720, 1280)
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print(f"\nPassing input tensor with shape {input_tensor.shape} through model...")
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# Forward pass
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results = model.forward(input_tensor)
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# Display results
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print(f"\nDensePose-RCNN Results:")
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print(f" Detected {len(results)} people")
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for i, person in enumerate(results):
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bbox = person['bbox']
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print(f" Person {i+1}:")
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print(f" Bounding box: [{bbox[0]:.3f}, {bbox[1]:.3f}, {bbox[2]:.3f}, {bbox[3]:.3f}]")
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print(f" Confidence: {person['confidence']:.3f}")
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print(f" Part labels shape: {person['part_labels'].shape}")
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print(f" UV coordinates shape: ({person['u_coords'].shape}, {person['v_coords'].shape})")
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print(f" Keypoints: {len(person['keypoints'])}")
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print("\nDensePose-RCNN demonstration completed!")
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print("This architecture forms the core of the WiFi-DensePose system")
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print("when combined with the CSI processing and modality translation components.") |