212 lines
9.5 KiB
Python
212 lines
9.5 KiB
Python
import math
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import torch
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import torch.nn as nn
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import torchvision
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from torchvision import models
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from utils import compute_offsets, assign_label, generate_proposal
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from loss import ClsScoreRegression, BboxRegression
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class FeatureExtractor(nn.Module):
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"""
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Image feature extraction with MobileNet.
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"""
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def __init__(self, reshape_size=224, pooling=False, verbose=False):
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super().__init__()
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self.mobilenet = models.mobilenet_v2(pretrained=True)
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self.mobilenet = nn.Sequential(*list(self.mobilenet.children())[:-1]) # Remove the last classifier
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# average pooling
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if pooling:
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self.mobilenet.add_module('LastAvgPool', nn.AvgPool2d(math.ceil(reshape_size/32.))) # input: N x 1280 x 7 x 7
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for i in self.mobilenet.named_parameters():
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i[1].requires_grad = True # fine-tune all
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def forward(self, img, verbose=False):
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"""
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Inputs:
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- img: Batch of resized images, of shape Nx3x224x224
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Outputs:
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- feat: Image feature, of shape Nx1280 (pooled) or Nx1280x7x7
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"""
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num_img = img.shape[0]
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img_prepro = img
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feat = []
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process_batch = 500
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for b in range(math.ceil(num_img/process_batch)):
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feat.append(self.mobilenet(img_prepro[b*process_batch:(b+1)*process_batch]
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).squeeze(-1).squeeze(-1)) # forward and squeeze
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feat = torch.cat(feat)
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if verbose:
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print('Output feature shape: ', feat.shape)
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return feat
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class FastRCNN(nn.Module):
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def __init__(self, in_dim=1280, hidden_dim=256, num_classes=20, \
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roi_output_w=2, roi_output_h=2, drop_ratio=0.3):
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super().__init__()
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assert(num_classes != 0)
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self.num_classes = num_classes
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self.roi_output_w, self.roi_output_h = roi_output_w, roi_output_h
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self.feat_extractor = FeatureExtractor()
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##############################################################################
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# TODO: Declare the cls & bbox heads (in Fast R-CNN). #
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# The cls & bbox heads share a sequential module with a Linear layer, #
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# followed by a Dropout (p=drop_ratio), a ReLU nonlinearity and another #
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# Linear layer. #
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# The cls head is a Linear layer that predicts num_classes + 1 (background). #
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# The det head is a Linear layer that predicts offsets(dim=4). #
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# HINT: The dimension of the two Linear layers are in_dim -> hidden_dim and #
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# hidden_dim -> hidden_dim. #
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##############################################################################
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# Replace "pass" statement with your code
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pass
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##############################################################################
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# END OF YOUR CODE #
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##############################################################################
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def forward(self, images, bboxes, bbox_batch_ids, proposals, proposal_batch_ids):
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"""
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Training-time forward pass for our two-stage Faster R-CNN detector.
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Inputs:
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- images: Tensor of shape (B, 3, H, W) giving input images
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- bboxes: Tensor of shape (N, 5) giving ground-truth bounding boxes
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and category labels, from the dataloader, where N is the total number
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of GT boxes in the batch
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- bbox_batch_ids: Tensor of shape (N, ) giving the index (in the batch)
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of the image that each GT box belongs to
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- proposals: Tensor of shape (M, 4) giving the proposals for input images,
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where M is the total number of proposals in the batch
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- proposal_batch_ids: Tensor of shape (M, ) giving the index of the image
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that each proposals belongs to
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Outputs:
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- total_loss: Torch scalar giving the overall training loss.
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"""
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w_cls = 1 # for cls_scores
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w_bbox = 1 # for offsets
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total_loss = None
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##############################################################################
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# TODO: Implement the forward pass of Fast R-CNN. #
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# A few key steps are outlined as follows: #
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# i) Extract image fearure. #
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# ii) Perform RoI Align on proposals, then meanpool the feature in the #
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# spatial dimension. #
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# iii) Pass the RoI feature through the shared-fc layer. Predict #
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# classification scores ans box offsets. #
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# iv) Assign the proposals with targets of each image. #
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# v) Compute the cls_loss between the predicted class_prob and GT_class #
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# (For poistive & negative proposals) #
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# Compute the bbox_loss between the offsets and GT_offsets #
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# (For positive proposals) #
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# Compute the total_loss which is formulated as: #
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# total_loss = w_cls*cls_loss + w_bbox*bbox_loss. #
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##############################################################################
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# Replace "pass" statement with your code
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B, _, H, W = images.shape
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# extract image feature
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pass
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# perform RoI Pool & mean pool
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pass
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# forward heads, get predicted cls scores & offsets
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pass
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# assign targets with proposals
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pos_masks, neg_masks, GT_labels, GT_bboxes = [], [], [], []
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for img_idx in range(B):
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# get the positive/negative proposals and corresponding
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# GT box & class label of this image
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pass
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# compute loss
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pass
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##############################################################################
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# END OF YOUR CODE #
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##############################################################################
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return total_loss
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def inference(self, images, proposals, proposal_batch_ids, thresh=0.5, nms_thresh=0.7):
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""""
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Inference-time forward pass for our two-stage Faster R-CNN detector
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Inputs:
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- images: Tensor of shape (B, 3, H, W) giving input images
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- proposals: Tensor of shape (M, 4) giving the proposals for input images,
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where M is the total number of proposals in the batch
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- proposal_batch_ids: Tensor of shape (M, ) giving the index of the image
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that each proposals belongs to
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- thresh: Threshold value on confidence probability. HINT: You can convert the
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classification score to probability using a softmax nonlinearity.
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- nms_thresh: IoU threshold for NMS
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We can output a variable number of predicted boxes per input image.
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In particular we assume that the input images[i] gives rise to P_i final
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predicted boxes.
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Outputs:
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- final_proposals: List of length (B,) where final_proposals[i] is a Tensor
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of shape (P_i, 4) giving the coordinates of the final predicted boxes for
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the input images[i]
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- final_conf_probs: List of length (B,) where final_conf_probs[i] is a
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Tensor of shape (P_i, 1) giving the predicted probabilites that the boxes
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in final_proposals[i] are objects (vs background)
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- final_class: List of length (B,), where final_class[i] is an int64 Tensor
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of shape (P_i, 1) giving the predicted category labels for each box in
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final_proposals[i].
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"""
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final_proposals, final_conf_probs, final_class = None, None, None
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##############################################################################
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# TODO: Predicting the final proposal coordinates `final_proposals`, #
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# confidence scores `final_conf_probs`, and the class index `final_class`. #
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# The overall steps are similar to the forward pass, but now you cannot #
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# decide the activated nor negative proposals without GT boxes. #
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# You should apply post-processing (thresholding and NMS) to all proposals #
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# and keep the final proposals. #
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##############################################################################
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# Replace "pass" statement with your code
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B = images.shape[0]
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# extract image feature
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pass
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# perform RoI Pool & mean pool
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pass
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# forward heads, get predicted cls scores & offsets
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pass
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# get predicted boxes & class label & confidence probability
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pass
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final_proposals = []
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final_conf_probs = []
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final_class = []
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# post-process to get final predictions
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for img_idx in range(B):
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# filter by threshold
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pass
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# nms
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pass
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##############################################################################
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# END OF YOUR CODE #
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##############################################################################
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return final_proposals, final_conf_probs, final_class |